calories.ipynb

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        "<a href=\"https://colab.research.google.com/github/darsh0p/Calorie_Burnt_Prediction/blob/main/calories.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# import required libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "#from sklearn.linear_model import Ridge,Lasso\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn import metrics\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "import pickle\n",
        "\n",
        "import warnings\n",
        "from warnings import filterwarnings\n",
        "filterwarnings(\"ignore\")\n",
        "\n",
        "sns.set()"
      ]
    },
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        "outputId": "79d1b809-e422-4866-d7cd-dae6d8c9f8ae"
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            "text/plain": [
              "    User_ID  Calories\n",
              "0  14733363     231.0\n",
              "1  14861698      66.0\n",
              "2  11179863      26.0\n",
              "3  16180408      71.0\n",
              "4  17771927      35.0"
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          "execution_count": 2,
          "metadata": {},
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        }
      ],
      "source": [
        "#Load the Calories dataset\n",
        "df1 = pd.read_csv(\"/content/calories.csv\")\n",
        "df1.head()"
      ]
    },
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              "(15000, 2)"
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        "df1.shape\n"
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              "  <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-8bc1f448-0d6e-4fad-9f70-d49a062930af 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-8bc1f448-0d6e-4fad-9f70-d49a062930af');\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-a403bacf-2313-4650-9bcb-6e0ba832fa6b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a403bacf-2313-4650-9bcb-6e0ba832fa6b')\"\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-a403bacf-2313-4650-9bcb-6e0ba832fa6b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    User_ID  Gender  Age  Height  Weight  Duration  Heart_Rate  Body_Temp\n",
              "0  14733363    male   68   190.0    94.0      29.0       105.0       40.8\n",
              "1  14861698  female   20   166.0    60.0      14.0        94.0       40.3\n",
              "2  11179863    male   69   179.0    79.0       5.0        88.0       38.7\n",
              "3  16180408  female   34   179.0    71.0      13.0       100.0       40.5\n",
              "4  17771927  female   27   154.0    58.0      10.0        81.0       39.8"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Load the Exercise Dataset\n",
        "df2 = pd.read_csv(\"/content/exercise.csv\")\n",
        "df2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nD--5p5HQZ6c",
        "outputId": "efd7b7fa-32d5-4679-c6cb-79db6611eff0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(15000, 8)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Abb3R8L8QdRB"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15uyYxyMQjTK"
      },
      "source": [
        "**Now Concatenate both the Dataframe i.e df1 and df2**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uVFS_vAvQlDm"
      },
      "outputs": [],
      "source": [
        "\n",
        "df = pd.concat([df2,df1[\"Calories\"]],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zWQ93zZbQovH",
        "outputId": "e6e9e3c2-4578-44db-962f-44e51b7e1bd7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15000,\n  \"fields\": [\n    {\n      \"column\": \"User_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2872851,\n        \"min\": 10001159,\n        \"max\": 19999647,\n        \"num_unique_values\": 15000,\n        \"samples\": [\n          12549363,\n          13995058,\n          17223277\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 20,\n        \"max\": 79,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          68,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.258113616551187,\n        \"min\": 123.0,\n        \"max\": 222.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          197.0,\n          183.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.035656739246907,\n        \"min\": 36.0,\n        \"max\": 132.0,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          91.0,\n          84.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.319203342244775,\n        \"min\": 1.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          26.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Heart_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.583328189839296,\n        \"min\": 67.0,\n        \"max\": 128.0,\n        \"num_unique_values\": 59,\n        \"samples\": [\n          105.0,\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_Temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7792299239893948,\n        \"min\": 37.1,\n        \"max\": 41.5,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          37.5,\n          40.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.45697795593845,\n        \"min\": 1.0,\n        \"max\": 314.0,\n        \"num_unique_values\": 277,\n        \"samples\": [\n          80.0,\n          84.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-bb150f30-47bb-45bd-9da3-eab4fb0e8c64\" 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>User_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Heart_Rate</th>\n",
              "      <th>Body_Temp</th>\n",
              "      <th>Calories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14733363</td>\n",
              "      <td>male</td>\n",
              "      <td>68</td>\n",
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              "      <td>94.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>40.8</td>\n",
              "      <td>231.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14861698</td>\n",
              "      <td>female</td>\n",
              "      <td>20</td>\n",
              "      <td>166.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>40.3</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11179863</td>\n",
              "      <td>male</td>\n",
              "      <td>69</td>\n",
              "      <td>179.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>38.7</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16180408</td>\n",
              "      <td>female</td>\n",
              "      <td>34</td>\n",
              "      <td>179.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>40.5</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17771927</td>\n",
              "      <td>female</td>\n",
              "      <td>27</td>\n",
              "      <td>154.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>39.8</td>\n",
              "      <td>35.0</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-bb150f30-47bb-45bd-9da3-eab4fb0e8c64')\"\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",
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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-bb150f30-47bb-45bd-9da3-eab4fb0e8c64 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-bb150f30-47bb-45bd-9da3-eab4fb0e8c64');\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-1d5c4aaa-4497-4cbe-baec-c3edb63d53eb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1d5c4aaa-4497-4cbe-baec-c3edb63d53eb')\"\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-1d5c4aaa-4497-4cbe-baec-c3edb63d53eb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    User_ID  Gender  Age  Height  Weight  Duration  Heart_Rate  Body_Temp  \\\n",
              "0  14733363    male   68   190.0    94.0      29.0       105.0       40.8   \n",
              "1  14861698  female   20   166.0    60.0      14.0        94.0       40.3   \n",
              "2  11179863    male   69   179.0    79.0       5.0        88.0       38.7   \n",
              "3  16180408  female   34   179.0    71.0      13.0       100.0       40.5   \n",
              "4  17771927  female   27   154.0    58.0      10.0        81.0       39.8   \n",
              "\n",
              "   Calories  \n",
              "0     231.0  \n",
              "1      66.0  \n",
              "2      26.0  \n",
              "3      71.0  \n",
              "4      35.0  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3sen9cXQssH",
        "outputId": "220841a8-3972-443d-c05d-b805f3be407b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15000 entries, 0 to 14999\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   User_ID     15000 non-null  int64  \n",
            " 1   Gender      15000 non-null  object \n",
            " 2   Age         15000 non-null  int64  \n",
            " 3   Height      15000 non-null  float64\n",
            " 4   Weight      15000 non-null  float64\n",
            " 5   Duration    15000 non-null  float64\n",
            " 6   Heart_Rate  15000 non-null  float64\n",
            " 7   Body_Temp   15000 non-null  float64\n",
            " 8   Calories    15000 non-null  float64\n",
            "dtypes: float64(6), int64(2), object(1)\n",
            "memory usage: 1.0+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "KW_1Ws-AQvQ5",
        "outputId": "e2d86741-d8a7-468d-afc8-d947da4af785"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"User_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6983777.67806388,\n        \"min\": 15000.0,\n        \"max\": 19999647.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14977358.538466666,\n          14997285.0,\n          15000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5289.107935311001,\n        \"min\": 16.98026416907042,\n        \"max\": 15000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          42.7898,\n          39.0,\n          15000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5250.244768336296,\n        \"min\": 14.258113616551187,\n        \"max\": 15000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          174.46513333333334,\n          175.0,\n          15000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5279.0695857079645,\n        \"min\": 15.035656739246907,\n        \"max\": 15000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74.96686666666666,\n          74.0,\n          15000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5298.164466159714,\n        \"min\": 1.0,\n        \"max\": 15000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.5306,\n          16.0,\n          15000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Heart_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5273.761731104034,\n        \"min\": 9.583328189839296,\n        \"max\": 15000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          95.51853333333334,\n          96.0,\n          15000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_Temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5291.206694887213,\n        \"min\": 0.7792299239893948,\n        \"max\": 15000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          40.02545333333334,\n          40.2,\n          15000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5267.84272445134,\n        \"min\": 1.0,\n        \"max\": 15000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          89.53953333333334,\n          79.0,\n          15000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-5dd18ad7-a386-46f2-ba74-48ebc33fb25f\" 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>User_ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Heart_Rate</th>\n",
              "      <th>Body_Temp</th>\n",
              "      <th>Calories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.500000e+04</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.497736e+07</td>\n",
              "      <td>42.789800</td>\n",
              "      <td>174.465133</td>\n",
              "      <td>74.966867</td>\n",
              "      <td>15.530600</td>\n",
              "      <td>95.518533</td>\n",
              "      <td>40.025453</td>\n",
              "      <td>89.539533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.872851e+06</td>\n",
              "      <td>16.980264</td>\n",
              "      <td>14.258114</td>\n",
              "      <td>15.035657</td>\n",
              "      <td>8.319203</td>\n",
              "      <td>9.583328</td>\n",
              "      <td>0.779230</td>\n",
              "      <td>62.456978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000116e+07</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>37.100000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.247419e+07</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>39.600000</td>\n",
              "      <td>35.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.499728e+07</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>40.200000</td>\n",
              "      <td>79.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.744928e+07</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>40.600000</td>\n",
              "      <td>138.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.999965e+07</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>222.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>314.000000</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-5dd18ad7-a386-46f2-ba74-48ebc33fb25f')\"\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",
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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-5dd18ad7-a386-46f2-ba74-48ebc33fb25f 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-5dd18ad7-a386-46f2-ba74-48ebc33fb25f');\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-6bb188d7-032d-4ecc-9223-8a0eac12918a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6bb188d7-032d-4ecc-9223-8a0eac12918a')\"\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-6bb188d7-032d-4ecc-9223-8a0eac12918a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            User_ID           Age        Height        Weight      Duration  \\\n",
              "count  1.500000e+04  15000.000000  15000.000000  15000.000000  15000.000000   \n",
              "mean   1.497736e+07     42.789800    174.465133     74.966867     15.530600   \n",
              "std    2.872851e+06     16.980264     14.258114     15.035657      8.319203   \n",
              "min    1.000116e+07     20.000000    123.000000     36.000000      1.000000   \n",
              "25%    1.247419e+07     28.000000    164.000000     63.000000      8.000000   \n",
              "50%    1.499728e+07     39.000000    175.000000     74.000000     16.000000   \n",
              "75%    1.744928e+07     56.000000    185.000000     87.000000     23.000000   \n",
              "max    1.999965e+07     79.000000    222.000000    132.000000     30.000000   \n",
              "\n",
              "         Heart_Rate     Body_Temp      Calories  \n",
              "count  15000.000000  15000.000000  15000.000000  \n",
              "mean      95.518533     40.025453     89.539533  \n",
              "std        9.583328      0.779230     62.456978  \n",
              "min       67.000000     37.100000      1.000000  \n",
              "25%       88.000000     39.600000     35.000000  \n",
              "50%       96.000000     40.200000     79.000000  \n",
              "75%      103.000000     40.600000    138.000000  \n",
              "max      128.000000     41.500000    314.000000  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ih203W-aQxmy",
        "outputId": "22076e2d-9e9b-434c-a20b-59e39efc4a14"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "User_ID       0\n",
              "Gender        0\n",
              "Age           0\n",
              "Height        0\n",
              "Weight        0\n",
              "Duration      0\n",
              "Heart_Rate    0\n",
              "Body_Temp     0\n",
              "Calories      0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XiZXmrgIQzEl"
      },
      "outputs": [],
      "source": [
        "# drop User_ID column because this is not required from Main Dataframe itself\n",
        "\n",
        "df.drop(columns = [\"User_ID\"],axis=1,inplace =True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sF07jXlMQ1PN",
        "outputId": "a57fed68-ebd9-4e9e-c1b4-2b0d62cc9407"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15000,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 20,\n        \"max\": 79,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          68,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.258113616551187,\n        \"min\": 123.0,\n        \"max\": 222.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          197.0,\n          183.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.035656739246907,\n        \"min\": 36.0,\n        \"max\": 132.0,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          91.0,\n          84.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.319203342244775,\n        \"min\": 1.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          26.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Heart_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.583328189839296,\n        \"min\": 67.0,\n        \"max\": 128.0,\n        \"num_unique_values\": 59,\n        \"samples\": [\n          105.0,\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_Temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7792299239893948,\n        \"min\": 37.1,\n        \"max\": 41.5,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          37.5,\n          40.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.45697795593845,\n        \"min\": 1.0,\n        \"max\": 314.0,\n        \"num_unique_values\": 277,\n        \"samples\": [\n          80.0,\n          84.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-0c6d3bb5-6c38-4f78-ae0e-d222b4db250c\" 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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Heart_Rate</th>\n",
              "      <th>Body_Temp</th>\n",
              "      <th>Calories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>male</td>\n",
              "      <td>68</td>\n",
              "      <td>190.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>40.8</td>\n",
              "      <td>231.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>female</td>\n",
              "      <td>20</td>\n",
              "      <td>166.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>40.3</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>male</td>\n",
              "      <td>69</td>\n",
              "      <td>179.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>38.7</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>female</td>\n",
              "      <td>34</td>\n",
              "      <td>179.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>40.5</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>female</td>\n",
              "      <td>27</td>\n",
              "      <td>154.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>39.8</td>\n",
              "      <td>35.0</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-0c6d3bb5-6c38-4f78-ae0e-d222b4db250c')\"\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-0c6d3bb5-6c38-4f78-ae0e-d222b4db250c 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-0c6d3bb5-6c38-4f78-ae0e-d222b4db250c');\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-0aa80549-53ea-4a58-959e-35e9744127a5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0aa80549-53ea-4a58-959e-35e9744127a5')\"\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-0aa80549-53ea-4a58-959e-35e9744127a5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Gender  Age  Height  Weight  Duration  Heart_Rate  Body_Temp  Calories\n",
              "0    male   68   190.0    94.0      29.0       105.0       40.8     231.0\n",
              "1  female   20   166.0    60.0      14.0        94.0       40.3      66.0\n",
              "2    male   69   179.0    79.0       5.0        88.0       38.7      26.0\n",
              "3  female   34   179.0    71.0      13.0       100.0       40.5      71.0\n",
              "4  female   27   154.0    58.0      10.0        81.0       39.8      35.0"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8sYh68HQ46P",
        "outputId": "82f29ca9-fb4a-441a-c7dc-a43e16d3d51a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15000 entries, 0 to 14999\n",
            "Data columns (total 8 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Gender      15000 non-null  object \n",
            " 1   Age         15000 non-null  int64  \n",
            " 2   Height      15000 non-null  float64\n",
            " 3   Weight      15000 non-null  float64\n",
            " 4   Duration    15000 non-null  float64\n",
            " 5   Heart_Rate  15000 non-null  float64\n",
            " 6   Body_Temp   15000 non-null  float64\n",
            " 7   Calories    15000 non-null  float64\n",
            "dtypes: float64(6), int64(1), object(1)\n",
            "memory usage: 937.6+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sGsxotOqQ64r"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksE5FshQQ_vd"
      },
      "source": [
        "#### **Separate Categorical and Numerical Features**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWodQ96oRB7f"
      },
      "source": [
        "**1. Categorical Feature**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "or-ijsazREiD",
        "outputId": "f431d96f-15f5-427c-908b-9ccc9a0157b8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Gender']"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Fatching Categorical Data\n",
        "cat_col=[col for col in df.columns if df[col].dtype=='O'] #-->Object-\"o\"\n",
        "cat_col"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyUr0SGjRGdU",
        "outputId": "a636700e-f812-46e8-958e-9a3b52ec2689"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Gender\n",
              "female    7553\n",
              "male      7447\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"Gender\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "CXqHhm4bRIFd",
        "outputId": "2d5cdd95-de45-4b98-e3bf-a62c5ff58010"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plotting the gender column in count plot\n",
        "sns.countplot(df['Gender'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "U6JDcjqwRKOb",
        "outputId": "95832712-baa0-4f8b-cab6-bf884db9a6c6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"categorical\",\n  \"rows\": 15000,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "categorical"
            },
            "text/html": [
              "\n",
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              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>female</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>female</td>\n",
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              "      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-381c082c-6e96-48c9-9922-ba3f528955cc 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-381c082c-6e96-48c9-9922-ba3f528955cc');\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-e7e6ddb7-8016-4267-8f64-681c8606713b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e7e6ddb7-8016-4267-8f64-681c8606713b')\"\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-e7e6ddb7-8016-4267-8f64-681c8606713b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Gender\n",
              "0    male\n",
              "1  female\n",
              "2    male\n",
              "3  female\n",
              "4  female"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "categorical = df[cat_col]\n",
        "categorical.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X3F8XBtaRMgR"
      },
      "outputs": [],
      "source": [
        "categorical = pd.get_dummies(categorical[\"Gender\"],drop_first=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Jm8tHEQxROyg",
        "outputId": "e04cd7b6-3ef9-4d2e-a9f3-72fd0ed3907a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"categorical\",\n  \"rows\": 15000,\n  \"fields\": [\n    {\n      \"column\": \"male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "categorical"
            },
            "text/html": [
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              "        vertical-align: middle;\n",
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              "      <th>14997</th>\n",
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              "      <th>14998</th>\n",
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              "      <th>14999</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>15000 rows × 1 columns</p>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\n",
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              "    .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",
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              "      display: none;\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",
              "    }\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-a161b0ad-2598-4993-b429-2b5a08d5b547 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-a161b0ad-2598-4993-b429-2b5a08d5b547');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-07340a9a-0200-44fd-8d85-0d1a2f9ae51c')\"\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",
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              "    </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",
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              "      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-07340a9a-0200-44fd-8d85-0d1a2f9ae51c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_6c189eb3-325f-4d6f-921d-1c4dac5ca742\">\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('categorical')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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              "        google.colab.notebook.generateWithVariable('categorical');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        male\n",
              "0       True\n",
              "1      False\n",
              "2       True\n",
              "3      False\n",
              "4      False\n",
              "...      ...\n",
              "14995  False\n",
              "14996  False\n",
              "14997  False\n",
              "14998   True\n",
              "14999   True\n",
              "\n",
              "[15000 rows x 1 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "categorical"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XUS8nV79RRlF"
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      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fgeigP_0RVEg"
      },
      "source": [
        "**2.Numerical Features**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjelD2caRWWG",
        "outputId": "b41cb87a-3b46-41ad-c70a-9def3cbdc412"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Age', 'Height', 'Weight', 'Duration', 'Heart_Rate', 'Body_Temp', 'Calories']"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Num_col = [col for col in df.columns if df[col].dtype != \"O\"]\n",
        "Num_col"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0tD_TIrRYUm",
        "outputId": "f3fcc9ec-39c7-477d-8a60-bea455ddc2e6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(15000, 7)"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[Num_col].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DutXwlb9Ralw",
        "outputId": "73edc344-85d0-453b-fcaa-d2a63981394d"
      },
      "outputs": [
        {
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Age  Height  Weight  Duration  Heart_Rate  Body_Temp  Calories\n",
              "0   68   190.0    94.0      29.0       105.0       40.8     231.0\n",
              "1   20   166.0    60.0      14.0        94.0       40.3      66.0\n",
              "2   69   179.0    79.0       5.0        88.0       38.7      26.0\n",
              "3   34   179.0    71.0      13.0       100.0       40.5      71.0\n",
              "4   27   154.0    58.0      10.0        81.0       39.8      35.0"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Numerical = df[Num_col]\n",
        "Numerical.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQ2xiSxyRcnK",
        "outputId": "9f597294-e83a-4fd5-8fa9-7af6922fa311"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(15000, 7)"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Numerical.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Q5tSGzn-Rem7",
        "outputId": "6374294b-cbeb-4dad-963f-d2a9b74a200b"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 2000x1500 with 7 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(20,15))\n",
        "plotnumber = 1\n",
        "\n",
        "for column in Numerical:\n",
        "  if plotnumber <= 8:\n",
        "    ax = plt.subplot(3,3,plotnumber)\n",
        "    sns.distplot(Numerical[column])\n",
        "    plt.xlabel(column,fontsize=15)\n",
        "  plotnumber+=1\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 859
        },
        "id": "-J1FaR9URgut",
        "outputId": "75a3052c-8fa8-4b61-fd09-f9fe32493dea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# constructing a heatmap to understand the correlation\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(Numerical.corr(), cmap='Blues',annot = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d8tw-DhTRoKW"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vZUCMkMaRrXT"
      },
      "source": [
        "### **Concatenate Categorical and Numerical**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8OkVcjZRseV"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RlfXzgA7RvOn"
      },
      "outputs": [],
      "source": [
        "data = pd.concat([categorical,Numerical],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gHKZc-0ARvvN",
        "outputId": "ba6b7794-fa7c-48ff-9f84-8b849783ce22"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    male  Age  Height  Weight  Duration  Heart_Rate  Body_Temp  Calories\n",
              "0   True   68   190.0    94.0      29.0       105.0       40.8     231.0\n",
              "1  False   20   166.0    60.0      14.0        94.0       40.3      66.0\n",
              "2   True   69   179.0    79.0       5.0        88.0       38.7      26.0\n",
              "3  False   34   179.0    71.0      13.0       100.0       40.5      71.0\n",
              "4  False   27   154.0    58.0      10.0        81.0       39.8      35.0"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "NmFKcDnZRx5c",
        "outputId": "aba03cf4-eea1-4b26-fae4-2d4c173991c8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig,ax = plt.subplots(figsize = (15,10))\n",
        "sns.boxplot(data=data,width = 0.5,fliersize = 3,ax=ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ToOic6SRR0Wz",
        "outputId": "f3e7c1a3-2856-499c-b35b-461128de10d8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2000x1500 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(20,15))\n",
        "plotnumber = 1\n",
        "\n",
        "for column in data:\n",
        "  if plotnumber <= 8:\n",
        "    ax = plt.subplot(3,3,plotnumber)\n",
        "    sns.distplot(data[column])\n",
        "    plt.xlabel(column,fontsize=15)\n",
        "  plotnumber+=1\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwCUg43rR5sH",
        "outputId": "f1fc0bd4-0df8-446d-d10d-b41b4cb16b47"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['male', 'Age', 'Height', 'Weight', 'Duration', 'Heart_Rate',\n",
              "       'Body_Temp', 'Calories'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lnjxdSM3R8AI"
      },
      "outputs": [],
      "source": [
        "X = data.drop(columns = [\"Calories\"],axis = 1)\n",
        "y = data[\"Calories\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w4_qyt8WR9_w",
        "outputId": "4af03a57-ada0-4e3d-ecbe-0e63b5580a4e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "\n",
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              "    async function quickchart(key) {\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    male  Age  Height  Weight  Duration  Heart_Rate  Body_Temp\n",
              "0   True   68   190.0    94.0      29.0       105.0       40.8\n",
              "1  False   20   166.0    60.0      14.0        94.0       40.3\n",
              "2   True   69   179.0    79.0       5.0        88.0       38.7\n",
              "3  False   34   179.0    71.0      13.0       100.0       40.5\n",
              "4  False   27   154.0    58.0      10.0        81.0       39.8"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5PPqepfR_76",
        "outputId": "61ad1048-695f-4d92-fab2-2fc5b0af2481"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    231.0\n",
              "1     66.0\n",
              "2     26.0\n",
              "3     71.0\n",
              "4     35.0\n",
              "Name: Calories, dtype: float64"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lvKwhBYLSCeO"
      },
      "outputs": [],
      "source": [
        "# Split the Data\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UBmgeuv_SEQC",
        "outputId": "33aeed3c-1c6f-4075-a652-1064736affd9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of X Train:  (12000, 7)\n",
            "Shape of X Test:  (3000, 7)\n",
            "Shape of y Train:  (12000,)\n",
            "Shape of y Test:  (3000,)\n"
          ]
        }
      ],
      "source": [
        "print(\"Shape of X Train: \",X_train.shape)\n",
        "print(\"Shape of X Test: \",X_test.shape)\n",
        "print(\"Shape of y Train: \",y_train.shape)\n",
        "print(\"Shape of y Test: \",y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sr_ettLbSGZF"
      },
      "outputs": [],
      "source": [
        "#from sklearn import metrics\n",
        "def predict(ml_model):\n",
        "    model=ml_model.fit(X_train,y_train)\n",
        "    print('Score : {}'.format(model.score(X_train,y_train)))\n",
        "    y_prediction=model.predict(X_test)\n",
        "    print('predictions are: \\n {}'.format(y_prediction))\n",
        "    print('\\n')\n",
        "\n",
        "    r2_score=metrics.r2_score(y_test,y_prediction)\n",
        "    print('r2 score: {}'.format(r2_score))\n",
        "\n",
        "    print('MAE:',metrics.mean_absolute_error(y_test,y_prediction))\n",
        "    print('MSE:',metrics.mean_squared_error(y_test,y_prediction))\n",
        "    print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test,y_prediction)))\n",
        "\n",
        "    sns.distplot(y_test-y_prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dBCpepjbSK9G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6cUS5lv1SPB6"
      },
      "source": [
        "**XGB Regressor**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "juOTSPtFSQK7",
        "outputId": "c02df39f-18be-4f4d-fc3c-f91c22332270"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Score : 0.9995380557081355\n",
            "predictions are: \n",
            " [197.06581   70.867226 196.99498  ...  29.043041 104.09284   14.61472 ]\n",
            "\n",
            "\n",
            "r2 score: 0.9986863132331905\n",
            "MAE: 1.5521575984954834\n",
            "MSE: 5.2744122853837005\n",
            "RMSE: 2.2966088664340956\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "regression = predict(XGBRegressor())\n",
        "regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yGMz4aavSSii"
      },
      "outputs": [],
      "source": [
        "# saving the model to the local file system\n",
        "filename = 'finalized_model.pickle'\n",
        "pickle.dump(regression, open(filename, 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EYhziNBhSbQ_"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2lvTkLEESex6"
      },
      "source": [
        "**Linear Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "OCFwD9c4Sfe9",
        "outputId": "c6b90ebf-fc57-4034-cd44-1618331919a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Score : 0.967592555473578\n",
            "predictions are: \n",
            " [198.81182363  80.43555305 194.40940033 ...  22.14745631 118.63504926\n",
            " -11.98134672]\n",
            "\n",
            "\n",
            "r2 score: 0.9655977245826504\n",
            "MAE: 8.479071745987955\n",
            "MSE: 138.12408611460899\n",
            "RMSE: 11.752620393538157\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "predict(LinearRegression())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XxFA8XfyShVX"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVgdrxpISkdQ"
      },
      "source": [
        "**DecisionTree Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "znU2WLvlSk6D",
        "outputId": "1a308dd0-48c7-47e3-c9a8-7b755cb1b4bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Score : 1.0\n",
            "predictions are: \n",
            " [194.  75. 204. ...  30. 109.  13.]\n",
            "\n",
            "\n",
            "r2 score: 0.9925230648060414\n",
            "MAE: 3.4663333333333335\n",
            "MSE: 30.019666666666666\n",
            "RMSE: 5.479020593743618\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "predict(DecisionTreeRegressor())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s_RsQd09SoHw"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qe342rLKSrNN"
      },
      "source": [
        "**RandomForest Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "LJJHl2dMSr1E",
        "outputId": "c929b064-3e7a-4826-bb61-f363329c0827"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Score : 0.9996727554469901\n",
            "predictions are: \n",
            " [197.44  67.42 196.8  ...  26.97 111.62  14.03]\n",
            "\n",
            "\n",
            "r2 score: 0.9976300459911249\n",
            "MAE: 1.8251700000000002\n",
            "MSE: 9.515293033333332\n",
            "RMSE: 3.084686861471247\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "predict(RandomForestRegressor())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wxdg0l2yStn9",
        "outputId": "ecdf098e-ae1b-4c65-ea33-866847a5f864"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting streamlit\n",
            "  Downloading streamlit-1.35.0-py2.py3-none-any.whl (8.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m37.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: altair<6,>=4.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (4.2.2)\n",
            "Requirement already satisfied: blinker<2,>=1.0.0 in /usr/lib/python3/dist-packages (from streamlit) (1.4)\n",
            "Requirement already satisfied: cachetools<6,>=4.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (5.3.3)\n",
            "Requirement already satisfied: click<9,>=7.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (8.1.7)\n",
            "Requirement already satisfied: numpy<2,>=1.19.3 in /usr/local/lib/python3.10/dist-packages (from streamlit) (1.25.2)\n",
            "Requirement already satisfied: packaging<25,>=16.8 in /usr/local/lib/python3.10/dist-packages (from streamlit) (24.0)\n",
            "Requirement already satisfied: pandas<3,>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (2.0.3)\n",
            "Requirement already satisfied: pillow<11,>=7.1.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (9.4.0)\n",
            "Requirement already satisfied: protobuf<5,>=3.20 in /usr/local/lib/python3.10/dist-packages (from streamlit) (3.20.3)\n",
            "Requirement already satisfied: pyarrow>=7.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (14.0.2)\n",
            "Requirement already satisfied: requests<3,>=2.27 in /usr/local/lib/python3.10/dist-packages (from streamlit) (2.31.0)\n",
            "Requirement already satisfied: rich<14,>=10.14.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (13.7.1)\n",
            "Requirement already satisfied: tenacity<9,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (8.3.0)\n",
            "Requirement already satisfied: toml<2,>=0.10.1 in /usr/local/lib/python3.10/dist-packages (from streamlit) (0.10.2)\n",
            "Requirement already satisfied: typing-extensions<5,>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (4.11.0)\n",
            "Collecting gitpython!=3.1.19,<4,>=3.0.7 (from streamlit)\n",
            "  Downloading GitPython-3.1.43-py3-none-any.whl (207 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.3/207.3 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pydeck<1,>=0.8.0b4 (from streamlit)\n",
            "  Downloading pydeck-0.9.1-py2.py3-none-any.whl (6.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.9/6.9 MB\u001b[0m \u001b[31m73.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tornado<7,>=6.0.3 in /usr/local/lib/python3.10/dist-packages (from streamlit) (6.3.3)\n",
            "Collecting watchdog>=2.1.5 (from streamlit)\n",
            "  Downloading watchdog-4.0.1-py3-none-manylinux2014_x86_64.whl (83 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.0/83.0 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: entrypoints in /usr/local/lib/python3.10/dist-packages (from altair<6,>=4.0->streamlit) (0.4)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from altair<6,>=4.0->streamlit) (3.1.4)\n",
            "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.10/dist-packages (from altair<6,>=4.0->streamlit) (4.19.2)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair<6,>=4.0->streamlit) (0.12.1)\n",
            "Collecting gitdb<5,>=4.0.1 (from gitpython!=3.1.19,<4,>=3.0.7->streamlit)\n",
            "  Downloading gitdb-4.0.11-py3-none-any.whl (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas<3,>=1.3.0->streamlit) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3,>=1.3.0->streamlit) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3,>=1.3.0->streamlit) (2024.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->streamlit) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->streamlit) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->streamlit) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->streamlit) (2024.2.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich<14,>=10.14.0->streamlit) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich<14,>=10.14.0->streamlit) (2.16.1)\n",
            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython!=3.1.19,<4,>=3.0.7->streamlit)\n",
            "  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->altair<6,>=4.0->streamlit) (2.1.5)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (23.2.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (0.35.1)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (0.18.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich<14,>=10.14.0->streamlit) (0.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas<3,>=1.3.0->streamlit) (1.16.0)\n",
            "Installing collected packages: watchdog, smmap, pydeck, gitdb, gitpython, streamlit\n",
            "Successfully installed gitdb-4.0.11 gitpython-3.1.43 pydeck-0.9.1 smmap-5.0.1 streamlit-1.35.0 watchdog-4.0.1\n"
          ]
        }
      ],
      "source": [
        "pip install streamlit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXhccMw4t9ug",
        "outputId": "d429d62b-fff2-4ad4-870a-d858c873fd5d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing app.py\n"
          ]
        }
      ],
      "source": [
        "%%writefile app.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sR0vlW77uN_v",
        "outputId": "701d3b3c-d5f4-41af-ab5c-bf70d95d56f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "35.196.61.39\n"
          ]
        }
      ],
      "source": [
        "! wget -q -O - ipv4.icanhazip.com"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "NoYTj4Hpud8q",
        "outputId": "e4faf184-e375-413c-8eb7-6bb53fd2d064"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to false.\n",
            "\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m\u001b[1m  You can now view your Streamlit app in your browser.\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m  Local URL: \u001b[0m\u001b[1mhttp://localhost:8501\u001b[0m\n",
            "\u001b[34m  Network URL: \u001b[0m\u001b[1mhttp://172.28.0.12:8501\u001b[0m\n",
            "\u001b[34m  External URL: \u001b[0m\u001b[1mhttp://35.196.61.39:8501\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[K\u001b[?25hnpx: installed 22 in 5.134s\n",
            "your url is: https://little-foxes-try.loca.lt\n",
            "lr  MSE: 131.9957457508168, Score: 0.9672937151257296\n",
            "rd  MSE: 131.99625903139352, Score: 0.9672935879435944\n",
            "ls  MSE: 143.82689461175067, Score: 0.9643621590908397\n",
            "dtr  MSE: 28.145, Score: 0.9930261510888078\n",
            "rfr  MSE: 7.369592666666666, Score: 0.9981739411691468\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "! streamlit run app.py & npx localtunnel --port 8501"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fuaS9WcTu0aq"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPE5dGJa+l8F8Leu9aiw5Dx",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}