{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# LAB5 [222-15-6259]"
      ],
      "metadata": {
        "id": "EpzTa8T6go0V"
      }
    },
    {
      "source": [
        "## Load Data to the Colab\n"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "LBivWRZcd5Jw"
      }
    },
    {
      "source": [
        "import pandas as pd\n",
        "\n",
        "try:\n",
        "    df_ad = pd.read_csv('advertising.csv')\n",
        "    display(df_ad.head())\n",
        "except FileNotFoundError as e:\n",
        "    print(e)\n",
        ""
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ufb5FCkBd5pD",
        "outputId": "b643b4fa-8529-4a65-c8c6-de5bb7c8cbf3"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
              "0                     68.95   35     61833.90                256.09   \n",
              "1                     80.23   31     68441.85                193.77   \n",
              "2                     69.47   26     59785.94                236.50   \n",
              "3                     74.15   29     54806.18                245.89   \n",
              "4                     68.37   35     73889.99                225.58   \n",
              "\n",
              "                           Ad Topic Line            City  Male     Country  \\\n",
              "0     Cloned 5thgeneration orchestration     Wrightburgh     0     Tunisia   \n",
              "1     Monitored national standardization       West Jodi     1       Nauru   \n",
              "2       Organic bottom-line service-desk        Davidton     0  San Marino   \n",
              "3  Triple-buffered reciprocal time-frame  West Terrifurt     1       Italy   \n",
              "4          Robust logistical utilization    South Manuel     0     Iceland   \n",
              "\n",
              "             Timestamp  Clicked on Ad  \n",
              "0  2016-03-27 00:53:11              0  \n",
              "1  2016-04-04 01:39:02              0  \n",
              "2  2016-03-13 20:35:42              0  \n",
              "3  2016-01-10 02:31:19              0  \n",
              "4  2016-06-03 03:36:18              0  "
            ],
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              "    <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>Daily Time Spent on Site</th>\n",
              "      <th>Age</th>\n",
              "      <th>Area Income</th>\n",
              "      <th>Daily Internet Usage</th>\n",
              "      <th>Ad Topic Line</th>\n",
              "      <th>City</th>\n",
              "      <th>Male</th>\n",
              "      <th>Country</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Clicked on Ad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>68.95</td>\n",
              "      <td>35</td>\n",
              "      <td>61833.90</td>\n",
              "      <td>256.09</td>\n",
              "      <td>Cloned 5thgeneration orchestration</td>\n",
              "      <td>Wrightburgh</td>\n",
              "      <td>0</td>\n",
              "      <td>Tunisia</td>\n",
              "      <td>2016-03-27 00:53:11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80.23</td>\n",
              "      <td>31</td>\n",
              "      <td>68441.85</td>\n",
              "      <td>193.77</td>\n",
              "      <td>Monitored national standardization</td>\n",
              "      <td>West Jodi</td>\n",
              "      <td>1</td>\n",
              "      <td>Nauru</td>\n",
              "      <td>2016-04-04 01:39:02</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69.47</td>\n",
              "      <td>26</td>\n",
              "      <td>59785.94</td>\n",
              "      <td>236.50</td>\n",
              "      <td>Organic bottom-line service-desk</td>\n",
              "      <td>Davidton</td>\n",
              "      <td>0</td>\n",
              "      <td>San Marino</td>\n",
              "      <td>2016-03-13 20:35:42</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>74.15</td>\n",
              "      <td>29</td>\n",
              "      <td>54806.18</td>\n",
              "      <td>245.89</td>\n",
              "      <td>Triple-buffered reciprocal time-frame</td>\n",
              "      <td>West Terrifurt</td>\n",
              "      <td>1</td>\n",
              "      <td>Italy</td>\n",
              "      <td>2016-01-10 02:31:19</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68.37</td>\n",
              "      <td>35</td>\n",
              "      <td>73889.99</td>\n",
              "      <td>225.58</td>\n",
              "      <td>Robust logistical utilization</td>\n",
              "      <td>South Manuel</td>\n",
              "      <td>0</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>2016-06-03 03:36:18</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-f210c359-6a37-470f-b0cb-0d0cfca3a7d2 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-f210c359-6a37-470f-b0cb-0d0cfca3a7d2');\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-e9f554e7-2900-4940-9333-964e691a9761\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9f554e7-2900-4940-9333-964e691a9761')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
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              "    20% {\n",
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              "    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",
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              "      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-e9f554e7-2900-4940-9333-964e691a9761 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"    print(\\\"Error: 'advertising\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Daily Time Spent on Site\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.02399044585079,\n        \"min\": 68.37,\n        \"max\": 80.23,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          80.23,\n          68.37,\n          69.47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 26,\n        \"max\": 35,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          31,\n          29,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7486.821227207849,\n        \"min\": 54806.18,\n        \"max\": 73889.99,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          68441.85,\n          73889.99,\n          59785.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Daily Internet Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.954737109807724,\n        \"min\": 193.77,\n        \"max\": 256.09,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          193.77,\n          225.58,\n          236.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ad Topic Line\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Monitored national standardization\",\n          \"Robust logistical utilization\",\n          \"Organic bottom-line service-desk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"West Jodi\",\n          \"South Manuel\",\n          \"Davidton\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Nauru\",\n          \"Iceland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Timestamp\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2016-04-04 01:39:02\",\n          \"2016-06-03 03:36:18\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Clicked on Ad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "## Data Management"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "1E9mqJfZd9qa"
      }
    },
    {
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Data Shape and Info\n",
        "print(\"Shape of the DataFrame:\", df_ad.shape)\n",
        "df_ad.info()\n",
        "\n",
        "# Descriptive Statistics\n",
        "print(\"\\nDescriptive Statistics:\")\n",
        "display(df_ad.describe())\n",
        "\n",
        "# Data Distribution (Histograms)\n",
        "numerical_cols = df_ad.select_dtypes(include=['number']).columns\n",
        "num_plots = len(numerical_cols)\n",
        "fig, axes = plt.subplots(nrows=(num_plots + 1) // 2, ncols=2, figsize=(15, 5 * ((num_plots + 1) // 2)))\n",
        "axes = axes.flatten()\n",
        "\n",
        "for i, col in enumerate(numerical_cols):\n",
        "    df_ad[col].hist(ax=axes[i], color='skyblue', edgecolor='black')\n",
        "    axes[i].set_title(f'Distribution of {col}')\n",
        "    axes[i].set_xlabel(col)\n",
        "    axes[i].set_ylabel('Frequency')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Missing Value Analysis\n",
        "print(\"\\nMissing Values:\")\n",
        "display(df_ad.isnull().sum())"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1772
        },
        "id": "Nx29KGMYd-1H",
        "outputId": "6ba2f111-61c5-4538-b1a3-b787ca430d5f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the DataFrame: (1000, 10)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 10 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Daily Time Spent on Site  1000 non-null   float64\n",
            " 1   Age                       1000 non-null   int64  \n",
            " 2   Area Income               1000 non-null   float64\n",
            " 3   Daily Internet Usage      1000 non-null   float64\n",
            " 4   Ad Topic Line             1000 non-null   object \n",
            " 5   City                      1000 non-null   object \n",
            " 6   Male                      1000 non-null   int64  \n",
            " 7   Country                   1000 non-null   object \n",
            " 8   Timestamp                 1000 non-null   object \n",
            " 9   Clicked on Ad             1000 non-null   int64  \n",
            "dtypes: float64(3), int64(3), object(4)\n",
            "memory usage: 78.3+ KB\n",
            "\n",
            "Descriptive Statistics:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       Daily Time Spent on Site          Age   Area Income  \\\n",
              "count               1000.000000  1000.000000   1000.000000   \n",
              "mean                  65.000200    36.009000  55000.000080   \n",
              "std                   15.853615     8.785562  13414.634022   \n",
              "min                   32.600000    19.000000  13996.500000   \n",
              "25%                   51.360000    29.000000  47031.802500   \n",
              "50%                   68.215000    35.000000  57012.300000   \n",
              "75%                   78.547500    42.000000  65470.635000   \n",
              "max                   91.430000    61.000000  79484.800000   \n",
              "\n",
              "       Daily Internet Usage         Male  Clicked on Ad  \n",
              "count           1000.000000  1000.000000     1000.00000  \n",
              "mean             180.000100     0.481000        0.50000  \n",
              "std               43.902339     0.499889        0.50025  \n",
              "min              104.780000     0.000000        0.00000  \n",
              "25%              138.830000     0.000000        0.00000  \n",
              "50%              183.130000     0.000000        0.50000  \n",
              "75%              218.792500     1.000000        1.00000  \n",
              "max              269.960000     1.000000        1.00000  "
            ],
            "text/html": [
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Daily Time Spent on Site</th>\n",
              "      <th>Age</th>\n",
              "      <th>Area Income</th>\n",
              "      <th>Daily Internet Usage</th>\n",
              "      <th>Male</th>\n",
              "      <th>Clicked on Ad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>65.000200</td>\n",
              "      <td>36.009000</td>\n",
              "      <td>55000.000080</td>\n",
              "      <td>180.000100</td>\n",
              "      <td>0.481000</td>\n",
              "      <td>0.50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>15.853615</td>\n",
              "      <td>8.785562</td>\n",
              "      <td>13414.634022</td>\n",
              "      <td>43.902339</td>\n",
              "      <td>0.499889</td>\n",
              "      <td>0.50025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>32.600000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>13996.500000</td>\n",
              "      <td>104.780000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>51.360000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>47031.802500</td>\n",
              "      <td>138.830000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>68.215000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>57012.300000</td>\n",
              "      <td>183.130000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>78.547500</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>65470.635000</td>\n",
              "      <td>218.792500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>91.430000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>79484.800000</td>\n",
              "      <td>269.960000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "        document.querySelector('#df-edb7ded0-b93b-446a-af19-7f1607539dec 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-edb7ded0-b93b-446a-af19-7f1607539dec');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    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-6c88b6e0-36d0-43c8-bc0f-adcc32590bc9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_ad\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Daily Time Spent on Site\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 334.0885412445993,\n        \"min\": 15.8536145675002,\n        \"max\": 1000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          65.0002,\n          68.215,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 342.2465542682817,\n        \"min\": 8.78556231012592,\n        \"max\": 1000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          36.009,\n          35.0,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28426.290342992994,\n        \"min\": 1000.0,\n        \"max\": 79484.8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          55000.000080000005,\n          57012.3,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Daily Internet Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 303.95932222293465,\n        \"min\": 43.902339301980106,\n        \"max\": 1000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          180.0001,\n          183.13,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 353.40307790688973,\n        \"min\": 0.0,\n        \"max\": 1000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.481,\n          1.0,\n          0.49988887654046626\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Clicked on Ad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 353.37680339312715,\n        \"min\": 0.0,\n        \"max\": 1000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5,\n          1.0,\n          0.5002501876563868\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing Values:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Daily Time Spent on Site    0\n",
              "Age                         0\n",
              "Area Income                 0\n",
              "Daily Internet Usage        0\n",
              "Ad Topic Line               0\n",
              "City                        0\n",
              "Male                        0\n",
              "Country                     0\n",
              "Timestamp                   0\n",
              "Clicked on Ad               0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Daily Time Spent on Site</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Area Income</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily Internet Usage</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ad Topic Line</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>City</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Timestamp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Clicked on Ad</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "## Data cleaning.\n"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "qql-_JtweDMw"
      }
    },
    {
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "numerical_cols = ['Daily Time Spent on Site', 'Age', 'Area Income', 'Daily Internet Usage']\n",
        "\n",
        "# Identify outliers using box plots and IQR\n",
        "plt.figure(figsize=(15, 10))\n",
        "for i, col in enumerate(numerical_cols):\n",
        "    plt.subplot(2, 2, i + 1)\n",
        "    plt.boxplot(df_ad[col])\n",
        "    plt.title(f'Box Plot of {col}')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "df_cleaned = df_ad.copy()\n",
        "for col in numerical_cols:\n",
        "    Q1 = df_ad[col].quantile(0.25)\n",
        "    Q3 = df_ad[col].quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "    lower_bound = Q1 - 1.5 * IQR\n",
        "    upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "    # Winsorize\n",
        "    df_cleaned[col] = np.clip(df_cleaned[col], lower_bound, upper_bound)\n",
        "\n",
        "# Verify outlier handling\n",
        "plt.figure(figsize=(15, 10))\n",
        "for i, col in enumerate(numerical_cols):\n",
        "    plt.subplot(2, 2, i + 1)\n",
        "    plt.boxplot(df_cleaned[col])\n",
        "    plt.title(f'Box Plot of {col} after Winsorizing')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"Minimum and Maximum values after Winsorizing:\")\n",
        "for col in numerical_cols:\n",
        "    print(f\"{col}: Min={df_cleaned[col].min()}, Max={df_cleaned[col].max()}\")"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1445
        },
        "id": "9PTb2DO2eEUh",
        "outputId": "84e88baf-3f6d-4d58-fc6c-f88f38e08030"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum and Maximum values after Winsorizing:\n",
            "Daily Time Spent on Site: Min=32.6, Max=91.43\n",
            "Age: Min=19, Max=61\n",
            "Area Income: Min=19373.553750000003, Max=79484.8\n",
            "Daily Internet Usage: Min=104.78, Max=269.96\n"
          ]
        }
      ]
    },
    {
      "source": [
        "## Data analysis\n",
        "\n"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "EYClDWiVeIUY"
      }
    },
    {
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "\n",
        "# 1. Descriptive Statistics\n",
        "display(df_cleaned.describe())\n",
        "\n",
        "# 2. Correlation Matrix and Heatmap\n",
        "numerical_features = ['Daily Time Spent on Site', 'Age', 'Area Income', 'Daily Internet Usage', 'Clicked on Ad']\n",
        "correlation_matrix = df_cleaned[numerical_features].corr()\n",
        "display(correlation_matrix)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
        "plt.title('Correlation Matrix of Numerical Features')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# 3. Hypothesis Testing\n",
        "advertising_channels = ['Daily Time Spent on Site', 'Age', 'Area Income', 'Daily Internet Usage']\n",
        "for channel in advertising_channels:\n",
        "    t_statistic, p_value = stats.ttest_ind(df_cleaned[channel], df_cleaned['Clicked on Ad'])\n",
        "    print(f\"Hypothesis Test for {channel}:\")\n",
        "    print(f\"  T-statistic: {t_statistic:.4f}\")\n",
        "    print(f\"  P-value: {p_value:.4f}\")\n",
        "    if p_value < 0.05:\n",
        "        print(f\"  There is a statistically significant relationship between {channel} and sales.\")\n",
        "    else:\n",
        "        print(f\"  There is no statistically significant relationship between {channel} and sales.\")\n",
        "\n",
        "# 4. Summarize Key Findings\n",
        "print(\"\\nSummary of Key Findings:\")\n",
        "print(\"1. Outlier Handling: Outliers in 'Daily Time Spent on Site', 'Age', 'Area Income', and 'Daily Internet Usage' were winsorized, with the minimum and maximum values after winsorizing reported.\")"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1522
        },
        "id": "zxiIGfbxeJax",
        "outputId": "aadf65d6-2dd3-41e4-fc6e-9b2feac57e09"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       Daily Time Spent on Site          Age   Area Income  \\\n",
              "count               1000.000000  1000.000000   1000.000000   \n",
              "mean                  65.000200    36.009000  55025.321364   \n",
              "std                   15.853615     8.785562  13343.228619   \n",
              "min                   32.600000    19.000000  19373.553750   \n",
              "25%                   51.360000    29.000000  47031.802500   \n",
              "50%                   68.215000    35.000000  57012.300000   \n",
              "75%                   78.547500    42.000000  65470.635000   \n",
              "max                   91.430000    61.000000  79484.800000   \n",
              "\n",
              "       Daily Internet Usage         Male  Clicked on Ad  \n",
              "count           1000.000000  1000.000000     1000.00000  \n",
              "mean             180.000100     0.481000        0.50000  \n",
              "std               43.902339     0.499889        0.50025  \n",
              "min              104.780000     0.000000        0.00000  \n",
              "25%              138.830000     0.000000        0.00000  \n",
              "50%              183.130000     0.000000        0.50000  \n",
              "75%              218.792500     1.000000        1.00000  \n",
              "max              269.960000     1.000000        1.00000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dad1e970-c4e7-4ceb-9a0c-d045ca12679b\" 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>Daily Time Spent on Site</th>\n",
              "      <th>Age</th>\n",
              "      <th>Area Income</th>\n",
              "      <th>Daily Internet Usage</th>\n",
              "      <th>Male</th>\n",
              "      <th>Clicked on Ad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>65.000200</td>\n",
              "      <td>36.009000</td>\n",
              "      <td>55025.321364</td>\n",
              "      <td>180.000100</td>\n",
              "      <td>0.481000</td>\n",
              "      <td>0.50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>15.853615</td>\n",
              "      <td>8.785562</td>\n",
              "      <td>13343.228619</td>\n",
              "      <td>43.902339</td>\n",
              "      <td>0.499889</td>\n",
              "      <td>0.50025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>32.600000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>19373.553750</td>\n",
              "      <td>104.780000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>51.360000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>47031.802500</td>\n",
              "      <td>138.830000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>68.215000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>57012.300000</td>\n",
              "      <td>183.130000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>78.547500</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>65470.635000</td>\n",
              "      <td>218.792500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>91.430000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>79484.800000</td>\n",
              "      <td>269.960000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</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-dad1e970-c4e7-4ceb-9a0c-d045ca12679b')\"\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-dad1e970-c4e7-4ceb-9a0c-d045ca12679b 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-dad1e970-c4e7-4ceb-9a0c-d045ca12679b');\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-a260be97-bd36-43a2-b0b8-ab4b226ceaeb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a260be97-bd36-43a2-b0b8-ab4b226ceaeb')\"\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-a260be97-bd36-43a2-b0b8-ab4b226ceaeb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"print(\\\"1\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Daily Time Spent on Site\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 334.0885412445993,\n        \"min\": 15.8536145675002,\n        \"max\": 1000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          65.0002,\n          68.215,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 342.2465542682817,\n        \"min\": 8.78556231012592,\n        \"max\": 1000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          36.009,\n          35.0,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27749.171349983284,\n        \"min\": 1000.0,\n        \"max\": 79484.8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          55025.321363749994,\n          57012.3,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Daily Internet Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 303.95932222293465,\n        \"min\": 43.902339301980106,\n        \"max\": 1000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          180.0001,\n          183.13,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 353.40307790688973,\n        \"min\": 0.0,\n        \"max\": 1000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.481,\n          1.0,\n          0.49988887654046626\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Clicked on Ad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 353.37680339312715,\n        \"min\": 0.0,\n        \"max\": 1000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5,\n          1.0,\n          0.5002501876563868\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "text/plain": [
              "                          Daily Time Spent on Site       Age  Area Income  \\\n",
              "Daily Time Spent on Site                  1.000000 -0.331513     0.312198   \n",
              "Age                                      -0.331513  1.000000    -0.182403   \n",
              "Area Income                               0.312198 -0.182403     1.000000   \n",
              "Daily Internet Usage                      0.518658 -0.367209     0.339280   \n",
              "Clicked on Ad                            -0.748117  0.492531    -0.476905   \n",
              "\n",
              "                          Daily Internet Usage  Clicked on Ad  \n",
              "Daily Time Spent on Site              0.518658      -0.748117  \n",
              "Age                                  -0.367209       0.492531  \n",
              "Area Income                           0.339280      -0.476905  \n",
              "Daily Internet Usage                  1.000000      -0.786539  \n",
              "Clicked on Ad                        -0.786539       1.000000  "
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Daily Time Spent on Site</th>\n",
              "      <th>Age</th>\n",
              "      <th>Area Income</th>\n",
              "      <th>Daily Internet Usage</th>\n",
              "      <th>Clicked on Ad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Daily Time Spent on Site</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.331513</td>\n",
              "      <td>0.312198</td>\n",
              "      <td>0.518658</td>\n",
              "      <td>-0.748117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.331513</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.182403</td>\n",
              "      <td>-0.367209</td>\n",
              "      <td>0.492531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Area Income</th>\n",
              "      <td>0.312198</td>\n",
              "      <td>-0.182403</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.339280</td>\n",
              "      <td>-0.476905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily Internet Usage</th>\n",
              "      <td>0.518658</td>\n",
              "      <td>-0.367209</td>\n",
              "      <td>0.339280</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.786539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Clicked on Ad</th>\n",
              "      <td>-0.748117</td>\n",
              "      <td>0.492531</td>\n",
              "      <td>-0.476905</td>\n",
              "      <td>-0.786539</td>\n",
              "      <td>1.000000</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-452dd621-ae12-48a4-8bda-f4285b81408d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </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-452dd621-ae12-48a4-8bda-f4285b81408d 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-452dd621-ae12-48a4-8bda-f4285b81408d');\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",
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              "\n",
              "\n",
              "<div id=\"df-ac47ba4b-e22d-4a32-a777-88d6a5f2ad0f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ac47ba4b-e22d-4a32-a777-88d6a5f2ad0f')\"\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",
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              "\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-ac47ba4b-e22d-4a32-a777-88d6a5f2ad0f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7213721f-98aa-4e42-9110-005ef65f36d8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('correlation_matrix')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7213721f-98aa-4e42-9110-005ef65f36d8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('correlation_matrix');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "correlation_matrix",
              "summary": "{\n  \"name\": \"correlation_matrix\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Daily Time Spent on Site\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6933948084478638,\n        \"min\": -0.7481165641196352,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.3315133427865839,\n          -0.7481165641196352,\n          0.31219805481326707\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6012149195231586,\n        \"min\": -0.367208560147359,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          0.49253126555603755,\n          -0.18240290289888908\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Area Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5648909243278424,\n        \"min\": -0.4769046351199011,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.18240290289888908,\n          -0.4769046351199011,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Daily Internet Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7138674165665029,\n        \"min\": -0.7865391758714916,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.367208560147359,\n          -0.7865391758714916,\n          0.3392803500883926\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Clicked on Ad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.805367789262577,\n        \"min\": -0.7865391758714916,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.49253126555603755,\n          1.0,\n          -0.4769046351199011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hypothesis Test for Daily Time Spent on Site:\n",
            "  T-statistic: 128.5928\n",
            "  P-value: 0.0000\n",
            "  There is a statistically significant relationship between Daily Time Spent on Site and sales.\n",
            "Hypothesis Test for Age:\n",
            "  T-statistic: 127.6045\n",
            "  P-value: 0.0000\n",
            "  There is a statistically significant relationship between Age and sales.\n",
            "Hypothesis Test for Area Income:\n",
            "  T-statistic: 130.4060\n",
            "  P-value: 0.0000\n",
            "  There is a statistically significant relationship between Area Income and sales.\n",
            "Hypothesis Test for Daily Internet Usage:\n",
            "  T-statistic: 129.2852\n",
            "  P-value: 0.0000\n",
            "  There is a statistically significant relationship between Daily Internet Usage and sales.\n",
            "\n",
            "Summary of Key Findings:\n",
            "1. Outlier Handling: Outliers in 'Daily Time Spent on Site', 'Age', 'Area Income', and 'Daily Internet Usage' were winsorized, with the minimum and maximum values after winsorizing reported.\n"
          ]
        }
      ]
    },
    {
      "source": [
        "## Data visualization\n",
        "\n",
        "\n"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "71k2U1WYeN0H"
      }
    },
    {
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# 1. Scatter Plots\n",
        "plt.figure(figsize=(15, 10))\n",
        "advertising_channels = ['Daily Time Spent on Site', 'Age', 'Area Income', 'Daily Internet Usage']\n",
        "for i, channel in enumerate(advertising_channels):\n",
        "    plt.subplot(2, 2, i + 1)\n",
        "    sns.scatterplot(x=channel, y='Clicked on Ad', data=df_cleaned, color=f\"C{i}\")\n",
        "    plt.xlabel(channel)\n",
        "    plt.ylabel('Clicked on Ad')\n",
        "    plt.title(f'{channel} vs. Clicked on Ad')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# 2. Histograms\n",
        "plt.figure(figsize=(15, 10))\n",
        "for i, channel in enumerate(advertising_channels):\n",
        "    plt.subplot(2, 2, i + 1)\n",
        "    df_cleaned[channel].hist(color=f\"C{i}\", edgecolor='black')\n",
        "    plt.xlabel(channel)\n",
        "    plt.ylabel('Frequency')\n",
        "    plt.title(f'Distribution of {channel}')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# 3. Correlation Heatmap\n",
        "correlation_matrix = df_cleaned[advertising_channels + ['Clicked on Ad']].corr()\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
        "plt.title('Correlation Matrix of Advertising Channels and Clicked on Ad')\n",
        "plt.show()"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1413
        },
        "id": "EqtUubSmeO8E",
        "outputId": "d1978a36-e425-4b1c-bdef-5c9fd3c1d403"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}