Calories_Burnt.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn import metrics\n"
      ]
    },
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        "import seaborn as sns"
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        "cal=pd.read_csv(\"/content/calories.csv\")"
      ],
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      "outputs": []
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              "    User_ID  Calories\n",
              "0  14733363     231.0\n",
              "1  14861698      66.0\n",
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              "3  16180408      71.0\n",
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              "    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",
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              "    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",
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              "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  "
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              "      <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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ecd7720b-009d-4941-850a-4b3e7d58ec6e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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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",
              "    [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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ecd7720b-009d-4941-850a-4b3e7d58ec6e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cal_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwbQXlTSDHs3",
        "outputId": "e65b3572-24b7-443e-d40f-3f87c4b7750d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cal_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPvXkcdqDNDv",
        "outputId": "6f9d6b20-cf66-431a-c0e1-f3cc911ee780"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cal_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYhgke_nDQNi",
        "outputId": "17e2f794-1d88-4d08-9b25-5147feb97eb5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cal_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "TILpU7mUDSmL",
        "outputId": "76d51851-ce63-4893-8df9-8eed43f975b8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e70eb0e-614e-4256-9619-1f350de19c08\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e70eb0e-614e-4256-9619-1f350de19c08')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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-2e70eb0e-614e-4256-9619-1f350de19c08 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-2e70eb0e-614e-4256-9619-1f350de19c08');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set()"
      ],
      "metadata": {
        "id": "9_HPm1l5Daik"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cal_data['Gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JM95B8tNEIrm",
        "outputId": "9a29fea9-9eb9-4cb5-90f6-261eadad1ab7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "female    7553\n",
              "male      7447\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#distributiuon for age using distribution plot\n",
        "sns.distplot(cal_data['Age'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "8UTcv7LYER02",
        "outputId": "23cb17cb-9291-4565-a595-77c21ff25a7e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-694538de27b4>:2: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(cal_data['Age'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(cal_data['Height'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "rG0ACH_yEfTW",
        "outputId": "faf95cc3-abcb-4df2-fdaf-e5bcb939ad53"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-e10a5de5672f>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(cal_data['Height'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Height', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(cal_data['Weight'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "q9xGDU-YFJ9K",
        "outputId": "7e9b2fa6-dd4b-4f03-921c-e4bf020641d9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-27-c44b2b93044a>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(cal_data['Weight'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Weight', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#coreltaion in data\n",
        "corr=cal_data.corr()"
      ],
      "metadata": {
        "id": "WoZy4F9NFPVq"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "RELxFTe8FdGq",
        "outputId": "52d11f39-ec1a-4ca1-b72f-220001ba74f9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             User_ID       Age    Height    Weight  Duration  Heart_Rate  \\\n",
              "User_ID     1.000000 -0.001827 -0.013520 -0.011603 -0.002751   -0.000457   \n",
              "Age        -0.001827  1.000000  0.009554  0.090094  0.013247    0.010482   \n",
              "Height     -0.013520  0.009554  1.000000  0.958451 -0.004625    0.000528   \n",
              "Weight     -0.011603  0.090094  0.958451  1.000000 -0.001884    0.004311   \n",
              "Duration   -0.002751  0.013247 -0.004625 -0.001884  1.000000    0.852869   \n",
              "Heart_Rate -0.000457  0.010482  0.000528  0.004311  0.852869    1.000000   \n",
              "Body_Temp   0.000923  0.013175  0.001200  0.004095  0.903167    0.771529   \n",
              "Calories   -0.001661  0.154395  0.017537  0.035481  0.955421    0.897882   \n",
              "\n",
              "            Body_Temp  Calories  \n",
              "User_ID      0.000923 -0.001661  \n",
              "Age          0.013175  0.154395  \n",
              "Height       0.001200  0.017537  \n",
              "Weight       0.004095  0.035481  \n",
              "Duration     0.903167  0.955421  \n",
              "Heart_Rate   0.771529  0.897882  \n",
              "Body_Temp    1.000000  0.824558  \n",
              "Calories     0.824558  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9e92e9a3-1acc-4f39-a4bd-3f7de9719a52\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>User_ID</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.001827</td>\n",
              "      <td>-0.013520</td>\n",
              "      <td>-0.011603</td>\n",
              "      <td>-0.002751</td>\n",
              "      <td>-0.000457</td>\n",
              "      <td>0.000923</td>\n",
              "      <td>-0.001661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.001827</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.009554</td>\n",
              "      <td>0.090094</td>\n",
              "      <td>0.013247</td>\n",
              "      <td>0.010482</td>\n",
              "      <td>0.013175</td>\n",
              "      <td>0.154395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Height</th>\n",
              "      <td>-0.013520</td>\n",
              "      <td>0.009554</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.958451</td>\n",
              "      <td>-0.004625</td>\n",
              "      <td>0.000528</td>\n",
              "      <td>0.001200</td>\n",
              "      <td>0.017537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>-0.011603</td>\n",
              "      <td>0.090094</td>\n",
              "      <td>0.958451</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.001884</td>\n",
              "      <td>0.004311</td>\n",
              "      <td>0.004095</td>\n",
              "      <td>0.035481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duration</th>\n",
              "      <td>-0.002751</td>\n",
              "      <td>0.013247</td>\n",
              "      <td>-0.004625</td>\n",
              "      <td>-0.001884</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.852869</td>\n",
              "      <td>0.903167</td>\n",
              "      <td>0.955421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Heart_Rate</th>\n",
              "      <td>-0.000457</td>\n",
              "      <td>0.010482</td>\n",
              "      <td>0.000528</td>\n",
              "      <td>0.004311</td>\n",
              "      <td>0.852869</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.771529</td>\n",
              "      <td>0.897882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Body_Temp</th>\n",
              "      <td>0.000923</td>\n",
              "      <td>0.013175</td>\n",
              "      <td>0.001200</td>\n",
              "      <td>0.004095</td>\n",
              "      <td>0.903167</td>\n",
              "      <td>0.771529</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.824558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calories</th>\n",
              "      <td>-0.001661</td>\n",
              "      <td>0.154395</td>\n",
              "      <td>0.017537</td>\n",
              "      <td>0.035481</td>\n",
              "      <td>0.955421</td>\n",
              "      <td>0.897882</td>\n",
              "      <td>0.824558</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e92e9a3-1acc-4f39-a4bd-3f7de9719a52')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9e92e9a3-1acc-4f39-a4bd-3f7de9719a52 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-9e92e9a3-1acc-4f39-a4bd-3f7de9719a52');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(corr,cbar=True,square=True,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "id": "2GHhoLRiFeiy",
        "outputId": "a8ffb1da-17b8-455d-fe92-96eb95df4d20"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cal_data.replace({'Gender':{'male':0,'female':1}},inplace=True)"
      ],
      "metadata": {
        "id": "31Oij189GKBC"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cal_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "rRXeQYtvHnQO",
        "outputId": "d39e2020-491e-4e6a-fdee-c6ac2c0aed65"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        User_ID  Gender  Age  Height  Weight  Duration  Heart_Rate  Body_Temp  \\\n",
              "0      14733363       0   68   190.0    94.0      29.0       105.0       40.8   \n",
              "1      14861698       1   20   166.0    60.0      14.0        94.0       40.3   \n",
              "2      11179863       0   69   179.0    79.0       5.0        88.0       38.7   \n",
              "3      16180408       1   34   179.0    71.0      13.0       100.0       40.5   \n",
              "4      17771927       1   27   154.0    58.0      10.0        81.0       39.8   \n",
              "...         ...     ...  ...     ...     ...       ...         ...        ...   \n",
              "14995  15644082       1   20   193.0    86.0      11.0        92.0       40.4   \n",
              "14996  17212577       1   27   165.0    65.0       6.0        85.0       39.2   \n",
              "14997  17271188       1   43   159.0    58.0      16.0        90.0       40.1   \n",
              "14998  18643037       0   78   193.0    97.0       2.0        84.0       38.3   \n",
              "14999  11751526       0   63   173.0    79.0      18.0        92.0       40.5   \n",
              "\n",
              "       Calories  \n",
              "0         231.0  \n",
              "1          66.0  \n",
              "2          26.0  \n",
              "3          71.0  \n",
              "4          35.0  \n",
              "...         ...  \n",
              "14995      45.0  \n",
              "14996      23.0  \n",
              "14997      75.0  \n",
              "14998      11.0  \n",
              "14999      98.0  \n",
              "\n",
              "[15000 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e719995-2acf-4c1b-b012-7fb3800c5fc4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>0</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>14861698</td>\n",
              "      <td>1</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>0</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>1</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>1</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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>15644082</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>193.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>40.4</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>17212577</td>\n",
              "      <td>1</td>\n",
              "      <td>27</td>\n",
              "      <td>165.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>39.2</td>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>17271188</td>\n",
              "      <td>1</td>\n",
              "      <td>43</td>\n",
              "      <td>159.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>40.1</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>18643037</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>193.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>38.3</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14999</th>\n",
              "      <td>11751526</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>173.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>40.5</td>\n",
              "      <td>98.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15000 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e719995-2acf-4c1b-b012-7fb3800c5fc4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e719995-2acf-4c1b-b012-7fb3800c5fc4 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-0e719995-2acf-4c1b-b012-7fb3800c5fc4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=cal_data.drop(['User_ID','Calories'],axis=1)"
      ],
      "metadata": {
        "id": "xhVnWa9OHpIU"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=cal_data['Calories']"
      ],
      "metadata": {
        "id": "bgNhvgcMH7XZ"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "kfb2ZkxWH-pl"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "Wx66hZtTIJPp"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(x_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Iv_I0BEHIQ-i",
        "outputId": "54b6e066-2d9c-4400-91a9-54dd3dcbaaac"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train=scaler.transform(x_train)"
      ],
      "metadata": {
        "id": "1ayZKEf3IoSp"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test=scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "LuLlGfSPJI3W"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=XGBRegressor()"
      ],
      "metadata": {
        "id": "oaUSZJXgJMih"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "Flo72bf9JU2w",
        "outputId": "8dedbd45-1a93-413c-f6ae-c8e2a0434932"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test_data_pred\n",
        "testpred=model.predict(X_test)"
      ],
      "metadata": {
        "id": "MTIQbwmOJYiM"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tables.tests.test_suite import test\n",
        "#mean aBSOLUTE  error\n",
        "mae=metrics.mean_absolute_error(y_test,testpred)\n",
        "print(mae)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8nQHANXNJr-9",
        "outputId": "ca98788f-e42e-4420-b2a4-a8fab8d7e4ff"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.5171571099360783\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ip=X_test[1:2]"
      ],
      "metadata": {
        "id": "xSuLFdLKKGJA"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ir7DJR_OKh6k",
        "outputId": "bfe89658-dd04-4667-a71c-516e848916c3"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(ip)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePk8bfQZKkqw",
        "outputId": "524e3d9c-02d5-4b82-98f9-2616f4af8b5f"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([192.5936], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[1:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jSxuQsrKxuw",
        "outputId": "f852d75a-ddf2-4e9b-c799-9199cb080357"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6475    189.0\n",
              "Name: Calories, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "pickle.dump(model,open(\"rgressor.pkl\",\"wb\"))\n"
      ],
      "metadata": {
        "id": "FTFIth3gLBMY"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O2wZNUlkMUsg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}