task1.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "q_6_NhhhFkAD"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import io\n",
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Read data"
      ],
      "metadata": {
        "id": "Vh3bL2w4F4sS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "up = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "JJHvCgDdF781",
        "outputId": "6155f739-4d4b-416a-bf45-c9d932306a30"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c30f57b8-80df-4df3-9dce-f3c8a42a3c68\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c30f57b8-80df-4df3-9dce-f3c8a42a3c68\">\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 KPMG_VI_New_raw_data_update_final.xlsx to KPMG_VI_New_raw_data_update_final (2).xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = pd.ExcelFile('KPMG_VI_New_raw_data_update_final (2).xlsx')"
      ],
      "metadata": {
        "id": "w6Uxs-b0HKkf"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Read separately\n"
      ],
      "metadata": {
        "id": "iHxUNnYnIH3p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Trsn = pd.read_excel(dt, 'Transactions')\n",
        "NCL = pd.read_excel(dt, 'NewCustomerList')\n",
        "CD = pd.read_excel(dt, 'CustomerDemographic')\n",
        "CA = pd.read_excel(dt, 'CustomerAddress')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APyEFla4IKMe",
        "outputId": "aa4526c3-76fb-4729-88e8-d25e698e854c"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-45-91d2a1137e86>:2: FutureWarning: Inferring datetime64[ns] from data containing strings is deprecated and will be removed in a future version. To retain the old behavior explicitly pass Series(data, dtype=datetime64[ns])\n",
            "  NCL = pd.read_excel(dt, 'NewCustomerList')\n",
            "<ipython-input-45-91d2a1137e86>:3: FutureWarning: Inferring datetime64[ns] from data containing strings is deprecated and will be removed in a future version. To retain the old behavior explicitly pass Series(data, dtype=datetime64[ns])\n",
            "  CD = pd.read_excel(dt, 'CustomerDemographic')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Transaction Dataset"
      ],
      "metadata": {
        "id": "KIvezc6dIiI4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Trsn.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "KwQnJDezIkrs",
        "outputId": "10227cd7-a6b0-4926-a9f1-adde15de94af"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   transaction_id  product_id  customer_id transaction_date  online_order  \\\n",
              "0               1           2         2950       2017-02-25           0.0   \n",
              "1               2           3         3120       2017-05-21           1.0   \n",
              "2               3          37          402       2017-10-16           0.0   \n",
              "3               4          88         3135       2017-08-31           0.0   \n",
              "4               5          78          787       2017-10-01           1.0   \n",
              "5               6          25         2339       2017-03-08           1.0   \n",
              "6               7          22         1542       2017-04-21           1.0   \n",
              "7               8          15         2459       2017-07-15           0.0   \n",
              "8               9          67         1305       2017-08-10           0.0   \n",
              "9              10          12         3262       2017-08-30           1.0   \n",
              "\n",
              "  order_status           brand product_line product_class product_size  \\\n",
              "0     Approved           Solex     Standard        medium       medium   \n",
              "1     Approved   Trek Bicycles     Standard        medium        large   \n",
              "2     Approved      OHM Cycles     Standard           low       medium   \n",
              "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
              "4     Approved  Giant Bicycles     Standard        medium        large   \n",
              "5     Approved  Giant Bicycles         Road        medium       medium   \n",
              "6     Approved        WeareA2B     Standard        medium       medium   \n",
              "7     Approved        WeareA2B     Standard        medium       medium   \n",
              "8     Approved           Solex     Standard        medium        large   \n",
              "9     Approved        WeareA2B     Standard        medium       medium   \n",
              "\n",
              "   list_price  standard_cost  product_first_sold_date  \n",
              "0       71.49          53.62                  41245.0  \n",
              "1     2091.47         388.92                  41701.0  \n",
              "2     1793.43         248.82                  36361.0  \n",
              "3     1198.46         381.10                  36145.0  \n",
              "4     1765.30         709.48                  42226.0  \n",
              "5     1538.99         829.65                  39031.0  \n",
              "6       60.34          45.26                  34165.0  \n",
              "7     1292.84          13.44                  39915.0  \n",
              "8     1071.23         380.74                  33455.0  \n",
              "9     1231.15         161.60                  38216.0  "
            ],
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              "\n",
              "  <div id=\"df-0976b3e0-8a4f-49bb-aea7-da8f3ddddb65\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>transaction_id</th>\n",
              "      <th>product_id</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>transaction_date</th>\n",
              "      <th>online_order</th>\n",
              "      <th>order_status</th>\n",
              "      <th>brand</th>\n",
              "      <th>product_line</th>\n",
              "      <th>product_class</th>\n",
              "      <th>product_size</th>\n",
              "      <th>list_price</th>\n",
              "      <th>standard_cost</th>\n",
              "      <th>product_first_sold_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2950</td>\n",
              "      <td>2017-02-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Solex</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>71.49</td>\n",
              "      <td>53.62</td>\n",
              "      <td>41245.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3120</td>\n",
              "      <td>2017-05-21</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Trek Bicycles</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>large</td>\n",
              "      <td>2091.47</td>\n",
              "      <td>388.92</td>\n",
              "      <td>41701.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>402</td>\n",
              "      <td>2017-10-16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>OHM Cycles</td>\n",
              "      <td>Standard</td>\n",
              "      <td>low</td>\n",
              "      <td>medium</td>\n",
              "      <td>1793.43</td>\n",
              "      <td>248.82</td>\n",
              "      <td>36361.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>88</td>\n",
              "      <td>3135</td>\n",
              "      <td>2017-08-31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Norco Bicycles</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>1198.46</td>\n",
              "      <td>381.10</td>\n",
              "      <td>36145.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>78</td>\n",
              "      <td>787</td>\n",
              "      <td>2017-10-01</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Giant Bicycles</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>large</td>\n",
              "      <td>1765.30</td>\n",
              "      <td>709.48</td>\n",
              "      <td>42226.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>25</td>\n",
              "      <td>2339</td>\n",
              "      <td>2017-03-08</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Giant Bicycles</td>\n",
              "      <td>Road</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>1538.99</td>\n",
              "      <td>829.65</td>\n",
              "      <td>39031.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "      <td>1542</td>\n",
              "      <td>2017-04-21</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>WeareA2B</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>60.34</td>\n",
              "      <td>45.26</td>\n",
              "      <td>34165.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>2459</td>\n",
              "      <td>2017-07-15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>WeareA2B</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>1292.84</td>\n",
              "      <td>13.44</td>\n",
              "      <td>39915.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>67</td>\n",
              "      <td>1305</td>\n",
              "      <td>2017-08-10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>Solex</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>large</td>\n",
              "      <td>1071.23</td>\n",
              "      <td>380.74</td>\n",
              "      <td>33455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>3262</td>\n",
              "      <td>2017-08-30</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Approved</td>\n",
              "      <td>WeareA2B</td>\n",
              "      <td>Standard</td>\n",
              "      <td>medium</td>\n",
              "      <td>medium</td>\n",
              "      <td>1231.15</td>\n",
              "      <td>161.60</td>\n",
              "      <td>38216.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0976b3e0-8a4f-49bb-aea7-da8f3ddddb65')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-0976b3e0-8a4f-49bb-aea7-da8f3ddddb65 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Trsn.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "utuTwtpRItB5",
        "outputId": "84ddc378-a3ea-4c29-df10-059999e07561"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20000 entries, 0 to 19999\n",
            "Data columns (total 13 columns):\n",
            " #   Column                   Non-Null Count  Dtype         \n",
            "---  ------                   --------------  -----         \n",
            " 0   transaction_id           20000 non-null  int64         \n",
            " 1   product_id               20000 non-null  int64         \n",
            " 2   customer_id              20000 non-null  int64         \n",
            " 3   transaction_date         20000 non-null  datetime64[ns]\n",
            " 4   online_order             19640 non-null  float64       \n",
            " 5   order_status             20000 non-null  object        \n",
            " 6   brand                    19803 non-null  object        \n",
            " 7   product_line             19803 non-null  object        \n",
            " 8   product_class            19803 non-null  object        \n",
            " 9   product_size             19803 non-null  object        \n",
            " 10  list_price               20000 non-null  float64       \n",
            " 11  standard_cost            19803 non-null  float64       \n",
            " 12  product_first_sold_date  19803 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(4), int64(3), object(5)\n",
            "memory usage: 2.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#removing the unnamed columns\n",
        "Trsn = Trsn.iloc[:, 0:13]\n",
        "Trsn.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "bjFRrcQ5IzzZ",
        "outputId": "60424660-e731-4746-ad41-65b243cdb8c4"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   transaction_id  product_id  customer_id transaction_date  online_order  \\\n",
              "0               1           2         2950       2017-02-25           0.0   \n",
              "1               2           3         3120       2017-05-21           1.0   \n",
              "2               3          37          402       2017-10-16           0.0   \n",
              "3               4          88         3135       2017-08-31           0.0   \n",
              "4               5          78          787       2017-10-01           1.0   \n",
              "\n",
              "  order_status           brand product_line product_class product_size  \\\n",
              "0     Approved           Solex     Standard        medium       medium   \n",
              "1     Approved   Trek Bicycles     Standard        medium        large   \n",
              "2     Approved      OHM Cycles     Standard           low       medium   \n",
              "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
              "4     Approved  Giant Bicycles     Standard        medium        large   \n",
              "\n",
              "   list_price  standard_cost  product_first_sold_date  \n",
              "0       71.49          53.62                  41245.0  \n",
              "1     2091.47         388.92                  41701.0  \n",
              "2     1793.43         248.82                  36361.0  \n",
              "3     1198.46         381.10                  36145.0  \n",
              "4     1765.30         709.48                  42226.0  "
            ],
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c5fae093-1b83-4764-a4a7-f80b3638566b');\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",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check data info and shape\n",
        "Trsn.info()\n",
        "Trsn.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9N0MakheJnjd",
        "outputId": "1444e5e3-ff70-4eb8-e3e2-081fda514dbf"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20000 entries, 0 to 19999\n",
            "Data columns (total 13 columns):\n",
            " #   Column                   Non-Null Count  Dtype         \n",
            "---  ------                   --------------  -----         \n",
            " 0   transaction_id           20000 non-null  int64         \n",
            " 1   product_id               20000 non-null  int64         \n",
            " 2   customer_id              20000 non-null  int64         \n",
            " 3   transaction_date         20000 non-null  datetime64[ns]\n",
            " 4   online_order             19640 non-null  float64       \n",
            " 5   order_status             20000 non-null  object        \n",
            " 6   brand                    19803 non-null  object        \n",
            " 7   product_line             19803 non-null  object        \n",
            " 8   product_class            19803 non-null  object        \n",
            " 9   product_size             19803 non-null  object        \n",
            " 10  list_price               20000 non-null  float64       \n",
            " 11  standard_cost            19803 non-null  float64       \n",
            " 12  product_first_sold_date  19803 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(4), int64(3), object(5)\n",
            "memory usage: 2.0+ MB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20000, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for null values\n",
        "\n",
        "Trsn.isnull().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DIPfkLN0Jr2z",
        "outputId": "72d683b7-4738-4827-de77-bee5df4aa096"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "transaction_id               0\n",
              "product_id                   0\n",
              "customer_id                  0\n",
              "transaction_date             0\n",
              "online_order               360\n",
              "order_status                 0\n",
              "brand                      197\n",
              "product_line               197\n",
              "product_class              197\n",
              "product_size               197\n",
              "list_price                   0\n",
              "standard_cost              197\n",
              "product_first_sold_date    197\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Therefore, there are 7 columns that have null values, column numbers being 4, 6, 7, 8, 9, 11, 12. These can be either dropped or treated later on the basis of the analysis."
      ],
      "metadata": {
        "id": "ZHKMUmUuK3zV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check for duplicates\n",
        "Trsn.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nDf1o3KQLIAK",
        "outputId": "5e9f7957-fbf6-4a3d-a224-2307c94c9b8a"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no duplicates in the dataset, so no action required."
      ],
      "metadata": {
        "id": "iWG_2a7QLRgR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#column wise\n",
        "Trsn.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpruGUdsLWwb",
        "outputId": "5a9f6b85-e1f5-4524-e586-be8d3c60c27b"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['transaction_id', 'product_id', 'customer_id', 'transaction_date',\n",
              "       'online_order', 'order_status', 'brand', 'product_line',\n",
              "       'product_class', 'product_size', 'list_price', 'standard_cost',\n",
              "       'product_first_sold_date'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Trsn['order_status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyZw-tFJLdos",
        "outputId": "84cda380-690a-4101-a776-3775dc7fc37b"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "medium    12990\n",
              "large      3976\n",
              "small      2837\n",
              "Name: product_size, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Trsn['brand'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hb8a3dLaU_y0",
        "outputId": "f1d470cd-db35-4fea-c441-1194d19cef41"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Solex             4253\n",
              "Giant Bicycles    3312\n",
              "WeareA2B          3295\n",
              "OHM Cycles        3043\n",
              "Trek Bicycles     2990\n",
              "Norco Bicycles    2910\n",
              "Name: brand, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Trsn['product_line'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vp3TEeV2VDNE",
        "outputId": "8cd49f03-58cb-4da5-f4e9-88c2d50ef56e"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Standard    14176\n",
              "Road         3970\n",
              "Touring      1234\n",
              "Mountain      423\n",
              "Name: product_line, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Trsn['product_class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EdYWp3YeVFvM",
        "outputId": "57f40e71-aa37-4ae1-d066-df65fe35c3e7"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "medium    13826\n",
              "high       3013\n",
              "low        2964\n",
              "Name: product_class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Trsn['product_size'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5Qg1fxkVHXE",
        "outputId": "3c26db93-3d03-49b3-eb79-6703b3ec74b2"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "medium    12990\n",
              "large      3976\n",
              "small      2837\n",
              "Name: product_size, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Trsn['product_first_sold_date'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MiQpAfhGVPRW",
        "outputId": "45292425-c390-491c-9a65-d2efebd294a9"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33879.0    234\n",
              "41064.0    229\n",
              "37823.0    227\n",
              "39880.0    222\n",
              "38216.0    220\n",
              "          ... \n",
              "41848.0    169\n",
              "42404.0    168\n",
              "41922.0    166\n",
              "37659.0    163\n",
              "34586.0    162\n",
              "Name: product_first_sold_date, Length: 100, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#convert int to time\n",
        "Trsn['product_first_sold_date'] = pd.to_datetime(Trsn['product_first_sold_date'], unit='s')"
      ],
      "metadata": {
        "id": "GRObQRxLVUQz"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Trsn['product_first_sold_date'].head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LV2X4XlMVmCQ",
        "outputId": "27dfae03-7fc6-49e6-ea55-aac8d6fe70ef"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1970-01-01 11:27:25\n",
              "1    1970-01-01 11:35:01\n",
              "2    1970-01-01 10:06:01\n",
              "3    1970-01-01 10:02:25\n",
              "4    1970-01-01 11:43:46\n",
              "5    1970-01-01 10:50:31\n",
              "6    1970-01-01 09:29:25\n",
              "7    1970-01-01 11:05:15\n",
              "8    1970-01-01 09:17:35\n",
              "9    1970-01-01 10:36:56\n",
              "10   1970-01-01 11:19:44\n",
              "11   1970-01-01 11:42:52\n",
              "12   1970-01-01 09:35:27\n",
              "13   1970-01-01 09:36:26\n",
              "14   1970-01-01 10:36:33\n",
              "15   1970-01-01 10:31:13\n",
              "16   1970-01-01 10:36:46\n",
              "17   1970-01-01 09:24:48\n",
              "18   1970-01-01 11:05:15\n",
              "19   1970-01-01 10:22:17\n",
              "Name: product_first_sold_date, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since all products have their activity in same day at different times therefore the column data is not correct."
      ],
      "metadata": {
        "id": "jJKP853YWE4n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "New Customer List Dataset"
      ],
      "metadata": {
        "id": "5wzZ028SZrvM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NCL.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 682
        },
        "id": "lmKo4RFpZytz",
        "outputId": "9e141490-07f5-4e8e-8108-b9d135383a1e"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  first_name  last_name  gender  past_3_years_bike_related_purchases  \\\n",
              "0    Chickie    Brister    Male                                   86   \n",
              "1      Morly     Genery    Male                                   69   \n",
              "2    Ardelis  Forrester  Female                                   10   \n",
              "3     Lucine      Stutt  Female                                   64   \n",
              "4    Melinda     Hadlee  Female                                   34   \n",
              "5      Druci    Brandli  Female                                   39   \n",
              "6   Rutledge      Hallt    Male                                   23   \n",
              "7     Nancie       Vian  Female                                   74   \n",
              "8       Duff  Karlowicz    Male                                   50   \n",
              "9    Barthel     Docket    Male                                   72   \n",
              "\n",
              "         DOB                     job_title job_industry_category  \\\n",
              "0 1957-07-12               General Manager         Manufacturing   \n",
              "1 1970-03-22           Structural Engineer              Property   \n",
              "2 1974-08-28        Senior Cost Accountant    Financial Services   \n",
              "3 1979-01-28    Account Representative III         Manufacturing   \n",
              "4 1965-09-21             Financial Analyst    Financial Services   \n",
              "5 1951-04-29       Assistant Media Planner         Entertainment   \n",
              "6 1976-10-06          Compensation Analyst    Financial Services   \n",
              "7 1972-12-27  Human Resources Assistant II                Retail   \n",
              "8 1972-04-28            Speech Pathologist         Manufacturing   \n",
              "9 1985-08-02       Accounting Assistant IV                    IT   \n",
              "\n",
              "      wealth_segment deceased_indicator owns_car  ...  state    country  \\\n",
              "0      Mass Customer                  N      Yes  ...    QLD  Australia   \n",
              "1      Mass Customer                  N       No  ...    NSW  Australia   \n",
              "2  Affluent Customer                  N       No  ...    VIC  Australia   \n",
              "3  Affluent Customer                  N      Yes  ...    QLD  Australia   \n",
              "4  Affluent Customer                  N       No  ...    NSW  Australia   \n",
              "5     High Net Worth                  N      Yes  ...    QLD  Australia   \n",
              "6      Mass Customer                  N       No  ...    NSW  Australia   \n",
              "7      Mass Customer                  N      Yes  ...    QLD  Australia   \n",
              "8      Mass Customer                  N      Yes  ...    NSW  Australia   \n",
              "9      Mass Customer                  N      Yes  ...    QLD  Australia   \n",
              "\n",
              "   property_valuation Unnamed: 16 Unnamed: 17  Unnamed: 18  Unnamed: 19  \\\n",
              "0                   6        0.60      0.7500       0.9375     0.796875   \n",
              "1                  11        0.76      0.7600       0.9500     0.807500   \n",
              "2                   5        0.40      0.4000       0.4000     0.400000   \n",
              "3                   1        0.75      0.9375       0.9375     0.937500   \n",
              "4                   9        1.09      1.0900       1.3625     1.362500   \n",
              "5                   7        0.74      0.9250       0.9250     0.925000   \n",
              "6                   7        0.96      0.9600       0.9600     0.816000   \n",
              "7                   5        0.81      1.0125       1.0125     0.860625   \n",
              "8                  10        0.76      0.9500       1.1875     1.009375   \n",
              "9                   5        1.02      1.2750       1.2750     1.083750   \n",
              "\n",
              "   Unnamed: 20  Rank     Value  \n",
              "0            1     1  1.718750  \n",
              "1            1     1  1.718750  \n",
              "2            1     1  1.718750  \n",
              "3            4     4  1.703125  \n",
              "4            4     4  1.703125  \n",
              "5            6     6  1.671875  \n",
              "6            6     6  1.671875  \n",
              "7            8     8  1.656250  \n",
              "8            8     8  1.656250  \n",
              "9           10    10  1.640625  \n",
              "\n",
              "[10 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c86da43c-5a1a-45bd-ab8b-74ac53aa77ef\">\n",
              "    <div 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>first_name</th>\n",
              "      <th>last_name</th>\n",
              "      <th>gender</th>\n",
              "      <th>past_3_years_bike_related_purchases</th>\n",
              "      <th>DOB</th>\n",
              "      <th>job_title</th>\n",
              "      <th>job_industry_category</th>\n",
              "      <th>wealth_segment</th>\n",
              "      <th>deceased_indicator</th>\n",
              "      <th>owns_car</th>\n",
              "      <th>...</th>\n",
              "      <th>state</th>\n",
              "      <th>country</th>\n",
              "      <th>property_valuation</th>\n",
              "      <th>Unnamed: 16</th>\n",
              "      <th>Unnamed: 17</th>\n",
              "      <th>Unnamed: 18</th>\n",
              "      <th>Unnamed: 19</th>\n",
              "      <th>Unnamed: 20</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Chickie</td>\n",
              "      <td>Brister</td>\n",
              "      <td>Male</td>\n",
              "      <td>86</td>\n",
              "      <td>1957-07-12</td>\n",
              "      <td>General Manager</td>\n",
              "      <td>Manufacturing</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>6</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.7500</td>\n",
              "      <td>0.9375</td>\n",
              "      <td>0.796875</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.718750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Morly</td>\n",
              "      <td>Genery</td>\n",
              "      <td>Male</td>\n",
              "      <td>69</td>\n",
              "      <td>1970-03-22</td>\n",
              "      <td>Structural Engineer</td>\n",
              "      <td>Property</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>11</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.7600</td>\n",
              "      <td>0.9500</td>\n",
              "      <td>0.807500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.718750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ardelis</td>\n",
              "      <td>Forrester</td>\n",
              "      <td>Female</td>\n",
              "      <td>10</td>\n",
              "      <td>1974-08-28</td>\n",
              "      <td>Senior Cost Accountant</td>\n",
              "      <td>Financial Services</td>\n",
              "      <td>Affluent Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>VIC</td>\n",
              "      <td>Australia</td>\n",
              "      <td>5</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.718750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lucine</td>\n",
              "      <td>Stutt</td>\n",
              "      <td>Female</td>\n",
              "      <td>64</td>\n",
              "      <td>1979-01-28</td>\n",
              "      <td>Account Representative III</td>\n",
              "      <td>Manufacturing</td>\n",
              "      <td>Affluent Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.9375</td>\n",
              "      <td>0.9375</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.703125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Melinda</td>\n",
              "      <td>Hadlee</td>\n",
              "      <td>Female</td>\n",
              "      <td>34</td>\n",
              "      <td>1965-09-21</td>\n",
              "      <td>Financial Analyst</td>\n",
              "      <td>Financial Services</td>\n",
              "      <td>Affluent Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>9</td>\n",
              "      <td>1.09</td>\n",
              "      <td>1.0900</td>\n",
              "      <td>1.3625</td>\n",
              "      <td>1.362500</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.703125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Druci</td>\n",
              "      <td>Brandli</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>1951-04-29</td>\n",
              "      <td>Assistant Media Planner</td>\n",
              "      <td>Entertainment</td>\n",
              "      <td>High Net Worth</td>\n",
              "      <td>N</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>7</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.9250</td>\n",
              "      <td>0.9250</td>\n",
              "      <td>0.925000</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1.671875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Rutledge</td>\n",
              "      <td>Hallt</td>\n",
              "      <td>Male</td>\n",
              "      <td>23</td>\n",
              "      <td>1976-10-06</td>\n",
              "      <td>Compensation Analyst</td>\n",
              "      <td>Financial Services</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>7</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.9600</td>\n",
              "      <td>0.9600</td>\n",
              "      <td>0.816000</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1.671875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nancie</td>\n",
              "      <td>Vian</td>\n",
              "      <td>Female</td>\n",
              "      <td>74</td>\n",
              "      <td>1972-12-27</td>\n",
              "      <td>Human Resources Assistant II</td>\n",
              "      <td>Retail</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>5</td>\n",
              "      <td>0.81</td>\n",
              "      <td>1.0125</td>\n",
              "      <td>1.0125</td>\n",
              "      <td>0.860625</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1.656250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Duff</td>\n",
              "      <td>Karlowicz</td>\n",
              "      <td>Male</td>\n",
              "      <td>50</td>\n",
              "      <td>1972-04-28</td>\n",
              "      <td>Speech Pathologist</td>\n",
              "      <td>Manufacturing</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>NSW</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.9500</td>\n",
              "      <td>1.1875</td>\n",
              "      <td>1.009375</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1.656250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Barthel</td>\n",
              "      <td>Docket</td>\n",
              "      <td>Male</td>\n",
              "      <td>72</td>\n",
              "      <td>1985-08-02</td>\n",
              "      <td>Accounting Assistant IV</td>\n",
              "      <td>IT</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>5</td>\n",
              "      <td>1.02</td>\n",
              "      <td>1.2750</td>\n",
              "      <td>1.2750</td>\n",
              "      <td>1.083750</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>1.640625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c86da43c-5a1a-45bd-ab8b-74ac53aa77ef')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      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",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-c86da43c-5a1a-45bd-ab8b-74ac53aa77ef');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NCL.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ee_eNRbWZ3GF",
        "outputId": "9f36f8ab-8d49-4893-8d11-19e7431b0f2c"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 23 columns):\n",
            " #   Column                               Non-Null Count  Dtype         \n",
            "---  ------                               --------------  -----         \n",
            " 0   first_name                           1000 non-null   object        \n",
            " 1   last_name                            971 non-null    object        \n",
            " 2   gender                               1000 non-null   object        \n",
            " 3   past_3_years_bike_related_purchases  1000 non-null   int64         \n",
            " 4   DOB                                  983 non-null    datetime64[ns]\n",
            " 5   job_title                            894 non-null    object        \n",
            " 6   job_industry_category                835 non-null    object        \n",
            " 7   wealth_segment                       1000 non-null   object        \n",
            " 8   deceased_indicator                   1000 non-null   object        \n",
            " 9   owns_car                             1000 non-null   object        \n",
            " 10  tenure                               1000 non-null   int64         \n",
            " 11  address                              1000 non-null   object        \n",
            " 12  postcode                             1000 non-null   int64         \n",
            " 13  state                                1000 non-null   object        \n",
            " 14  country                              1000 non-null   object        \n",
            " 15  property_valuation                   1000 non-null   int64         \n",
            " 16  Unnamed: 16                          1000 non-null   float64       \n",
            " 17  Unnamed: 17                          1000 non-null   float64       \n",
            " 18  Unnamed: 18                          1000 non-null   float64       \n",
            " 19  Unnamed: 19                          1000 non-null   float64       \n",
            " 20  Unnamed: 20                          1000 non-null   int64         \n",
            " 21  Rank                                 1000 non-null   int64         \n",
            " 22  Value                                1000 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(5), int64(6), object(11)\n",
            "memory usage: 179.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#removing unnamed values\n",
        "NCL = NCL.loc[:]"
      ],
      "metadata": {
        "id": "WOlAOZDzZ7EI"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NCL.head()\n",
        "NCL.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZswIl4ladHt",
        "outputId": "9aefd29c-03ce-4aef-803c-f1673d0023b5"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for null values\n",
        "NCL.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOJDkK-waTjt",
        "outputId": "5becb129-888c-4104-e82d-eda37d63ac2f"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "first_name                               0\n",
              "last_name                               29\n",
              "gender                                   0\n",
              "past_3_years_bike_related_purchases      0\n",
              "DOB                                     17\n",
              "job_title                              106\n",
              "job_industry_category                  165\n",
              "wealth_segment                           0\n",
              "deceased_indicator                       0\n",
              "owns_car                                 0\n",
              "tenure                                   0\n",
              "address                                  0\n",
              "postcode                                 0\n",
              "state                                    0\n",
              "country                                  0\n",
              "property_valuation                       0\n",
              "Rank                                     0\n",
              "Value                                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Therefore, there are 4 columns with null values and can be dropped or treated accordingly."
      ],
      "metadata": {
        "id": "Fg1xmjcIdscQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NCL.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pW2knU2Cdzc3",
        "outputId": "4d525b8e-358d-4204-d6fa-73710fcf8001"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Therefore, no duplicate values, hence no action required."
      ],
      "metadata": {
        "id": "zSVksDm6d9A7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check if column is unique\n",
        "NCL.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86kEwCwGeBjY",
        "outputId": "6983c1e0-1b6c-46fb-a8bc-9c7994a9565b"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "first_name                              940\n",
              "last_name                               961\n",
              "gender                                    3\n",
              "past_3_years_bike_related_purchases     100\n",
              "DOB                                     958\n",
              "job_title                               184\n",
              "job_industry_category                     9\n",
              "wealth_segment                            3\n",
              "deceased_indicator                        1\n",
              "owns_car                                  2\n",
              "tenure                                   23\n",
              "address                                1000\n",
              "postcode                                522\n",
              "state                                     3\n",
              "country                                   1\n",
              "property_valuation                       12\n",
              "Rank                                    324\n",
              "Value                                   324\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#column wise\n",
        "NCL.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfZWb2WieRHg",
        "outputId": "aa1dd0cf-4b16-4ad8-a369-c5d95c56b83d"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['first_name', 'last_name', 'gender',\n",
              "       'past_3_years_bike_related_purchases', 'DOB', 'job_title',\n",
              "       'job_industry_category', 'wealth_segment', 'deceased_indicator',\n",
              "       'owns_car', 'tenure', 'address', 'postcode', 'state', 'country',\n",
              "       'property_valuation', 'Rank', 'Value'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NCL['gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKOKuooqeVFM",
        "outputId": "e8a39dbf-d70a-4979-f824-079f6172b612"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female    513\n",
              "Male      470\n",
              "U          17\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NCL[NCL.gender == 'U'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvV5NmvLebYf",
        "outputId": "5143aec4-892c-48b9-85bf-ec0fa4248105"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "first_name                             17\n",
              "last_name                              17\n",
              "gender                                 17\n",
              "past_3_years_bike_related_purchases    17\n",
              "DOB                                     0\n",
              "job_title                              16\n",
              "job_industry_category                  17\n",
              "wealth_segment                         17\n",
              "deceased_indicator                     17\n",
              "owns_car                               17\n",
              "tenure                                 17\n",
              "address                                17\n",
              "postcode                               17\n",
              "state                                  17\n",
              "country                                17\n",
              "property_valuation                     17\n",
              "Rank                                   17\n",
              "Value                                  17\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 17 unidentified gender column."
      ],
      "metadata": {
        "id": "zHz72E_VetNB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NCL['DOB'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiycocEmewve",
        "outputId": "ce2e82f1-18ad-48bd-a9a8-78a1983182e1"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1998-02-05    2\n",
              "1978-01-15    2\n",
              "1977-11-08    2\n",
              "1951-11-28    2\n",
              "1979-07-28    2\n",
              "             ..\n",
              "1945-08-08    1\n",
              "1943-08-27    1\n",
              "1999-10-24    1\n",
              "1976-01-24    1\n",
              "1955-10-02    1\n",
              "Name: DOB, Length: 958, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NCL['job_industry_category'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9iMMs309e0yR",
        "outputId": "147919e3-48c9-48ab-93ae-0ea6f628eb7d"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Financial Services    203\n",
              "Manufacturing         199\n",
              "Health                152\n",
              "Retail                 78\n",
              "Property               64\n",
              "IT                     51\n",
              "Entertainment          37\n",
              "Argiculture            26\n",
              "Telecommunications     25\n",
              "Name: job_industry_category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NCL['wealth_segment'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwv2UDPce6H8",
        "outputId": "8688d3d6-eea0-4d34-cf63-b42d8d5d4e31"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mass Customer        508\n",
              "High Net Worth       251\n",
              "Affluent Customer    241\n",
              "Name: wealth_segment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NCL['state'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22RVNqcFe-I_",
        "outputId": "7e95f6e4-a8b5-408d-9883-1a3c49216229"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NSW    506\n",
              "VIC    266\n",
              "QLD    228\n",
              "Name: state, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NCL['owns_car'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yF8oQSlifA7d",
        "outputId": "acee5ea7-de9d-492e-abd6-6e49791d4e2f"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     507\n",
              "Yes    493\n",
              "Name: owns_car, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NCL['deceased_indicator'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wO-HVDc8fEaa",
        "outputId": "8fcf2007-eec2-47bb-e0d3-848070adfdfb"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N    1000\n",
              "Name: deceased_indicator, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Customer Demographics Dataset"
      ],
      "metadata": {
        "id": "dEy1PUTnfLMz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CD.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "mK1K75zTfOMy",
        "outputId": "ada9c904-f301-4cea-bd64-9ca923929b5f"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_id      first_name  last_name  gender  \\\n",
              "0            1         Laraine  Medendorp       F   \n",
              "1            2             Eli    Bockman    Male   \n",
              "2            3           Arlin     Dearle    Male   \n",
              "3            4          Talbot        NaN    Male   \n",
              "4            5  Sheila-kathryn     Calton  Female   \n",
              "5            6            Curr  Duckhouse    Male   \n",
              "6            7            Fina     Merali  Female   \n",
              "7            8             Rod      Inder    Male   \n",
              "8            9            Mala       Lind  Female   \n",
              "9           10        Fiorenze    Birdall  Female   \n",
              "\n",
              "   past_3_years_bike_related_purchases        DOB  \\\n",
              "0                                   93 1953-10-12   \n",
              "1                                   81 1980-12-16   \n",
              "2                                   61 1954-01-20   \n",
              "3                                   33 1961-10-03   \n",
              "4                                   56 1977-05-13   \n",
              "5                                   35 1966-09-16   \n",
              "6                                    6 1976-02-23   \n",
              "7                                   31 1962-03-30   \n",
              "8                                   97 1973-03-10   \n",
              "9                                   49 1988-10-11   \n",
              "\n",
              "                              job_title job_industry_category  \\\n",
              "0                   Executive Secretary                Health   \n",
              "1                Administrative Officer    Financial Services   \n",
              "2                    Recruiting Manager              Property   \n",
              "3                                   NaN                    IT   \n",
              "4                         Senior Editor                   NaN   \n",
              "5                                   NaN                Retail   \n",
              "6                                   NaN    Financial Services   \n",
              "7                       Media Manager I                   NaN   \n",
              "8  Business Systems Development Analyst           Argiculture   \n",
              "9               Senior Quality Engineer    Financial Services   \n",
              "\n",
              "      wealth_segment deceased_indicator  \\\n",
              "0      Mass Customer                  N   \n",
              "1      Mass Customer                  N   \n",
              "2      Mass Customer                  N   \n",
              "3      Mass Customer                  N   \n",
              "4  Affluent Customer                  N   \n",
              "5     High Net Worth                  N   \n",
              "6  Affluent Customer                  N   \n",
              "7      Mass Customer                  N   \n",
              "8  Affluent Customer                  N   \n",
              "9      Mass Customer                  N   \n",
              "\n",
              "                                             default owns_car  tenure  \n",
              "0                                                 \"'      Yes    11.0  \n",
              "1                       <script>alert('hi')</script>      Yes    16.0  \n",
              "2                                2018-02-01 00:00:00      Yes    15.0  \n",
              "3  () { _; } >_[$($())] { touch /tmp/blns.shellsh...       No     7.0  \n",
              "4                                                NIL      Yes     8.0  \n",
              "5                                           ðµ ð ð ð      Yes    13.0  \n",
              "6                                          â°â´âµâââ      Yes    11.0  \n",
              "7                            (â¯Â°â¡Â°ï¼â¯ï¸µ â»ââ»)       No     7.0  \n",
              "8                                                0/0      Yes     8.0  \n",
              "9                                               ð©ð½      Yes    20.0  "
            ],
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              "      <th></th>\n",
              "      <th>customer_id</th>\n",
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              "      <th>default</th>\n",
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              "      <th>tenure</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Laraine</td>\n",
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              "      <td>F</td>\n",
              "      <td>93</td>\n",
              "      <td>1953-10-12</td>\n",
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              "      <td>Health</td>\n",
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              "      <td>81</td>\n",
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              "      <td>N</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Arlin</td>\n",
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              "      <td>Male</td>\n",
              "      <td>61</td>\n",
              "      <td>1954-01-20</td>\n",
              "      <td>Recruiting Manager</td>\n",
              "      <td>Property</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>2018-02-01 00:00:00</td>\n",
              "      <td>Yes</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Talbot</td>\n",
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              "      <td>33</td>\n",
              "      <td>1961-10-03</td>\n",
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              "      <td>N</td>\n",
              "      <td>() { _; } &gt;_[$($())] { touch /tmp/blns.shellsh...</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Sheila-kathryn</td>\n",
              "      <td>Calton</td>\n",
              "      <td>Female</td>\n",
              "      <td>56</td>\n",
              "      <td>1977-05-13</td>\n",
              "      <td>Senior Editor</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Affluent Customer</td>\n",
              "      <td>N</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>8.0</td>\n",
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              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Curr</td>\n",
              "      <td>Duckhouse</td>\n",
              "      <td>Male</td>\n",
              "      <td>35</td>\n",
              "      <td>1966-09-16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Retail</td>\n",
              "      <td>High Net Worth</td>\n",
              "      <td>N</td>\n",
              "      <td>ðµ ð ð ð</td>\n",
              "      <td>Yes</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Fina</td>\n",
              "      <td>Merali</td>\n",
              "      <td>Female</td>\n",
              "      <td>6</td>\n",
              "      <td>1976-02-23</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Financial Services</td>\n",
              "      <td>Affluent Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>â°â´âµâââ</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Rod</td>\n",
              "      <td>Inder</td>\n",
              "      <td>Male</td>\n",
              "      <td>31</td>\n",
              "      <td>1962-03-30</td>\n",
              "      <td>Media Manager I</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>(â¯Â°â¡Â°ï¼â¯ï¸µ â»ââ»)</td>\n",
              "      <td>No</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Mala</td>\n",
              "      <td>Lind</td>\n",
              "      <td>Female</td>\n",
              "      <td>97</td>\n",
              "      <td>1973-03-10</td>\n",
              "      <td>Business Systems Development Analyst</td>\n",
              "      <td>Argiculture</td>\n",
              "      <td>Affluent Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>0/0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>8.0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Fiorenze</td>\n",
              "      <td>Birdall</td>\n",
              "      <td>Female</td>\n",
              "      <td>49</td>\n",
              "      <td>1988-10-11</td>\n",
              "      <td>Senior Quality Engineer</td>\n",
              "      <td>Financial Services</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>ð©ð½</td>\n",
              "      <td>Yes</td>\n",
              "      <td>20.0</td>\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
          "execution_count": 95
        }
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    {
      "cell_type": "code",
      "source": [
        "CD.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t2iGBcGVfQyV",
        "outputId": "20c1c9bd-bb35-4da9-e27c-15a71689550b"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4000 entries, 0 to 3999\n",
            "Data columns (total 13 columns):\n",
            " #   Column                               Non-Null Count  Dtype         \n",
            "---  ------                               --------------  -----         \n",
            " 0   customer_id                          4000 non-null   int64         \n",
            " 1   first_name                           4000 non-null   object        \n",
            " 2   last_name                            3875 non-null   object        \n",
            " 3   gender                               4000 non-null   object        \n",
            " 4   past_3_years_bike_related_purchases  4000 non-null   int64         \n",
            " 5   DOB                                  3913 non-null   datetime64[ns]\n",
            " 6   job_title                            3494 non-null   object        \n",
            " 7   job_industry_category                3344 non-null   object        \n",
            " 8   wealth_segment                       4000 non-null   object        \n",
            " 9   deceased_indicator                   4000 non-null   object        \n",
            " 10  default                              3698 non-null   object        \n",
            " 11  owns_car                             4000 non-null   object        \n",
            " 12  tenure                               3913 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(1), int64(2), object(9)\n",
            "memory usage: 406.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for null values\n",
        "CD.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XQu3A0fVff9i",
        "outputId": "0d5f28f0-e4cd-4234-a176-608c3235bb16"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id                              0\n",
              "first_name                               0\n",
              "last_name                              125\n",
              "gender                                   0\n",
              "past_3_years_bike_related_purchases      0\n",
              "DOB                                     87\n",
              "job_title                              506\n",
              "job_industry_category                  656\n",
              "wealth_segment                           0\n",
              "deceased_indicator                       0\n",
              "default                                302\n",
              "owns_car                                 0\n",
              "tenure                                  87\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Therefore, there are 6 columns with null values and can be dropped or maybe treated later on."
      ],
      "metadata": {
        "id": "73D6vK6dfmQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check for duplicates\n",
        "CD.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lY55TZAnfvJa",
        "outputId": "17e80927-f4a8-4b96-f5dc-804aa7db61ba"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Therefore, no duplicate values so no action required."
      ],
      "metadata": {
        "id": "VirVluxsf2kt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check for uniqueness column wise\n",
        "CD.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9DLzvohf944",
        "outputId": "e8ad0c1c-f07c-490b-b722-8e25b110fec1"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id                            4000\n",
              "first_name                             3139\n",
              "last_name                              3725\n",
              "gender                                    6\n",
              "past_3_years_bike_related_purchases     100\n",
              "DOB                                    3448\n",
              "job_title                               195\n",
              "job_industry_category                     9\n",
              "wealth_segment                            3\n",
              "deceased_indicator                        2\n",
              "default                                  90\n",
              "owns_car                                  2\n",
              "tenure                                   22\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#column wise\n",
        "CD.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Dm8dS4pgFge",
        "outputId": "00b1ba84-8d62-459c-a853-6bafaa6e3058"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['customer_id', 'first_name', 'last_name', 'gender',\n",
              "       'past_3_years_bike_related_purchases', 'DOB', 'job_title',\n",
              "       'job_industry_category', 'wealth_segment', 'deceased_indicator',\n",
              "       'default', 'owns_car', 'tenure'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CD['gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ITa6Ltg6gJTg",
        "outputId": "18849209-8b34-40be-f094-689ab70dfdc5"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female    2037\n",
              "Male      1872\n",
              "U           88\n",
              "F            1\n",
              "Femal        1\n",
              "M            1\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CD['gender'] = CD['gender'].replace('F','Female').replace('M','Male').replace('Femal','Female')\n"
      ],
      "metadata": {
        "id": "HdtbJLXogM4Q"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CD['gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqoZwaQCgd1a",
        "outputId": "678658a0-c2ff-4a3b-a1eb-4e3465217c62"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Female    2039\n",
              "Male      1873\n",
              "U           88\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are total of 88 unspecified gender."
      ],
      "metadata": {
        "id": "Mp6kIVxjgkg8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CD['past_3_years_bike_related_purchases'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0tDTJ3-gphH",
        "outputId": "3dd6c648-8b32-42c9-c2a4-9f4396e2a09a"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16    56\n",
              "19    56\n",
              "67    54\n",
              "20    54\n",
              "2     50\n",
              "      ..\n",
              "8     28\n",
              "95    27\n",
              "85    27\n",
              "86    27\n",
              "92    24\n",
              "Name: past_3_years_bike_related_purchases, Length: 100, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CD['DOB'].value_counts()"
      ],
      "metadata": {
        "id": "g1eqU_Pfgwzs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CD['job_title'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMN7zCGmgyc9",
        "outputId": "5bc0a289-1aad-4232-ced8-764253d09795"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Business Systems Development Analyst    45\n",
              "Tax Accountant                          44\n",
              "Social Worker                           44\n",
              "Internal Auditor                        42\n",
              "Recruiting Manager                      41\n",
              "                                        ..\n",
              "Database Administrator I                 4\n",
              "Health Coach I                           3\n",
              "Health Coach III                         3\n",
              "Research Assistant III                   3\n",
              "Developer I                              1\n",
              "Name: job_title, Length: 195, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CD['job_industry_category'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GiNm4OQdg03K",
        "outputId": "aa001e09-d401-4030-854f-2901618ac696"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Manufacturing         799\n",
              "Financial Services    774\n",
              "Health                602\n",
              "Retail                358\n",
              "Property              267\n",
              "IT                    223\n",
              "Entertainment         136\n",
              "Argiculture           113\n",
              "Telecommunications     72\n",
              "Name: job_industry_category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CD['deceased_indicator'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tV4HHU2ug8aM",
        "outputId": "39a8bca7-a9e7-4269-9ded-d415a3b5d111"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N    3998\n",
              "Y       2\n",
              "Name: deceased_indicator, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CD['wealth_segment'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ATc6fiuYg_Zn",
        "outputId": "6dd418b8-05d4-4b08-bb8f-e28178b0dfcf"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mass Customer        2000\n",
              "High Net Worth       1021\n",
              "Affluent Customer     979\n",
              "Name: wealth_segment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#removed default values"
      ],
      "metadata": {
        "id": "bBiakQ8YhDQp"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CD.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "MlXgncnKhSFN",
        "outputId": "d8fb3e7c-bea9-4cbb-93fa-cdde60faf1cc"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_id      first_name  last_name  gender  \\\n",
              "0            1         Laraine  Medendorp  Female   \n",
              "1            2             Eli    Bockman    Male   \n",
              "2            3           Arlin     Dearle    Male   \n",
              "3            4          Talbot        NaN    Male   \n",
              "4            5  Sheila-kathryn     Calton  Female   \n",
              "5            6            Curr  Duckhouse    Male   \n",
              "6            7            Fina     Merali  Female   \n",
              "7            8             Rod      Inder    Male   \n",
              "8            9            Mala       Lind  Female   \n",
              "9           10        Fiorenze    Birdall  Female   \n",
              "\n",
              "   past_3_years_bike_related_purchases        DOB  \\\n",
              "0                                   93 1953-10-12   \n",
              "1                                   81 1980-12-16   \n",
              "2                                   61 1954-01-20   \n",
              "3                                   33 1961-10-03   \n",
              "4                                   56 1977-05-13   \n",
              "5                                   35 1966-09-16   \n",
              "6                                    6 1976-02-23   \n",
              "7                                   31 1962-03-30   \n",
              "8                                   97 1973-03-10   \n",
              "9                                   49 1988-10-11   \n",
              "\n",
              "                              job_title job_industry_category  \\\n",
              "0                   Executive Secretary                Health   \n",
              "1                Administrative Officer    Financial Services   \n",
              "2                    Recruiting Manager              Property   \n",
              "3                                   NaN                    IT   \n",
              "4                         Senior Editor                   NaN   \n",
              "5                                   NaN                Retail   \n",
              "6                                   NaN    Financial Services   \n",
              "7                       Media Manager I                   NaN   \n",
              "8  Business Systems Development Analyst           Argiculture   \n",
              "9               Senior Quality Engineer    Financial Services   \n",
              "\n",
              "      wealth_segment deceased_indicator owns_car  tenure  \n",
              "0      Mass Customer                  N      Yes    11.0  \n",
              "1      Mass Customer                  N      Yes    16.0  \n",
              "2      Mass Customer                  N      Yes    15.0  \n",
              "3      Mass Customer                  N       No     7.0  \n",
              "4  Affluent Customer                  N      Yes     8.0  \n",
              "5     High Net Worth                  N      Yes    13.0  \n",
              "6  Affluent Customer                  N      Yes    11.0  \n",
              "7      Mass Customer                  N       No     7.0  \n",
              "8  Affluent Customer                  N      Yes     8.0  \n",
              "9      Mass Customer                  N      Yes    20.0  "
            ],
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              "      <td>Male</td>\n",
              "      <td>35</td>\n",
              "      <td>1966-09-16</td>\n",
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              "      <td>13.0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Rod</td>\n",
              "      <td>Inder</td>\n",
              "      <td>Male</td>\n",
              "      <td>31</td>\n",
              "      <td>1962-03-30</td>\n",
              "      <td>Media Manager I</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mass Customer</td>\n",
              "      <td>N</td>\n",
              "      <td>No</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Mala</td>\n",
              "      <td>Lind</td>\n",
              "      <td>Female</td>\n",
              "      <td>97</td>\n",
              "      <td>1973-03-10</td>\n",
              "      <td>Business Systems Development Analyst</td>\n",
              "      <td>Argiculture</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
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              "      <td>Female</td>\n",
              "      <td>49</td>\n",
              "      <td>1988-10-11</td>\n",
              "      <td>Senior Quality Engineer</td>\n",
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              "      <td>N</td>\n",
              "      <td>Yes</td>\n",
              "      <td>20.0</td>\n",
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              "  </tbody>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
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      ]
    },
    {
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      "source": [
        "CD['tenure'].value_counts()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "VZL9DXo-hrYM",
        "outputId": "19d9b839-770e-46b9-ea8c-bc2a333cbe4d"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.0     235\n",
              "5.0     228\n",
              "11.0    221\n",
              "10.0    218\n",
              "16.0    215\n",
              "8.0     211\n",
              "18.0    208\n",
              "12.0    202\n",
              "9.0     200\n",
              "14.0    200\n",
              "6.0     192\n",
              "13.0    191\n",
              "4.0     191\n",
              "17.0    182\n",
              "15.0    179\n",
              "1.0     166\n",
              "3.0     160\n",
              "19.0    159\n",
              "2.0     150\n",
              "20.0     96\n",
              "22.0     55\n",
              "21.0     54\n",
              "Name: tenure, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
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      "source": [
        "CD['owns_car'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0V5-9uahys7",
        "outputId": "22710ec3-b80c-4063-b637-38296509b787"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Yes    2024\n",
              "No     1976\n",
              "Name: owns_car, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Customer Address Dataset"
      ],
      "metadata": {
        "id": "O6mqFo61h432"
      }
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    {
      "cell_type": "code",
      "source": [
        "CA.head(10)"
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      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 545
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        "id": "b0C6cw9bh8yr",
        "outputId": "25958b9e-6c2f-4974-b218-64e93f09533f"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_id                  address  postcode            state    country  \\\n",
              "0            1       060 Morning Avenue      2016  New South Wales  Australia   \n",
              "1            2      6 Meadow Vale Court      2153  New South Wales  Australia   \n",
              "2            4       0 Holy Cross Court      4211              QLD  Australia   \n",
              "3            5      17979 Del Mar Point      2448  New South Wales  Australia   \n",
              "4            6         9 Oakridge Court      3216              VIC  Australia   \n",
              "5            7         4 Delaware Trail      2210  New South Wales  Australia   \n",
              "6            8      49 Londonderry Lane      2650  New South Wales  Australia   \n",
              "7            9          97736 7th Trail      2023  New South Wales  Australia   \n",
              "8           11     93405 Ludington Park      3044              VIC  Australia   \n",
              "9           12  44339 Golden Leaf Alley      4557              QLD  Australia   \n",
              "\n",
              "   property_valuation  \n",
              "0                  10  \n",
              "1                  10  \n",
              "2                   9  \n",
              "3                   4  \n",
              "4                   9  \n",
              "5                   9  \n",
              "6                   4  \n",
              "7                  12  \n",
              "8                   8  \n",
              "9                   4  "
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              "      <th>address</th>\n",
              "      <th>postcode</th>\n",
              "      <th>state</th>\n",
              "      <th>country</th>\n",
              "      <th>property_valuation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>060 Morning Avenue</td>\n",
              "      <td>2016</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>6 Meadow Vale Court</td>\n",
              "      <td>2153</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>0 Holy Cross Court</td>\n",
              "      <td>4211</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>17979 Del Mar Point</td>\n",
              "      <td>2448</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>9 Oakridge Court</td>\n",
              "      <td>3216</td>\n",
              "      <td>VIC</td>\n",
              "      <td>Australia</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7</td>\n",
              "      <td>4 Delaware Trail</td>\n",
              "      <td>2210</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>49 Londonderry Lane</td>\n",
              "      <td>2650</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9</td>\n",
              "      <td>97736 7th Trail</td>\n",
              "      <td>2023</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>11</td>\n",
              "      <td>93405 Ludington Park</td>\n",
              "      <td>3044</td>\n",
              "      <td>VIC</td>\n",
              "      <td>Australia</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12</td>\n",
              "      <td>44339 Golden Leaf Alley</td>\n",
              "      <td>4557</td>\n",
              "      <td>QLD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37d292a5-2c02-4c34-9c97-890d707d9905')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-37d292a5-2c02-4c34-9c97-890d707d9905 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-37d292a5-2c02-4c34-9c97-890d707d9905');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CA.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6h3lIoM8iAcn",
        "outputId": "8766933c-233b-4fb2-f16c-ceb1e988a793"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3999 entries, 0 to 3998\n",
            "Data columns (total 6 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   customer_id         3999 non-null   int64 \n",
            " 1   address             3999 non-null   object\n",
            " 2   postcode            3999 non-null   int64 \n",
            " 3   state               3999 non-null   object\n",
            " 4   country             3999 non-null   object\n",
            " 5   property_valuation  3999 non-null   int64 \n",
            "dtypes: int64(3), object(3)\n",
            "memory usage: 187.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for duplicates\n",
        "CA.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D63ju7USiFjP",
        "outputId": "8a8d0453-71cf-47ad-96ae-de8b522094c1"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for null values\n",
        "CA.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJ_1gTDTi6sA",
        "outputId": "8816afde-4717-4963-f9c1-fa9c8235ccb6"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id           0\n",
              "address               0\n",
              "postcode              0\n",
              "state                 0\n",
              "country               0\n",
              "property_valuation    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Therefore, no duplicate as well as null values so no actions required."
      ],
      "metadata": {
        "id": "VYvzmCpSjAFv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CA.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrqrYS1cjFmX",
        "outputId": "0f23fd07-9dbd-4a76-d885-03dbd8444a7d"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id           3999\n",
              "address               3996\n",
              "postcode               873\n",
              "state                    5\n",
              "country                  1\n",
              "property_valuation      12\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#column wise\n",
        "CA.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHrMlV8LjIYV",
        "outputId": "c6e2c76d-1a1e-4e27-a8ce-9a973bfa9818"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['customer_id', 'address', 'postcode', 'state', 'country',\n",
              "       'property_valuation'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CA['postcode'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaxKeWpLjY6K",
        "outputId": "ad73b8a7-6f46-4406-ec7d-6a552a18e7ee"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2170    31\n",
              "2155    30\n",
              "2145    30\n",
              "2153    29\n",
              "3977    26\n",
              "        ..\n",
              "3808     1\n",
              "3114     1\n",
              "4721     1\n",
              "4799     1\n",
              "3089     1\n",
              "Name: postcode, Length: 873, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CA['state'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3GB0lQ2sjfpb",
        "outputId": "875e06f0-b0de-43ab-ba2c-f691d4774749"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NSW                2054\n",
              "VIC                 939\n",
              "QLD                 838\n",
              "New South Wales      86\n",
              "Victoria             82\n",
              "Name: state, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CA['address'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wRa1FT15jtZo",
        "outputId": "91704bf9-f8d9-4a85-ab90-7e051d9be085"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3 Mariners Cove Terrace      2\n",
              "3 Talisman Place             2\n",
              "64 Macpherson Junction       2\n",
              "359 Briar Crest Road         1\n",
              "4543 Service Terrace         1\n",
              "                            ..\n",
              "5063 Shopko Pass             1\n",
              "09 Hagan Pass                1\n",
              "87897 Lighthouse Bay Pass    1\n",
              "294 Lawn Junction            1\n",
              "320 Acker Drive              1\n",
              "Name: address, Length: 3996, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CA['country'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IqIF9BpkjzUU",
        "outputId": "ca9f27ba-907b-4e19-a01c-0109675c943f"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Australia    3999\n",
              "Name: country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CA['property_valuation'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pUiNkNe6j8gv",
        "outputId": "6bec9c04-8e8b-4a3d-a6c3-8fcd24d7da18"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9     647\n",
              "8     646\n",
              "10    577\n",
              "7     493\n",
              "11    281\n",
              "6     238\n",
              "5     225\n",
              "4     214\n",
              "12    195\n",
              "3     186\n",
              "1     154\n",
              "2     143\n",
              "Name: property_valuation, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    }
  ]
}