diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb
index a05da5cd3..70e4a4996 100644
--- a/02_activities/assignments/assignment_2.ipynb
+++ b/02_activities/assignments/assignment_2.ipynb
@@ -1,413 +1,1378 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7b0bcac6-5086-4f4e-928a-570a9ff7ae58",
-   "metadata": {},
-   "source": [
-    "# Assignment 2"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "7b0bcac6-5086-4f4e-928a-570a9ff7ae58",
+      "metadata": {
+        "id": "7b0bcac6-5086-4f4e-928a-570a9ff7ae58"
+      },
+      "source": [
+        "# Assignment 2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5fce0350-2a17-4e93-8d4c-0b8748fdfc32",
+      "metadata": {
+        "id": "5fce0350-2a17-4e93-8d4c-0b8748fdfc32"
+      },
+      "source": [
+        "You only need to write one line of code for each question. When answering questions that ask you to identify or interpret something, the length of your response doesn’t matter. For example, if the answer is just ‘yes,’ ‘no,’ or a number, you can just give that answer without adding anything else.\n",
+        "\n",
+        "We will go through comparable code and concepts in the live learning session. If you run into trouble, start by using the help `help()` function in Python, to get information about the datasets and function in question. The internet is also a great resource when coding (though note that **no outside searches are required by the assignment!**). If you do incorporate code from the internet, please cite the source within your code (providing a URL is sufficient).\n",
+        "\n",
+        "Please bring questions that you cannot work out on your own to office hours, work periods or share with your peers on Slack. We will work with you through the issue."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5fc5001c-7715-4ebe-b0f7-e4bd04349629",
+      "metadata": {
+        "id": "5fc5001c-7715-4ebe-b0f7-e4bd04349629"
+      },
+      "source": [
+        "### Linear Regression\n",
+        "\n",
+        "Let's set up our workspace and use the **Auto MPG dataset**. This dataset contains several features (such as horsepower, weight, displacement, and acceleration) and a target variable indicating the car's **miles per gallon (MPG)**.\n",
+        "\n",
+        "Here, we will model **MPG (continuous outcome)** based on the car's physical and performance characteristics."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "4a3485d6-ba58-4660-a983-5680821c5719",
+      "metadata": {
+        "id": "4a3485d6-ba58-4660-a983-5680821c5719"
+      },
+      "outputs": [],
+      "source": [
+        "# Import standard libraries\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.metrics import mean_squared_error"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 574
+        },
+        "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+        "outputId": "8495b8f4-40c7-4621-afd5-1e7ffa25715d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+              "0  18.0          8         307.0       130.0    3504          12.0   \n",
+              "1  15.0          8         350.0       165.0    3693          11.5   \n",
+              "2  18.0          8         318.0       150.0    3436          11.0   \n",
+              "3  16.0          8         304.0       150.0    3433          12.0   \n",
+              "4  17.0          8         302.0       140.0    3449          10.5   \n",
+              "5  15.0          8         429.0       198.0    4341          10.0   \n",
+              "6  14.0          8         454.0       220.0    4354           9.0   \n",
+              "7  14.0          8         440.0       215.0    4312           8.5   \n",
+              "8  14.0          8         455.0       225.0    4425          10.0   \n",
+              "9  15.0          8         390.0       190.0    3850           8.5   \n",
+              "\n",
+              "   model_year origin                       name  \n",
+              "0          70    usa  chevrolet chevelle malibu  \n",
+              "1          70    usa          buick skylark 320  \n",
+              "2          70    usa         plymouth satellite  \n",
+              "3          70    usa              amc rebel sst  \n",
+              "4          70    usa                ford torino  \n",
+              "5          70    usa           ford galaxie 500  \n",
+              "6          70    usa           chevrolet impala  \n",
+              "7          70    usa          plymouth fury iii  \n",
+              "8          70    usa           pontiac catalina  \n",
+              "9          70    usa         amc ambassador dpl  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4e4ede25-268a-415f-95c2-ca3e69b90796\" 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>mpg</th>\n",
+              "      <th>cylinders</th>\n",
+              "      <th>displacement</th>\n",
+              "      <th>horsepower</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>acceleration</th>\n",
+              "      <th>model_year</th>\n",
+              "      <th>origin</th>\n",
+              "      <th>name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>307.0</td>\n",
+              "      <td>130.0</td>\n",
+              "      <td>3504</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>chevrolet chevelle malibu</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>15.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>350.0</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>3693</td>\n",
+              "      <td>11.5</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>buick skylark 320</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>18.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>318.0</td>\n",
+              "      <td>150.0</td>\n",
+              "      <td>3436</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>plymouth satellite</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>16.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>304.0</td>\n",
+              "      <td>150.0</td>\n",
+              "      <td>3433</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>amc rebel sst</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>302.0</td>\n",
+              "      <td>140.0</td>\n",
+              "      <td>3449</td>\n",
+              "      <td>10.5</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>ford torino</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>15.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>429.0</td>\n",
+              "      <td>198.0</td>\n",
+              "      <td>4341</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>ford galaxie 500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>14.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>454.0</td>\n",
+              "      <td>220.0</td>\n",
+              "      <td>4354</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>chevrolet impala</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>14.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>440.0</td>\n",
+              "      <td>215.0</td>\n",
+              "      <td>4312</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>plymouth fury iii</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>14.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>455.0</td>\n",
+              "      <td>225.0</td>\n",
+              "      <td>4425</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>pontiac catalina</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>15.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>390.0</td>\n",
+              "      <td>190.0</td>\n",
+              "      <td>3850</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>amc ambassador dpl</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e4ede25-268a-415f-95c2-ca3e69b90796')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4e4ede25-268a-415f-95c2-ca3e69b90796 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-4e4ede25-268a-415f-95c2-ca3e69b90796');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-7ea7d11a-63d1-4fd6-b7d6-c877122cfda9\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7ea7d11a-63d1-4fd6-b7d6-c877122cfda9')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-7ea7d11a-63d1-4fd6-b7d6-c877122cfda9 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "mpg_data",
+              "summary": "{\n  \"name\": \"mpg_data\",\n  \"rows\": 392,\n  \"fields\": [\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.805007486571799,\n        \"min\": 9.0,\n        \"max\": 46.6,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          17.5,\n          35.1,\n          28.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cylinders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          5,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"displacement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.64400390890465,\n        \"min\": 68.0,\n        \"max\": 455.0,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          116.0,\n          307.0,\n          360.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"horsepower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.49115993282855,\n        \"min\": 46.0,\n        \"max\": 230.0,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          92.0,\n          100.0,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 849,\n        \"min\": 1613,\n        \"max\": 5140,\n        \"num_unique_values\": 346,\n        \"samples\": [\n          2472,\n          3221,\n          2700\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acceleration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7588641191880816,\n        \"min\": 8.0,\n        \"max\": 24.8,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          14.7,\n          18.0,\n          14.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 70,\n        \"max\": 82,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          81,\n          79,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"origin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"usa\",\n          \"japan\",\n          \"europe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 301,\n        \"samples\": [\n          \"subaru dl\",\n          \"buick century limited\",\n          \"plymouth horizon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "import seaborn as sns\n",
+        "\n",
+        "# Load the Auto MPG dataset\n",
+        "mpg_data = sns.load_dataset('mpg')\n",
+        "\n",
+        "# Drop rows with missing values\n",
+        "mpg_data = mpg_data.dropna(subset=['mpg', 'horsepower', 'weight', 'displacement'])\n",
+        "\n",
+        "# Display the DataFrame\n",
+        "mpg_data.head(10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "721b2b17",
+      "metadata": {
+        "id": "721b2b17"
+      },
+      "source": [
+        "#### **Question 1:**  \n",
+        "#### Data inspection\n",
+        "\n",
+        "Before fitting any model, it is essential to understand our data. **Use Python code** to answer the following questions about the **Auto MPG dataset**:\n",
+        "\n",
+        "_(i)_ How many observations (rows) does the dataset contain and also how many variables (columns) does the dataset contain?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "5d79f1cf",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5d79f1cf",
+        "outputId": "7fa3822b-ee96-49f2-9d6b-1b9517e571f2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Observations (rows): 392\n",
+            "Variables (columns): 9\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"Observations (rows): {mpg_data.shape[0]}\")\n",
+        "print(f\"Variables (columns): {mpg_data.shape[1]}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a599c73b",
+      "metadata": {
+        "id": "a599c73b"
+      },
+      "source": [
+        "_(ii)_ What is the 'variable type' of the response variable `mpg` (e.g., 'integer', 'category', etc.) and explain what it is in short? (1-2 sentences at max)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "ac306190",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ac306190",
+        "outputId": "34e95d84-b242-4a3a-c4ce-74cc23a8f0b2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The variable type of 'mpg' is float64.\n"
+          ]
+        }
+      ],
+      "source": [
+        "type_of_mpg = mpg_data['mpg'].dtype\n",
+        "print(f\"The variable type of 'mpg' is {type_of_mpg}.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6d759089",
+      "metadata": {
+        "id": "6d759089"
+      },
+      "source": [
+        "Float64, also known as double-precision floating-point, is a data type that uses 64 bits of computer memory to store numbers with decimal points.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "be0119ad",
+      "metadata": {
+        "id": "be0119ad"
+      },
+      "source": [
+        "_(iii)_ Find the 5 rows with the greatest `horsepower`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "9f034a5d",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 400
+        },
+        "id": "9f034a5d",
+        "outputId": "82dc0e75-40cc-419d-b925-9d1d3206ed33"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+              "116  16.0          8         400.0       230.0    4278           9.5   \n",
+              "95   12.0          8         455.0       225.0    4951          11.0   \n",
+              "8    14.0          8         455.0       225.0    4425          10.0   \n",
+              "13   14.0          8         455.0       225.0    3086          10.0   \n",
+              "6    14.0          8         454.0       220.0    4354           9.0   \n",
+              "\n",
+              "     model_year origin                      name  \n",
+              "116          73    usa        pontiac grand prix  \n",
+              "95           73    usa  buick electra 225 custom  \n",
+              "8            70    usa          pontiac catalina  \n",
+              "13           70    usa   buick estate wagon (sw)  \n",
+              "6            70    usa          chevrolet impala  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2c7cb52f-32b4-4813-8272-6307dc2c33fc\" 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",
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+              "\n",
+              "    .dataframe thead th {\n",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mpg</th>\n",
+              "      <th>cylinders</th>\n",
+              "      <th>displacement</th>\n",
+              "      <th>horsepower</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>acceleration</th>\n",
+              "      <th>model_year</th>\n",
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+              "      <th>116</th>\n",
+              "      <td>16.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>400.0</td>\n",
+              "      <td>230.0</td>\n",
+              "      <td>4278</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>73</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>pontiac grand prix</td>\n",
+              "    </tr>\n",
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+              "      <td>12.0</td>\n",
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+              "      <td>11.0</td>\n",
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+              "      <td>225.0</td>\n",
+              "      <td>4425</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>pontiac catalina</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>14.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>455.0</td>\n",
+              "      <td>225.0</td>\n",
+              "      <td>3086</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>buick estate wagon (sw)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>14.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>454.0</td>\n",
+              "      <td>220.0</td>\n",
+              "      <td>4354</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>70</td>\n",
+              "      <td>usa</td>\n",
+              "      <td>chevrolet impala</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c7cb52f-32b4-4813-8272-6307dc2c33fc')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
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+              "  </svg>\n",
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+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      width: 32px;\n",
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+              "\n",
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+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2c7cb52f-32b4-4813-8272-6307dc2c33fc 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-2c7cb52f-32b4-4813-8272-6307dc2c33fc');\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",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "\n",
+              "    <div id=\"df-b2d70c99-a3ec-42a5-b6bc-82a0df075530\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b2d70c99-a3ec-42a5-b6bc-82a0df075530')\"\n",
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+              "                style=\"display:none;\">\n",
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+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
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+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
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+              "    height: 32px;\n",
+              "    padding: 0;\n",
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+              "  }\n",
+              "\n",
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+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-b2d70c99-a3ec-42a5-b6bc-82a0df075530 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "sorted_mpg_data",
+              "summary": "{\n  \"name\": \"sorted_mpg_data\",\n  \"rows\": 392,\n  \"fields\": [\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.805007486571798,\n        \"min\": 9.0,\n        \"max\": 46.6,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          20.0,\n          34.4,\n          28.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cylinders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6,\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"displacement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.64400390890464,\n        \"min\": 68.0,\n        \"max\": 455.0,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          225.0,\n          400.0,\n          258.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"horsepower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.491159932828545,\n        \"min\": 46.0,\n        \"max\": 230.0,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          112.0,\n          148.0,\n          91.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 849,\n        \"min\": 1613,\n        \"max\": 5140,\n        \"num_unique_values\": 346,\n        \"samples\": [\n          2933,\n          3525,\n          2158\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acceleration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.758864119188081,\n        \"min\": 8.0,\n        \"max\": 24.8,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          17.1,\n          13.2,\n          18.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 70,\n        \"max\": 82,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          81,\n          74,\n          73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"origin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"usa\",\n          \"europe\",\n          \"japan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 301,\n        \"samples\": [\n          \"ford fairmont (man)\",\n          \"honda civic cvcc\",\n          \"vw rabbit\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "sorted_mpg_data=mpg_data.sort_values(by='horsepower',ascending=False)\n",
+        "print(sorted_mpg_data.head(5))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cbc54d2c",
+      "metadata": {
+        "id": "cbc54d2c"
+      },
+      "source": [
+        "_(iv)_ How many predictor variables do we have (Hint: all variables other than `mpg`)?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "1b91233e",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1b91233e",
+        "outputId": "9c7f494e-0aae-4e5d-d261-c9e748cbacbd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "8\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(mpg_data.shape[1]-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1741cf23",
+      "metadata": {
+        "id": "1741cf23"
+      },
+      "source": [
+        "You can use `print()` and `describe()` to help answer these questions."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fa3832d7",
+      "metadata": {
+        "id": "fa3832d7"
+      },
+      "source": [
+        "#### **Question 2:**\n",
+        "#### Data-visualization\n",
+        "\n",
+        "Before we fit and review model outputs, we should visualize our data. Review the code and plot, shown below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "732784d8",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "732784d8",
+        "outputId": "b79c43ed-6d91-419a-e1cf-0f7481339871"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Exclude the 'mpg' (target variable) and non-numeric columns from the feature names\n",
+        "feature_names = mpg_data.select_dtypes(include=[float, int]).columns.difference(['mpg'])\n",
+        "\n",
+        "# Loop through each numeric feature (column) in mpg_data\n",
+        "for feature in feature_names:\n",
+        "    # Extract the feature column and target variable (mpg)\n",
+        "    X_feature = mpg_data[[feature]].values  # Extract as a 2D array\n",
+        "    y = mpg_data['mpg'].values  # Target variable (mpg)\n",
+        "\n",
+        "    # Create a scatter plot for the feature against the target (mpg)\n",
+        "    plt.figure(figsize=(6, 4))\n",
+        "    plt.scatter(X_feature, y, label='Data', color='blue')\n",
+        "\n",
+        "    # Fit a linear regression model\n",
+        "    lm = LinearRegression()\n",
+        "    lm.fit(X_feature, y)\n",
+        "\n",
+        "    # Plot the regression line\n",
+        "    plt.plot(X_feature, lm.predict(X_feature), color='red', label='Regression Line')\n",
+        "\n",
+        "    # Add labels and title\n",
+        "    plt.xlabel(feature)\n",
+        "    plt.ylabel('Miles Per Gallon')\n",
+        "    plt.title(f'{feature} vs Miles Per Gallon')\n",
+        "\n",
+        "    # Add a legend\n",
+        "    plt.legend()\n",
+        "\n",
+        "    # Show the plot\n",
+        "    plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "785c6a78",
+      "metadata": {
+        "id": "785c6a78"
+      },
+      "source": [
+        "Answer the following questions:\n",
+        "\n",
+        "_(i)_ Describe the associations being plotted ? (i.e., positive association, negative association, no association)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f67e57ab",
+      "metadata": {
+        "id": "f67e57ab"
+      },
+      "source": [
+        "Acceleration and model-year seems to have a positive association, displacement, horsepower and weight have negative association with MPG which is our target variable. While, Cylender shows negative assiciation with MPG, it seems they have no significant association with each other."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5325992e",
+      "metadata": {
+        "id": "5325992e"
+      },
+      "source": [
+        "_(ii)_ What concept ‘defines’ the plotted line?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "843f9eef",
+      "metadata": {
+        "id": "843f9eef"
+      },
+      "source": [
+        "Slop of the linear regression."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b1ff3cf",
+      "metadata": {
+        "id": "2b1ff3cf"
+      },
+      "source": [
+        "_(iii)_ Do all data points in the dataset fall perfectly along the plotted line? If not, why might there be deviations between the data points and the line, and what do these deviations indicate about the relationship between the variables?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2ea782fc",
+      "metadata": {
+        "id": "2ea782fc"
+      },
+      "source": [
+        "No, it is not the case in all the relationships between x varibales vs. y. Deviation are because of outliers in the dataset or there may be non-linear relationship btw x and y variables. Also, in some cases, only the effect of one x variable on y cannot capture the relationship as there may be other confounder variables effect on y."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4604ee03",
+      "metadata": {
+        "id": "4604ee03"
+      },
+      "source": [
+        "#### **Question 3:**  \n",
+        "#### Model fit  \n",
+        "Now, let’s fit a multivariable linear regression model using the general syntax `lm()`. As above, use **mpg** as the response variable **Y**, and all other variables as the predictors.\n",
+        "\n",
+        "**Step 1: Split the dataset into train and test sets, using a 75-25 split. (use random_state=42)**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "399225f4",
+      "metadata": {
+        "id": "399225f4"
+      },
+      "outputs": [],
+      "source": [
+        "X = mpg_data.drop(columns=['mpg', 'name', 'origin'])\n",
+        "y = mpg_data['mpg']\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f76b8f5c",
+      "metadata": {
+        "id": "f76b8f5c"
+      },
+      "source": [
+        "**Step 2: Fit the linear regression model.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "ac1e1117",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ac1e1117",
+        "outputId": "ad2a06dd-f368-4ba9-8206-0aecb4990a3c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "      predictor     slope  intercept\n",
+            "0     cylinders -0.160143 -15.047371\n",
+            "1  displacement  0.000373 -15.047371\n",
+            "2    horsepower -0.001899 -15.047371\n",
+            "3        weight -0.006457 -15.047371\n",
+            "4  acceleration  0.057588 -15.047371\n",
+            "5    model_year  0.762270 -15.047371\n"
+          ]
+        }
+      ],
+      "source": [
+        "lm = LinearRegression()\n",
+        "lm.fit(X_train, y_train)\n",
+        "\n",
+        "numeric_predictors = X_train\n",
+        "\n",
+        "# Create a DataFrame containing the slope (coefficients) and intercept\n",
+        "coefficients_df = pd.DataFrame({\n",
+        "    \"predictor\": numeric_predictors.columns,\n",
+        "    \"slope\": lm.coef_,\n",
+        "    \"intercept\": [lm.intercept_] * len(lm.coef_)\n",
+        "})\n",
+        "\n",
+        "# Display the coefficients DataFrame\n",
+        "print(coefficients_df)\n",
+        "\n",
+        "# lm.coef_ gives the coefficients for each predictor (change in miles per gallon per unit change in each predictor variable)\n",
+        "# lm.intercept_ gives the intercept b_0 (the predicted miles per gallon when all predictors are set to 0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3f76bf62",
+      "metadata": {
+        "id": "3f76bf62"
+      },
+      "source": [
+        "#### **Question 4:**\n",
+        "#### RMSPE\n",
+        "**Step 3. Finally, we predict on the test data set to assess how well our model does.**\n",
+        "\n",
+        "We will evaluate our final model's test error measured by RMSPE."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "ffefa9f2",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ffefa9f2",
+        "outputId": "8007f875-b535-4468-a389-3013b02db45a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Root Mean Squared Error (RMSE): 3.19\n",
+            "Root Mean Square Percentage Error (RMSPE): 0.16\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = lm.predict(X_test)\n",
+        "\n",
+        "# Calculate RMSE (Root Mean Squared Error)\n",
+        "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+        "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
+        "\n",
+        "# Calculate RMSPE (Root Mean Square Percentage Error)\n",
+        "# RMSPE = sqrt(mean(( (y_true - y_pred) / y_true )^2))\n",
+        "# To avoid division by zero, we can filter out y_true values that are 0 or very close to 0.\n",
+        "# For MPG, values are generally positive and non-zero, so this is less of a concern.\n",
+        "percentage_error = (y_test - y_pred) / y_test\n",
+        "rmspe = np.sqrt(np.mean(percentage_error**2))\n",
+        "print(f\"Root Mean Square Percentage Error (RMSPE): {rmspe:.2f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6f8a69db",
+      "metadata": {
+        "id": "6f8a69db"
+      },
+      "source": [
+        "# Criteria\n",
+        "\n",
+        "| **Criteria**                                           | **Complete**                                      | **Incomplete**                                    |\n",
+        "|--------------------------------------------------------|---------------------------------------------------|--------------------------------------------------|\n",
+        "| **Data Inspection**                                    | Data is inspected for the number of variables, observations, and data types. | Data inspection is missing or incomplete.         |\n",
+        "| **Data Visualization**                                 | Visualizations (e.g., scatter plots, histograms) are properly interepreted to explore the relationships between variables. | Data visualization were not correctly interpreted. |\n",
+        "| **Model Initialization**                               | The linear regression model is correctly initialized.            | The linear regression model is not initialized or is incorrect. |\n",
+        "| **Model Evaluation on Test Data**                      | The model is evaluated on the test data using appropriate metrics (e.g., RMSE). | The model evaluation is missing or uses the wrong metric. |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0b4390cc",
+      "metadata": {
+        "id": "0b4390cc"
+      },
+      "source": [
+        "## Submission Information\n",
+        "\n",
+        "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
+        "\n",
+        "### Note:\n",
+        "\n",
+        "If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.\n",
+        "\n",
+        "### Submission Parameters:\n",
+        "* Submission Due Date: `11:59 PM - 12/03/2025`\n",
+        "* The branch name for your repo should be: `assignment-2`\n",
+        "* What to submit for this assignment:\n",
+        "    * This Jupyter Notebook (assignment_2.ipynb) should be populated and should be the only change in your pull request.\n",
+        "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/LCR/pull/<pr_id>`\n",
+        "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
+        "\n",
+        "Checklist:\n",
+        "- [ ] Created a branch with the correct naming convention.\n",
+        "- [ ] Ensured that the repository is public.\n",
+        "- [ ] Reviewed the PR description guidelines and adhered to them.\n",
+        "- [ ] Verify that the link is accessible in a private browser window.\n",
+        "\n",
+        "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#dc2-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
+      }
+    },
+    "colab": {
+      "provenance": []
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "5fce0350-2a17-4e93-8d4c-0b8748fdfc32",
-   "metadata": {},
-   "source": [
-    "You only need to write one line of code for each question. When answering questions that ask you to identify or interpret something, the length of your response doesn’t matter. For example, if the answer is just ‘yes,’ ‘no,’ or a number, you can just give that answer without adding anything else.\n",
-    "\n",
-    "We will go through comparable code and concepts in the live learning session. If you run into trouble, start by using the help `help()` function in Python, to get information about the datasets and function in question. The internet is also a great resource when coding (though note that **no outside searches are required by the assignment!**). If you do incorporate code from the internet, please cite the source within your code (providing a URL is sufficient).\n",
-    "\n",
-    "Please bring questions that you cannot work out on your own to office hours, work periods or share with your peers on Slack. We will work with you through the issue."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fc5001c-7715-4ebe-b0f7-e4bd04349629",
-   "metadata": {},
-   "source": [
-    "### Linear Regression\n",
-    "\n",
-    "Let's set up our workspace and use the **Auto MPG dataset**. This dataset contains several features (such as horsepower, weight, displacement, and acceleration) and a target variable indicating the car's **miles per gallon (MPG)**.\n",
-    "\n",
-    "Here, we will model **MPG (continuous outcome)** based on the car's physical and performance characteristics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "4a3485d6-ba58-4660-a983-5680821c5719",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import standard libraries\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.metrics import mean_squared_error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "\n",
-    "# Load the Auto MPG dataset\n",
-    "mpg_data = sns.load_dataset('mpg')\n",
-    "\n",
-    "# Drop rows with missing values\n",
-    "mpg_data = mpg_data.dropna(subset=['mpg', 'horsepower', 'weight', 'displacement'])\n",
-    "\n",
-    "# Display the DataFrame\n",
-    "mpg_data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "721b2b17",
-   "metadata": {},
-   "source": [
-    "#### **Question 1:**  \n",
-    "#### Data inspection\n",
-    "\n",
-    "Before fitting any model, it is essential to understand our data. **Use Python code** to answer the following questions about the **Auto MPG dataset**:\n",
-    "\n",
-    "_(i)_ How many observations (rows) does the dataset contain and also how many variables (columns) does the dataset contain?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5d79f1cf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your answer here..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a599c73b",
-   "metadata": {},
-   "source": [
-    "_(ii)_ What is the 'variable type' of the response variable `mpg` (e.g., 'integer', 'category', etc.) and explain what it is in short? (1-2 sentences at max)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ac306190",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your answer here..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d759089",
-   "metadata": {},
-   "source": [
-    "Your explanation... \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be0119ad",
-   "metadata": {},
-   "source": [
-    "_(iii)_ Find the 5 rows with the greatest `horsepower`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f034a5d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your answer here... "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cbc54d2c",
-   "metadata": {},
-   "source": [
-    "_(iv)_ How many predictor variables do we have (Hint: all variables other than `mpg`)?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1b91233e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your answer here..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1741cf23",
-   "metadata": {},
-   "source": [
-    "You can use `print()` and `describe()` to help answer these questions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fa3832d7",
-   "metadata": {},
-   "source": [
-    "#### **Question 2:** \n",
-    "#### Data-visualization\n",
-    "\n",
-    "Before we fit and review model outputs, we should visualize our data. Review the code and plot, shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "732784d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Exclude the 'mpg' (target variable) and non-numeric columns from the feature names\n",
-    "feature_names = mpg_data.select_dtypes(include=[float, int]).columns.difference(['mpg'])\n",
-    "\n",
-    "# Loop through each numeric feature (column) in mpg_data\n",
-    "for feature in feature_names:\n",
-    "    # Extract the feature column and target variable (mpg)\n",
-    "    X_feature = mpg_data[[feature]].values  # Extract as a 2D array\n",
-    "    y = mpg_data['mpg'].values  # Target variable (mpg)\n",
-    "    \n",
-    "    # Create a scatter plot for the feature against the target (mpg)\n",
-    "    plt.figure(figsize=(6, 4))\n",
-    "    plt.scatter(X_feature, y, label='Data', color='blue')\n",
-    "\n",
-    "    # Fit a linear regression model\n",
-    "    lm = LinearRegression()\n",
-    "    lm.fit(X_feature, y)\n",
-    "\n",
-    "    # Plot the regression line\n",
-    "    plt.plot(X_feature, lm.predict(X_feature), color='red', label='Regression Line')\n",
-    "\n",
-    "    # Add labels and title\n",
-    "    plt.xlabel(feature)\n",
-    "    plt.ylabel('Miles Per Gallon')\n",
-    "    plt.title(f'{feature} vs Miles Per Gallon')\n",
-    "\n",
-    "    # Add a legend\n",
-    "    plt.legend()\n",
-    "\n",
-    "    # Show the plot\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "785c6a78",
-   "metadata": {},
-   "source": [
-    "Answer the following questions:\n",
-    "\n",
-    "_(i)_ Describe the associations being plotted ? (i.e., positive association, negative association, no association)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f67e57ab",
-   "metadata": {},
-   "source": [
-    "> Your answer here..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5325992e",
-   "metadata": {},
-   "source": [
-    "_(ii)_ What concept ‘defines’ the plotted line?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "843f9eef",
-   "metadata": {},
-   "source": [
-    "> Your answer here..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b1ff3cf",
-   "metadata": {},
-   "source": [
-    "_(iii)_ Do all data points in the dataset fall perfectly along the plotted line? If not, why might there be deviations between the data points and the line, and what do these deviations indicate about the relationship between the variables?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ea782fc",
-   "metadata": {},
-   "source": [
-    "> Your answer here..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4604ee03",
-   "metadata": {},
-   "source": [
-    "#### **Question 3:**  \n",
-    "#### Model fit  \n",
-    "Now, let’s fit a multivariable linear regression model using the general syntax `lm()`. As above, use **mpg** as the response variable **Y**, and all other variables as the predictors.\n",
-    "\n",
-    "**Step 1: Split the dataset into train and test sets, using a 75-25 split. (use random_state=42)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "399225f4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your answer here..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f76b8f5c",
-   "metadata": {},
-   "source": [
-    "**Step 2: Fit the linear regression model.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ac1e1117",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here ...\n",
-    "\n",
-    "numeric_predictors = 🤷‍♂️\n",
-    "\n",
-    "\n",
-    "# Create a DataFrame containing the slope (coefficients) and intercept\n",
-    "coefficients_df = pd.DataFrame({\n",
-    "    \"predictor\": numeric_predictors.columns,\n",
-    "    \"slope\": lm.coef_,\n",
-    "    \"intercept\": [lm.intercept_] * len(lm.coef_)\n",
-    "})\n",
-    "\n",
-    "# Display the coefficients DataFrame\n",
-    "print(coefficients_df)\n",
-    "\n",
-    "# lm.coef_ gives the coefficients for each predictor (change in miles per gallon per unit change in each predictor variable)\n",
-    "# lm.intercept_ gives the intercept b_0 (the predicted miles per gallon when all predictors are set to 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f76bf62",
-   "metadata": {},
-   "source": [
-    "#### **Question 4:** \n",
-    "#### RMSPE\n",
-    "**Step 3. Finally, we predict on the test data set to assess how well our model does.** \n",
-    "\n",
-    "We will evaluate our final model's test error measured by RMSPE."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ffefa9f2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f8a69db",
-   "metadata": {},
-   "source": [
-    "# Criteria\n",
-    "\n",
-    "| **Criteria**                                           | **Complete**                                      | **Incomplete**                                    |\n",
-    "|--------------------------------------------------------|---------------------------------------------------|--------------------------------------------------|\n",
-    "| **Data Inspection**                                    | Data is inspected for the number of variables, observations, and data types. | Data inspection is missing or incomplete.         |\n",
-    "| **Data Visualization**                                 | Visualizations (e.g., scatter plots, histograms) are properly interepreted to explore the relationships between variables. | Data visualization were not correctly interpreted. |\n",
-    "| **Model Initialization**                               | The linear regression model is correctly initialized.            | The linear regression model is not initialized or is incorrect. |\n",
-    "| **Model Evaluation on Test Data**                      | The model is evaluated on the test data using appropriate metrics (e.g., RMSE). | The model evaluation is missing or uses the wrong metric. |\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b4390cc",
-   "metadata": {},
-   "source": [
-    "## Submission Information\n",
-    "\n",
-    "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
-    "\n",
-    "### Note:\n",
-    "\n",
-    "If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.\n",
-    "\n",
-    "### Submission Parameters:\n",
-    "* Submission Due Date: `11:59 PM - 12/03/2025`\n",
-    "* The branch name for your repo should be: `assignment-2`\n",
-    "* What to submit for this assignment:\n",
-    "    * This Jupyter Notebook (assignment_2.ipynb) should be populated and should be the only change in your pull request.\n",
-    "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/LCR/pull/<pr_id>`\n",
-    "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
-    "\n",
-    "Checklist:\n",
-    "- [ ] Created a branch with the correct naming convention.\n",
-    "- [ ] Ensured that the repository is public.\n",
-    "- [ ] Reviewed the PR description guidelines and adhered to them.\n",
-    "- [ ] Verify that the link is accessible in a private browser window.\n",
-    "\n",
-    "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#dc2-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file