diff --git "a/Week16_\353\263\265\354\212\265\352\263\274\354\240\234_\354\236\245\354\235\270\354\204\234.ipynb" "b/Week16_\353\263\265\354\212\265\352\263\274\354\240\234_\354\236\245\354\235\270\354\204\234.ipynb"
new file mode 100644
index 0000000..7b5c13a
--- /dev/null
+++ "b/Week16_\353\263\265\354\212\265\352\263\274\354\240\234_\354\236\245\354\235\270\354\204\234.ipynb"
@@ -0,0 +1,6869 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 78
+        },
+        "id": "I4ULgXMx0xHo",
+        "outputId": "770d0de2-e5ba-4dad-889b-8fce043d6c20"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-996c7102-756c-4aeb-addf-5f598310c56a\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-996c7102-756c-4aeb-addf-5f598310c56a\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving tmdb_5000_movies.csv to tmdb_5000_movies.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import files\n",
+        "uploaded = files.upload()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import warnings; warnings.filterwarnings('ignore')\n",
+        "\n",
+        "movies =pd.read_csv('tmdb_5000_movies.csv')\n",
+        "print(movies.shape)\n",
+        "movies.head(1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "WrVqcATW1C7p",
+        "outputId": "afc12700-b2b7-4d27-c136-0102906ae1df"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(4803, 20)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      budget                                             genres  \\\n",
+              "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
+              "\n",
+              "                      homepage     id  \\\n",
+              "0  http://www.avatarmovie.com/  19995   \n",
+              "\n",
+              "                                            keywords original_language  \\\n",
+              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
+              "\n",
+              "  original_title                                           overview  \\\n",
+              "0         Avatar  In the 22nd century, a paraplegic Marine is di...   \n",
+              "\n",
+              "   popularity                               production_companies  \\\n",
+              "0  150.437577  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
+              "\n",
+              "                                production_countries release_date     revenue  \\\n",
+              "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
+              "\n",
+              "   runtime                                   spoken_languages    status  \\\n",
+              "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
+              "\n",
+              "                       tagline   title  vote_average  vote_count  \n",
+              "0  Enter the World of Pandora.  Avatar           7.2       11800  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e9bece2d-75ac-438c-969e-c7aa48efbb87\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>budget</th>\n",
+              "      <th>genres</th>\n",
+              "      <th>homepage</th>\n",
+              "      <th>id</th>\n",
+              "      <th>keywords</th>\n",
+              "      <th>original_language</th>\n",
+              "      <th>original_title</th>\n",
+              "      <th>overview</th>\n",
+              "      <th>popularity</th>\n",
+              "      <th>production_companies</th>\n",
+              "      <th>production_countries</th>\n",
+              "      <th>release_date</th>\n",
+              "      <th>revenue</th>\n",
+              "      <th>runtime</th>\n",
+              "      <th>spoken_languages</th>\n",
+              "      <th>status</th>\n",
+              "      <th>tagline</th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>vote_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>237000000</td>\n",
+              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
+              "      <td>http://www.avatarmovie.com/</td>\n",
+              "      <td>19995</td>\n",
+              "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
+              "      <td>en</td>\n",
+              "      <td>Avatar</td>\n",
+              "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
+              "      <td>150.437577</td>\n",
+              "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
+              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
+              "      <td>2009-12-10</td>\n",
+              "      <td>2787965087</td>\n",
+              "      <td>162.0</td>\n",
+              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
+              "      <td>Released</td>\n",
+              "      <td>Enter the World of Pandora.</td>\n",
+              "      <td>Avatar</td>\n",
+              "      <td>7.2</td>\n",
+              "      <td>11800</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9bece2d-75ac-438c-969e-c7aa48efbb87')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e9bece2d-75ac-438c-969e-c7aa48efbb87 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e9bece2d-75ac-438c-969e-c7aa48efbb87');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "movies",
+              "summary": "{\n  \"name\": \"movies\",\n  \"rows\": 4803,\n  \"fields\": [\n    {\n      \"column\": \"budget\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40722391,\n        \"min\": 0,\n        \"max\": 380000000,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          439000,\n          68000000,\n          700000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1175,\n        \"samples\": [\n          \"[{\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 16, \\\"name\\\": \\\"Animation\\\"}]\",\n          \"[{\\\"id\\\": 28, \\\"name\\\": \\\"Action\\\"}, {\\\"id\\\": 35, \\\"name\\\": \\\"Comedy\\\"}, {\\\"id\\\": 80, \\\"name\\\": \\\"Crime\\\"}, {\\\"id\\\": 18, \\\"name\\\": \\\"Drama\\\"}]\",\n          \"[{\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 16, \\\"name\\\": \\\"Animation\\\"}, {\\\"id\\\": 10751, \\\"name\\\": \\\"Family\\\"}, {\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 878, \\\"name\\\": \\\"Science Fiction\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"homepage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1691,\n        \"samples\": [\n          \"https://www.warnerbros.com/running-scared\",\n          \"http://www.51birchstreet.com/index.php\",\n          \"http://movies2.foxjapan.com/glee/\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88694,\n        \"min\": 5,\n        \"max\": 459488,\n        \"num_unique_values\": 4803,\n        \"samples\": [\n          8427,\n          13006,\n          18041\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4222,\n        \"samples\": [\n          \"[{\\\"id\\\": 782, \\\"name\\\": \\\"assassin\\\"}, {\\\"id\\\": 1872, \\\"name\\\": \\\"loss of father\\\"}, {\\\"id\\\": 2908, \\\"name\\\": \\\"secret society\\\"}, {\\\"id\\\": 3045, \\\"name\\\": \\\"mission of murder\\\"}, {\\\"id\\\": 9748, \\\"name\\\": \\\"revenge\\\"}]\",\n          \"[{\\\"id\\\": 2987, \\\"name\\\": \\\"gang war\\\"}, {\\\"id\\\": 4942, \\\"name\\\": \\\"victim of murder\\\"}, {\\\"id\\\": 5332, \\\"name\\\": \\\"greed\\\"}, {\\\"id\\\": 6062, \\\"name\\\": \\\"hostility\\\"}, {\\\"id\\\": 156212, \\\"name\\\": \\\"spaghetti western\\\"}]\",\n          \"[{\\\"id\\\": 703, \\\"name\\\": \\\"detective\\\"}, {\\\"id\\\": 1299, \\\"name\\\": \\\"monster\\\"}, {\\\"id\\\": 6101, \\\"name\\\": \\\"engine\\\"}, {\\\"id\\\": 10988, \\\"name\\\": \\\"based on tv series\\\"}, {\\\"id\\\": 15162, \\\"name\\\": \\\"dog\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"xx\",\n          \"ta\",\n          \"es\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4801,\n        \"samples\": [\n          \"I Spy\",\n          \"Love Letters\",\n          \"Sleepover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"overview\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4800,\n        \"samples\": [\n          \"When the Switchblade, the most sophisticated prototype stealth fighter created yet, is stolen from the U.S. government, one of the United States' top spies, Alex Scott, is called to action. What he doesn't expect is to get teamed up with a cocky civilian, World Class Boxing Champion Kelly Robinson, on a dangerous top secret espionage mission. Their assignment: using equal parts skill and humor, catch Arnold Gundars, one of the world's most successful arms dealers.\",\n          \"When \\\"street smart\\\" rapper Christopher \\\"C-Note\\\" Hawkins (Big Boi) applies for a membership to all-white Carolina Pines Country Club, the establishment's proprietors are hardly ready to oblige him.\",\n          \"As their first year of high school looms ahead, best friends Julie, Hannah, Yancy and Farrah have one last summer sleepover. Little do they know they're about to embark on the adventure of a lifetime. Desperate to shed their nerdy status, they take part in a night-long scavenger hunt that pits them against their popular archrivals. Everything under the sun goes on -- from taking Yancy's father's car to sneaking into nightclubs!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.816649749537806,\n        \"min\": 0.0,\n        \"max\": 875.581305,\n        \"num_unique_values\": 4802,\n        \"samples\": [\n          13.267631,\n          0.010909,\n          5.842299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production_companies\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3697,\n        \"samples\": [\n          \"[{\\\"name\\\": \\\"Paramount Pictures\\\", \\\"id\\\": 4}, {\\\"name\\\": \\\"Cherry Alley Productions\\\", \\\"id\\\": 2232}]\",\n          \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}, {\\\"name\\\": \\\"Dune Entertainment\\\", \\\"id\\\": 444}, {\\\"name\\\": \\\"Regency Enterprises\\\", \\\"id\\\": 508}, {\\\"name\\\": \\\"Guy Walks into a Bar Productions\\\", \\\"id\\\": 2645}, {\\\"name\\\": \\\"Deep River Productions\\\", \\\"id\\\": 2646}, {\\\"name\\\": \\\"Friendly Films (II)\\\", \\\"id\\\": 81136}]\",\n          \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production_countries\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 469,\n        \"samples\": [\n          \"[{\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"FR\\\", \\\"name\\\": \\\"France\\\"}]\",\n          \"[{\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"CA\\\", \\\"name\\\": \\\"Canada\\\"}, {\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}]\",\n          \"[{\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}, {\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"release_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3280,\n        \"samples\": [\n          \"1966-10-16\",\n          \"1987-07-31\",\n          \"1993-09-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162857100,\n        \"min\": 0,\n        \"max\": 2787965087,\n        \"num_unique_values\": 3297,\n        \"samples\": [\n          11833696,\n          10462500,\n          17807569\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runtime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.611934588844207,\n        \"min\": 0.0,\n        \"max\": 338.0,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          74.0,\n          85.0,\n          170.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spoken_languages\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 544,\n        \"samples\": [\n          \"[{\\\"iso_639_1\\\": \\\"es\\\", \\\"name\\\": \\\"Espa\\\\u00f1ol\\\"}, {\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"fr\\\", \\\"name\\\": \\\"Fran\\\\u00e7ais\\\"}, {\\\"iso_639_1\\\": \\\"hu\\\", \\\"name\\\": \\\"Magyar\\\"}]\",\n          \"[{\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"pt\\\", \\\"name\\\": \\\"Portugu\\\\u00eas\\\"}]\",\n          \"[{\\\"iso_639_1\\\": \\\"de\\\", \\\"name\\\": \\\"Deutsch\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"la\\\", \\\"name\\\": \\\"Latin\\\"}, {\\\"iso_639_1\\\": \\\"pl\\\", \\\"name\\\": \\\"Polski\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Released\",\n          \"Post Production\",\n          \"Rumored\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tagline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3944,\n        \"samples\": [\n          \"When you're 17, every day is war.\",\n          \"An Unspeakable Horror. A Creative Genius. Captured For Eternity.\",\n          \"May the schwartz be with you\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4800,\n        \"samples\": [\n          \"I Spy\",\n          \"Who's Your Caddy?\",\n          \"Sleepover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1946121628478925,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          5.1,\n          7.2,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1234,\n        \"min\": 0,\n        \"max\": 13752,\n        \"num_unique_values\": 1609,\n        \"samples\": [\n          7604,\n          3428,\n          225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df = movies[['id','title', 'genres', 'vote_average', 'vote_count',\n",
+        "                 'popularity', 'keywords', 'overview']]"
+      ],
+      "metadata": {
+        "id": "eHR7I5OU1LxI"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.set_option('max_colwidth', 100)\n",
+        "movies_df[['genres','keywords']][:1]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 115
+        },
+        "id": "L-FLF9At1Lzj",
+        "outputId": "bb22e85e-2988-439b-8311-4169ba2cca53"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                                                                genres  \\\n",
+              "0  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...   \n",
+              "\n",
+              "                                                                                              keywords  \n",
+              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-090d6da2-36df-4c78-a20f-113884e63c8b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>genres</th>\n",
+              "      <th>keywords</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...</td>\n",
+              "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"sp...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-090d6da2-36df-4c78-a20f-113884e63c8b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-090d6da2-36df-4c78-a20f-113884e63c8b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-090d6da2-36df-4c78-a20f-113884e63c8b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['genres','keywords']][:1]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"[{\\\"id\\\": 28, \\\"name\\\": \\\"Action\\\"}, {\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 878, \\\"name\\\": \\\"Science Fiction\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"[{\\\"id\\\": 1463, \\\"name\\\": \\\"culture clash\\\"}, {\\\"id\\\": 2964, \\\"name\\\": \\\"future\\\"}, {\\\"id\\\": 3386, \\\"name\\\": \\\"space war\\\"}, {\\\"id\\\": 3388, \\\"name\\\": \\\"space colony\\\"}, {\\\"id\\\": 3679, \\\"name\\\": \\\"society\\\"}, {\\\"id\\\": 3801, \\\"name\\\": \\\"space travel\\\"}, {\\\"id\\\": 9685, \\\"name\\\": \\\"futuristic\\\"}, {\\\"id\\\": 9840, \\\"name\\\": \\\"romance\\\"}, {\\\"id\\\": 9882, \\\"name\\\": \\\"space\\\"}, {\\\"id\\\": 9951, \\\"name\\\": \\\"alien\\\"}, {\\\"id\\\": 10148, \\\"name\\\": \\\"tribe\\\"}, {\\\"id\\\": 10158, \\\"name\\\": \\\"alien planet\\\"}, {\\\"id\\\": 10987, \\\"name\\\": \\\"cgi\\\"}, {\\\"id\\\": 11399, \\\"name\\\": \\\"marine\\\"}, {\\\"id\\\": 13065, \\\"name\\\": \\\"soldier\\\"}, {\\\"id\\\": 14643, \\\"name\\\": \\\"battle\\\"}, {\\\"id\\\": 14720, \\\"name\\\": \\\"love affair\\\"}, {\\\"id\\\": 165431, \\\"name\\\": \\\"anti war\\\"}, {\\\"id\\\": 193554, \\\"name\\\": \\\"power relations\\\"}, {\\\"id\\\": 206690, \\\"name\\\": \\\"mind and soul\\\"}, {\\\"id\\\": 209714, \\\"name\\\": \\\"3d\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from ast import literal_eval\n",
+        "\n",
+        "movies_df['genres'] = movies_df['genres'].apply(literal_eval)\n",
+        "movies_df['keywords'] = movies_df['keywords'].apply(literal_eval)"
+      ],
+      "metadata": {
+        "id": "pvA69CCe1L1i"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df['genres'] = movies_df['genres'].apply(lambda x : [ y['name'] for y in x])\n",
+        "movies_df['keywords'] = movies_df['keywords'].apply(lambda x : [ y['name'] for y in x])\n",
+        "movies_df[['genres', 'keywords']][:1]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 98
+        },
+        "id": "w8AL36Nz1L3m",
+        "outputId": "955c7006-35b9-421e-8d33-08c9bae8c922"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                          genres  \\\n",
+              "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
+              "\n",
+              "                                                                                              keywords  \n",
+              "0  [culture clash, future, space war, space colony, society, space travel, futuristic, romance, spa...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0be373c2-9116-4d11-bc0a-100000186fa3\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>genres</th>\n",
+              "      <th>keywords</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
+              "      <td>[culture clash, future, space war, space colony, society, space travel, futuristic, romance, spa...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0be373c2-9116-4d11-bc0a-100000186fa3')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0be373c2-9116-4d11-bc0a-100000186fa3 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0be373c2-9116-4d11-bc0a-100000186fa3');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['genres', 'keywords']][:1]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.feature_extraction.text import CountVectorizer\n",
+        "\n",
+        "movies_df['genres_literal'] = movies_df['genres'].apply(lambda x : (' ').join(x))\n",
+        "count_vect = CountVectorizer(min_df=0.01, ngram_range=(1,2))\n",
+        "genre_mat = count_vect.fit_transform(movies_df['genres_literal'])\n",
+        "print(genre_mat.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8O9cjRlT1L5e",
+        "outputId": "3fc1aab3-c527-4d81-e9e8-e1ed5eb985ba"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(4803, 60)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics.pairwise import cosine_similarity\n",
+        "\n",
+        "genre_sim = cosine_similarity(genre_mat, genre_mat)\n",
+        "print(genre_sim.shape)\n",
+        "print(genre_sim[:2])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_YuCl9UN1L7E",
+        "outputId": "7824f4b4-0632-47f3-9213-867ce57705d2"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(4803, 4803)\n",
+            "[[1.         0.63245553 0.53033009 ... 0.         0.         0.        ]\n",
+            " [0.63245553 1.         0.4472136  ... 0.         0.         0.        ]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "genre_sim_sorted_ind = genre_sim.argsort()[:, ::-1]\n",
+        "print(genre_sim_sorted_ind[:1])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iL3QZQpE1L8n",
+        "outputId": "498f5ad3-b4c2-47a5-9482-60f8b59ae9f5"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[   0   14  870 ... 3330 3792 2117]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def find_sim_movie(df, sorted_ind, title_name, top_n=10):\n",
+        "\n",
+        "\n",
+        "    title_movie = df[df['title'] == title_name]\n",
+        "\n",
+        "    title_index = title_movie.index.values\n",
+        "    similar_indexes = sorted_ind[title_index, :(top_n)]\n",
+        "\n",
+        "    print(similar_indexes)\n",
+        "    similar_indexes = similar_indexes.reshape(-1)\n",
+        "\n",
+        "    return df.iloc[similar_indexes]"
+      ],
+      "metadata": {
+        "id": "v2Jq_E6I1L-x"
+      },
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "similar_movies = find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather',10)\n",
+        "similar_movies[['title', 'vote_average']]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 381
+        },
+        "id": "cIC8-Um31MAR",
+        "outputId": "5043e73e-5c34-44ff-f9f1-729bc4e178aa"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[1663 3337 4217 1847 1464 3378 3112 1946 4065 1881]]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                               title  vote_average\n",
+              "1663                     Once Upon a Time in America           8.2\n",
+              "3337                                   The Godfather           8.4\n",
+              "4217                                            Kids           6.8\n",
+              "1847                                      GoodFellas           8.2\n",
+              "1464                             Black Water Transit           0.0\n",
+              "3378                                      Auto Focus           6.1\n",
+              "3112                         Blood Done Sign My Name           6.0\n",
+              "1946  The Bad Lieutenant: Port of Call - New Orleans           6.0\n",
+              "4065                                      Mi America           0.0\n",
+              "1881                        The Shawshank Redemption           8.5"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e243791b-683c-449b-9f6c-78b53059ff8f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1663</th>\n",
+              "      <td>Once Upon a Time in America</td>\n",
+              "      <td>8.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3337</th>\n",
+              "      <td>The Godfather</td>\n",
+              "      <td>8.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4217</th>\n",
+              "      <td>Kids</td>\n",
+              "      <td>6.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1847</th>\n",
+              "      <td>GoodFellas</td>\n",
+              "      <td>8.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1464</th>\n",
+              "      <td>Black Water Transit</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3378</th>\n",
+              "      <td>Auto Focus</td>\n",
+              "      <td>6.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3112</th>\n",
+              "      <td>Blood Done Sign My Name</td>\n",
+              "      <td>6.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1946</th>\n",
+              "      <td>The Bad Lieutenant: Port of Call - New Orleans</td>\n",
+              "      <td>6.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4065</th>\n",
+              "      <td>Mi America</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1881</th>\n",
+              "      <td>The Shawshank Redemption</td>\n",
+              "      <td>8.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e243791b-683c-449b-9f6c-78b53059ff8f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e243791b-683c-449b-9f6c-78b53059ff8f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e243791b-683c-449b-9f6c-78b53059ff8f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d110893d-e0b3-47d0-bba3-46ecfad70da2\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d110893d-e0b3-47d0-bba3-46ecfad70da2')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d110893d-e0b3-47d0-bba3-46ecfad70da2 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"similar_movies[['title', 'vote_average']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Mi America\",\n          \"The Godfather\",\n          \"Auto Focus\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.2320616055734805,\n        \"min\": 0.0,\n        \"max\": 8.5,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          8.2,\n          8.4,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df[['title','vote_average','vote_count']].sort_values('vote_average', ascending=False)[:10]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 362
+        },
+        "id": "n7vGQmYQ1MCF",
+        "outputId": "940b281d-250b-4aac-fa2f-0343045366a3"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                         title  vote_average  vote_count\n",
+              "4662            Little Big Top          10.0           1\n",
+              "3519          Stiff Upper Lips          10.0           1\n",
+              "4045     Dancer, Texas Pop. 81          10.0           1\n",
+              "4247     Me You and Five Bucks          10.0           2\n",
+              "3992                 Sardaarji           9.5           2\n",
+              "2386            One Man's Hero           9.3           2\n",
+              "1881  The Shawshank Redemption           8.5        8205\n",
+              "2970        There Goes My Baby           8.5           2\n",
+              "3337             The Godfather           8.4        5893\n",
+              "2796     The Prisoner of Zenda           8.4          11"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b1825a60-0b23-4018-bb1d-68a08fe476ad\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>vote_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>4662</th>\n",
+              "      <td>Little Big Top</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3519</th>\n",
+              "      <td>Stiff Upper Lips</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4045</th>\n",
+              "      <td>Dancer, Texas Pop. 81</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4247</th>\n",
+              "      <td>Me You and Five Bucks</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3992</th>\n",
+              "      <td>Sardaarji</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2386</th>\n",
+              "      <td>One Man's Hero</td>\n",
+              "      <td>9.3</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1881</th>\n",
+              "      <td>The Shawshank Redemption</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>8205</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2970</th>\n",
+              "      <td>There Goes My Baby</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3337</th>\n",
+              "      <td>The Godfather</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>5893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2796</th>\n",
+              "      <td>The Prisoner of Zenda</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1825a60-0b23-4018-bb1d-68a08fe476ad')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b1825a60-0b23-4018-bb1d-68a08fe476ad button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b1825a60-0b23-4018-bb1d-68a08fe476ad');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-73ad0e59-4366-4dd0-86f5-d5f4d766db88\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-73ad0e59-4366-4dd0-86f5-d5f4d766db88')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-73ad0e59-4366-4dd0-86f5-d5f4d766db88 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"movies_df[['title','vote_average','vote_count']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"The Godfather\",\n          \"Stiff Upper Lips\",\n          \"One Man's Hero\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7366591251499343,\n        \"min\": 8.4,\n        \"max\": 10.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.5,\n          8.4,\n          9.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3020,\n        \"min\": 1,\n        \"max\": 8205,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          11,\n          8205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "C = movies_df['vote_average'].mean()\n",
+        "m = movies_df['vote_count'].quantile(0.6)\n",
+        "print('C:',round(C,3), 'm:',round(m,3))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4Tt2OB-V1MEU",
+        "outputId": "a8e2677c-0167-4165-de94-cf4200740bc5"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "C: 6.092 m: 370.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "percentile = 0.6\n",
+        "m = movies_df['vote_count'].quantile(percentile)\n",
+        "C = movies_df['vote_average'].mean()\n",
+        "\n",
+        "def weighted_vote_average(record):\n",
+        "    v = record['vote_count']\n",
+        "    R = record['vote_average']\n",
+        "\n",
+        "    return ( (v/(v+m)) * R ) + ( (m/(m+v)) * C )\n",
+        "\n",
+        "movies_df['weighted_vote'] = movies_df.apply(weighted_vote_average, axis=1)\n"
+      ],
+      "metadata": {
+        "id": "5LLDJdDm1MGs"
+      },
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "movies_df[['title','vote_average','weighted_vote','vote_count']].sort_values('weighted_vote',\n",
+        "                                                                          ascending=False)[:10]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 362
+        },
+        "id": "xdxSA2PN1MIf",
+        "outputId": "57317bbd-52f2-43e8-c430-a10c1972cd8f"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                         title  vote_average  weighted_vote  vote_count\n",
+              "1881  The Shawshank Redemption           8.5       8.396052        8205\n",
+              "3337             The Godfather           8.4       8.263591        5893\n",
+              "662                 Fight Club           8.3       8.216455        9413\n",
+              "3232              Pulp Fiction           8.3       8.207102        8428\n",
+              "65             The Dark Knight           8.2       8.136930       12002\n",
+              "1818          Schindler's List           8.3       8.126069        4329\n",
+              "3865                  Whiplash           8.3       8.123248        4254\n",
+              "809               Forrest Gump           8.2       8.105954        7927\n",
+              "2294             Spirited Away           8.3       8.105867        3840\n",
+              "2731    The Godfather: Part II           8.3       8.079586        3338"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3fb762e4-c89d-4e40-a378-7e62b5727049\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>weighted_vote</th>\n",
+              "      <th>vote_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1881</th>\n",
+              "      <td>The Shawshank Redemption</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>8.396052</td>\n",
+              "      <td>8205</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3337</th>\n",
+              "      <td>The Godfather</td>\n",
+              "      <td>8.4</td>\n",
+              "      <td>8.263591</td>\n",
+              "      <td>5893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>662</th>\n",
+              "      <td>Fight Club</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.216455</td>\n",
+              "      <td>9413</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3232</th>\n",
+              "      <td>Pulp Fiction</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.207102</td>\n",
+              "      <td>8428</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>65</th>\n",
+              "      <td>The Dark Knight</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>8.136930</td>\n",
+              "      <td>12002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1818</th>\n",
+              "      <td>Schindler's List</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.126069</td>\n",
+              "      <td>4329</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3865</th>\n",
+              "      <td>Whiplash</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.123248</td>\n",
+              "      <td>4254</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>809</th>\n",
+              "      <td>Forrest Gump</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>8.105954</td>\n",
+              "      <td>7927</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2294</th>\n",
+              "      <td>Spirited Away</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.105867</td>\n",
+              "      <td>3840</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2731</th>\n",
+              "      <td>The Godfather: Part II</td>\n",
+              "      <td>8.3</td>\n",
+              "      <td>8.079586</td>\n",
+              "      <td>3338</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3fb762e4-c89d-4e40-a378-7e62b5727049')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3fb762e4-c89d-4e40-a378-7e62b5727049 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3fb762e4-c89d-4e40-a378-7e62b5727049');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-dc4e5a73-45f2-4f9b-b302-90c2f4f662c1\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc4e5a73-45f2-4f9b-b302-90c2f4f662c1')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-dc4e5a73-45f2-4f9b-b302-90c2f4f662c1 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"                                                                          ascending=False)[:10]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Spirited Away\",\n          \"The Godfather\",\n          \"Schindler's List\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08755950357709151,\n        \"min\": 8.2,\n        \"max\": 8.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8.4,\n          8.2,\n          8.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weighted_vote\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09696608479450805,\n        \"min\": 8.07958629828635,\n        \"max\": 8.39605162693645,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8.105867158639835,\n          8.263590802034972,\n          8.126068673669016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2866,\n        \"min\": 3338,\n        \"max\": 12002,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3840,\n          5893,\n          4329\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def find_sim_movie(df, sorted_ind, title_name, top_n=10):\n",
+        "    title_movie = df[df['title'] == title_name]\n",
+        "    title_index = title_movie.index.values\n",
+        "\n",
+        "\n",
+        "    similar_indexes = sorted_ind[title_index, :(top_n*2)]\n",
+        "    similar_indexes = similar_indexes.reshape(-1)\n",
+        "\n",
+        "    similar_indexes = similar_indexes[similar_indexes != title_index]\n",
+        "\n",
+        "\n",
+        "    return df.iloc[similar_indexes].sort_values('weighted_vote', ascending=False)[:top_n]\n",
+        "\n",
+        "similar_movies = find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather',10)\n",
+        "similar_movies[['title', 'vote_average', 'weighted_vote']]\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 362
+        },
+        "id": "j7Wefsh71MKp",
+        "outputId": "5a2ea5d4-7f9d-4087-ca60-89b64291beb0"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                            title  vote_average  weighted_vote\n",
+              "1881     The Shawshank Redemption           8.5       8.396052\n",
+              "1847                   GoodFellas           8.2       7.976937\n",
+              "1663  Once Upon a Time in America           8.2       7.657811\n",
+              "883           Catch Me If You Can           7.7       7.557097\n",
+              "892                        Casino           7.8       7.423040\n",
+              "4041              This Is England           7.4       6.739664\n",
+              "2582   The Place Beyond the Pines           6.8       6.670483\n",
+              "2839                     Rounders           6.9       6.530427\n",
+              "1370                           21           6.5       6.413490\n",
+              "4217                         Kids           6.8       6.396368"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8320a39e-2580-4e49-a65e-ce4d07ccdf05\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>weighted_vote</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1881</th>\n",
+              "      <td>The Shawshank Redemption</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>8.396052</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1847</th>\n",
+              "      <td>GoodFellas</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>7.976937</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1663</th>\n",
+              "      <td>Once Upon a Time in America</td>\n",
+              "      <td>8.2</td>\n",
+              "      <td>7.657811</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>883</th>\n",
+              "      <td>Catch Me If You Can</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>7.557097</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>892</th>\n",
+              "      <td>Casino</td>\n",
+              "      <td>7.8</td>\n",
+              "      <td>7.423040</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4041</th>\n",
+              "      <td>This Is England</td>\n",
+              "      <td>7.4</td>\n",
+              "      <td>6.739664</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2582</th>\n",
+              "      <td>The Place Beyond the Pines</td>\n",
+              "      <td>6.8</td>\n",
+              "      <td>6.670483</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2839</th>\n",
+              "      <td>Rounders</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>6.530427</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1370</th>\n",
+              "      <td>21</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>6.413490</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4217</th>\n",
+              "      <td>Kids</td>\n",
+              "      <td>6.8</td>\n",
+              "      <td>6.396368</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8320a39e-2580-4e49-a65e-ce4d07ccdf05')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8320a39e-2580-4e49-a65e-ce4d07ccdf05 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8320a39e-2580-4e49-a65e-ce4d07ccdf05');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-3060b2d1-7d27-4284-91f9-ebec67c5cc36\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3060b2d1-7d27-4284-91f9-ebec67c5cc36')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3060b2d1-7d27-4284-91f9-ebec67c5cc36 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"similar_movies[['title', 'vote_average', 'weighted_vote']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"21\",\n          \"GoodFellas\",\n          \"This Is England\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7036413227711338,\n        \"min\": 6.5,\n        \"max\": 8.5,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.2,\n          6.8,\n          8.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weighted_vote\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7165023508809135,\n        \"min\": 6.396367700716935,\n        \"max\": 8.39605162693645,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6.413489520573822,\n          7.976937256676415,\n          6.739664363482589\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113
+        },
+        "id": "vpBT_fxw1myA",
+        "outputId": "26f72bab-84b3-4e32-cfbb-27a3927b2a87"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-b8ff47ef-b8b0-4308-b6a9-e5ff8f471f55\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-b8ff47ef-b8b0-4308-b6a9-e5ff8f471f55\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving movies.csv to movies (1).csv\n",
+            "Saving ratings.csv to ratings.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "movies = pd.read_csv('movies.csv')\n",
+        "ratings = pd.read_csv('ratings.csv')\n",
+        "print(movies.shape)\n",
+        "print(ratings.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SDKre161CZN2",
+        "outputId": "d8c08218-e805-4b6a-ee5c-b9ea7137839f"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(9742, 3)\n",
+            "(100836, 4)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings = ratings[['userId', 'movieId', 'rating']]\n",
+        "ratings_matrix = ratings.pivot_table('rating', index='userId', columns='movieId')\n",
+        "ratings_matrix.head(3)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 224
+        },
+        "id": "xqxMB71nCZTp",
+        "outputId": "fc592ae9-8ca6-4ba3-9e2d-aed35952b8fb"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "movieId  1       2       3       4       5       6       7       8       \\\n",
+              "userId                                                                    \n",
+              "1           4.0     NaN     4.0     NaN     NaN     4.0     NaN     NaN   \n",
+              "2           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
+              "3           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
+              "\n",
+              "movieId  9       10      ...  193565  193567  193571  193573  193579  193581  \\\n",
+              "userId                   ...                                                   \n",
+              "1           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
+              "2           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
+              "3           NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
+              "\n",
+              "movieId  193583  193585  193587  193609  \n",
+              "userId                                   \n",
+              "1           NaN     NaN     NaN     NaN  \n",
+              "2           NaN     NaN     NaN     NaN  \n",
+              "3           NaN     NaN     NaN     NaN  \n",
+              "\n",
+              "[3 rows x 9724 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d9ffa871-1d8a-47a4-b33c-e0fb8d0a2766\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>movieId</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>193565</th>\n",
+              "      <th>193567</th>\n",
+              "      <th>193571</th>\n",
+              "      <th>193573</th>\n",
+              "      <th>193579</th>\n",
+              "      <th>193581</th>\n",
+              "      <th>193583</th>\n",
+              "      <th>193585</th>\n",
+              "      <th>193587</th>\n",
+              "      <th>193609</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>userId</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9724 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9ffa871-1d8a-47a4-b33c-e0fb8d0a2766')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d9ffa871-1d8a-47a4-b33c-e0fb8d0a2766 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d9ffa871-1d8a-47a4-b33c-e0fb8d0a2766');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-3bd34dbc-24ff-46a4-a152-9523b3561eca\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3bd34dbc-24ff-46a4-a152-9523b3561eca')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3bd34dbc-24ff-46a4-a152-9523b3561eca button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rating_movies = pd.merge(ratings, movies, on='movieId')\n",
+        "\n",
+        "ratings_matrix = rating_movies.pivot_table('rating', index='userId', columns='title')\n",
+        "\n",
+        "ratings_matrix = ratings_matrix.fillna(0)\n",
+        "ratings_matrix.head(3)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "4F-DV17CCZVF",
+        "outputId": "6adcac4a-bb3e-4227-9715-cd85c6457374"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "userId                                                        \n",
+              "1              0.0                                      0.0   \n",
+              "2              0.0                                      0.0   \n",
+              "3              0.0                                      0.0   \n",
+              "\n",
+              "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
+              "userId                                                \n",
+              "1                          0.0                  0.0   \n",
+              "2                          0.0                  0.0   \n",
+              "3                          0.0                  0.0   \n",
+              "\n",
+              "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
+              "userId                                                               \n",
+              "1                             0.0                              0.0   \n",
+              "2                             0.0                              0.0   \n",
+              "3                             0.0                              0.0   \n",
+              "\n",
+              "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
+              "userId                                                                          \n",
+              "1                      0.0                   0.0                          0.0   \n",
+              "2                      0.0                   0.0                          0.0   \n",
+              "3                      0.0                   0.0                          0.0   \n",
+              "\n",
+              "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
+              "userId                                  ...                              \n",
+              "1                                  0.0  ...          0.0           0.0   \n",
+              "2                                  0.0  ...          0.0           0.0   \n",
+              "3                                  0.0  ...          0.0           0.0   \n",
+              "\n",
+              "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
+              "userId                                           \n",
+              "1                 0.0                      0.0   \n",
+              "2                 0.0                      0.0   \n",
+              "3                 0.0                      0.0   \n",
+              "\n",
+              "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "userId                                                           \n",
+              "1                                                          0.0   \n",
+              "2                                                          0.0   \n",
+              "3                                                          0.0   \n",
+              "\n",
+              "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
+              "userId                                                                \n",
+              "1                   0.0         0.0                             0.0   \n",
+              "2                   0.0         0.0                             0.0   \n",
+              "3                   0.0         0.0                             0.0   \n",
+              "\n",
+              "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
+              "userId                                                                    \n",
+              "1                         4.0                                        0.0  \n",
+              "2                         0.0                                        0.0  \n",
+              "3                         0.0                                        0.0  \n",
+              "\n",
+              "[3 rows x 9719 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9078652e-3854-4a8a-a5b3-734f06ad5f0d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>title</th>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <th>'Salem's Lot (2004)</th>\n",
+              "      <th>'Til There Was You (1997)</th>\n",
+              "      <th>'Tis the Season for Love (2015)</th>\n",
+              "      <th>'burbs, The (1989)</th>\n",
+              "      <th>'night Mother (1986)</th>\n",
+              "      <th>(500) Days of Summer (2009)</th>\n",
+              "      <th>*batteries not included (1987)</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Zulu (2013)</th>\n",
+              "      <th>[REC] (2007)</th>\n",
+              "      <th>[REC]² (2009)</th>\n",
+              "      <th>[REC]³ 3 Génesis (2012)</th>\n",
+              "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
+              "      <th>eXistenZ (1999)</th>\n",
+              "      <th>xXx (2002)</th>\n",
+              "      <th>xXx: State of the Union (2005)</th>\n",
+              "      <th>¡Three Amigos! (1986)</th>\n",
+              "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>userId</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9078652e-3854-4a8a-a5b3-734f06ad5f0d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9078652e-3854-4a8a-a5b3-734f06ad5f0d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9078652e-3854-4a8a-a5b3-734f06ad5f0d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-9f109c92-12b5-44c4-8c50-9b3ed13b6e36\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9f109c92-12b5-44c4-8c50-9b3ed13b6e36')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-9f109c92-12b5-44c4-8c50-9b3ed13b6e36 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings_matrix_T = ratings_matrix.transpose()\n",
+        "ratings_matrix_T.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "ruB3_Po41m0Y",
+        "outputId": "0ffb7b11-a62e-43d4-8afd-50d330d384c5"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "userId                                   1    2    3    4    5    6    7    \\\n",
+              "title                                                                        \n",
+              "'71 (2014)                               0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+              "'Round Midnight (1986)                   0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+              "\n",
+              "userId                                   8    9    10   ...  601  602  603  \\\n",
+              "title                                                   ...                  \n",
+              "'71 (2014)                               0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
+              "'Round Midnight (1986)                   0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
+              "\n",
+              "userId                                   604  605  606  607  608  609  610  \n",
+              "title                                                                       \n",
+              "'71 (2014)                               0.0  0.0  0.0  0.0  0.0  0.0  4.0  \n",
+              "'Hellboy': The Seeds of Creation (2004)  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+              "'Round Midnight (1986)                   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
+              "\n",
+              "[3 rows x 610 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b2573479-06dd-4c74-9a83-29f3ab745a6f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>userId</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>601</th>\n",
+              "      <th>602</th>\n",
+              "      <th>603</th>\n",
+              "      <th>604</th>\n",
+              "      <th>605</th>\n",
+              "      <th>606</th>\n",
+              "      <th>607</th>\n",
+              "      <th>608</th>\n",
+              "      <th>609</th>\n",
+              "      <th>610</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 610 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2573479-06dd-4c74-9a83-29f3ab745a6f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b2573479-06dd-4c74-9a83-29f3ab745a6f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b2573479-06dd-4c74-9a83-29f3ab745a6f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-5b1394d2-6dca-4a8e-a330-71ad49d5d243\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5b1394d2-6dca-4a8e-a330-71ad49d5d243')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-5b1394d2-6dca-4a8e-a330-71ad49d5d243 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_matrix_T"
+            }
+          },
+          "metadata": {},
+          "execution_count": 47
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics.pairwise import cosine_similarity\n",
+        "\n",
+        "item_sim = cosine_similarity(ratings_matrix_T, ratings_matrix_T)\n",
+        "\n",
+        "item_sim_df = pd.DataFrame(data=item_sim, index=ratings_matrix.columns,\n",
+        "                          columns=ratings_matrix.columns)\n",
+        "print(item_sim_df.shape)\n",
+        "item_sim_df.head(3)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 485
+        },
+        "id": "W7_QtyfT1m2S",
+        "outputId": "70bff159-81c5-4f0c-f7ef-0c16dbe5bf52"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(9719, 9719)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title                                    '71 (2014)  \\\n",
+              "title                                                 \n",
+              "'71 (2014)                                      1.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)         0.0   \n",
+              "'Round Midnight (1986)                          0.0   \n",
+              "\n",
+              "title                                    'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "title                                                                              \n",
+              "'71 (2014)                                                              0.000000   \n",
+              "'Hellboy': The Seeds of Creation (2004)                                 1.000000   \n",
+              "'Round Midnight (1986)                                                  0.707107   \n",
+              "\n",
+              "title                                    'Round Midnight (1986)  \\\n",
+              "title                                                             \n",
+              "'71 (2014)                                             0.000000   \n",
+              "'Hellboy': The Seeds of Creation (2004)                0.707107   \n",
+              "'Round Midnight (1986)                                 1.000000   \n",
+              "\n",
+              "title                                    'Salem's Lot (2004)  \\\n",
+              "title                                                          \n",
+              "'71 (2014)                                               0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                  0.0   \n",
+              "'Round Midnight (1986)                                   0.0   \n",
+              "\n",
+              "title                                    'Til There Was You (1997)  \\\n",
+              "title                                                                \n",
+              "'71 (2014)                                                     0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                        0.0   \n",
+              "'Round Midnight (1986)                                         0.0   \n",
+              "\n",
+              "title                                    'Tis the Season for Love (2015)  \\\n",
+              "title                                                                      \n",
+              "'71 (2014)                                                           0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                              0.0   \n",
+              "'Round Midnight (1986)                                               0.0   \n",
+              "\n",
+              "title                                    'burbs, The (1989)  \\\n",
+              "title                                                         \n",
+              "'71 (2014)                                         0.000000   \n",
+              "'Hellboy': The Seeds of Creation (2004)            0.000000   \n",
+              "'Round Midnight (1986)                             0.176777   \n",
+              "\n",
+              "title                                    'night Mother (1986)  \\\n",
+              "title                                                           \n",
+              "'71 (2014)                                                0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                   0.0   \n",
+              "'Round Midnight (1986)                                    0.0   \n",
+              "\n",
+              "title                                    (500) Days of Summer (2009)  \\\n",
+              "title                                                                  \n",
+              "'71 (2014)                                                  0.141653   \n",
+              "'Hellboy': The Seeds of Creation (2004)                     0.000000   \n",
+              "'Round Midnight (1986)                                      0.000000   \n",
+              "\n",
+              "title                                    *batteries not included (1987)  ...  \\\n",
+              "title                                                                    ...   \n",
+              "'71 (2014)                                                          0.0  ...   \n",
+              "'Hellboy': The Seeds of Creation (2004)                             0.0  ...   \n",
+              "'Round Midnight (1986)                                              0.0  ...   \n",
+              "\n",
+              "title                                    Zulu (2013)  [REC] (2007)  \\\n",
+              "title                                                                \n",
+              "'71 (2014)                                       0.0      0.342055   \n",
+              "'Hellboy': The Seeds of Creation (2004)          0.0      0.000000   \n",
+              "'Round Midnight (1986)                           0.0      0.000000   \n",
+              "\n",
+              "title                                    [REC]² (2009)  \\\n",
+              "title                                                    \n",
+              "'71 (2014)                                    0.543305   \n",
+              "'Hellboy': The Seeds of Creation (2004)       0.000000   \n",
+              "'Round Midnight (1986)                        0.000000   \n",
+              "\n",
+              "title                                    [REC]³ 3 Génesis (2012)  \\\n",
+              "title                                                              \n",
+              "'71 (2014)                                              0.707107   \n",
+              "'Hellboy': The Seeds of Creation (2004)                 0.000000   \n",
+              "'Round Midnight (1986)                                  0.000000   \n",
+              "\n",
+              "title                                    anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "title                                                                                             \n",
+              "'71 (2014)                                                                                  0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                                                     0.0   \n",
+              "'Round Midnight (1986)                                                                      0.0   \n",
+              "\n",
+              "title                                    eXistenZ (1999)  xXx (2002)  \\\n",
+              "title                                                                  \n",
+              "'71 (2014)                                           0.0    0.139431   \n",
+              "'Hellboy': The Seeds of Creation (2004)              0.0    0.000000   \n",
+              "'Round Midnight (1986)                               0.0    0.000000   \n",
+              "\n",
+              "title                                    xXx: State of the Union (2005)  \\\n",
+              "title                                                                     \n",
+              "'71 (2014)                                                     0.327327   \n",
+              "'Hellboy': The Seeds of Creation (2004)                        0.000000   \n",
+              "'Round Midnight (1986)                                         0.000000   \n",
+              "\n",
+              "title                                    ¡Three Amigos! (1986)  \\\n",
+              "title                                                            \n",
+              "'71 (2014)                                                 0.0   \n",
+              "'Hellboy': The Seeds of Creation (2004)                    0.0   \n",
+              "'Round Midnight (1986)                                     0.0   \n",
+              "\n",
+              "title                                    À nous la liberté (Freedom for Us) (1931)  \n",
+              "title                                                                               \n",
+              "'71 (2014)                                                                     0.0  \n",
+              "'Hellboy': The Seeds of Creation (2004)                                        0.0  \n",
+              "'Round Midnight (1986)                                                         0.0  \n",
+              "\n",
+              "[3 rows x 9719 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7d1965bb-789b-4a4d-8d98-cbf0fe3aaaf6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>title</th>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <th>'Salem's Lot (2004)</th>\n",
+              "      <th>'Til There Was You (1997)</th>\n",
+              "      <th>'Tis the Season for Love (2015)</th>\n",
+              "      <th>'burbs, The (1989)</th>\n",
+              "      <th>'night Mother (1986)</th>\n",
+              "      <th>(500) Days of Summer (2009)</th>\n",
+              "      <th>*batteries not included (1987)</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Zulu (2013)</th>\n",
+              "      <th>[REC] (2007)</th>\n",
+              "      <th>[REC]² (2009)</th>\n",
+              "      <th>[REC]³ 3 Génesis (2012)</th>\n",
+              "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
+              "      <th>eXistenZ (1999)</th>\n",
+              "      <th>xXx (2002)</th>\n",
+              "      <th>xXx: State of the Union (2005)</th>\n",
+              "      <th>¡Three Amigos! (1986)</th>\n",
+              "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.141653</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.342055</td>\n",
+              "      <td>0.543305</td>\n",
+              "      <td>0.707107</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.139431</td>\n",
+              "      <td>0.327327</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.707107</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.707107</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.176777</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d1965bb-789b-4a4d-8d98-cbf0fe3aaaf6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7d1965bb-789b-4a4d-8d98-cbf0fe3aaaf6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7d1965bb-789b-4a4d-8d98-cbf0fe3aaaf6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d9813b59-0462-45e1-af71-ec64da7833ae\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d9813b59-0462-45e1-af71-ec64da7833ae')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d9813b59-0462-45e1-af71-ec64da7833ae button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "item_sim_df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "item_sim_df[\"Godfather, The (1972)\"].sort_values(ascending=False)[:6]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 303
+        },
+        "id": "IUd33H411m4P",
+        "outputId": "eb12e4d6-d888-40d5-f748-8dedba351e9f"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title\n",
+              "Godfather, The (1972)                        1.000000\n",
+              "Godfather: Part II, The (1974)               0.821773\n",
+              "Goodfellas (1990)                            0.664841\n",
+              "One Flew Over the Cuckoo's Nest (1975)       0.620536\n",
+              "Star Wars: Episode IV - A New Hope (1977)    0.595317\n",
+              "Fargo (1996)                                 0.588614\n",
+              "Name: Godfather, The (1972), dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Godfather, The (1972)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Godfather, The (1972)</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Godfather: Part II, The (1974)</th>\n",
+              "      <td>0.821773</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Goodfellas (1990)</th>\n",
+              "      <td>0.664841</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>One Flew Over the Cuckoo's Nest (1975)</th>\n",
+              "      <td>0.620536</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n",
+              "      <td>0.595317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fargo (1996)</th>\n",
+              "      <td>0.588614</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 49
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "item_sim_df[\"Inception (2010)\"].sort_values(ascending=False)[1:6]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "id": "TbpMMG6m1m6D",
+        "outputId": "650e7e97-b734-4f9f-f47a-a7661e694c31"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title\n",
+              "Dark Knight, The (2008)          0.727263\n",
+              "Inglourious Basterds (2009)      0.646103\n",
+              "Shutter Island (2010)            0.617736\n",
+              "Dark Knight Rises, The (2012)    0.617504\n",
+              "Fight Club (1999)                0.615417\n",
+              "Name: Inception (2010), dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Inception (2010)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Dark Knight, The (2008)</th>\n",
+              "      <td>0.727263</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Inglourious Basterds (2009)</th>\n",
+              "      <td>0.646103</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Shutter Island (2010)</th>\n",
+              "      <td>0.617736</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Dark Knight Rises, The (2012)</th>\n",
+              "      <td>0.617504</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fight Club (1999)</th>\n",
+              "      <td>0.615417</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 50
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def predict_rating(ratings_arr, item_sim_arr ):\n",
+        "    ratings_pred = ratings_arr.dot(item_sim_arr)/ np.array([np.abs(item_sim_arr).sum(axis=1)])\n",
+        "    return ratings_pred\n"
+      ],
+      "metadata": {
+        "id": "MrZq7npT1m8I"
+      },
+      "execution_count": 51,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings_pred = predict_rating(ratings_matrix.values , item_sim_df.values)\n",
+        "ratings_pred_matrix = pd.DataFrame(data=ratings_pred, index= ratings_matrix.index,\n",
+        "                                   columns = ratings_matrix.columns)\n",
+        "ratings_pred_matrix.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "Ail4whyR1m-F",
+        "outputId": "abd58e34-fdcc-4fc8-ef79-a2640c0dc22e"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "userId                                                        \n",
+              "1         0.070345                                 0.577855   \n",
+              "2         0.018260                                 0.042744   \n",
+              "3         0.011884                                 0.030279   \n",
+              "\n",
+              "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
+              "userId                                                \n",
+              "1                     0.321696             0.227055   \n",
+              "2                     0.018861             0.000000   \n",
+              "3                     0.064437             0.003762   \n",
+              "\n",
+              "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
+              "userId                                                               \n",
+              "1                        0.206958                         0.194615   \n",
+              "2                        0.000000                         0.035995   \n",
+              "3                        0.003749                         0.002722   \n",
+              "\n",
+              "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
+              "userId                                                                          \n",
+              "1                 0.249883              0.102542                     0.157084   \n",
+              "2                 0.013413              0.002314                     0.032213   \n",
+              "3                 0.014625              0.002085                     0.005666   \n",
+              "\n",
+              "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
+              "userId                                  ...                              \n",
+              "1                             0.178197  ...     0.113608      0.181738   \n",
+              "2                             0.014863  ...     0.015640      0.020855   \n",
+              "3                             0.006272  ...     0.006923      0.011665   \n",
+              "\n",
+              "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
+              "userId                                           \n",
+              "1            0.133962                 0.128574   \n",
+              "2            0.020119                 0.015745   \n",
+              "3            0.011800                 0.012225   \n",
+              "\n",
+              "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "userId                                                           \n",
+              "1                                                     0.006179   \n",
+              "2                                                     0.049983   \n",
+              "3                                                     0.000000   \n",
+              "\n",
+              "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
+              "userId                                                                \n",
+              "1              0.212070    0.192921                        0.136024   \n",
+              "2              0.014876    0.021616                        0.024528   \n",
+              "3              0.008194    0.007017                        0.009229   \n",
+              "\n",
+              "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
+              "userId                                                                    \n",
+              "1                    0.292955                                   0.720347  \n",
+              "2                    0.017563                                   0.000000  \n",
+              "3                    0.010420                                   0.084501  \n",
+              "\n",
+              "[3 rows x 9719 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1ec65842-c32e-459c-bb4a-9a9b3349cae6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>title</th>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <th>'Salem's Lot (2004)</th>\n",
+              "      <th>'Til There Was You (1997)</th>\n",
+              "      <th>'Tis the Season for Love (2015)</th>\n",
+              "      <th>'burbs, The (1989)</th>\n",
+              "      <th>'night Mother (1986)</th>\n",
+              "      <th>(500) Days of Summer (2009)</th>\n",
+              "      <th>*batteries not included (1987)</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Zulu (2013)</th>\n",
+              "      <th>[REC] (2007)</th>\n",
+              "      <th>[REC]² (2009)</th>\n",
+              "      <th>[REC]³ 3 Génesis (2012)</th>\n",
+              "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
+              "      <th>eXistenZ (1999)</th>\n",
+              "      <th>xXx (2002)</th>\n",
+              "      <th>xXx: State of the Union (2005)</th>\n",
+              "      <th>¡Three Amigos! (1986)</th>\n",
+              "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>userId</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.070345</td>\n",
+              "      <td>0.577855</td>\n",
+              "      <td>0.321696</td>\n",
+              "      <td>0.227055</td>\n",
+              "      <td>0.206958</td>\n",
+              "      <td>0.194615</td>\n",
+              "      <td>0.249883</td>\n",
+              "      <td>0.102542</td>\n",
+              "      <td>0.157084</td>\n",
+              "      <td>0.178197</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.113608</td>\n",
+              "      <td>0.181738</td>\n",
+              "      <td>0.133962</td>\n",
+              "      <td>0.128574</td>\n",
+              "      <td>0.006179</td>\n",
+              "      <td>0.212070</td>\n",
+              "      <td>0.192921</td>\n",
+              "      <td>0.136024</td>\n",
+              "      <td>0.292955</td>\n",
+              "      <td>0.720347</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.018260</td>\n",
+              "      <td>0.042744</td>\n",
+              "      <td>0.018861</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.035995</td>\n",
+              "      <td>0.013413</td>\n",
+              "      <td>0.002314</td>\n",
+              "      <td>0.032213</td>\n",
+              "      <td>0.014863</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.015640</td>\n",
+              "      <td>0.020855</td>\n",
+              "      <td>0.020119</td>\n",
+              "      <td>0.015745</td>\n",
+              "      <td>0.049983</td>\n",
+              "      <td>0.014876</td>\n",
+              "      <td>0.021616</td>\n",
+              "      <td>0.024528</td>\n",
+              "      <td>0.017563</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.011884</td>\n",
+              "      <td>0.030279</td>\n",
+              "      <td>0.064437</td>\n",
+              "      <td>0.003762</td>\n",
+              "      <td>0.003749</td>\n",
+              "      <td>0.002722</td>\n",
+              "      <td>0.014625</td>\n",
+              "      <td>0.002085</td>\n",
+              "      <td>0.005666</td>\n",
+              "      <td>0.006272</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.006923</td>\n",
+              "      <td>0.011665</td>\n",
+              "      <td>0.011800</td>\n",
+              "      <td>0.012225</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.008194</td>\n",
+              "      <td>0.007017</td>\n",
+              "      <td>0.009229</td>\n",
+              "      <td>0.010420</td>\n",
+              "      <td>0.084501</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ec65842-c32e-459c-bb4a-9a9b3349cae6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1ec65842-c32e-459c-bb4a-9a9b3349cae6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-1ec65842-c32e-459c-bb4a-9a9b3349cae6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c3e9a23e-f838-4686-895e-880e2109cddf\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c3e9a23e-f838-4686-895e-880e2109cddf')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c3e9a23e-f838-4686-895e-880e2109cddf button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_pred_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "def get_mse(pred, actual):\n",
+        "\n",
+        "    pred = pred[actual.nonzero()].flatten()\n",
+        "    actual = actual[actual.nonzero()].flatten()\n",
+        "    return mean_squared_error(pred, actual)\n",
+        "\n",
+        "print('아이템 기반 모든 인접 이웃 MSE: ', get_mse(ratings_pred, ratings_matrix.values ))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6o11izyk1nAB",
+        "outputId": "01ee4981-365f-47d7-fa3b-28853a213c0e"
+      },
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "아이템 기반 모든 인접 이웃 MSE:  9.895354759094706\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def predict_rating_topsim(ratings_arr, item_sim_arr, n=20):\n",
+        "\n",
+        "    pred = np.zeros(ratings_arr.shape)\n",
+        "\n",
+        "    for col in range(ratings_arr.shape[1]):\n",
+        "\n",
+        "        top_n_items = [np.argsort(item_sim_arr[:, col])[:-n-1:-1]]\n",
+        "\n",
+        "        for row in range(ratings_arr.shape[0]):\n",
+        "            pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+        "            pred[row, col] /= np.sum(np.abs(item_sim_arr[col, :][top_n_items]))\n",
+        "    return pred\n"
+      ],
+      "metadata": {
+        "id": "tsMRVwPj1nCF"
+      },
+      "execution_count": 54,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings_pred = predict_rating_topsim(ratings_matrix.values , item_sim_df.values, n=20)\n",
+        "print('아이템 기반 인접 TOP-20 이웃 MSE: ', get_mse(ratings_pred, ratings_matrix.values ))\n",
+        "\n",
+        "\n",
+        "ratings_pred_matrix = pd.DataFrame(data=ratings_pred, index= ratings_matrix.index,\n",
+        "                                   columns = ratings_matrix.columns)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "S7WrkrN71nEH",
+        "outputId": "3972ba4c-6501-4989-9515-7f0e98f88d2a"
+      },
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n",
+            "/tmp/ipython-input-54-124057145.py:10: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "아이템 기반 인접 TOP-20 이웃 MSE:  3.694409449382562\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "user_rating_id = ratings_matrix.loc[9, :]\n",
+        "user_rating_id[ user_rating_id > 0].sort_values(ascending=False)[:10]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 429
+        },
+        "id": "Rixs_bAPGJ4E",
+        "outputId": "1e6f884c-211e-4d59-f414-a1d55fe7dfc6"
+      },
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title\n",
+              "Adaptation (2002)                                                                 5.0\n",
+              "Austin Powers in Goldmember (2002)                                                5.0\n",
+              "Back to the Future (1985)                                                         5.0\n",
+              "Citizen Kane (1941)                                                               5.0\n",
+              "Lord of the Rings: The Fellowship of the Ring, The (2001)                         5.0\n",
+              "Lord of the Rings: The Two Towers, The (2002)                                     5.0\n",
+              "Producers, The (1968)                                                             5.0\n",
+              "Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)    5.0\n",
+              "Elling (2001)                                                                     4.0\n",
+              "King of Comedy, The (1983)                                                        4.0\n",
+              "Name: 9, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>9</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Adaptation (2002)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Austin Powers in Goldmember (2002)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Back to the Future (1985)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Citizen Kane (1941)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Lord of the Rings: The Fellowship of the Ring, The (2001)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Lord of the Rings: The Two Towers, The (2002)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Producers, The (1968)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)</th>\n",
+              "      <td>5.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Elling (2001)</th>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>King of Comedy, The (1983)</th>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_unseen_movies(ratings_matrix, userId):\n",
+        "\n",
+        "    user_rating = ratings_matrix.loc[userId,:]\n",
+        "\n",
+        "    already_seen = user_rating[ user_rating > 0].index.tolist()\n",
+        "\n",
+        "    movies_list = ratings_matrix.columns.tolist()\n",
+        "\n",
+        "    unseen_list = [ movie for movie in movies_list if movie not in already_seen]\n",
+        "\n",
+        "    return unseen_list\n"
+      ],
+      "metadata": {
+        "id": "M8ELnAqPGJ6H"
+      },
+      "execution_count": 57,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def recomm_movie_by_userid(pred_df, userId, unseen_list, top_n=10):\n",
+        "\n",
+        "    recomm_movies = pred_df.loc[userId, unseen_list].sort_values(ascending=False)[:top_n]\n",
+        "    return recomm_movies\n",
+        "\n",
+        "unseen_list = get_unseen_movies(ratings_matrix, 9)\n",
+        "\n",
+        "recomm_movies = recomm_movie_by_userid(ratings_pred_matrix, 9, unseen_list, top_n=10)\n",
+        "\n",
+        "recomm_movies = pd.DataFrame(data=recomm_movies.values,index=recomm_movies.index,columns=['pred_score'])\n",
+        "recomm_movies\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 411
+        },
+        "id": "RaWlNCrQGJ78",
+        "outputId": "9ba20918-d7e7-4a85-fd17-b8882342648f"
+      },
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                                                                pred_score\n",
+              "title                                                                                                     \n",
+              "Shrek (2001)                                                                                      0.866202\n",
+              "Spider-Man (2002)                                                                                 0.857854\n",
+              "Last Samurai, The (2003)                                                                          0.817473\n",
+              "Indiana Jones and the Temple of Doom (1984)                                                       0.816626\n",
+              "Matrix Reloaded, The (2003)                                                                       0.800990\n",
+              "Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)    0.765159\n",
+              "Gladiator (2000)                                                                                  0.740956\n",
+              "Matrix, The (1999)                                                                                0.732693\n",
+              "Pirates of the Caribbean: The Curse of the Black Pearl (2003)                                     0.689591\n",
+              "Lord of the Rings: The Return of the King, The (2003)                                             0.676711"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-980afbdb-0b45-4898-99e2-0d0cef437cbf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pred_score</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Shrek (2001)</th>\n",
+              "      <td>0.866202</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Spider-Man (2002)</th>\n",
+              "      <td>0.857854</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Last Samurai, The (2003)</th>\n",
+              "      <td>0.817473</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Indiana Jones and the Temple of Doom (1984)</th>\n",
+              "      <td>0.816626</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Matrix Reloaded, The (2003)</th>\n",
+              "      <td>0.800990</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)</th>\n",
+              "      <td>0.765159</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gladiator (2000)</th>\n",
+              "      <td>0.740956</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Matrix, The (1999)</th>\n",
+              "      <td>0.732693</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pirates of the Caribbean: The Curse of the Black Pearl (2003)</th>\n",
+              "      <td>0.689591</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Lord of the Rings: The Return of the King, The (2003)</th>\n",
+              "      <td>0.676711</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-980afbdb-0b45-4898-99e2-0d0cef437cbf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-980afbdb-0b45-4898-99e2-0d0cef437cbf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-980afbdb-0b45-4898-99e2-0d0cef437cbf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-9382653d-eb86-4620-a8b1-e53d798f2495\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9382653d-eb86-4620-a8b1-e53d798f2495')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-9382653d-eb86-4620-a8b1-e53d798f2495 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_918d7917-d315-4ed8-9ef6-94d892d43a57\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('recomm_movies')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_918d7917-d315-4ed8-9ef6-94d892d43a57 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('recomm_movies');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "recomm_movies",
+              "summary": "{\n  \"name\": \"recomm_movies\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Pirates of the Caribbean: The Curse of the Black Pearl (2003)\",\n          \"Spider-Man (2002)\",\n          \"Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06614432811511851,\n        \"min\": 0.6767108283499336,\n        \"max\": 0.8662018746933645,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.6895905595608812,\n          0.8578535950426878,\n          0.7651586070058114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 58
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "def get_rmse(R, P, Q, non_zeros):\n",
+        "    error = 0\n",
+        "\n",
+        "    full_pred_matrix = np.dot(P, Q.T)\n",
+        "\n",
+        "    x_non_zero_ind = [non_zero[0] for non_zero in non_zeros]\n",
+        "    y_non_zero_ind = [non_zero[1] for non_zero in non_zeros]\n",
+        "    R_non_zeros = R[x_non_zero_ind, y_non_zero_ind]\n",
+        "\n",
+        "    full_pred_matrix_non_zeros = full_pred_matrix[x_non_zero_ind, y_non_zero_ind]\n",
+        "\n",
+        "    mse = mean_squared_error(R_non_zeros, full_pred_matrix_non_zeros)\n",
+        "    rmse = np.sqrt(mse)\n",
+        "\n",
+        "    return rmse\n"
+      ],
+      "metadata": {
+        "id": "cIPedxStGJ9_"
+      },
+      "execution_count": 62,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def matrix_factorization(R, K, steps=200, learning_rate=0.01, r_lambda = 0.01):\n",
+        "    num_users, num_items = R.shape\n",
+        "\n",
+        "    np.random.seed(1)\n",
+        "    P = np.random.normal(scale=1./K, size=(num_users, K))\n",
+        "    Q = np.random.normal(scale=1./K, size=(num_items, K))\n",
+        "\n",
+        "    non_zeros = [ (i, j, R[i,j]) for i in range(num_users) for j in range(num_items) if R[i,j] > 0 ]\n",
+        "\n",
+        "    for step in range(steps):\n",
+        "        for i, j, r in non_zeros:\n",
+        "\n",
+        "            eij = r - np.dot(P[i, :], Q[j, :].T)\n",
+        "\n",
+        "            P[i,:] = P[i,:] + learning_rate*(eij * Q[j, :] - r_lambda*P[i,:])\n",
+        "            Q[j,:] = Q[j,:] + learning_rate*(eij * P[i, :] - r_lambda*Q[j,:])\n",
+        "\n",
+        "        rmse = get_rmse(R, P, Q, non_zeros)\n",
+        "        if (step % 10) == 0 :\n",
+        "            print(\"### iteration step : \", step,\" rmse : \", rmse)\n",
+        "\n",
+        "    return P, Q"
+      ],
+      "metadata": {
+        "id": "ObQRjW83GKAn"
+      },
+      "execution_count": 63,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "movies = pd.read_csv('movies.csv')\n",
+        "ratings = pd.read_csv('ratings.csv')\n",
+        "ratings = ratings[['userId', 'movieId', 'rating']]\n",
+        "ratings_matrix = ratings.pivot_table('rating', index='userId', columns='movieId')\n",
+        "\n",
+        "rating_movies = pd.merge(ratings, movies, on='movieId')\n",
+        "\n",
+        "ratings_matrix = rating_movies.pivot_table('rating', index='userId', columns='title')"
+      ],
+      "metadata": {
+        "id": "U6fn8fDrGKC9"
+      },
+      "execution_count": 64,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "P, Q = matrix_factorization(ratings_matrix.values, K=50, steps=200, learning_rate=0.01, r_lambda = 0.01)\n",
+        "pred_matrix = np.dot(P, Q.T)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "A9ZsIti1GKE3",
+        "outputId": "d9d11893-05d9-483e-e5df-7e0b76a07340"
+      },
+      "execution_count": 65,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "### iteration step :  0  rmse :  2.9023619751336867\n",
+            "### iteration step :  10  rmse :  0.7335768591017927\n",
+            "### iteration step :  20  rmse :  0.5115539026853442\n",
+            "### iteration step :  30  rmse :  0.37261628282537446\n",
+            "### iteration step :  40  rmse :  0.2960818299181014\n",
+            "### iteration step :  50  rmse :  0.2520353192341642\n",
+            "### iteration step :  60  rmse :  0.22487503275269854\n",
+            "### iteration step :  70  rmse :  0.2068545530233154\n",
+            "### iteration step :  80  rmse :  0.19413418783028685\n",
+            "### iteration step :  90  rmse :  0.18470082002720406\n",
+            "### iteration step :  100  rmse :  0.17742927527209104\n",
+            "### iteration step :  110  rmse :  0.1716522696470749\n",
+            "### iteration step :  120  rmse :  0.16695181946871726\n",
+            "### iteration step :  130  rmse :  0.16305292191997542\n",
+            "### iteration step :  140  rmse :  0.15976691929679646\n",
+            "### iteration step :  150  rmse :  0.1569598699945732\n",
+            "### iteration step :  160  rmse :  0.15453398186715425\n",
+            "### iteration step :  170  rmse :  0.15241618551077643\n",
+            "### iteration step :  180  rmse :  0.1505508073962831\n",
+            "### iteration step :  190  rmse :  0.1488947091323209\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ratings_pred_matrix = pd.DataFrame(data=pred_matrix, index= ratings_matrix.index,\n",
+        "                                   columns = ratings_matrix.columns)\n",
+        "\n",
+        "ratings_pred_matrix.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "S64lj_etHy_p",
+        "outputId": "a4f7b2e4-8e28-4d0f-e72e-12ff3d9127b0"
+      },
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
+              "userId                                                        \n",
+              "1         3.055084                                 4.092018   \n",
+              "2         3.170119                                 3.657992   \n",
+              "3         2.307073                                 1.658853   \n",
+              "\n",
+              "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
+              "userId                                                \n",
+              "1                     3.564130             4.502167   \n",
+              "2                     3.308707             4.166521   \n",
+              "3                     1.443538             2.208859   \n",
+              "\n",
+              "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
+              "userId                                                               \n",
+              "1                        3.981215                         1.271694   \n",
+              "2                        4.311890                         1.275469   \n",
+              "3                        2.229486                         0.780760   \n",
+              "\n",
+              "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
+              "userId                                                                          \n",
+              "1                 3.603274              2.333266                     5.091749   \n",
+              "2                 4.237972              1.900366                     3.392859   \n",
+              "3                 1.997043              0.924908                     2.970700   \n",
+              "\n",
+              "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
+              "userId                                  ...                              \n",
+              "1                             3.972454  ...     1.402608      4.208382   \n",
+              "2                             3.647421  ...     0.973811      3.528264   \n",
+              "3                             2.551446  ...     0.520354      1.709494   \n",
+              "\n",
+              "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
+              "userId                                           \n",
+              "1            3.705957                 2.720514   \n",
+              "2            3.361532                 2.672535   \n",
+              "3            2.281596                 1.782833   \n",
+              "\n",
+              "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
+              "userId                                                           \n",
+              "1                                                     2.787331   \n",
+              "2                                                     2.404456   \n",
+              "3                                                     1.635173   \n",
+              "\n",
+              "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
+              "userId                                                                \n",
+              "1              3.475076    3.253458                        2.161087   \n",
+              "2              4.232789    2.911602                        1.634576   \n",
+              "3              1.323276    2.887580                        1.042618   \n",
+              "\n",
+              "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
+              "userId                                                                    \n",
+              "1                    4.010495                                   0.859474  \n",
+              "2                    4.135735                                   0.725684  \n",
+              "3                    2.293890                                   0.396941  \n",
+              "\n",
+              "[3 rows x 9719 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b54fc03b-28d4-4198-b409-225252f62c04\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>title</th>\n",
+              "      <th>'71 (2014)</th>\n",
+              "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
+              "      <th>'Round Midnight (1986)</th>\n",
+              "      <th>'Salem's Lot (2004)</th>\n",
+              "      <th>'Til There Was You (1997)</th>\n",
+              "      <th>'Tis the Season for Love (2015)</th>\n",
+              "      <th>'burbs, The (1989)</th>\n",
+              "      <th>'night Mother (1986)</th>\n",
+              "      <th>(500) Days of Summer (2009)</th>\n",
+              "      <th>*batteries not included (1987)</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Zulu (2013)</th>\n",
+              "      <th>[REC] (2007)</th>\n",
+              "      <th>[REC]² (2009)</th>\n",
+              "      <th>[REC]³ 3 Génesis (2012)</th>\n",
+              "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
+              "      <th>eXistenZ (1999)</th>\n",
+              "      <th>xXx (2002)</th>\n",
+              "      <th>xXx: State of the Union (2005)</th>\n",
+              "      <th>¡Three Amigos! (1986)</th>\n",
+              "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>userId</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3.055084</td>\n",
+              "      <td>4.092018</td>\n",
+              "      <td>3.564130</td>\n",
+              "      <td>4.502167</td>\n",
+              "      <td>3.981215</td>\n",
+              "      <td>1.271694</td>\n",
+              "      <td>3.603274</td>\n",
+              "      <td>2.333266</td>\n",
+              "      <td>5.091749</td>\n",
+              "      <td>3.972454</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.402608</td>\n",
+              "      <td>4.208382</td>\n",
+              "      <td>3.705957</td>\n",
+              "      <td>2.720514</td>\n",
+              "      <td>2.787331</td>\n",
+              "      <td>3.475076</td>\n",
+              "      <td>3.253458</td>\n",
+              "      <td>2.161087</td>\n",
+              "      <td>4.010495</td>\n",
+              "      <td>0.859474</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.170119</td>\n",
+              "      <td>3.657992</td>\n",
+              "      <td>3.308707</td>\n",
+              "      <td>4.166521</td>\n",
+              "      <td>4.311890</td>\n",
+              "      <td>1.275469</td>\n",
+              "      <td>4.237972</td>\n",
+              "      <td>1.900366</td>\n",
+              "      <td>3.392859</td>\n",
+              "      <td>3.647421</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.973811</td>\n",
+              "      <td>3.528264</td>\n",
+              "      <td>3.361532</td>\n",
+              "      <td>2.672535</td>\n",
+              "      <td>2.404456</td>\n",
+              "      <td>4.232789</td>\n",
+              "      <td>2.911602</td>\n",
+              "      <td>1.634576</td>\n",
+              "      <td>4.135735</td>\n",
+              "      <td>0.725684</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2.307073</td>\n",
+              "      <td>1.658853</td>\n",
+              "      <td>1.443538</td>\n",
+              "      <td>2.208859</td>\n",
+              "      <td>2.229486</td>\n",
+              "      <td>0.780760</td>\n",
+              "      <td>1.997043</td>\n",
+              "      <td>0.924908</td>\n",
+              "      <td>2.970700</td>\n",
+              "      <td>2.551446</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.520354</td>\n",
+              "      <td>1.709494</td>\n",
+              "      <td>2.281596</td>\n",
+              "      <td>1.782833</td>\n",
+              "      <td>1.635173</td>\n",
+              "      <td>1.323276</td>\n",
+              "      <td>2.887580</td>\n",
+              "      <td>1.042618</td>\n",
+              "      <td>2.293890</td>\n",
+              "      <td>0.396941</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 9719 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b54fc03b-28d4-4198-b409-225252f62c04')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b54fc03b-28d4-4198-b409-225252f62c04 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b54fc03b-28d4-4198-b409-225252f62c04');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c5c9a189-11cf-421b-9f17-7f4f314b6488\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5c9a189-11cf-421b-9f17-7f4f314b6488')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c5c9a189-11cf-421b-9f17-7f4f314b6488 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings_pred_matrix"
+            }
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_unseen_movies(ratings_matrix, userId):\n",
+        "\n",
+        "    user_rating = ratings_matrix.loc[userId,:]\n",
+        "\n",
+        "    already_seen = user_rating[ user_rating > 0].index.tolist()\n",
+        "\n",
+        "    movies_list = ratings_matrix.columns.tolist()\n",
+        "\n",
+        "    unseen_list = [ movie for movie in movies_list if movie not in already_seen]\n",
+        "\n",
+        "    return unseen_list"
+      ],
+      "metadata": {
+        "id": "OMDohuMEHzB1"
+      },
+      "execution_count": 67,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def recomm_movie_by_userid(pred_df, userId, unseen_list, top_n=10):\n",
+        "    recomm_movies = pred_df.loc[userId, unseen_list].sort_values(ascending=False)[:top_n]\n",
+        "    return recomm_movies"
+      ],
+      "metadata": {
+        "id": "O6Ulv6cnHzDn"
+      },
+      "execution_count": 68,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "unseen_list = get_unseen_movies(ratings_matrix, 9)\n",
+        "\n",
+        "recomm_movies = recomm_movie_by_userid(ratings_pred_matrix, 9, unseen_list, top_n=10)\n",
+        "\n",
+        "recomm_movies = pd.DataFrame(data=recomm_movies.values,index=recomm_movies.index,columns=['pred_score'])\n",
+        "recomm_movies"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "x520S7O8HzHl",
+        "outputId": "4644047e-dd48-4b4a-8213-74ad8e988594"
+      },
+      "execution_count": 69,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                       pred_score\n",
+              "title                                                            \n",
+              "Rear Window (1954)                                       5.704612\n",
+              "South Park: Bigger, Longer and Uncut (1999)              5.451100\n",
+              "Rounders (1998)                                          5.298393\n",
+              "Blade Runner (1982)                                      5.244951\n",
+              "Roger & Me (1989)                                        5.191962\n",
+              "Gattaca (1997)                                           5.183179\n",
+              "Ben-Hur (1959)                                           5.130463\n",
+              "Rosencrantz and Guildenstern Are Dead (1990)             5.087375\n",
+              "Big Lebowski, The (1998)                                 5.038690\n",
+              "Star Wars: Episode V - The Empire Strikes Back (1980)    4.989601"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8ee59643-3d45-4e23-adcb-b56ab3999946\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>pred_score</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>title</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Rear Window (1954)</th>\n",
+              "      <td>5.704612</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>South Park: Bigger, Longer and Uncut (1999)</th>\n",
+              "      <td>5.451100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rounders (1998)</th>\n",
+              "      <td>5.298393</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Blade Runner (1982)</th>\n",
+              "      <td>5.244951</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Roger &amp; Me (1989)</th>\n",
+              "      <td>5.191962</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gattaca (1997)</th>\n",
+              "      <td>5.183179</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ben-Hur (1959)</th>\n",
+              "      <td>5.130463</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rosencrantz and Guildenstern Are Dead (1990)</th>\n",
+              "      <td>5.087375</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Big Lebowski, The (1998)</th>\n",
+              "      <td>5.038690</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Star Wars: Episode V - The Empire Strikes Back (1980)</th>\n",
+              "      <td>4.989601</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ee59643-3d45-4e23-adcb-b56ab3999946')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8ee59643-3d45-4e23-adcb-b56ab3999946 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8ee59643-3d45-4e23-adcb-b56ab3999946');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-68cd3734-a3d4-4d36-a155-47c23cca548d\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-68cd3734-a3d4-4d36-a155-47c23cca548d')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-68cd3734-a3d4-4d36-a155-47c23cca548d button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_f4ddf921-bde2-490b-be1a-ec7829e9fcff\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('recomm_movies')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_f4ddf921-bde2-490b-be1a-ec7829e9fcff button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('recomm_movies');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "recomm_movies",
+              "summary": "{\n  \"name\": \"recomm_movies\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Big Lebowski, The (1998)\",\n          \"South Park: Bigger, Longer and Uncut (1999)\",\n          \"Gattaca (1997)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21272885538651393,\n        \"min\": 4.989601238872484,\n        \"max\": 5.704612469838172,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          5.0386897288205725,\n          5.451100205772531,\n          5.183178550884765\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 69
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\236\245\354\235\270\354\204\234.pdf" "b/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\236\245\354\235\270\354\204\234.pdf"
new file mode 100644
index 0000000..38ba90d
Binary files /dev/null and "b/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\236\245\354\235\270\354\204\234.pdf" differ
diff --git "a/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\275\224\353\223\234\355\225\204\354\202\254.ipynb" "b/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\275\224\353\223\234\355\225\204\354\202\254.ipynb"
new file mode 100644
index 0000000..a3ec077
--- /dev/null
+++ "b/Week16_\354\230\210\354\212\265\352\263\274\354\240\234_\354\275\224\353\223\234\355\225\204\354\202\254.ipynb"
@@ -0,0 +1,1511 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JoWGPoPKCTDh"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "R = np.array([[4, np.nan, np.nan, 2, np.nan],\n",
+        "              [np.nan, 5, np.nan, 3, 1],\n",
+        "              [np.nan, np.nan, 3, 4, 4],\n",
+        "              [5, 2, 1, 2, np.nan]])\n",
+        "num_users, num_items = R.shape\n",
+        "K=3\n",
+        "\n",
+        "np.random.seed(1)\n",
+        "P = np.random.normal(scale=1./K, size=(num_users, K))\n",
+        "Q = np.random.normal(scale=1./K, size=(num_items, K))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "def get_rmse(R, P, Q, non_zeros):\n",
+        "    error = 0\n",
+        "\n",
+        "    full_pred_matrix = np.dot(P, Q.T)\n",
+        "\n",
+        "    x_non_zero_ind = [non_zero[0] for non_zero in non_zeros]\n",
+        "    y_non_zero_ind = [non_zero[1] for non_zero in non_zeros]\n",
+        "    R_non_zeros = R[x_non_zero_ind, y_non_zero_ind]\n",
+        "    full_pred_matrix_non_zeros = full_pred_matrix[x_non_zero_ind, y_non_zero_ind]\n",
+        "\n",
+        "    mse = mean_squared_error(R_non_zeros, full_pred_matrix_non_zeros)\n",
+        "    rmse = np.sqrt(mse)\n",
+        "\n",
+        "    return rmse"
+      ],
+      "metadata": {
+        "id": "-QKFXlmpDGbL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "non_zeros = [(i, j, R[i, j]) for i in range(num_users) for j in range(num_items) if R[i, j] > 0 ]\n",
+        "\n",
+        "steps = 1000\n",
+        "learning_rate = 0.01\n",
+        "r_lambda = 0.01\n",
+        "\n",
+        "for step in range(steps):\n",
+        "    for i, j, r in non_zeros:\n",
+        "        eij = r - np.dot(P[i, :], Q[j, :].T)\n",
+        "        P[i, :] = P[i, :] + learning_rate * (eij * Q[j, :] - r_lambda * P[i, :])\n",
+        "        Q[j, :] = Q[j, :] + learning_rate * (eij * P[i, :] - r_lambda * Q[j, :])\n",
+        "\n",
+        "        rmse = get_rmse(R, P, Q, non_zeros)\n",
+        "        if (step % 50) == 0:\n",
+        "            print(\"### iteration step : \", step, \" rmse : \", rmse)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Rgsftl94DljP",
+        "outputId": "0883d2d4-b95a-470b-eb04-757d56122739"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "### iteration step :  0  rmse :  3.261355059488935\n",
+            "### iteration step :  0  rmse :  3.26040057174686\n",
+            "### iteration step :  0  rmse :  3.253984404542389\n",
+            "### iteration step :  0  rmse :  3.2521583839863624\n",
+            "### iteration step :  0  rmse :  3.252335303789125\n",
+            "### iteration step :  0  rmse :  3.251072196430487\n",
+            "### iteration step :  0  rmse :  3.2492449982564864\n",
+            "### iteration step :  0  rmse :  3.247416477570409\n",
+            "### iteration step :  0  rmse :  3.241926055455223\n",
+            "### iteration step :  0  rmse :  3.2400454107613084\n",
+            "### iteration step :  0  rmse :  3.240166740749792\n",
+            "### iteration step :  0  rmse :  3.2388050277987723\n",
+            "### iteration step :  50  rmse :  0.5003190892212748\n",
+            "### iteration step :  50  rmse :  0.5001616291326989\n",
+            "### iteration step :  50  rmse :  0.49899601202578087\n",
+            "### iteration step :  50  rmse :  0.4988483450145831\n",
+            "### iteration step :  50  rmse :  0.49895189256631756\n",
+            "### iteration step :  50  rmse :  0.49833236830090993\n",
+            "### iteration step :  50  rmse :  0.4984148489378701\n",
+            "### iteration step :  50  rmse :  0.49792599580240876\n",
+            "### iteration step :  50  rmse :  0.4900605568692785\n",
+            "### iteration step :  50  rmse :  0.4890370238665435\n",
+            "### iteration step :  50  rmse :  0.48869176023997846\n",
+            "### iteration step :  50  rmse :  0.4876723101369648\n",
+            "### iteration step :  100  rmse :  0.15911521988578564\n",
+            "### iteration step :  100  rmse :  0.1588091617801093\n",
+            "### iteration step :  100  rmse :  0.1587409221708901\n",
+            "### iteration step :  100  rmse :  0.1582856952842508\n",
+            "### iteration step :  100  rmse :  0.1583080948216876\n",
+            "### iteration step :  100  rmse :  0.15828832993767403\n",
+            "### iteration step :  100  rmse :  0.15787486893092847\n",
+            "### iteration step :  100  rmse :  0.15792073606567072\n",
+            "### iteration step :  100  rmse :  0.15725245215457084\n",
+            "### iteration step :  100  rmse :  0.15710664164665206\n",
+            "### iteration step :  100  rmse :  0.15690252144190003\n",
+            "### iteration step :  100  rmse :  0.1564340384819247\n",
+            "### iteration step :  150  rmse :  0.07546004875264435\n",
+            "### iteration step :  150  rmse :  0.07544589133447106\n",
+            "### iteration step :  150  rmse :  0.07543234329653023\n",
+            "### iteration step :  150  rmse :  0.07514800672233914\n",
+            "### iteration step :  150  rmse :  0.07518867696418177\n",
+            "### iteration step :  150  rmse :  0.0752288950993841\n",
+            "### iteration step :  150  rmse :  0.07489318864469259\n",
+            "### iteration step :  150  rmse :  0.07493400425933257\n",
+            "### iteration step :  150  rmse :  0.07462695506527872\n",
+            "### iteration step :  150  rmse :  0.07464332131959663\n",
+            "### iteration step :  150  rmse :  0.0746444164156341\n",
+            "### iteration step :  150  rmse :  0.07455141311978046\n",
+            "### iteration step :  200  rmse :  0.04361016579439073\n",
+            "### iteration step :  200  rmse :  0.04370913068953006\n",
+            "### iteration step :  200  rmse :  0.04369072102767977\n",
+            "### iteration step :  200  rmse :  0.043475549832271414\n",
+            "### iteration step :  200  rmse :  0.0435313092537358\n",
+            "### iteration step :  200  rmse :  0.04359240037575283\n",
+            "### iteration step :  200  rmse :  0.04329647906053838\n",
+            "### iteration step :  200  rmse :  0.04332057192123618\n",
+            "### iteration step :  200  rmse :  0.04310448294502512\n",
+            "### iteration step :  200  rmse :  0.04313550286658552\n",
+            "### iteration step :  200  rmse :  0.04313786864806258\n",
+            "### iteration step :  200  rmse :  0.04325226798579314\n",
+            "### iteration step :  250  rmse :  0.029395183185609734\n",
+            "### iteration step :  250  rmse :  0.02954402948437167\n",
+            "### iteration step :  250  rmse :  0.02950187436758184\n",
+            "### iteration step :  250  rmse :  0.029329609713572593\n",
+            "### iteration step :  250  rmse :  0.02940211807327667\n",
+            "### iteration step :  250  rmse :  0.02946720568417511\n",
+            "### iteration step :  250  rmse :  0.029189294191791375\n",
+            "### iteration step :  250  rmse :  0.029198757426747605\n",
+            "### iteration step :  250  rmse :  0.028995742260002243\n",
+            "### iteration step :  250  rmse :  0.02904415445054541\n",
+            "### iteration step :  250  rmse :  0.029049587101179365\n",
+            "### iteration step :  250  rmse :  0.029248328780878973\n",
+            "### iteration step :  300  rmse :  0.022678715233749362\n",
+            "### iteration step :  300  rmse :  0.022844873864300484\n",
+            "### iteration step :  300  rmse :  0.022773566650325074\n",
+            "### iteration step :  300  rmse :  0.02263234507322516\n",
+            "### iteration step :  300  rmse :  0.02272006255153119\n",
+            "### iteration step :  300  rmse :  0.022778917442558434\n",
+            "### iteration step :  300  rmse :  0.022516243062381223\n",
+            "### iteration step :  300  rmse :  0.022515508246519694\n",
+            "### iteration step :  300  rmse :  0.02229491665298542\n",
+            "### iteration step :  300  rmse :  0.022367287171783136\n",
+            "### iteration step :  300  rmse :  0.022392303480653113\n",
+            "### iteration step :  300  rmse :  0.022621116143829466\n",
+            "### iteration step :  350  rmse :  0.019516973680183715\n",
+            "### iteration step :  350  rmse :  0.019681605297160464\n",
+            "### iteration step :  350  rmse :  0.019585635379668415\n",
+            "### iteration step :  350  rmse :  0.01946716545524988\n",
+            "### iteration step :  350  rmse :  0.01956568678979253\n",
+            "### iteration step :  350  rmse :  0.019614020075870497\n",
+            "### iteration step :  350  rmse :  0.019368393329296258\n",
+            "### iteration step :  350  rmse :  0.019361014872334943\n",
+            "### iteration step :  350  rmse :  0.019116038405167533\n",
+            "### iteration step :  350  rmse :  0.01920981547997513\n",
+            "### iteration step :  350  rmse :  0.019255623979392192\n",
+            "### iteration step :  350  rmse :  0.019493636196525135\n",
+            "### iteration step :  400  rmse :  0.01803666559195465\n",
+            "### iteration step :  400  rmse :  0.01819133106334419\n",
+            "### iteration step :  400  rmse :  0.018078504374883574\n",
+            "### iteration step :  400  rmse :  0.01797554592952707\n",
+            "### iteration step :  400  rmse :  0.018080509676855847\n",
+            "### iteration step :  400  rmse :  0.018118882879536648\n",
+            "### iteration step :  400  rmse :  0.017889686482489363\n",
+            "### iteration step :  400  rmse :  0.017878066671070433\n",
+            "### iteration step :  400  rmse :  0.01761224433968553\n",
+            "### iteration step :  400  rmse :  0.01772096734904666\n",
+            "### iteration step :  400  rmse :  0.01778179645659777\n",
+            "### iteration step :  400  rmse :  0.018022719092132704\n",
+            "### iteration step :  450  rmse :  0.017334045429542092\n",
+            "### iteration step :  450  rmse :  0.01747683493759156\n",
+            "### iteration step :  450  rmse :  0.01735361907510825\n",
+            "### iteration step :  450  rmse :  0.017260553985290646\n",
+            "### iteration step :  450  rmse :  0.01736909385010645\n",
+            "### iteration step :  450  rmse :  0.017399933857257726\n",
+            "### iteration step :  450  rmse :  0.01718431757863743\n",
+            "### iteration step :  450  rmse :  0.01716990649625117\n",
+            "### iteration step :  450  rmse :  0.01688861579579296\n",
+            "### iteration step :  450  rmse :  0.017006638154083088\n",
+            "### iteration step :  450  rmse :  0.01707679250866153\n",
+            "### iteration step :  450  rmse :  0.01731968595344266\n",
+            "### iteration step :  500  rmse :  0.016991609248052833\n",
+            "### iteration step :  500  rmse :  0.01712340891578616\n",
+            "### iteration step :  500  rmse :  0.01699398405641037\n",
+            "### iteration step :  500  rmse :  0.01690707049203008\n",
+            "### iteration step :  500  rmse :  0.01701760577221745\n",
+            "### iteration step :  500  rmse :  0.017043277556700362\n",
+            "### iteration step :  500  rmse :  0.01683803145900356\n",
+            "### iteration step :  500  rmse :  0.016821674312725313\n",
+            "### iteration step :  500  rmse :  0.016529281264429145\n",
+            "### iteration step :  500  rmse :  0.0166528887951985\n",
+            "### iteration step :  500  rmse :  0.016728541275490984\n",
+            "### iteration step :  500  rmse :  0.016973657887570753\n",
+            "### iteration step :  550  rmse :  0.016818969716266233\n",
+            "### iteration step :  550  rmse :  0.016941445597444732\n",
+            "### iteration step :  550  rmse :  0.0168082592988841\n",
+            "### iteration step :  550  rmse :  0.016725234339747562\n",
+            "### iteration step :  550  rmse :  0.01683693849143515\n",
+            "### iteration step :  550  rmse :  0.016859187050621206\n",
+            "### iteration step :  550  rmse :  0.016661644526141564\n",
+            "### iteration step :  550  rmse :  0.01664385102006508\n",
+            "### iteration step :  550  rmse :  0.016343446075494233\n",
+            "### iteration step :  550  rmse :  0.01647044082182643\n",
+            "### iteration step :  550  rmse :  0.01654932331426952\n",
+            "### iteration step :  550  rmse :  0.016796804595895633\n",
+            "### iteration step :  600  rmse :  0.016727439717439115\n",
+            "### iteration step :  600  rmse :  0.016842259158977232\n",
+            "### iteration step :  600  rmse :  0.016706687924467476\n",
+            "### iteration step :  600  rmse :  0.016626255644609397\n",
+            "### iteration step :  600  rmse :  0.016738696939262717\n",
+            "### iteration step :  600  rmse :  0.016758682415985614\n",
+            "### iteration step :  600  rmse :  0.0165668572000528\n",
+            "### iteration step :  600  rmse :  0.016547954461110684\n",
+            "### iteration step :  600  rmse :  0.016241668760761063\n",
+            "### iteration step :  600  rmse :  0.016370800056137867\n",
+            "### iteration step :  600  rmse :  0.016451627209257007\n",
+            "### iteration step :  600  rmse :  0.01670132290188466\n",
+            "### iteration step :  650  rmse :  0.016674291334806343\n",
+            "### iteration step :  650  rmse :  0.016782895588885082\n",
+            "### iteration step :  650  rmse :  0.016645698091647773\n",
+            "### iteration step :  650  rmse :  0.01656714079916223\n",
+            "### iteration step :  650  rmse :  0.016680091021598568\n",
+            "### iteration step :  650  rmse :  0.016698554271430792\n",
+            "### iteration step :  650  rmse :  0.016511017732427972\n",
+            "### iteration step :  650  rmse :  0.016491228766905293\n",
+            "### iteration step :  650  rmse :  0.01618054419796173\n",
+            "### iteration step :  650  rmse :  0.01631111150707529\n",
+            "### iteration step :  650  rmse :  0.01639316772050061\n",
+            "### iteration step :  650  rmse :  0.01664473691247669\n",
+            "### iteration step :  700  rmse :  0.0166383624426085\n",
+            "### iteration step :  700  rmse :  0.016741936743323586\n",
+            "### iteration step :  700  rmse :  0.016603524189001625\n",
+            "### iteration step :  700  rmse :  0.016526454393300468\n",
+            "### iteration step :  700  rmse :  0.016639792083379498\n",
+            "### iteration step :  700  rmse :  0.016657201345297346\n",
+            "### iteration step :  700  rmse :  0.016472928381641428\n",
+            "### iteration step :  700  rmse :  0.01645241257047358\n",
+            "### iteration step :  700  rmse :  0.016138379086448083\n",
+            "### iteration step :  700  rmse :  0.016269993747904915\n",
+            "### iteration step :  700  rmse :  0.01635288508504558\n",
+            "### iteration step :  700  rmse :  0.016605910068210026\n",
+            "### iteration step :  750  rmse :  0.01660906046895522\n",
+            "### iteration step :  750  rmse :  0.016708562969098305\n",
+            "### iteration step :  750  rmse :  0.016569153528341783\n",
+            "### iteration step :  750  rmse :  0.016493367054249922\n",
+            "### iteration step :  750  rmse :  0.016607027966870924\n",
+            "### iteration step :  750  rmse :  0.01662368102752549\n",
+            "### iteration step :  750  rmse :  0.016441927271724666\n",
+            "### iteration step :  750  rmse :  0.0164208024653437\n",
+            "### iteration step :  750  rmse :  0.016104179990850755\n",
+            "### iteration step :  750  rmse :  0.016236628551952913\n",
+            "### iteration step :  750  rmse :  0.016320141009292095\n",
+            "### iteration step :  750  rmse :  0.016574200475705\n",
+            "### iteration step :  800  rmse :  0.016581161561119846\n",
+            "### iteration step :  800  rmse :  0.016677363428436936\n",
+            "### iteration step :  800  rmse :  0.016537069269613652\n",
+            "### iteration step :  800  rmse :  0.0164624613777787\n",
+            "### iteration step :  800  rmse :  0.016576412350487568\n",
+            "### iteration step :  800  rmse :  0.01659250180024954\n",
+            "### iteration step :  800  rmse :  0.01641271740942833\n",
+            "### iteration step :  800  rmse :  0.016391072859801518\n",
+            "### iteration step :  800  rmse :  0.01607242307736876\n",
+            "### iteration step :  800  rmse :  0.016205589842521878\n",
+            "### iteration step :  800  rmse :  0.016289609430091494\n",
+            "### iteration step :  800  rmse :  0.01654431582921597\n",
+            "### iteration step :  850  rmse :  0.01655222898431553\n",
+            "### iteration step :  850  rmse :  0.01664575121547569\n",
+            "### iteration step :  850  rmse :  0.016504627328190514\n",
+            "### iteration step :  850  rmse :  0.016431145801748863\n",
+            "### iteration step :  850  rmse :  0.016545370571042432\n",
+            "### iteration step :  850  rmse :  0.016561024020105147\n",
+            "### iteration step :  850  rmse :  0.016382795627019747\n",
+            "### iteration step :  850  rmse :  0.016360700076085824\n",
+            "### iteration step :  850  rmse :  0.016040446344395578\n",
+            "### iteration step :  850  rmse :  0.016174269580681345\n",
+            "### iteration step :  850  rmse :  0.016258737354641353\n",
+            "### iteration step :  850  rmse :  0.01651375177473524\n",
+            "### iteration step :  900  rmse :  0.016521280433777957\n",
+            "### iteration step :  900  rmse :  0.016612624200841405\n",
+            "### iteration step :  900  rmse :  0.016470695682261876\n",
+            "### iteration step :  900  rmse :  0.016398314989165292\n",
+            "### iteration step :  900  rmse :  0.016512806333073466\n",
+            "### iteration step :  900  rmse :  0.01652811087350182\n",
+            "### iteration step :  900  rmse :  0.016351122754892394\n",
+            "### iteration step :  900  rmse :  0.016328629783842166\n",
+            "### iteration step :  900  rmse :  0.016007096878603234\n",
+            "### iteration step :  900  rmse :  0.016141544071514122\n",
+            "### iteration step :  900  rmse :  0.016226430843994055\n",
+            "### iteration step :  900  rmse :  0.01648146573819501\n",
+            "### iteration step :  950  rmse :  0.016488081335748316\n",
+            "### iteration step :  950  rmse :  0.016577652134974717\n",
+            "### iteration step :  950  rmse :  0.01643492933498176\n",
+            "### iteration step :  950  rmse :  0.0163636366204062\n",
+            "### iteration step :  950  rmse :  0.01647839195954869\n",
+            "### iteration step :  950  rmse :  0.01649340903060659\n",
+            "### iteration step :  950  rmse :  0.016317416842511007\n",
+            "### iteration step :  950  rmse :  0.016294568571753248\n",
+            "### iteration step :  950  rmse :  0.015972009545965248\n",
+            "### iteration step :  950  rmse :  0.0161070634587959\n",
+            "### iteration step :  950  rmse :  0.016192355609214733\n",
+            "### iteration step :  950  rmse :  0.016447171683479155\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pred_matrix = np.dot(P, Q.T)\n",
+        "print('예측 행렬 : \\n', np.round(pred_matrix, 3))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GYJZkGEXEJX7",
+        "outputId": "e6613491-71f9-45dd-ad80-99a8cb1b4c3a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "예측 행렬 : \n",
+            " [[3.991 0.897 1.306 2.002 1.663]\n",
+            " [6.696 4.978 0.979 2.981 1.003]\n",
+            " [6.677 0.391 2.987 3.977 3.986]\n",
+            " [4.968 2.005 1.006 2.017 1.14 ]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install numpy==1.24.4\n",
+        "!pip install scikit-surprise"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "P24i-zwaRqMy",
+        "outputId": "85b6f042-7cdd-4fbb-bae0-d5fef04dcd03",
+        "collapsed": true
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting numpy==1.24.4\n",
+            "  Downloading numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB)\n",
+            "Downloading numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.3/17.3 MB\u001b[0m \u001b[31m113.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: numpy\n",
+            "  Attempting uninstall: numpy\n",
+            "    Found existing installation: numpy 2.0.2\n",
+            "    Uninstalling numpy-2.0.2:\n",
+            "      Successfully uninstalled numpy-2.0.2\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "pymc 5.23.0 requires numpy>=1.25.0, but you have numpy 1.24.4 which is incompatible.\n",
+            "tensorflow 2.18.0 requires numpy<2.1.0,>=1.26.0, but you have numpy 1.24.4 which is incompatible.\n",
+            "xarray-einstats 0.9.0 requires numpy>=1.25, but you have numpy 1.24.4 which is incompatible.\n",
+            "jaxlib 0.5.1 requires numpy>=1.25, but you have numpy 1.24.4 which is incompatible.\n",
+            "jax 0.5.2 requires numpy>=1.25, but you have numpy 1.24.4 which is incompatible.\n",
+            "treescope 0.1.9 requires numpy>=1.25.2, but you have numpy 1.24.4 which is incompatible.\n",
+            "blosc2 3.4.0 requires numpy>=1.26, but you have numpy 1.24.4 which is incompatible.\n",
+            "thinc 8.3.6 requires numpy<3.0.0,>=2.0.0, but you have numpy 1.24.4 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0mSuccessfully installed numpy-1.24.4\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "pip_warning": {
+                "packages": [
+                  "numpy"
+                ]
+              },
+              "id": "99b0f1fec0ba498387fc014f2e06b471"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: scikit-surprise in /usr/local/lib/python3.11/dist-packages (1.1.4)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-surprise) (1.5.1)\n",
+            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-surprise) (1.24.4)\n",
+            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-surprise) (1.15.3)\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-4-472363651.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pip install numpy==1.24.4'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pip install scikit-surprise'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/colab/_shell.py\u001b[0m in \u001b[0;36msystem\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpip_warn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m       \u001b[0m_pip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_previous_import_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_send_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_content\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/colab/_pip.py\u001b[0m in \u001b[0;36mprint_previous_import_warning\u001b[0;34m(output)\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mprint_previous_import_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m   \u001b[0;34m\"\"\"Prints a warning about previously imported packages.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m   \u001b[0mpackages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_previously_imported_packages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mpackages\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;31m# display a list of packages using the colab-display-data mimetype, which\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/colab/_pip.py\u001b[0m in \u001b[0;36m_previously_imported_packages\u001b[0;34m(pip_output)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_previously_imported_packages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpip_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m   \u001b[0;34m\"\"\"List all previously imported packages from a pip install.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m   \u001b[0minstalled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_extract_toplevel_packages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpip_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstalled\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/colab/_pip.py\u001b[0m in \u001b[0;36m_extract_toplevel_packages\u001b[0;34m(pip_output)\u001b[0m\n\u001b[1;32m     37\u001b[0m   \u001b[0;34m\"\"\"Extract the list of toplevel packages associated with a pip install.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m   \u001b[0mtoplevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m   \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mps\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpackages_distributions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m       \u001b[0mtoplevel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.11/importlib/metadata/__init__.py\u001b[0m in \u001b[0;36mpackages_distributions\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1073\u001b[0m     \u001b[0mpkg_to_dist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1074\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdist\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdistributions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1075\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mpkg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_top_level_declared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_top_level_inferred\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1076\u001b[0m             \u001b[0mpkg_to_dist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpkg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1077\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpkg_to_dist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.11/importlib/metadata/__init__.py\u001b[0m in \u001b[0;36m_top_level_inferred\u001b[0;34m(dist)\u001b[0m\n\u001b[1;32m   1085\u001b[0m     return {\n\u001b[1;32m   1086\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparts\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_suffix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1087\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0malways_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1088\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuffix\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\".py\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1089\u001b[0m     }\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/importlib_metadata/__init__.py\u001b[0m in \u001b[0;36mfiles\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    602\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 604\u001b[0;31m         return skip_missing_files(\n\u001b[0m\u001b[1;32m    605\u001b[0m             make_files(\n\u001b[1;32m    606\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_files_distinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/importlib_metadata/_functools.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(param, *args, **kwargs)\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/importlib_metadata/__init__.py\u001b[0m in \u001b[0;36mskip_missing_files\u001b[0;34m(package_paths)\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mpass_none\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mskip_missing_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpackage_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m         return skip_missing_files(\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/importlib_metadata/__init__.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mpass_none\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mskip_missing_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpackage_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m         return skip_missing_files(\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/importlib_metadata/__init__.py\u001b[0m in \u001b[0;36mlocate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    384\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mlocate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSimplePath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m         \u001b[0;34m\"\"\"Return a path-like object for this path\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocate_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/importlib_metadata/__init__.py\u001b[0m in \u001b[0;36mlocate_file\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m   1003\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mlocate_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPathLike\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSimplePath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1005\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1007\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.11/pathlib.py\u001b[0m in \u001b[0;36m__truediv__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__truediv__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 767\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    768\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.11/pathlib.py\u001b[0m in \u001b[0;36m_make_child\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m         \u001b[0mdrv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m         drv, root, parts = self._flavour.join_parsed_parts(\n\u001b[1;32m    533\u001b[0m             self._drv, self._root, self._parts, drv, root, parts)\n",
+            "\u001b[0;32m/usr/lib/python3.11/pathlib.py\u001b[0m in \u001b[0;36m_parse_args\u001b[0;34m(cls, args)\u001b[0m\n\u001b[1;32m    500\u001b[0m                         \u001b[0;34m\"object returning str, not %r\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m                         % type(a))\n\u001b[0;32m--> 502\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flavour\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_parts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/lib/python3.11/pathlib.py\u001b[0m in \u001b[0;36mparse_parts\u001b[0;34m(self, parts)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mrel\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrel\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m                     \u001b[0mparsed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintern\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mdrv\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdrv\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import surprise\n",
+        "print(surprise.__version__)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9ZOWk8U0MLoF",
+        "outputId": "2f82f1e3-cbed-4c41-c3f7-b17871c14658"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1.1.4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise import SVD\n",
+        "from surprise import Dataset\n",
+        "from surprise import accuracy\n",
+        "from surprise.model_selection import train_test_split"
+      ],
+      "metadata": {
+        "id": "o6tYN-v-MLqH"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = Dataset.load_builtin('ml-100k')\n",
+        "\n",
+        "trainset, testset = train_test_split(data, test_size=.25, random_state=0)"
+      ],
+      "metadata": {
+        "id": "vcMYLw3TMLsN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5afd80f0-078b-493c-ff71-248b6d7564da"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset ml-100k could not be found. Do you want to download it? [Y/n] Y\n",
+            "Trying to download dataset from https://files.grouplens.org/datasets/movielens/ml-100k.zip...\n",
+            "Done! Dataset ml-100k has been saved to /root/.surprise_data/ml-100k\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "algo = SVD(random_state=0)\n",
+        "algo.fit(trainset)"
+      ],
+      "metadata": {
+        "id": "XliZb1CAMLuV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8e8cc8b6-0440-4531-a61c-d44ad7da7e50"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7affc8fefa10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predictions = algo.test( testset )\n",
+        "print('prediction type :',type(predictions), ' size:',len(predictions))\n",
+        "print('prediction 결과의 최초 5개 추출')\n",
+        "predictions[:5]"
+      ],
+      "metadata": {
+        "id": "O4B3M1wpMLwI",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a2db785d-217a-4fcc-eef7-52e60528152f"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "prediction type : <class 'list'>  size: 25000\n",
+            "prediction 결과의 최초 5개 추출\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Prediction(uid='120', iid='282', r_ui=4.0, est=3.5114147666251547, details={'was_impossible': False}),\n",
+              " Prediction(uid='882', iid='291', r_ui=4.0, est=3.573872419581491, details={'was_impossible': False}),\n",
+              " Prediction(uid='535', iid='507', r_ui=5.0, est=4.033583485472447, details={'was_impossible': False}),\n",
+              " Prediction(uid='697', iid='244', r_ui=5.0, est=3.8463639495936905, details={'was_impossible': False}),\n",
+              " Prediction(uid='751', iid='385', r_ui=4.0, est=3.1807542478219157, details={'was_impossible': False})]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "[ (pred.uid, pred.iid, pred.est) for pred in predictions[:3] ]"
+      ],
+      "metadata": {
+        "id": "_qIo9STtMLyS",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bb518534-3694-492f-84a4-59a5b12d362e"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[('120', '282', 3.5114147666251547),\n",
+              " ('882', '291', 3.573872419581491),\n",
+              " ('535', '507', 4.033583485472447)]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "uid = str(196)\n",
+        "iid = str(302)\n",
+        "pred = algo.predict(uid, iid)\n",
+        "print(pred)"
+      ],
+      "metadata": {
+        "id": "TtQVJAaNML0R",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b1a74999-1c6f-4862-f73e-fb131f96c43e"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "user: 196        item: 302        r_ui = None   est = 4.49   {'was_impossible': False}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "accuracy.rmse(predictions)"
+      ],
+      "metadata": {
+        "id": "DTK1sdcvML2E",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b08983d7-e602-42c9-a92c-9cbaeb906210"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 0.9467\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9466860806937948"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 78
+        },
+        "id": "5Ht2RP_dtsgY",
+        "outputId": "408201b8-2adf-4529-c916-a9a4fcd6e58f"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-ba4165a1-b87f-4fc5-b56a-1b8a453f766e\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-ba4165a1-b87f-4fc5-b56a-1b8a453f766e\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving ratings.csv to ratings.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "ratings = pd.read_csv('ratings.csv')\n",
+        "\n",
+        "ratings.to_csv('ratings_noh.csv', index=False, header=False)"
+      ],
+      "metadata": {
+        "id": "5brLiG_yML4U"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise import Reader\n",
+        "\n",
+        "reader = Reader(line_format='user item rating timestamp', sep=',', rating_scale=(0.5, 5))\n",
+        "data=Dataset.load_from_file('ratings_noh.csv',reader=reader)"
+      ],
+      "metadata": {
+        "id": "r_OCTFdsML72"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainset, testset = train_test_split(data, test_size=.25, random_state=0)\n",
+        "\n",
+        "algo = SVD(n_factors=50, random_state=0)\n",
+        "\n",
+        "algo.fit(trainset)\n",
+        "predictions = algo.test( testset )\n",
+        "accuracy.rmse(predictions)\n"
+      ],
+      "metadata": {
+        "id": "Qr_DNeO1ML-K",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9945b562-ba58-44d1-c014-fe0b0e76a11b"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 0.8682\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8681952927143516"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "from surprise import Reader, Dataset\n",
+        "\n",
+        "ratings = pd.read_csv('ratings.csv')\n",
+        "reader = Reader(rating_scale=(0.5, 5.0))\n",
+        "\n",
+        "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n",
+        "trainset, testset = train_test_split(data, test_size=.25, random_state=0)\n",
+        "\n",
+        "algo = SVD(n_factors=50, random_state=0)\n",
+        "algo.fit(trainset)\n",
+        "predictions = algo.test( testset )\n",
+        "accuracy.rmse(predictions)\n"
+      ],
+      "metadata": {
+        "id": "MxYtrCyAkFFl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d6ca6999-1b6c-4499-9943-ab9747ef3718"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 0.8682\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8681952927143516"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise.model_selection import cross_validate\n",
+        "\n",
+        "# 판다스 DataFrame에서 Surprise 데이터 세트로 데이터 로딩\n",
+        "ratings = pd.read_csv('ratings.csv') # reading data in pandas df\n",
+        "reader = Reader(rating_scale=(0.5, 5.0))\n",
+        "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n",
+        "\n",
+        "algo = SVD(random_state=0)\n",
+        "cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)"
+      ],
+      "metadata": {
+        "id": "DAbwC8elkFJV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2d4b7349-bbdc-4c9c-a559-ac53f5455cc9"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
+            "\n",
+            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
+            "RMSE (testset)    0.8827  0.8646  0.8758  0.8764  0.8749  0.8749  0.0058  \n",
+            "MAE (testset)     0.6768  0.6644  0.6738  0.6724  0.6709  0.6717  0.0041  \n",
+            "Fit time          1.56    1.60    1.61    1.65    1.72    1.63    0.06    \n",
+            "Test time         0.09    0.32    0.11    0.31    0.10    0.19    0.11    \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'test_rmse': array([0.88265688, 0.86459918, 0.87581833, 0.87639267, 0.87486001]),\n",
+              " 'test_mae': array([0.67678983, 0.66439488, 0.67382928, 0.67240378, 0.6708778 ]),\n",
+              " 'fit_time': (1.5565800666809082,\n",
+              "  1.6041619777679443,\n",
+              "  1.6122362613677979,\n",
+              "  1.6494355201721191,\n",
+              "  1.7216756343841553),\n",
+              " 'test_time': (0.09142255783081055,\n",
+              "  0.32407689094543457,\n",
+              "  0.11034798622131348,\n",
+              "  0.3142893314361572,\n",
+              "  0.10209774971008301)}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise.model_selection import GridSearchCV\n",
+        "\n",
+        "# 최적화할 파라미터를 딕셔너리 형태로 지정.\n",
+        "param_grid = {'n_epochs': [20, 40, 60], 'n_factors': [50, 100, 200] }\n",
+        "\n",
+        "# CV를 3개 폴드 세트로 지정, 성능 평가는 rmse, mse로 수행하도록 GridSearchCV 구성\n",
+        "gs = GridSearchCV(SVD, param_grid, measures=['rmse', 'mae'], cv=3)\n",
+        "gs.fit(data)\n",
+        "\n",
+        "# 최고 RMSE Evaluation 점수와 그때의 하이퍼 파라미터\n",
+        "print(gs.best_score['rmse'])\n",
+        "print(gs.best_params['rmse'])"
+      ],
+      "metadata": {
+        "id": "yz_jQptlkFLq",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a4e8017c-7c1b-42d0-b186-8136720a1d13"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.8772390973772396\n",
+            "{'n_epochs': 20, 'n_factors': 50}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 다음 코드는 train_test_split( )으로 분리되지 않는 데이터 세트에 fit( )을 호출해 오류가 발생합니다.\n",
+        "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n",
+        "algo = SVD(n_factors=50, random_state=0)\n",
+        "algo.fit(data)"
+      ],
+      "metadata": {
+        "id": "I12TarXjwcaf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise.dataset import DatasetAutoFolds\n",
+        "\n",
+        "reader = Reader(line_format='user item rating timestamp', sep=',', rating_scale=(0.5, 5))\n",
+        "# DatasetAutoFolds 클래스를 ratings_noh.csv 파일 기반으로 생성.\n",
+        "data_folds = DatasetAutoFolds(ratings_file='ratings_noh.csv', reader=reader)\n",
+        "\n",
+        "#전체 데이터를 학습데이터로 생성함.\n",
+        "trainset = data_folds.build_full_trainset()\n"
+      ],
+      "metadata": {
+        "id": "BLWPhqeUkFRo"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "algo = SVD(n_epochs=20, n_factors=50, random_state=0)\n",
+        "algo.fit(trainset)\n"
+      ],
+      "metadata": {
+        "id": "L43bnI3dkLjJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cd956b2f-2671-4f45-8f22-4c620e4f450b"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7affcab8d550>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 78
+        },
+        "id": "X9m1gLVDuKY6",
+        "outputId": "28e37bda-581b-4060-d838-b5dde8fa3890"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-5036137f-69a3-43e6-aeb5-171209b04581\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-5036137f-69a3-43e6-aeb5-171209b04581\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving movies.csv to movies.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 영화에 대한 상세 속성 정보 DataFrame로딩\n",
+        "movies = pd.read_csv('movies.csv')\n",
+        "\n",
+        "# userId=9 의 movieId 데이터 추출하여 movieId=42 데이터가 있는지 확인.\n",
+        "movieIds = ratings[ratings['userId']==9]['movieId']\n",
+        "if movieIds[movieIds==42].count() == 0:\n",
+        "    print('사용자 아이디 9는 영화 아이디 42의 평점 없음')\n",
+        "\n",
+        "print(movies[movies['movieId']==42])\n"
+      ],
+      "metadata": {
+        "id": "N5VunUbUkLlU",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e4618472-2109-4b98-8a82-1b9bfdbe8375"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "사용자 아이디 9는 영화 아이디 42의 평점 없음\n",
+            "    movieId                   title              genres\n",
+            "38       42  Dead Presidents (1995)  Action|Crime|Drama\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "uid = str(9)\n",
+        "iid = str(42)\n",
+        "\n",
+        "pred = algo.predict(uid, iid, verbose=True)"
+      ],
+      "metadata": {
+        "id": "9ynTS07swfKL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_unseen_surprise(ratings, movies, userId):\n",
+        "    #입력값으로 들어온 userId에 해당하는 사용자가 평점을 매긴 모든 영화를 리스트로 생성\n",
+        "    seen_movies = ratings[ratings['userId']== userId]['movieId'].tolist()\n",
+        "\n",
+        "    # 모든 영화들의 movieId를 리스트로 생성.\n",
+        "    total_movies = movies['movieId'].tolist()\n",
+        "\n",
+        "    # 모든 영화들의 movieId중 이미 평점을 매긴 영화의 movieId를 제외하여 리스트로 생성\n",
+        "    unseen_movies= [movie for movie in total_movies if movie not in seen_movies]\n",
+        "    print('평점 매긴 영화수:',len(seen_movies), '추천대상 영화수:',len(unseen_movies), \\\n",
+        "          '전체 영화수:',len(total_movies))\n",
+        "\n",
+        "    return unseen_movies\n",
+        "\n",
+        "unseen_movies = get_unseen_surprise(ratings, movies, 9)"
+      ],
+      "metadata": {
+        "id": "LcueWYIHkLz_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f29d22f3-a5ff-4e4f-9749-d8a1033c3e4f"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "평점 매긴 영화수: 46 추천대상 영화수: 9696 전체 영화수: 9742\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def recomm_movie_by_surprise(algo, userId, unseen_movies, top_n=10):\n",
+        "    # 알고리즘 객체의 predict() 메서드를 평점이 없는 영화에 반복 수행한 후 결과를 list 객체로 저장\n",
+        "    predictions = [algo.predict(str(userId), str(movieId)) for movieId in unseen_movies]\n",
+        "\n",
+        "    # predictions list 객체는 surprise의 Predictions 객체를 원소로 가지고 있음.\n",
+        "    # [Prediction(uid='9', iid='1', est=3.69), Prediction(uid='9', iid='2', est=2.98),,,,]\n",
+        "    # 이를 est 값으로 정렬하기 위해서 아래의 sortkey_est 함수를 정의함.\n",
+        "    # sortkey_est 함수는 list 객체의 sort() 함수의 키 값으로 사용되어 정렬 수행.\n",
+        "    def sortkey_est(pred):\n",
+        "        return pred.est\n",
+        "\n",
+        "    # sortkey_est( ) 반환값의 내림 차순으로 정렬 수행하고 top_n개의 최상위 값 추출.\n",
+        "    predictions.sort(key=sortkey_est, reverse=True)\n",
+        "    top_predictions= predictions[:top_n]\n",
+        "\n",
+        "    # top_n으로 추출된 영화의 정보 추출. 영화 아이디, 추천 예상 평점, 제목 추출\n",
+        "    top_movie_ids = [ int(pred.iid) for pred in top_predictions]\n",
+        "    top_movie_rating = [ pred.est for pred in top_predictions]\n",
+        "    top_movie_titles = movies[movies.movieId.isin(top_movie_ids)]['title']\n",
+        "    top_movie_preds = [ (id, title, rating) for id, title, rating in zip(top_movie_ids, top_movie_titles, top_movie_rating)]\n",
+        "\n",
+        "    return top_movie_preds\n",
+        "\n",
+        "unseen_movies = get_unseen_surprise(ratings, movies, 9)\n",
+        "top_movie_preds = recomm_movie_by_surprise(algo, 9, unseen_movies, top_n=10)\n",
+        "print('##### Top-10 추천 영화 리스트 #####')\n",
+        "\n",
+        "for top_movie in top_movie_preds:\n",
+        "    print(top_movie[1], \":\", top_movie[2])"
+      ],
+      "metadata": {
+        "id": "VkjMn0EAvm0-"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file