Feynn_labs.ipynb

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        "<a href=\"https://colab.research.google.com/github/aaditshukla98710/Data-Analysis/blob/main/Feynn_labs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# data visualisation and manipulation\n",
        "import pandas as pd\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline \n",
        "from datetime import datetime\n",
        "import calendar\n",
        "\n",
        "\n",
        "#label Encoding on Train and Test \n",
        "from sklearn import preprocessing\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "\n",
        "#model selection\n",
        "from sklearn.model_selection import train_test_split,cross_validate\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "#import the necessary modelling algos\n",
        "from sklearn.ensemble import RandomForestRegressor,BaggingRegressor,GradientBoostingRegressor,AdaBoostRegressor\n",
        "#from sklearn.svm import SVR\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "\n",
        "#evaluation metrics\n",
        "from sklearn.metrics import mean_squared_log_error,mean_squared_error, r2_score,mean_absolute_error\n"
      ],
      "metadata": {
        "id": "No0FqOta8CPF"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
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      "source": [
        "data=pd.read_csv('/content/traindataset.txt')\n",
        "data1=pd.read_csv('/content/testdataset.txt')"
      ],
      "metadata": {
        "id": "Rx42-_ThBF9t"
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      "execution_count": 6,
      "outputs": []
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        "data.head()"
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          "height": 206
        },
        "outputId": "3dd244c0-046a-4701-84f9-35738c63a7b2"
      },
      "execution_count": 5,
      "outputs": [
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              "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
              "0  2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
              "1  2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
              "2  2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
              "3  2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
              "4  2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
              "\n",
              "   humidity  windspeed  \n",
              "0        56    26.0027  \n",
              "1        56     0.0000  \n",
              "2        56     0.0000  \n",
              "3        56    11.0014  \n",
              "4        56    11.0014  "
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        "data1.head()"
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            "text/plain": [
              "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
              "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
              "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
              "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
              "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
              "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
              "\n",
              "   humidity  windspeed  casual  registered  count  \n",
              "0        81        0.0       3          13     16  \n",
              "1        80        0.0       8          32     40  \n",
              "2        80        0.0       5          27     32  \n",
              "3        75        0.0       3          10     13  \n",
              "4        75        0.0       0           1      1  "
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    },
    {
      "cell_type": "code",
      "source": [
        "# Shape of the dataset\n",
        "print('Shape of the Train data:',data.shape)\n",
        "print('Shape of the Test data:',data1.shape)\n"
      ],
      "metadata": {
        "id": "d_U5bydQA1So",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e411c7e1-d18a-4694-99b4-d55f006efd23"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the Train data: (6493, 9)\n",
            "Shape of the Test data: (10886, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the data types of each column of this dataset.\n",
        "print('Data type of Train data :\\n', data.dtypes,'\\n\\n')\n",
        "print('Data type of Test data :\\n', data1.dtypes)\n"
      ],
      "metadata": {
        "id": "vh7NVTgeA1Vc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48cff2cf-726f-4fa2-b5fc-56347e69e533"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data type of Train data :\n",
            " datetime       object\n",
            "season          int64\n",
            "holiday         int64\n",
            "workingday      int64\n",
            "weather         int64\n",
            "temp          float64\n",
            "atemp         float64\n",
            "humidity        int64\n",
            "windspeed     float64\n",
            "dtype: object \n",
            "\n",
            "\n",
            "Data type of Test data :\n",
            " datetime       object\n",
            "season          int64\n",
            "holiday         int64\n",
            "workingday      int64\n",
            "weather         int64\n",
            "temp          float64\n",
            "atemp         float64\n",
            "humidity        int64\n",
            "windspeed     float64\n",
            "casual          int64\n",
            "registered      int64\n",
            "count           int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the columns of both the data.\n",
        "print('Columns of Train data :\\n' , data.columns,'\\n\\n\\n')\n",
        "print('Columns of Test data :\\n', data1.columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Gv4tBYM_YgR",
        "outputId": "ff431ce4-d6ba-4180-fbee-3097bcc27ee4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns of Train data :\n",
            " Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',\n",
            "       'atemp', 'humidity', 'windspeed'],\n",
            "      dtype='object') \n",
            "\n",
            "\n",
            "\n",
            "Columns of Test data :\n",
            " Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',\n",
            "       'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Information of both the data\n",
        "print( data.info(),'\\n\\n')\n",
        "print( data1.info())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKEu9xdS_Yjg",
        "outputId": "4bca4cf5-9ad0-470e-cdfa-399e95c4b36c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6493 entries, 0 to 6492\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   datetime    6493 non-null   object \n",
            " 1   season      6493 non-null   int64  \n",
            " 2   holiday     6493 non-null   int64  \n",
            " 3   workingday  6493 non-null   int64  \n",
            " 4   weather     6493 non-null   int64  \n",
            " 5   temp        6493 non-null   float64\n",
            " 6   atemp       6493 non-null   float64\n",
            " 7   humidity    6493 non-null   int64  \n",
            " 8   windspeed   6493 non-null   float64\n",
            "dtypes: float64(3), int64(5), object(1)\n",
            "memory usage: 456.7+ KB\n",
            "None \n",
            "\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10886 entries, 0 to 10885\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   datetime    10886 non-null  object \n",
            " 1   season      10886 non-null  int64  \n",
            " 2   holiday     10886 non-null  int64  \n",
            " 3   workingday  10886 non-null  int64  \n",
            " 4   weather     10886 non-null  int64  \n",
            " 5   temp        10886 non-null  float64\n",
            " 6   atemp       10886 non-null  float64\n",
            " 7   humidity    10886 non-null  int64  \n",
            " 8   windspeed   10886 non-null  float64\n",
            " 9   casual      10886 non-null  int64  \n",
            " 10  registered  10886 non-null  int64  \n",
            " 11  count       10886 non-null  int64  \n",
            "dtypes: float64(3), int64(8), object(1)\n",
            "memory usage: 1020.7+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Describe both data\n",
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "IWHH75RE_Ylf",
        "outputId": "ad8e15e7-2ab5-49f7-cada-7dd63063bb13"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            season      holiday   workingday      weather         temp  \\\n",
              "count  6493.000000  6493.000000  6493.000000  6493.000000  6493.000000   \n",
              "mean      2.493300     0.029108     0.685815     1.436778    20.620607   \n",
              "std       1.091258     0.168123     0.464226     0.648390     8.059583   \n",
              "min       1.000000     0.000000     0.000000     1.000000     0.820000   \n",
              "25%       2.000000     0.000000     0.000000     1.000000    13.940000   \n",
              "50%       3.000000     0.000000     1.000000     1.000000    21.320000   \n",
              "75%       3.000000     0.000000     1.000000     2.000000    27.060000   \n",
              "max       4.000000     1.000000     1.000000     4.000000    40.180000   \n",
              "\n",
              "             atemp     humidity    windspeed  \n",
              "count  6493.000000  6493.000000  6493.000000  \n",
              "mean     24.012865    64.125212    12.631157  \n",
              "std       8.782741    19.293391     8.250151  \n",
              "min       0.000000    16.000000     0.000000  \n",
              "25%      16.665000    49.000000     7.001500  \n",
              "50%      25.000000    65.000000    11.001400  \n",
              "75%      31.060000    81.000000    16.997900  \n",
              "max      50.000000   100.000000    55.998600  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "      <td>6493.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.493300</td>\n",
              "      <td>0.029108</td>\n",
              "      <td>0.685815</td>\n",
              "      <td>1.436778</td>\n",
              "      <td>20.620607</td>\n",
              "      <td>24.012865</td>\n",
              "      <td>64.125212</td>\n",
              "      <td>12.631157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.091258</td>\n",
              "      <td>0.168123</td>\n",
              "      <td>0.464226</td>\n",
              "      <td>0.648390</td>\n",
              "      <td>8.059583</td>\n",
              "      <td>8.782741</td>\n",
              "      <td>19.293391</td>\n",
              "      <td>8.250151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.820000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.940000</td>\n",
              "      <td>16.665000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>7.001500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>21.320000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>11.001400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>27.060000</td>\n",
              "      <td>31.060000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>16.997900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>40.180000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>55.998600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7fe799e-bb85-4d5b-96a1-8f845d347e2a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f7fe799e-bb85-4d5b-96a1-8f845d347e2a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-f7fe799e-bb85-4d5b-96a1-8f845d347e2a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "data1.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "g2mJ36io_Yn-",
        "outputId": "cdeeede5-1151-4ac4-f03c-150f28d45d92"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             season       holiday    workingday       weather         temp  \\\n",
              "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.00000   \n",
              "mean       2.506614      0.028569      0.680875      1.418427     20.23086   \n",
              "std        1.116174      0.166599      0.466159      0.633839      7.79159   \n",
              "min        1.000000      0.000000      0.000000      1.000000      0.82000   \n",
              "25%        2.000000      0.000000      0.000000      1.000000     13.94000   \n",
              "50%        3.000000      0.000000      1.000000      1.000000     20.50000   \n",
              "75%        4.000000      0.000000      1.000000      2.000000     26.24000   \n",
              "max        4.000000      1.000000      1.000000      4.000000     41.00000   \n",
              "\n",
              "              atemp      humidity     windspeed        casual    registered  \\\n",
              "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.000000   \n",
              "mean      23.655084     61.886460     12.799395     36.021955    155.552177   \n",
              "std        8.474601     19.245033      8.164537     49.960477    151.039033   \n",
              "min        0.760000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%       16.665000     47.000000      7.001500      4.000000     36.000000   \n",
              "50%       24.240000     62.000000     12.998000     17.000000    118.000000   \n",
              "75%       31.060000     77.000000     16.997900     49.000000    222.000000   \n",
              "max       45.455000    100.000000     56.996900    367.000000    886.000000   \n",
              "\n",
              "              count  \n",
              "count  10886.000000  \n",
              "mean     191.574132  \n",
              "std      181.144454  \n",
              "min        1.000000  \n",
              "25%       42.000000  \n",
              "50%      145.000000  \n",
              "75%      284.000000  \n",
              "max      977.000000  "
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              "\n",
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              "      <th></th>\n",
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              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>10886.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.506614</td>\n",
              "      <td>0.028569</td>\n",
              "      <td>0.680875</td>\n",
              "      <td>1.418427</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.116174</td>\n",
              "      <td>0.166599</td>\n",
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              "      <td>0.633839</td>\n",
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              "      <td>19.245033</td>\n",
              "      <td>8.164537</td>\n",
              "      <td>49.960477</td>\n",
              "      <td>151.039033</td>\n",
              "      <td>181.144454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.82000</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.94000</td>\n",
              "      <td>16.665000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>7.001500</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>42.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.50000</td>\n",
              "      <td>24.240000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>12.998000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>145.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.24000</td>\n",
              "      <td>31.060000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>16.997900</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>222.000000</td>\n",
              "      <td>284.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>41.00000</td>\n",
              "      <td>45.455000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>56.996900</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>977.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1991cde-fcb3-453a-8d0c-190d0a2a2c72')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d1991cde-fcb3-453a-8d0c-190d0a2a2c72 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-d1991cde-fcb3-453a-8d0c-190d0a2a2c72');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"date\"] = data.datetime.apply(lambda x : x.split()[0])\n",
        "data[\"day\"] = data.datetime.apply(lambda x : x.split()[0].split(\"-\")[2])\n",
        "data[\"hour\"] = data.datetime.apply(lambda x : x.split()[1].split(\":\")[0])\n",
        "data[\"year\"] = data.datetime.apply(lambda x : x.split()[0].split(\"-\")[0])\n",
        "data[\"weekday\"] = data.date.apply(lambda x : calendar.day_name[datetime.strptime(x,\"%Y-%m-%d\").weekday()])\n",
        "data[\"month\"] = data.date.apply(lambda dateString : calendar.month_name[datetime.strptime(dateString,\"%Y-%m-%d\").month])\n"
      ],
      "metadata": {
        "id": "g6OeOjrE_Yqr"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
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        "id": "1T7UmvwL_YtD",
        "outputId": "163167c0-c9fc-45e4-f694-739f5dc2ae43"
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      "execution_count": 15,
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            "text/plain": [
              "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
              "0  2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
              "1  2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
              "2  2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
              "3  2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
              "4  2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
              "\n",
              "   humidity  windspeed        date day hour  year   weekday    month  \n",
              "0        56    26.0027  2011-01-20  20   00  2011  Thursday  January  \n",
              "1        56     0.0000  2011-01-20  20   01  2011  Thursday  January  \n",
              "2        56     0.0000  2011-01-20  20   02  2011  Thursday  January  \n",
              "3        56    11.0014  2011-01-20  20   03  2011  Thursday  January  \n",
              "4        56    11.0014  2011-01-20  20   04  2011  Thursday  January  "
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              "      <td>Thursday</td>\n",
              "      <td>January</td>\n",
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              "      <td>2011-01-20 04:00:00</td>\n",
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              "      <td>Thursday</td>\n",
              "      <td>January</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5f7b3cd-ee8e-4c3a-91c6-46cfab861ae7')\"\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-c5f7b3cd-ee8e-4c3a-91c6-46cfab861ae7 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-c5f7b3cd-ee8e-4c3a-91c6-46cfab861ae7');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data1[\"date\"] = data1.datetime.apply(lambda x : x.split()[0])\n",
        "data1[\"day\"] = data1.datetime.apply(lambda x : x.split()[0].split(\"-\")[2])\n",
        "data1[\"hour\"] = data1.datetime.apply(lambda x : x.split()[1].split(\":\")[0])\n",
        "data1[\"year\"] = data1.datetime.apply(lambda x : x.split()[0].split(\"-\")[0])\n",
        "data1[\"weekday\"] = data1.date.apply(lambda x : calendar.day_name[datetime.strptime(x,\"%Y-%m-%d\").weekday()])\n",
        "data1[\"month\"] = data1.date.apply(lambda dateString : calendar.month_name[datetime.strptime(dateString,\"%Y-%m-%d\").month])\n"
      ],
      "metadata": {
        "id": "voILzIGp_YvS"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "zkx9SGxJ_YyU",
        "outputId": "bd08a4d1-2f27-46a4-f278-475151ae4840"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
              "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
              "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
              "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
              "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
              "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
              "\n",
              "   humidity  windspeed  casual  registered  count        date day hour  year  \\\n",
              "0        81        0.0       3          13     16  2011-01-01  01   00  2011   \n",
              "1        80        0.0       8          32     40  2011-01-01  01   01  2011   \n",
              "2        80        0.0       5          27     32  2011-01-01  01   02  2011   \n",
              "3        75        0.0       3          10     13  2011-01-01  01   03  2011   \n",
              "4        75        0.0       0           1      1  2011-01-01  01   04  2011   \n",
              "\n",
              "    weekday    month  \n",
              "0  Saturday  January  \n",
              "1  Saturday  January  \n",
              "2  Saturday  January  \n",
              "3  Saturday  January  \n",
              "4  Saturday  January  "
            ],
            "text/html": [
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              "<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>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>count</th>\n",
              "      <th>date</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>year</th>\n",
              "      <th>weekday</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>01</td>\n",
              "      <td>00</td>\n",
              "      <td>2011</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>01</td>\n",
              "      <td>01</td>\n",
              "      <td>2011</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>01</td>\n",
              "      <td>02</td>\n",
              "      <td>2011</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>01</td>\n",
              "      <td>03</td>\n",
              "      <td>2011</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>01</td>\n",
              "      <td>04</td>\n",
              "      <td>2011</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "    [theme=dark] .colab-df-convert {\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-74431d70-e2a8-4ec2-9e26-ecc6bd73fff9');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of unique values in each columns and unique values in each columns.\n",
        "\n",
        "for i in data.columns:\n",
        "    print(\"Unique value of = {} [[{}]]\\n{}\\n\".format(i, len(data[i].unique()), data[i].unique()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrV-9LWn_Y1M",
        "outputId": "05efaf2e-b85f-4ac2-8fef-710c6a654305"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique value of = datetime [[6493]]\n",
            "['2011-01-20 00:00:00' '2011-01-20 01:00:00' '2011-01-20 02:00:00' ...\n",
            " '2012-12-31 21:00:00' '2012-12-31 22:00:00' '2012-12-31 23:00:00']\n",
            "\n",
            "Unique value of = season [[4]]\n",
            "[1 2 3 4]\n",
            "\n",
            "Unique value of = holiday [[2]]\n",
            "[0 1]\n",
            "\n",
            "Unique value of = workingday [[2]]\n",
            "[1 0]\n",
            "\n",
            "Unique value of = weather [[4]]\n",
            "[1 2 3 4]\n",
            "\n",
            "Unique value of = temp [[49]]\n",
            "[10.66  9.84  9.02 11.48 12.3  13.12  8.2   6.56  5.74  4.92  4.1   3.28\n",
            "  2.46  1.64  0.82  7.38 13.94 14.76 17.22 15.58 16.4  21.32 22.14 22.96\n",
            " 18.86 18.04 19.68 20.5  23.78 25.42 27.06 28.7  30.34 31.16 27.88 24.6\n",
            " 26.24 29.52 31.98 33.62 32.8  35.26 36.08 36.9  34.44 37.72 38.54 39.36\n",
            " 40.18]\n",
            "\n",
            "Unique value of = atemp [[65]]\n",
            "[11.365 13.635 12.88  10.605 16.665 14.395 15.15  15.91  12.12   9.85\n",
            "  9.09   8.335  7.575  6.06   6.82   5.305  3.79   1.515  2.275  0.\n",
            "  0.76   3.03   4.545 17.425 18.18  21.21  19.695 20.455 25.    25.76\n",
            " 26.515 22.725 21.97  23.485 24.24  27.275 30.305 31.06  32.575 33.335\n",
            " 31.82  29.545 28.79  28.03  34.09  34.85  37.12  38.635 37.88  36.365\n",
            " 35.605 40.15  39.395 41.665 40.91  42.425 43.18  44.695 46.21  45.455\n",
            " 47.725 49.24  50.    43.94  18.94 ]\n",
            "\n",
            "Unique value of = humidity [[79]]\n",
            "[ 56  60  55  52  48  45  42  49  65  70  75  80  87  51  47  41  27  21\n",
            "  25  26  28  30  33  38  44  35  57  62  58  54  46  43  37  36  40  50\n",
            "  59  63  64  69  74  86  93  61  29  53  66  81  76  72 100  94  77  96\n",
            "  82  88  39  34  32  71  90  23  24  31  22  20  83  78  73  67  68  89\n",
            "  79  84  18  17  16  19  85]\n",
            "\n",
            "Unique value of = windspeed [[27]]\n",
            "[26.0027  0.     11.0014 15.0013 19.0012 19.9995  7.0015  8.9981 12.998\n",
            " 22.0028 16.9979  6.0032 27.9993 23.9994 31.0009 39.0007 35.0008 30.0026\n",
            " 36.9974 40.9973 54.002  50.0021 32.9975 43.0006 55.9986 46.0022 43.9989]\n",
            "\n",
            "Unique value of = date [[275]]\n",
            "['2011-01-20' '2011-01-21' '2011-01-22' '2011-01-23' '2011-01-24'\n",
            " '2011-01-25' '2011-01-26' '2011-01-27' '2011-01-28' '2011-01-29'\n",
            " '2011-01-30' '2011-01-31' '2011-02-20' '2011-02-21' '2011-02-22'\n",
            " '2011-02-23' '2011-02-24' '2011-02-25' '2011-02-26' '2011-02-27'\n",
            " '2011-02-28' '2011-03-20' '2011-03-21' '2011-03-22' '2011-03-23'\n",
            " '2011-03-24' '2011-03-25' '2011-03-26' '2011-03-27' '2011-03-28'\n",
            " '2011-03-29' '2011-03-30' '2011-03-31' '2011-04-20' '2011-04-21'\n",
            " '2011-04-22' '2011-04-23' '2011-04-24' '2011-04-25' '2011-04-26'\n",
            " '2011-04-27' '2011-04-28' '2011-04-29' '2011-04-30' '2011-05-20'\n",
            " '2011-05-21' '2011-05-22' '2011-05-23' '2011-05-24' '2011-05-25'\n",
            " '2011-05-26' '2011-05-27' '2011-05-28' '2011-05-29' '2011-05-30'\n",
            " '2011-05-31' '2011-06-20' '2011-06-21' '2011-06-22' '2011-06-23'\n",
            " '2011-06-24' '2011-06-25' '2011-06-26' '2011-06-27' '2011-06-28'\n",
            " '2011-06-29' '2011-06-30' '2011-07-20' '2011-07-21' '2011-07-22'\n",
            " '2011-07-23' '2011-07-24' '2011-07-25' '2011-07-26' '2011-07-27'\n",
            " '2011-07-28' '2011-07-29' '2011-07-30' '2011-07-31' '2011-08-20'\n",
            " '2011-08-21' '2011-08-22' '2011-08-23' '2011-08-24' '2011-08-25'\n",
            " '2011-08-26' '2011-08-27' '2011-08-28' '2011-08-29' '2011-08-30'\n",
            " '2011-08-31' '2011-09-20' '2011-09-21' '2011-09-22' '2011-09-23'\n",
            " '2011-09-24' '2011-09-25' '2011-09-26' '2011-09-27' '2011-09-28'\n",
            " '2011-09-29' '2011-09-30' '2011-10-20' '2011-10-21' '2011-10-22'\n",
            " '2011-10-23' '2011-10-24' '2011-10-25' '2011-10-26' '2011-10-27'\n",
            " '2011-10-28' '2011-10-29' '2011-10-30' '2011-10-31' '2011-11-20'\n",
            " '2011-11-21' '2011-11-22' '2011-11-23' '2011-11-24' '2011-11-25'\n",
            " '2011-11-26' '2011-11-27' '2011-11-28' '2011-11-29' '2011-11-30'\n",
            " '2011-12-20' '2011-12-21' '2011-12-22' '2011-12-23' '2011-12-24'\n",
            " '2011-12-25' '2011-12-26' '2011-12-27' '2011-12-28' '2011-12-29'\n",
            " '2011-12-30' '2011-12-31' '2012-01-20' '2012-01-21' '2012-01-22'\n",
            " '2012-01-23' '2012-01-24' '2012-01-25' '2012-01-26' '2012-01-27'\n",
            " '2012-01-28' '2012-01-29' '2012-01-30' '2012-01-31' '2012-02-20'\n",
            " '2012-02-21' '2012-02-22' '2012-02-23' '2012-02-24' '2012-02-25'\n",
            " '2012-02-26' '2012-02-27' '2012-02-28' '2012-02-29' '2012-03-20'\n",
            " '2012-03-21' '2012-03-22' '2012-03-23' '2012-03-24' '2012-03-25'\n",
            " '2012-03-26' '2012-03-27' '2012-03-28' '2012-03-29' '2012-03-30'\n",
            " '2012-03-31' '2012-04-20' '2012-04-21' '2012-04-22' '2012-04-23'\n",
            " '2012-04-24' '2012-04-25' '2012-04-26' '2012-04-27' '2012-04-28'\n",
            " '2012-04-29' '2012-04-30' '2012-05-20' '2012-05-21' '2012-05-22'\n",
            " '2012-05-23' '2012-05-24' '2012-05-25' '2012-05-26' '2012-05-27'\n",
            " '2012-05-28' '2012-05-29' '2012-05-30' '2012-05-31' '2012-06-20'\n",
            " '2012-06-21' '2012-06-22' '2012-06-23' '2012-06-24' '2012-06-25'\n",
            " '2012-06-26' '2012-06-27' '2012-06-28' '2012-06-29' '2012-06-30'\n",
            " '2012-07-20' '2012-07-21' '2012-07-22' '2012-07-23' '2012-07-24'\n",
            " '2012-07-25' '2012-07-26' '2012-07-27' '2012-07-28' '2012-07-29'\n",
            " '2012-07-30' '2012-07-31' '2012-08-20' '2012-08-21' '2012-08-22'\n",
            " '2012-08-23' '2012-08-24' '2012-08-25' '2012-08-26' '2012-08-27'\n",
            " '2012-08-28' '2012-08-29' '2012-08-30' '2012-08-31' '2012-09-20'\n",
            " '2012-09-21' '2012-09-22' '2012-09-23' '2012-09-24' '2012-09-25'\n",
            " '2012-09-26' '2012-09-27' '2012-09-28' '2012-09-29' '2012-09-30'\n",
            " '2012-10-20' '2012-10-21' '2012-10-22' '2012-10-23' '2012-10-24'\n",
            " '2012-10-25' '2012-10-26' '2012-10-27' '2012-10-28' '2012-10-29'\n",
            " '2012-10-30' '2012-10-31' '2012-11-20' '2012-11-21' '2012-11-22'\n",
            " '2012-11-23' '2012-11-24' '2012-11-25' '2012-11-26' '2012-11-27'\n",
            " '2012-11-28' '2012-11-29' '2012-11-30' '2012-12-20' '2012-12-21'\n",
            " '2012-12-22' '2012-12-23' '2012-12-24' '2012-12-25' '2012-12-26'\n",
            " '2012-12-27' '2012-12-28' '2012-12-29' '2012-12-30' '2012-12-31']\n",
            "\n",
            "Unique value of = day [[12]]\n",
            "['20' '21' '22' '23' '24' '25' '26' '27' '28' '29' '30' '31']\n",
            "\n",
            "Unique value of = hour [[24]]\n",
            "['00' '01' '02' '03' '04' '05' '06' '07' '08' '09' '10' '11' '12' '13'\n",
            " '14' '15' '16' '17' '18' '19' '20' '21' '22' '23']\n",
            "\n",
            "Unique value of = year [[2]]\n",
            "['2011' '2012']\n",
            "\n",
            "Unique value of = weekday [[7]]\n",
            "['Thursday' 'Friday' 'Saturday' 'Sunday' 'Monday' 'Tuesday' 'Wednesday']\n",
            "\n",
            "Unique value of = month [[12]]\n",
            "['January' 'February' 'March' 'April' 'May' 'June' 'July' 'August'\n",
            " 'September' 'October' 'November' 'December']\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(15,15))    \n",
        "ax = sns.heatmap(data.corr(), cmap = \"coolwarm\", annot=True, linewidth=1)\n",
        "bottom, top = ax.get_ylim()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "id": "a8BDTStg_Y4F",
        "outputId": "133e3533-14e1-4922-ad5e-5e7d4fc43c46"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data1.hour.value_counts())\n",
        "values=data1['hour'].value_counts().values\n",
        "label=['00', '01', '02', '03', '04', '05','06', '07', '08', '09', '10', '11','12','13','14', '15','16','17','18','19', '20', '21', '22', '23']\n",
        "fig,ax1=plt.subplots()\n",
        "ax1.pie(values,labels=label,shadow=True,startangle=200,autopct='%1.1f%%')\n",
        "plt.show()\n",
        "\n",
        "sns.barplot(x='hour',y='count',data=data1,estimator = np.sum)\n",
        "label=['00', '01', '02', '03', '04', '05','06', '07', '08', '09', '10', '11','12','13','14', '15','16','17','18','19', '20', '21', '22', '23']\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 944
        },
        "id": "hpjXKU3o_Y5q",
        "outputId": "78a5e588-a07f-4ba6-e76a-40113d83f79d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12    456\n",
            "13    456\n",
            "22    456\n",
            "21    456\n",
            "20    456\n",
            "19    456\n",
            "18    456\n",
            "17    456\n",
            "16    456\n",
            "15    456\n",
            "14    456\n",
            "23    456\n",
            "11    455\n",
            "10    455\n",
            "09    455\n",
            "08    455\n",
            "07    455\n",
            "06    455\n",
            "00    455\n",
            "01    454\n",
            "05    452\n",
            "02    448\n",
            "04    442\n",
            "03    433\n",
            "Name: hour, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data1.season.value_counts())\n",
        "values=data1['season'].value_counts().values\n",
        "label=[ \"Spring\", \"Summer\", \"Fall\", \"Winter\"]\n",
        "fig,ax1=plt.subplots()\n",
        "ax1.pie(values,labels=label,shadow=True,startangle=90,autopct='%1.1f%%')\n",
        "plt.show()\n",
        "\n",
        "sns.barplot(x='season',y='count',data=data1,estimator = np.sum)\n",
        "label=[ \"Spring\", \"Summer\", \"Fall\", \"Winter\"]\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "2oUsgIuS_Y7V",
        "outputId": "8a853dae-6b21-406a-9e53-c1adc9ddeb81"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4    2734\n",
            "2    2733\n",
            "3    2733\n",
            "1    2686\n",
            "Name: season, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data1.holiday.value_counts())\n",
        "values=data['holiday'].value_counts().values\n",
        "label=[0,1]\n",
        "fig,ax1=plt.subplots()\n",
        "ax1.pie(values,labels=label,shadow=True,startangle=90,autopct='%1.1f%%')\n",
        "plt.show()\n",
        "\n",
        "sns.barplot(x='holiday',y='count',data=data1, estimator = np.sum)\n",
        "label=[0,1]\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "sqovJIE__Y_A",
        "outputId": "2363f803-cb60-4112-c32b-096b005d6538"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    10575\n",
            "1      311\n",
            "Name: holiday, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data1.weather.value_counts())\n",
        "values=data1['weather'].value_counts().values\n",
        "label=[\" Clear + Few clouds + Partly cloudy + Partly cloudy\", \" Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist \",  \" Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\", \" Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog \"]\n",
        "fig,ax1=plt.subplots()\n",
        "ax1.pie(values,labels=label,shadow=True,startangle=120,autopct='%1.1f%%')\n",
        "plt.show()\n",
        "\n",
        "sns.barplot(x='weather',y='count',data=data1,estimator = np.sum)\n",
        "label=[\" Clear + Few clouds + Partly cloudy + Partly cloudy\", \" Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist \",  \" Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\", \" Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog \"]\n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "id": "l9unzL4SCv2S",
        "outputId": "64af8999-2a05-43ef-b3ab-759bbb0083e6"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    7192\n",
            "2    2834\n",
            "3     859\n",
            "4       1\n",
            "Name: weather, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data1.weekday.value_counts())\n",
        "values=data1['weekday'].value_counts().values\n",
        "label=['Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']\n",
        "fig,ax1=plt.subplots()\n",
        "ax1.pie(values,labels=label,shadow=True,startangle=90,autopct='%1.1f%%')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "VEdJEFUpC3co",
        "outputId": "81f05b31-4f83-407c-87e7-cd8cec135b50"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saturday     1584\n",
            "Sunday       1579\n",
            "Thursday     1553\n",
            "Monday       1551\n",
            "Wednesday    1551\n",
            "Tuesday      1539\n",
            "Friday       1529\n",
            "Name: weekday, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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HJmpGanjq1HqKTIM1aa3ur9xU18ZSP5l3CkJdGYT6cpTMexhF790N0VqF0k/+BNFWe0o7lSkBmuT+UMekgDge+sxJcJcfO6WN/egv0KQMAPM44akrReJVs2A/vAWSx+lTm/PqilFYX4rp8+7GWe/dgFJrJS795A+osFWf0i7FlIhSS8WJ12XWCqSYTh2j+ix7Ca4dfgl2luyHWWvEf698AR/uWOAzW49UH7H+VPaxXuJs6m52dc2cmVmK4LRBxAoOgPPP6cuPHJrId2kE0qQSDZ+a30/5q/PtPMb8G2FpEjOQ/vCXSHtgHtIemAfelIDUO98CHxV7arvUTEhO24kZLGf+HmgS0k+cZ6IAy29LYZ54LZjgwomxICYBouBTm4cknoFdDy/DtgcWYtsDC5FqSsTKOz9CUtSpkeu5/SZgY94O1DmtqHNasTFvB87tN+HE+TqnFetytuG64dPhEJwg4kBEcAoun9i5rXizdWf9YhM40Vf/C+/OmZml7L1rhYgUnBmD1AlqDtfdPUYzrjv9cAR6IGZbxiLxz8Umsc5nLkLlsn+h7PMn4KkpRtG7d8C6e3WrbV2lR1G98j8AAOJ4xE67B+Xzn0bJ3AcBMESNmn6irXXnCkQNvwCcWgd1Yj8wwYWSuQ9CkzIAnK570eGDy17EVZ8/gOM1BRj/7rWYv3t5q213lx7CkytfBQDE6s145Ow7kPXpfcj69D48evadiG0y+PzWlk/w8Nm3gSMO5/abgO1Fu3HR3Dtx7bDprfTeMQRJEH/MXWwvcG8x+bjaRjKArsWREUDEVd70DhT/8f5x6tsuy1RP9FW/VW615S7HY+692lH+G9UMIuFUedPmanCuKZpPbq7KX1sTGICRjy9Yvs9P/fdYItHDGZpmpvMu6q8a48tOEzQe8xLTq7F3OT8v8GW/Cr6l2Fpq+6HoI5UfxQaQY1WlxlULRJTgzBik1gK444/jNcPUPLW1erRLqDjwz8es7POh+28FWsnh20ERhW6zr2KvdVPFF0bGOQMxO3vtnJlZIwJwnx5FRAkOgAv6x1K/oYmcX9NOXGzO77OWe6i+r+d4wFYnK7SOxBj7uXCNbX/DDybipEClCVW8nBaIGMGZMUhtAnDlbSM1GVwAavKm6xzxPxqe1Wc5lwdkdbJCy7gEt2dF7nxnmbAzGJU2rpkzM2tUEO4bskSM4AA4O15PxpHJXMA+AHqead+J+SrtFec/8znmCfjq5Ein1lFn/z5/HrNTQUs7vQOB4uWcRkQIjnfsJuv2Ueo+ap66u7ir09wUs6/vCvZIZYJQ3hDoe0cquXV51jUlH2tFrt7nY3Wd5Ko5M7M6vJI93IkIwQFwpk6F6Elp/JnBMmCIoT75J+0TmOTaWhYsGyKF30q3W3+tWWBinDsUCtgpXk4Twl5wZgxS8wCuumm4OkWvpqCW5zWrReNX5neSH3O8mxdMO8IVURKltfnfNxxzrjf5f5SuU1w1Z2bW0GAbEQqEveAAGMoRks7vpwqad9MUjkCPxG7JWOD5c3GUWO+b9fkKsHscruW5X7irpQOhWvO9eWayCCSsBce7qnhG1kBVUrSO2kw/EWgmmsp7/6R61DnUvbe6/dYKbVHRUNmwouAjzsmVdXWndyC4dc7MrFAI8YJKWAsOgH4ABlyWqQpJdzZJ647+LuqV6Fsc85XVyV3kUNVh609ln+olzh7wyYBO0gvAhcE2ItiEu+Bc2ttEfEoUhWzOWQ0H1cuxy/q853qmQKOsTu4wjDFsLd5o22VZYiLf7fT2NxEfVvWUP1SnmTFIHQNgbNZAVUIgFvp1l0ujj/dZQw/XpXnyLcG2JdTxiILwY94ie6F7W1QP+NM25ao5M7Oa52GNIMJWcAAMAUCjU/ghwTako/TV2xPWGJ7SXuL8sTjYtoQqFpfV8X3+x6IFxwzBtqUL6AHcEGwjgkk4C87UZCN5Uk2hG061hJ5n2veiP+v9D+e/8oiJkZU7pB2KLMW2H4vmqj1U05MrXkZ0WBWWguPdNzU4a6AqpSeEU6dDBNwWsyvje+nh8jix0h5se0KB3eW7rFsqvzQyztXT83BPmTMz64yONCSieCLa5X2UEVGx93kdER3wt6FEdCcRvePLPsNScCCHUxiTyofk7FRHGW6sS1mveUya4Pq1PNi2BAuJSWx9waqGQ/ZVJnCsx315tMLNHWnEGKtmjI1mjI0G8D6AN73PRwPo8t48IgqaaIer4EyO05PQ20T9gm1Id4lWi1Ffm/+d+Ijzg4grO+wSXJ4VeV+7KsRdobqYr6tc7IM+eCL6HxHtJ6LVRKQHACLaQETjvM8TiCjP+/xOIlpGRD8BWEdEqUS00esx7SOiqd52dxHRESLaDmBy482I6Aoi+pWIsoloLRElExFHREeJKNHbhiOinMbXLRF2gjNjkNoIYPgVA1VJPEdh8f54AvdYzM99v/Q8XmQQre5g2xMIqu219u/z58GOolBezNdVJs2ZmdW80mHnyATwLmNsGIA6dKw8zZkArmOMnQvZy1rl9ZhGAdhFRKkAXoQsNFMANI0QNgOYxBgbA2A+gL8wuXrAFwAaKypeCGA3Y6yyNQM69A9JRE97lXSPVxG7nQuYiF4goie6208LDAZAY3v1nNmpjjLZVJq2XvWwfZD7QFivTj5We9y6tuxjrchZQn0xX1dRATivm33kMsZ2eZ//DiCjA9esYYzVeJ/vAHAXEb0AYARjzApgIoANjLFKxpgbQNNaPGkAVhHRXgBPAmgsiz0PwO3e53cD+LgtA9oVHCI6C0AWgDMZYyMhq1hhB95csDiLIzh6mSgj2Ib4g2StO+b7qJfNNzq+CcvVydtLfrHuqF1oAnnCfRvARd28vuk+PBEni1oKOPl/fbp3eCI9CmNsI4BzABQD+ISIbkfbvA3gHcbYCAD/19g3Y6wQQDkRnQ9gAoCVbXXSEQ8nFUAVY8zlvUEVY6yEiPKIKAEAiGgcEW3wPn+BiOZ5Y8njRPRIY0deT+kIEW0GMKjJ8XuJaAcR7SaiRURkICITEeUSyflriMjc9HVLePPejBqbyqk0fshZHCpoOKaeHbukz9uu5/LVkksMtj2+QJREcXXeUnuu62dfl20JVc7zU795AMZ6n1/XWiMi6gugnDH2PwAfQQ63fgVwrnd2TA3g+iaXREMWJ6D51P5HkEOrbxhjbX4eOyI4qwGke4Xiv0R0bgeuGQxgOmTFe56I1EQ0FsCNkEfYLwMwvkn7xYyx8YyxUQAOArjH6+JtAHC5t82N3naeNu6bBoAbk8r37oCNPZ4ronP6rqaHanp5Cnr06uQGt935fe5nQi071BMX83WVYXNmZkX7od/XATxARNkA2ipZdB6A3d52MwH8mzFWCuAFANsAbIH8v9jICwC+IaLfAVSd1tcyAFFoJ5wCOlBbnDFm84rFVADTACwgolntXLbC6xG5iKgCcnGwqQCWMMbsAEBEy5q0H05ELwGI8Rq+ynv8IwB/AfAdgLsA3NvOffsAoDNiubCon9QR+ukbEtcITzkftd1ZslZ3Ya9g29NZymzltk3lC7U9YPOlr+EAnAXgx440Zoy90OR5HoDhTV6/3uT5IQAjm1z6jPf4JwA+adLuUwCftnCfj9GCcDDGlgJY2op5oyAPFh9q7310aNCYMSYyxjYwxp4H8BDkEfG2YsXW4svW+ATAQ9748EWcjA+3AMggovMA8Iyx9gqLDQNg72WKHMEBAKNK0v0vel6v55xz8tCDVicfqDpg/bniM0MEik0jZwfbgO7idT4WAfhbR9p3ZNB4EBFlNjk0GkA+To0VOzIltxHAVUSkJyITgCuanDMBKPXGjbecdt1nAL5CO+6aN/fNkBgdHGYtQir3TSAgAu6O+T1jmfRIWaxQFdKrkxlj2Fy03rbX+r0JJHVp6cKC7bvx/NI1eO3Hn5ud23D4OJ5YuAINrpZXEPxv43Y8s2QV5m7accrxL3/JxpxVG/HDnpNf1GsPHMW+Yr9lhe3xgsMYm80Y68sY29yR9h35Y0cB+JSIDhDRHshz8y9A9kT+TUS/QfZi2jNsJ+Rptt2QR7Kb/rWfhTxgtQXA6W7ZlwBiAXzdzi3iAejGpPBxETLo2CIjjbWpP2keE8e6fqsIti0t4RY9wg95Cx3Fnu3dKtsyrl8a7j1nQrPjdXYHjpRVIsbQeqGG8wb1x00TT81rXlJngZrn8fj0c1BYWw+H2wOLw4n86joM753SHVPbokevhO8KHRnD+R0tK/EmAANbaP/Caa+bxpovA3i5hWveA/BeKyZMAfAtY6y9onIpADAwnktup13YE6sRTAtVbxhft1yQ/57unpDZvFrvtDjWFc/nPVxtt8u2nJEYj5qG5o7c0l0HkDVqCD7e/Fur12YmJyCn4tSlTDxH8IgiJMYgShI4IqzadwTThzf7iPuSlDkzs/SPL1ju8OdNQomQXolLRG8DmA3gHx1o3gsA0qM5n38d3b3UgaTXrBj+X1uzc3O2ukAvWlBlb3lrC/93C0a/b8Po922Y8fXJf5BbFtsx8j0bnlrnPHHspY0ufHeorUm4jsNz4P4as67vZ54nC3WSzTeddoOC+kLbquK5Gg9X67flCvuKyxCt16FXTOdTziSbTYjSavDWms0Y2isZVbYGMDCkxfpjIukEBKC/P28QaoT0zlvG2MOdaJ4JwJFkpCRf23HnaDUemqDB7UtO/SIqrJew+riAPtGth3B6FbDr/lOjhz3lIvQqwp4HonDR5w2odzLYPQy/Fot45hzfZl44x1Scvt75cO1t7lnI0QyK9WnnHSS7bKf1cMPaKPLj5ku3IGLdwWO4r4Uwq6NcOWbYiedzN+3AdeNGYO2Boyips2JgcgImndHHF6aeTn8A+/3RcSgS0h5OJ+kPwBalIZ9nVDunrwpx+ub/K39e5cS/LtShs/9Fag5wCAwSY/CIAM8Bz6134cXz/JPmJVXnil1h/LvxWufigK4Ql5gkrStYaTviWGPyp9gAQLWtATUNdryxehNeXv4T6h1OvLlmEywOZ/sXn8a+4jKkxUbDLQiottlx+9lnYk9RKdyCX9ZYKh5OT8O7wjgOQL5OhYDsLF56yIPeJg6jUtpege8UgHEf2qDiCLOmaHDVYDWGJPJINHA484MG3DZSjZwaCRIDzkz132p+Lc80c2K+TZ9Svzf/Cc3f0kXS+PXLxim43KsLFkoOKglITe/UGDNevPLkboGXl/+EP100BUZt5yI4UZKw6Ugu7pk6AVW2BjTOPzDv2A7g879Rh3LjhAthITiQZ9KkOD1pVZz/c33YPQz/3OzC6lvb17b8P0Wht5nD8VoJ53/agBFJPM6I4/DWJSeXLl3xtR0fZOnw8kYXdpeLuKi/CveO9c9Qx9XRh/uOsD9Ucav4vL5M1bu7O5ZbpKqhumFD2Xy1yNn8ttP7i23ZOFZZjQaXG//4fh0uHpaJif1bDnkKa+qw7VgBbhgvr4d796etqLA2wCUI+Mf363DD+JEYlCJnVNiSk49xGWnQqHikRpvgFiS8vmojBqckQq/xy3IhxcPpgRgBsNSowFTWPFYjIbeWYdT78iBykYXhzA8asP1eI1KiTnUcepvl1/1jOZyXoUJ2mYgz4k62WXrIg7GpHGxuhmO1EhZeb8D0Lxpwy0g1DGr/RCEDDLaktcJfHQ/Z/lC6QXdeqi/7PlqTY91Zu9QITvCrB3XrWWPaPP901vknnqfHxSA9LubE6wfPb335yzkDT6ZQIqJ27+MDIkpwwmUMxwgAScbACM6IZB4VT5qQ9yf5kWYm7Py/5mJT62BwCfLC3yq7hC2FIoYmnmzjERne+tWNv0zWwuHBibEgUQLcft6SGaWS9POiP0x5yvlWvpzWpPv8UrzF+nvdtyZ/i02Y0W/OzKyIWTgWLh8MIwAuweAfwblpkR1nzW3A4WoJaW9YMXdn6zmwfisR8Ydl8mzWwSoR4/7XgFHv2zDtUztmTdZgaOLJMYB3d7hxxyjZkxmZzMEuMIx4z4axqTxidP7/DHIEui9me9/vxEdLooXaLq8FESRBXJW32J7v3hwpO719iQ6InJXxxFiP2XrTKjMGqc8FcOd9Y9UJWQPVWcG2pydS41ZZ7nL82bVbO6bF9JAfNHiKhhnjm+1Rs+IKUeYAACAASURBVLkbnGsK55Obq+rJlRSCTfrjC5YXBduIQBAuHk48AMGsDUxIFY7EaQTzItNrcfc6P+lw7uQSa5ltZeFclSI23SZiNq+Gi+DEAvCYNIrgdAcVB/7pmNV957n/WqiV7G2uTt5fud+ysfJzo8Q5wmXiIZiEbbK40wknwXEb1Oj2Hh0F4HxzYfpP3EPWfp6c2tPPSYyxjYVrbftsy81EkjJg4xsUwelhxABwSww9f0AqROitc8atNDxvnGH57sQGMLfg9vyQt8BZKvwekMV8EUTEhFTh4g7rAbhFBiHYhoQTOp5p/pO0UHNB3f7CCsedmkNl3zCBq1e8SN8TMR5OuAiOAIATpPbz8ih0DJEB6wWtYzmi2b7kupgL922WPPGZNZKn0EWsWqPl3TEqnlPExzcoHk4PwwNAI0iKh9Md9kh83fcqvWWrUccVmXUJkkYWlEnrRdf04mzVz31vUKvV55xIdO50llgdjqP1gidf5FitVqvyxKo4Tpmx6jyKh9PD8ADQCVLbJSoUTiWPcbalnK72Z4MeuWZdnKDjYyCPh51C1u+M6ZjAq47/AAw6WXlEp+tl0ul6nbIfy+EsrHc6jloFT6HIs1qtViXE8ooItYciOD2MxpBK8XDaoEIi5/ecruonvV48bNLFuIyqaMgbX1slc5/HNtBDUQBwTukm7bp+lzZoNMZWlx/odenRel36iaxVkiTB5SqsczqPWgVPocSzOp1WJcXxXOv1xSKQkM5B7UvCRXA8AMgjKmM4TamX4PmR01Wt0epc+0x6U0OUKg5EnapocdN6sQ5QRQGAGgIfl7+mypZ5VYfXO3EcB72+b4xe3/eE5yRJEnM682qczhyb6CliKqrXa3kpjgvATv8QpTzYBgSKcPkDu6F4OHBKTPqJtJUrtXrnriidri5KkwieurwbPKFEcA218r2aZhgbU7wufV3fC+s1mqgu597kOI4Mhv5xBkP/uMZjkiQwhzOvxuk4amNiMeNhMWhVLI4jCveSvwAQkgnv/UG4CI4HAOeJsDEcgTG2Derq5WqD7bconbbCpEmEyndJ5G9YJVRwpEpvekwNiUvMXVlfP+h6nyb75TgVGQ0D4oyGASdESJQ8ksNxvMrlzLFLQglUZG0UoXBZPwbIn91mCyzDlbASHLvnlAJ8YckuSVW3TKWr32bU8yVmbaKk4RLQdknXLqGzieLZpXxSS/lTR5f8nL4mY3qtRmv2a45knlNzUcZBCVHGE2XoIYou0eE8VulyHrMzoZRUZDVqVYilnitCFY8vWB4xC1bDRXDcAKjIIoXdN8UxibMu5XW1Gw0G5Jm18WIrM0m+Jmu1p0xDqhZrtPPEKPX497bqIbcEPCk7z2v5KOPQxCjjyZJOgugUHI6cKrfzmIOJZZyKbI0i1BO2XkTM+A0QPoJjAaA+Wi3VBNuQ7lImkWMZr6ter9OLR8y6WLdBZYZcmTRgkCDh0sNcm/ccUbY1fVXGZdVafWzQc7moeJ3KFDU8CVEnSqBBEB0eu/1Irdt13AGxjFNzdpOGR0wIalDEjN8A4SM4pQD4QgtrcIvMreGpx6xrqJfgXkG6yrV6vbDfpIuyG1XxnZ1J8jVTN3oqTeBbzIvTCEdA+rGlzorhdwbIqs6h4vVqs2lUEkyjThzzCFaXw5FT53Ydd0Is59Scw6zhER1kEVI8nB5INQAJAKwuVhNvIL/VZu0uTomJa0hb+aNW79odpdfXm9QJ4KjF0CVYXPt7xzbBDq/a0XulPatSa0hoU5xCBbXKpFWbxiTDdDJPsUewOh2yJ+SCVKHScA6zRsX5vNRQGyiC0wOpgTclcK0TNfEGhIzgCIyxzUxT9YNG37AjSq+tMqkTofJ9dVBfMWS3uz5V4DtcTLBfzmKhZOR9/jTJr6hVJp3aPDYVGHvimNtd53A4jtR63LluSJVqDec0a1Rth5jdoNRP/YYk4SI4dZAFh6rsUs2AuOBOWPzOVLXf8zrLL1F6VYlZm8jUXCKAHuEF3PizZAW4Dk95D67ZnZprKy/XRiWHTU13jSZGr9FM0AMnq3i63TV2h+Nwnced5yGpSqXhXTFqnvNFwreIqboJhIngLDvsEWYMUlcB0JXZWMAHjo9InGUZr6/bZNRTvlkbL2r5WMhJwXoUSUWCc5CN793ZUqIDjn7DCsc85B+jQgSNJs6g0ZxlAM46cczlqmxwOI7Uedx5Aseq1RreHaPmOUMb3bTEbt9aGtqEheB4KQHQv7De/zNVJYzsy0hXvV6vZzlmXYx3JimQcb9fmLlKqDx9oV9HyKw/mJJjLS7TmnqHbKjoD7TaRKNWm2gEJp845nSVW52Ow/Ued4HAsWqtlve0msaDMVb2xMIVyixVD6UQwLCjNb4XnFoJ7hWcrnKtTi8cMOlMDqMqDkSd/SYLafQWUZxUzid3ulC6l8FHvuFyx/7Jt0b1QHTaZJNOm3zKeI/TWWJxOI5YBE+BwLFanZb3xKp4TktEEeXdAOElOKUA+Lw6ZnUKzKFTUZeTQzkkJq4mbeWPWoNzj0lntESF3kySr7lyladM3cpCv47Qz3o06XB9QbEmuk9Y/566gk7Xy6zT9TrhAUuSBI+nrIpH9fpg2hUMwklwagB5OrfUygr6xdKgdtqfwMMY2wRN5Q9qvf23KL2u2qxJBB+6U+u+hgQJF+d0fyp42NEF2iNjn0AILq4LKTiOg1bbKwHodSjYtgSacBKcMninxnNqpPx+sVybgrNDUtV8r9ZbfzHqVGXyTFKHp4LDjfM2eCqi0PGp8NZIt+Ul7K/LLdLE9g/qwsUexLZgGxBowkZwlh321M0YpK4GYNhVJuZfdMapb+2gxFuWqXR1Wwx6KjBrE0QtHwcgrsXOIoxrdnZ15KY5I4/ONxwc/zfWQ/YxBQ3G2PGHPrggogaMgTASHC+7AUzdViSW5gpUt1Kts27QG1iOWRvr0YfHTJKvGb7TXZcstr2NoTP0shfH7a05UqiOH9Tp2a5Igoh+CbYNwaCnbulvjYMA1A4J2quNCQ3vZSSmH0w29vHoVQHd/NiTmLlJavB1n6NzFpgYkyRf9xtmrAu2AcEgrATHBeTnqFUDD2g0V1pzHe5g2xPqpBQIjoENfC9f95vsKI8Rqw4U+brfcIExJgJYGmw7gkFYCc42g766RKXKruW55ZZS9/Jg2xPq3LhaqPTXWMvYnIUxkiRFVAbGjsKYtPnB98+vDrYdwSCsBMeSbWES0SYQRbuKXDWiQwy7hFy+wlAnCuMreL9N/ce7qs2o3KN4OS3AcfzCYNsQLMJKcLzsg3cw3F3hPhpkW0KWq1d5ytTk37xB43IWxkuSGNGJ7U+HMcYALAm2HcEi3GapACAX8gJAruFgw159X/2E9i6INHi3xC46zvk9TWmMpz6KK9+Zj9TxfTtz3RcbXsO+/F9g0sfg6RvmnnJu3e6FWPLLB5h9+2JE6U/d1F5UlYP5m96C02MHRxymj7kFYwdMAwB8su6fKKk5juF9JmHGxD8AAH7c+QVSYzMwqt+U7rzNTsGYtP2hDy6KqJQUTQk7D8eSbXFAnq2KbTjUUCQ2iJXBtinUmLbBXWEA12YBPF8x/ti3iaIkeDpzzaSB0/HgZa80O15rq8DBot8RG9XyGkW1Sovbz5+FZ26Yhz9eNhuLtv0XdpcNxdXHoFZp8NT1HyG/8jAcLhvqG6qRV34woGIDRHY4BYSh4HhZD8AIAI48R3aQbQk5rs4OXK0nk2AzqEu3l3TmmgG9RsKga75katHW/+KqSfeBWlmnmByTjqRoeZFzjDEBJl0MbM468JwKHsENiUkQJREcx2PFb5/g8nF3dOEddZvFwbhpqBCOIRUgj+O4Aajrf6vfYxxivJC4wJQRKZpbBOsuK1RmFTJfzjzlXNXKKpQtKMPgtwdDZTr1V++ucqPg7QJAApjIEH9hPOLOj4PkkVDw7wJ4aj2IOz8O8RfIOcuLPy5G3LQ46DM6t0d15G/u2kSJ93lZmbYYd3xJ8raU8S6eV3e5xvievC2IMSYgLf6MDrXPqzgEQRKQYO4FjjhE6aLx6qL7MSHzQlTWF4MxCemJA7tqTpeQJHH3wx9elBfQm4YYYSk4lmyLyzzGvAHA+UKtUOKp8hzRJGkGB+LesVNiEX9BPIr+d+oEjbvaDdt+G9TxLZfUVsWo0P+Z/uDUHESniJync2AaY4IjzwHDQAMSsxJx/OXjiL8gHo4CB5jEOi02AHDjZskOcAFNDhYl2nWakq15Yvq5GV253u1xYlX2V3joslc71L6+oRqf/fQKbpv2VzR+z1w3+cET599f+TRuPOfP+HHnlyiuPobBaWMxecjlXTGtU3Ac/4XfbxLihGtIBcgb49QA0HC4IWBhlXGQEbyxecRS9nUZkm9oPQsnp+LAqeU/BxMYGtOYE0+Q3BKYePJYxeIKJF/T+YyevfIE+xl2PijpI8bnLksVBLejK9dWWkpQbSnDK9/eh+e+vBl1DZV4dfH9sNibpz5yuBvw3o9P4YoJd6Nf8tBm5/fkbUF64kC4PA5UWUpwz0XPIfv4Rrg9zq6Y1mEkJjkAfOTXm/QAwtLD8VIAOQug2ZJtORo9MdrGaQIzUHo6lp0WqGPV0Pdp2yNxV7uR/2Y+3BVupNyQAnWsGiqzCnVb63D8H8eRcGkCLNkW6PrqoI5t2VNqixtXC9VEqqAkDjNITq2haGOeO+PCjM5e2zu+P2bfsejE6+e+vBl/uea9ZrNUgujB/1Y9j4mZF2NM/3Ob9SOKAtbvXYQHLvknKizFJ0aCGJMgSAL8uUbAI7g/f2zuZXV+vEWPIGw9HEu2hQFYDSAGEpiz0LkrGHZILgmVyyuRdHX72R808RpkvpSJga8ORN2WOgj1AognpN+fjgF/H4Do8dGoXl2NhEsSUPp1KQreKYAl29IhO6JqRc+4Kv8t9OsI4/OX9/YITnt77T5e+xLmfPcwyusL8cwXM7H10A+tts2vPIwvf34dALDz2AbklO3BL0dW4ZVv78Mr396HoqqcE2037l+KiQMvhkatQ++4/nALLrz8zR+QnpAJg9Z/30WMMUmr1jWfdotASF6HFJ6Yx5hNAN4EUKpN00YnX5v8cCCyJrgr3ch/Kx+ZL2fCWehE7r9ywWlkbffUeqCOUaP/c/2hjmndSymaWwTTSBOix5/8Fq9aXQVez0Mdq4Y9x47EGYnIfTUX/f/Wv12bbvvaWXRFniroeWq29bk839H/sk6ty+npuDyOFY/NvTwr2HaEAmHr4QCAJdtiBfAbgARXkavGXeE+EGgbdOk6DHl7CAbNGYRBcwZBHavGGS+e0UxsPDUeSG55g7XYIMJ+xA5tyslJHbFBhHW3FTGTY+R2Xt1k7va/MFRuiV2YG9iB4tYYV7AqzeNx2IJtRyBR89oXg21DqBDWguPlZwB6AKjbUree+dmlK3yvEMdfOg5XmQuH/nwINT+3ntPdketA8bxiAICrxIVjfz+GnGdzcPwVebxGl6470bZiaQUSsxJBHCFqeBQajjQg55kcxJzd/oLhC9Z5KvTkkxpK3UYNgY/JWx0xGxfdgnPnwx9euCPYdoQKYR1SAYB5jJkD8AzkQnTVyTckX6XrpRvVzmVhxXuvuqrjJT4+2HY04gEnrTt7tk2jMYZ9QjRREq5+5MOLvwu2HaFC2Hs4lmyLBOAbeLP91W2q28AkFjHJocb86q4NJbEBADUkLiFvZX2w7fA3HtFdwHOqiMx70xphLzheDkHeX5XoKnXVOYucO4NtUKCYuZV1ae2LvxlTvCHN7bKEdfoQxtiLD75/fniHEJ0kIgTHO0X+LeT9VVS7sXYjE1nYp01IzxEa+jk4n2f08wU8MUo5vtzn6U1DBYe74YBGpf042HaEGhEhOF6OAdgFIMlT5bE6ChxhP5B341qhJpSLJ4ws25LmctaF3QAyY4w1OOvvU7yb5kSM4Hi9nCWQZ6yo9ufazUxgYZv32FQjes6s4VODbUdbcASkH1sakiFfd6i3Vy99/qtbtwTbjlAkYgQHACzZlnwAvwJIEeoEu/2YPWw/FNeu9JTzRCG/dWV45fY0l726Kth2+AqP6HZIknhfsO0IVSJKcLwsBaABwFWvqd4s2ISyYBvka1ROiU3L53pMkb+MnMVh42la7bX/fPbLm5Skb60QcYJjybaUQE7Q1ZsJTKr5qea7cJsmn77OU6YnLiibNLvCkJpdvVy2ivJg29Fd7C5bfpwp+Z/BtiOUiTjB8bIIgAWA2XHcUW4/av852Ab5khl70eVEV8FiQM43PXqAlTEGu8ty74Pvnx9WX16+JiIFx5JtaYCcmyQeAFe1umqzYBXCIrH1uG2umljGtxlOPV1aiik5RzEj93izcx/XVGPo4UOoFZqvGjjodOKm/DxckXscV+XmYqXl5E71J0tKcFVuLt6sPBlNvF9dhbVWa4fszqw7kOKylvTY8NZir1n5/Fe3rgm2HaFORAoOAFiyLfsBrAXQGyKkmnU13zGJ9fjCbTdsg6u9NldHR+PDtOalv0s9HmxtsCNV1fJYs57j8EpqL3zfrz8+TE/DKxXlsIgiDjud0HGE7/r1wz6nA1ZRRKUgYI/DgQtNHa+yPPjowh75eXS4bDX19uqbg21HT6BH/oF9yCIAdQCiHXmOCvuRnh1a9T3isWW42p8KH2cwIJpv/qd/taICjycmtpKiHMjQaJChkdNUJanUiFepUCOKUBHBKTFIjEFgDBwR3q6qxEMJiZ2yv5/laJK7vqBTCdeDjSRJUn7l4XtfXXR/xCfX6ggRLTiWbIsdcmgVh8bQyiL0qA98U25a1/VKo+usViSpVBis07XfGMAehwMextBHrcYZWi3iVDyuzc/DeVFRKHC7IQEY2sG+mjLs6AK/FufzNUXVOV+8vfzJiK7E0BkiWnAAwJJtOQhgDYA0SGDVa6uXMJF1qo5SKBBdKbhH1fJd2sbgkCR8WFONhxM6VsyhUhAwq7QUL6ekgvOuZP5bUjKWZPTDXXHxeLuqEo8kJOD96ir8uaQY39R1/Ms/3ZaX4K49XtyV9xFoamwVR7cd/vEPwbajJxHxguNlMYBaANHOAmdV/a/1S3pa2o7rfhTKeepavalCjxvFHg+uzsvFhcdyUC4IuDY/D5UtDBzbRBH3FxXi0cQEjNI3z9G8zmrFUJ0Odomh0O3Bm716Y7XVCofU8cmbETkLdP7OW9RdHC6b9XDRzis27vuux305BRNFcHAitPoAQAwATf32+oP2nJ4znqN2StK5hVyXa00N1OqweUAm1p4xAGvPGIBklQqL+mYg8bTBYzdjeLikGFeaozHd1DyVjYcxfF5bi3vi4uGUJDRu4xLB4OmEfvRuKIoXao4Wtd8yOIiSIB4s/v2PX2z41+G22hHRm0T0pyavVxHRR01ezyGix9q7HxFlENG+7lnd7j0CkoVRERwvlmzLEQBfAEgDwFWtqNrgrnAfCrJZHeKS1e5yHXEdLlL1REkxbsrPR57bjWnHcrCojZBnn9OBZ8vkFQM/Wiz43W7HEks9rs7LxdV5uTjoPFle5evaWlwZbYae4zBIq4VTknBlbi6G6XQw851zvkbnzDcxJoWkl3O0ZM+H24+s+bIDTbcAOBsAiIgDkABgWJPzZwPY6nsLQ5ewz/jXGcxjzATgDgDnAsjnDJwm9ebUu1RRqqBWO2gTScKH/xJqY1ho5Cz2JauH/bFAlTisT7DtaEph5dHNK37/9Py9eVvbDaWIqBeAXxlj6UQ0AsATAFIBzARgB1AOYDqAfwGIAlAF4E7GWCkRjQUwz9vVagCXMsaGE9GdAGYAMAA4A8ASxthfvPe7GMCLALSQsyPcxRizEdFs7zUCgNWMsSeIqB+Ar7z3XQrgT4yxKCJqfB0Lua7bM4yxpUT0dwA1jLG3vPd6GUAFY+zfnfn9KR5OE7w7yr8CkAMgVbJL7orvKr6SnFLIZqebuNVTHY5iAwBn5iyMkVgnBn/8TFFVzoHVu+fP6IjYAABjrASAQER9IHsz2yBvHj4LwDjISeHeBHAdY6xRYF72Xv4xgIcZYy2lwx0NWbRGAJhJROlElAA5le6FjLEzIRcPeIyI4gFcDWAYY2wkgJe8ffwbwHuMsREAmi56dQK42tvHNABzSM5xMg/A7cAJb+1GyBFBpwio4BARI6IvmrxWEVElES33Uf8vENET3enDkm1xA/gvABuAeE+Vx1q5svJLSZD8W5qxi9zwK8J20DLBVWVGxd7CYNsBACU1ucdXZX911c6c9Z1derAVstg0Cs62Jq+LAQwHsIaIdkEWjDQiigEQwxjb6O3j89P6XMcYq2eMOQEcANAXwCQAQwFs8fZ1h/d4PWQRmUtE10D2rABgMoCvW+ifAPyTiPagcWEskMwYywNQTURjAFwMIJsx1ulcRoH2cBoADCeixvGGiyD/0kMKS7alFsAbkCuTmp35zsraDbULQm0lcr9DHmu6O7jF7fzN2GML4yVJDGp2xvK6wsLVu+ZfvfPYhqNduLxxHGcEgH0AfoHs4ZwNYBOA/Yyx0d7HCMbYxR3os+lqchHy55QArGnS11DG2D2MMQHABMgZL7MA/Njk2pbGU26BXHBgLGNsNOSwr3FB1UcA7gRwF06Ge50iGCHVDwAaK8ffhJMqCyKKI6LviGgPEf1CRCO9x18gonlEtIGIjhPRI02ueZqIjhDRZgCDmhy/l4h2ENFuIlpERAYiMhFRLhGpvW3MTV83xZJtKYbs7sYA0Nv22fJqN9XOD6XUpDevE8N+dWusuy6KK88O2pdSlaW0dO3uBdftOLJmTxe72Ar5H72GMSYyxmogf6bOgvzZTySiswCAiNRENIwxVgegjoimePu4pQP3+QXAZCIa4O3LSEQDvWMy0YyxHwD8GUBjiLYFclh0ev/RkMdmPEQ0DbKX1MgSAJcAGA9gVSd+BycIhuDMB3AjEekAjIQc0zbyImRXbSSApwB81uTcYMgDbBMAPO/944yF/EsbDeAyyL+IRhYzxsZ7Y+CDAO5hjFkBbMBJwbvR267FsMQ7c/UegBQAWmu2Nafmp5ovmMDa3a/kb2LLBNeIer53sO0IBOOOfZsoSkLAQ8daW0XFuj3f3Lz14A/bu9HNXsizU7+cdqyeMVYB4DoArxLRbsgpcM/2trkLwLve8KjdPLGMsUrI3sfX3nBoG+T/GROA5d5jmwE0TsM/CuBBItoLOWxq5EsA47zHb4dcgKDxHm7IqV0WMtY1bz+gs1REZPOOhP8G4F0AmZBH4J9gjGURUTaAaxljx73tCyFPIz4GwMMYe9l7/CDkcOw6AHGMsee8x98AUMIYe52IzoU8QBYDeSR+FWPsfiKaDOAvjLEriWgbgHsZY22ucTCPMU8FcA9k99JhyDT0ir84/lZO3fGpaF/zf586Cy8oUTXfgRmmrM+8NY/1PisjUPert9fUrNk1/7b1e75tvbB5hOEdLN4J4HrGWFfCy6DNUi0D8DqahFMdoKW4tS0+AfCQdxT+RXjjUMbYFgAZRHQeAL49sQEAS7ZlE4B3ACQBMNqP2ksqV1R+LLmkjuVe8DEauyhNKeY7tzOyhzP++OJkUfQExLOsb6iuXr/n2/sVsTkJEQ2FPHu7rqtiAwRPcOYBeJExtve045vgjSe9glDFGLOgdTYCuIqI9ERkAnBFk3MmAKXe8ZnTY+DPIE9/d7iMhyXb8huAOZA3epqdec7KiqUVH4vOwI+jXLbaU6aVQ9KIIUq06zUl2/yeL6e0Nq/g+x3z7i+rK/jW3/fqSTDGDjDG+jPGHu9OP0ERHMZYEWPsPy2cegHAWG+8ORvy1F5b/ewEsADAbgArATQt/fIs5PGhLWgSh3r5EvLCps54WLBkW/YBeBVyiBbjKnHVli8qnyc2iAFLAk6ihMsP9pz0ob5kfO7SFEF0+215wuHinXuX/Tr3MZuzftHevK3Kilg/EJErjYnoOgBXMsZu68r15jHmDABPApAAVKtiVIbka5JvVplVfh/EnbzRVfXoFr7L+6Z6Opszrsl3Z1zQt/2WHUeSRHFHzrot2cc3PgNgsyI2/iPiBIeI3gZwKYDLGGNHutqPeYy5N2TRUQOoJDXxiZcnXqTP0E/0kakt8ubrrvLeHj7Zn/cIZVyk9myYPNujVul84uW5BZfj531LfjxWtu9ve/O2trkZU6H7RJzg+BLzGHMSgMch50YuBsDM482DoydGX8mpOJ+PsQzY77H8cxk136YdYWzrk5Xv6H9pt70cm6O+dvWurxdU1Be9uDdva4/Np9yTUPZSdQNLtqUCwD8gTxX2A6Cx7LAcqlhU8YE/MgfetF5sawA9YhhX8GOax+PoVjqF8rqioiW/fjCnor7oSUVsAofi4fgA8xgzB+A8ALcCsAKo9XWIFV8qON/9mGk4eS1ExPNr2vT8hgEzOu3lCKLHlX18487fj61/D8D8jm7EVPANiuD4EPMYcz8AD0JebOjTEOuBj52F08oiZ6Ffe3jASevOnm3TaIwdDjGrLKX563Yv/LW2ofJDAD8pg8OBRxEcH2MeY46CvCR8EoAiAG5tqjYm/uL4K9Wx6oyu9KmzieJH/5EEDVGPK3DnT37rNa3AMvC6dvPleL2a334/tn49gI/25m3ND4B5Ci2gCI4fOC3EsgGoAYDoSdHDzGPMF3NarlMDv9cudpbMPKzqUoL0cEZkxNac/Uq9RmuKaa1NE6/mKwAr9+ZtDZs65j0RRXD8iDfEug9ylrdSAC5Ox6njLoibYjjDMJm49pOekyjho38J9SZw0f62tyeyK3lKYc2Qm5qFmopXE5ooguNnzGPMasgpS6+HPCtYCkDS9tLGxk2Lm65J1Axq6/qpG1yVD2+LrH1TnUFiwKqzXq7W6mLi5deSVFSVc2DzweX7Lfaar6F4NSGFIjgBwjzGHAvgGgBTIc9kVQOAaYxpQPT46Et4Ax/f0nVvveYq7yVE7kK/jrA3cUJR5bA7caMPoAAABmhJREFU0irqiw5vPrD8QEV90X4oXk1IoghOgDGPMZ8B4DbI63bKAdhJRVzsebETjJnGszktd6IY9+A9bsvfV3RuvCcSKZC4Y++YztyfV32sBHL55o2KVxOaKIITBMxjzDzkWaybIafNKAUgkJr4mLNiRhkHGqbyUaqYF952Fg21qdKCamwIc9QjVnwt6Cv2kfFXACsArNmbtzUg9ZUUuoYiOEHEO4V+GeRkYhyACgCupDp2bvzAqKh/HTMmJqnUPt2o2NMRGRMLPO5DKyyWo/td7nq7Pumww5D0wd68rTXBtk2hfRTBCQHMY8zRAM4BcBkvsqh+ZRhrcuB7DnCM1ulSzo8yTein0YzgidpLOha21ApC6W6nc/cqq6WqXpLcANYBWL3eag1YahCF7qMITghhHmM2GJzson5luEEnwI2Ta3hYAs/rp5vMowZptUPjeT7NWysorHFJkj3H7d6zzmY9fsjlcgPwAPgJwJr1VmunS5QoBB9FcEKQaSaTCnIu50sgJ8IWIFdldAFAikplPMcYNXiQVjskWaXqF077qyTGWKkgHN1ub9i/3mar85bIOAq5RtK+9VarI5j2KXQPRXBCnGkmU2/IU+lTAOgh1xKqBuAAgBiO154TZRw4VKsb0kutHqBqoeRNqGMRxcpijyf3iMuV/6u9wVYnSQS5gNtaADvWW63lQTZRwUcogtNDmGYy8QAyINcVmgJ5gyggh1w2ADAQqSYbjf37a7TpSSpVahzP99Jywass0Ro2UawtEYTcHJcrd4fDXlouCBrIicwkANsh56o+ut5qDanCgwrdRxGcHsg0k4kApEEuEzsVct0sQK5sagVwYg1KX7U6eohWl9pHo+mVfFKEApITWWRMsElSjUUUa+oksabMI1T87rCXFHg8HIDGjai1ALIB7IcsMsq0dhijCE4Pxys+SZDrSo8AMABykncGuYCaHbIInSixkq5Wm8/QaBOjec4YxfGGKI4zGjjOaODIqCPOoJV/GlVEmpbuKclV0NwCY26BMbeHMZdNkupqRbGmShRqyjxCTb7HXVPs8diZHAYaIK83YpBFcTeAPQByAVSvt1qVD2GEoAhOmOEVoGjIG0bTIJc/zoRcNkcCwEMehPZA9oQ8TR6nfBj0RCotcbwExkSAeYWGuU+tuqiCLCp6nBQVBnldkQfyauoCyB5MLoByRWAiF0VwIgCvCJkhi1Ay5NpacZDHgWIgC1SjV9SRDwQ1ebggr5QuAlAIeTatxvuwKuKi0BRFcBQAANNMJg6yl2L0PjSQxUfCSSESIXtFpzwUUVHoKIrgKCgoBIywWTCmoKAQ+iiCo9BpiCieiHZ5H2VEVNzkdYszW128z3lEtNxX/SkEn4jdDKjQdRhj1QBGAwARvQDAxhh7/f/bu38Qqa4wDOPP0wkRtopF0ggRo5ViwM4UQUVQFBEUGxELwS6NlTYhISFaBOxERQUbsVJY8A/ioqKIqGy0EBS0sg0JsqQQvxTnrg4pBHX26ML7g4GZO3MP9xTzznfn3nPOJz2omBdS4cRYqKeGNdtnX78ceb5fvav+qf40bPtCnVSn1UfqjmH7BvWxep82Q+JsG6vV2+oD9Zb67bD9urpy5HM31RUduhwfIBVOzCl1Pe0+oNW0y+gX1O+BL4EXVbVx+NyEugA4BvwAPAXOjjT1GFhTVa/UtcCvwDbgBLAb+FFdCiyoqukunYv3lgon5tr64fGAtiTyMloAPQTWqb+ra6rq7+G9Z1X1pNrl0zMj7UwA59RHwB+00fQA54BNtkGre4BTHfoUHygVTozLK4YfMNt0GbN/Hgv8VlVH/7+Duoo24+Ev6lXgwjva/xm4VlVb1cXAFEBVzahXgC3AduC7cXQm5kYqnBiX57z9sm+mjf4GuATsURcCqF+ri9SvgJmqOgMcBlbRTpsWq98M++4caX+CtnwytFOoUceBI8DdqvprbD2KsUuFE+NyDDivTgMXaYM0qarL6nLg9jBJ4UvaiqRLgMPqa9qYq31V9a+6F5hUZ4AbtDFgAIeA0+pB2oTpb1TVPfUf4ORcdzI+Tu40jnlvqJamgGVV9foTH068Q06pYl5TdwF3gAMJm89fKpyI6CYVTkR0k8CJiG4SOBHRTQInIrpJ4ERENwmciOgmgRMR3SRwIqKbBE5EdJPAiYhuEjgR0U0CJyK6SeBERDf/AQS5S0Z/Lv+ZAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data1.month.value_counts())\n",
        "values=data1['month'].value_counts().values\n",
        "label=['January', 'February', 'March', 'April', 'May', 'June','July', 'August', 'September', 'October', 'November', 'December']\n",
        "fig,ax1=plt.subplots()\n",
        "ax1.pie(values,labels=label,shadow=True,startangle=150,autopct='%1.1f%%')\n",
        "plt.show()\n",
        "\n",
        "sns.barplot(x='weekday',y='count',data=data1, estimator = np.sum)\n",
        "label=['Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "id": "Lr80ci4MC3gU",
        "outputId": "9165081d-3fc6-4e01-bf8d-babc6f922d8b"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "May          912\n",
            "June         912\n",
            "July         912\n",
            "August       912\n",
            "December     912\n",
            "October      911\n",
            "November     911\n",
            "April        909\n",
            "September    909\n",
            "February     901\n",
            "March        901\n",
            "January      884\n",
            "Name: month, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data1  = data1.drop([\"datetime\", 'casual', 'registered','date' ],axis=1)\n"
      ],
      "metadata": {
        "id": "A5HjghAsC3hx"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "WBACn-HZC3kP",
        "outputId": "2235047c-bc34-416b-a3f4-94986eb6452a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   season  holiday  workingday  weather  temp   atemp  humidity  windspeed  \\\n",
              "0       1        0           0        1  9.84  14.395        81        0.0   \n",
              "1       1        0           0        1  9.02  13.635        80        0.0   \n",
              "2       1        0           0        1  9.02  13.635        80        0.0   \n",
              "3       1        0           0        1  9.84  14.395        75        0.0   \n",
              "4       1        0           0        1  9.84  14.395        75        0.0   \n",
              "\n",
              "   count day hour  year   weekday    month  \n",
              "0     16  01   00  2011  Saturday  January  \n",
              "1     40  01   01  2011  Saturday  January  \n",
              "2     32  01   02  2011  Saturday  January  \n",
              "3     13  01   03  2011  Saturday  January  \n",
              "4      1  01   04  2011  Saturday  January  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fcd49844-f85d-477d-b549-c41977331bf4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>count</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>year</th>\n",
              "      <th>weekday</th>\n",
              "      <th>month</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16</td>\n",
              "      <td>01</td>\n",
              "      <td>00</td>\n",
              "      <td>2011</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40</td>\n",
              "      <td>01</td>\n",
              "      <td>01</td>\n",
              "      <td>2011</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32</td>\n",
              "      <td>01</td>\n",
              "      <td>02</td>\n",
              "      <td>2011</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13</td>\n",
              "      <td>01</td>\n",
              "      <td>03</td>\n",
              "      <td>2011</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>01</td>\n",
              "      <td>04</td>\n",
              "      <td>2011</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fcd49844-f85d-477d-b549-c41977331bf4')\"\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-fcd49844-f85d-477d-b549-c41977331bf4 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-fcd49844-f85d-477d-b549-c41977331bf4');\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": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=data.drop(['date'],axis=1)"
      ],
      "metadata": {
        "id": "frpuyhWoC3nF"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnB3O2efC3pg",
        "outputId": "ccf257d2-2893-4b6e-910e-c8845f532670"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6493, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train data set\n",
        "print(data1.isnull().sum())\n",
        "\n",
        "# Visualizing the same using heatmap.\n",
        "sns.heatmap(data1.isnull(), cmap = 'YlGnBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "hpaRZ3UXC3rz",
        "outputId": "07af5a5d-979b-404c-d6f0-f4d356e5d5f2"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "season        0\n",
            "holiday       0\n",
            "workingday    0\n",
            "weather       0\n",
            "temp          0\n",
            "atemp         0\n",
            "humidity      0\n",
            "windspeed     0\n",
            "count         0\n",
            "day           0\n",
            "hour          0\n",
            "year          0\n",
            "weekday       0\n",
            "month         0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fad221d1130>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train data set\n",
        "print(data.isnull().sum())\n",
        "\n",
        "# Visualizing the same using heatmap.\n",
        "sns.heatmap(data.isnull(), cmap = 'YlGnBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "mDMJ_Gmx_ZDc",
        "outputId": "718d3b30-1c2b-499c-dac2-d4f3d45b88d2"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "datetime      0\n",
            "season        0\n",
            "holiday       0\n",
            "workingday    0\n",
            "weather       0\n",
            "temp          0\n",
            "atemp         0\n",
            "humidity      0\n",
            "windspeed     0\n",
            "day           0\n",
            "hour          0\n",
            "year          0\n",
            "weekday       0\n",
            "month         0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fad1d9b3a30>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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QzPyz+bRrXPL9giAIOqPHHvBlKeM1JJKo3F22/6eMUUnjgN3I+wMwd68hEtgHQdC1hMTEwHyQ5P1zW06SAPAfwALA94BlgF9Lmml7m3x9U+DhRv6BApHAPgiC7iU2i1tj+w8MHGZx0QD3XElyNW0ujwT2QRB0L2N0aSgii4MgCBqM0Y6gcmRx4fqXJFnS0vl8MUm/knRrrr9Poe4USfflY0rzewVBEIwoauMoY07aVtI9WU3h4BbXF5B0br5+XSMtsKStJN2UlRhukrRF4Z4rs82GesOynXxkKDcjaEQW3yxpEeAmSdNt3ylpBWBr4KFC/f2BO23vKGkZ4B5JZwELkySs1yflPL5J0lTbT3f6IYIgCOqgTukISeOBE4GtSHFTN+RnXjFX+77A07ZXkTQZ+BYp/urvwI62/yJpTZKEfzHuas+81F4LQ84IbD9m++b8+nlSoplGg44Hvkp6sM+9BVgkewYtDDxF6ky2Aabbfio//KcTEhNBEHQTUvljaDYAZtu+3/arJC/K5kxbOwNn5NfnA1tKku1bbP8ll98BLChpgRo+YUvaylBWjCyWtDPwqO1bm6p9H3g3KYvZbcBBtl+ndQL7iCwOgqB7GK/SRzHwNR/7NVkr88ybW8f2HOBZYKmmOrsAN9t+pVB2el4W+mYd4p2VIotJI/z/AA5tUXUbYCbwVmAd4PuSFm2nURFZHATBSDBuXPmjmFI3HyfX3R5Ja5CWiz5bKN7T9nuATfLxyU7fp1RH0CKyeGVgInCrpAdJUcI3S3oLsA9woROzgQeA1WgjgX3kLA6CYCSod2Wo1DNvbp2c+ncx4Ml8/naSi/5etv/UuMH2o/n/54GzSUtQHVEpstj2bbaXtb2S7ZVIU551bT9O2jjeMt+7HPAu4H7SZsfWkpaQtARpk/nSTj9AEARBXdTcEdwATJI0UdL8JNmdqU11pgIND8pdgcttW9LiwK+Bg21f09c+TSh4aM4H7EBKCdARlSOLcw7iVhwF/FjSbSQnq6/Z/juApKNIPxyAI20/Vb3pQRAE9VJnrizbcyQdQBrwjgdOs32HpCOBG21PJQ2yfyJpNsmxZnK+/QBgFeBQSY0l+K2BF4FLcycwHvgd8KNO2yq7uzXdQnQuCIIyXLH9Bzt+ik/64VWlnzf3fXbTnok+i8jiIAiCjNryo+wdoiMIgiDI9JioaGk6SV5/biHE+cHG/oGk+SWdnkOjb5W0WcFWJK8PgqBrGaN5aTqSmPhEo4Kk40iBEJCS02P7PVkD4xJJ78tBZY3k9dcB00iRxSFFHQRBVzBWh6adSkw03Et3oy8b2erA5bn+E8AzwPoqJK932qFuJK8PgiDoCmp2Hx011JG8fhPgr7bvy+e3AjtJ+hkpUGK9/P/rRPL6IAi6mHFjNDFNHcnrd6d/buLTSA/5G4H/Ba4FXmunUSExEQTBSBAzgkFoITHRKJ8AfIw06gfmCid9oVDnWuBe4GkieX0QBF1Mrz3gy9Jp8voPA3fbfqRQ/82SFsqvtwLm2L4zktcHQdDtxIxgYAaTmJhM/2UhgGVJIdCvk0b8RWW8SF4fBEHX0mtuoWXpKHm97b1blD1IEpprVT+S1wdB0LX02ki/LBFZHARBkAmvoQGQ9CZJ16svGf0RufyAHCE8N3F9LleOGp4taZakdQvXInl9EARdS+wRDMwrwBa2X8jeQ3+QdAlwDXAxcGVT/e2ASfnYkBRNvKGkJYnk9UEQdDG99oAvS5nIYtt+IZ/Olw/n5MoPtrhlZ+DMfN8MYPEcVRzJ64Mg6GrG6oygbKrK8dlj6AnSw/y6QaoPlLA5ktcHQdDVjFXRuVIdge3XbK9DCgLbQNI89fyJyOIgCEaCcePLH71EW2kYbD8DXMHgSzoDJWyO5PVBEHQ1sTQ0AJKWyYmUkbQgsBVw9yC3TAX2yt5DGwHP5qjiSF4fBEFXI6n0UdLetpLuyV6UB7e4vkDO7TJb0nVZ2LNx7ZBcfo+kbcrarEKZGcHywBWSZpESz0+3fbGkAyU9QhrZz5J0Sq4/DbgfmE1Kqvx5gJyovpG8/gYieX0QBF1GnTMCSeOBE0melKsDu0tavanavsDTtlcBjge+le9dnaTcsAZpBeYHea+2jM22KRNZPIskPd1cfgJwQotyA/sPYOs0kjppEARB11Hzks8GwGzb9yfbOofkVXlnoc7OwOH59fnA97MW287AObZfAR6QNDvbo4TNthmjqZqDIAjeSDszgqJTSz72azJXxlNybp2s3PwssNQg984T78vKkcWF6ydIeqFwvqmkmyXNkbRrU92ILA6CoGuZMK78UXRqycfJI93+qlSOLLY9Q9L6wBJN9R8C9ga+XCyMyOIgCLqdcao1/UkZT8lGnUdyfpfFgCeHuLeU92U7VI4szpsWxwJfbar/YN5XeL3JVEQWB0HQ1dQcUHYDMEnSREnzkzZ/pzbVmQo0Vkd2BS7P+6xTgcnZq2giSbLn+pI226ZshrLxwE3AKsCJtq+TdBAw1fZjJV2pIrI4CIKups5NU9tzJB1AcpMfD5xm+w5JRwI32p5KSvr1k7wZ/BTpwU6udx5pE3gOsL/t1yAJfjbb7LStpTqC3IB1cjzBRZI2BT4ObNZpA4IgCLqFmpeGyAm8pjWVHVp4/TLpWdrq3qOBo8vY7JSqkcWbk2YHsyU9CLw592iDUTqyOCQmgiAYCUJraAAGiCy+yfZbbK9keyXgpRwQMRilI4tDYiIIgpFggsofvUSZpaHlgTPyPsE44DzbFw9UWdL7gItI3kQ7SjrC9hq2n5LUiCyGiCwOgqDLUM1LQ6OFypHFTXUWLry+gbTs06peRBYHQdC19NqST1kiZ3EQBEFmrEotREcQBEGQqdtraLTQSfL6qyXNzMdfJP0il++plLT+NknXSlq7YKt2+dQgCIK6iM3igRlIYmKTRgVJFwANP88HgA/ZflrSdsDJpOT1DfnUrUjBZDdkiYmOVPOCIAjqIvYIBiCHO79BYqJxXdKiwBbAPrn+tYXbZ9C3cVxGkjUIgmDEiKWhQdDgyes/Clxm+7kWt+4LXJJfh8REEARdTQSUDcIQyet3B37WfI+kzUkdwdfabVREFgdBMBKMa+PoJTpKXi9padKSz6+L9SStBZwC7Gz7yVwcyeuDIOhqxsmlj16i0+T1uwIXZ+GkRv0VgQuBT9q+t2BqnsinBkEQ1EU7iWl6iU4lJiYDxzTVP5SUau0HWZ56Th7dt5RkreNDBEEQ1EGPPd9L05HEhO3NWpR9Gvj0APVrl08NgiCoi15b8ilLRBYHQRBkes0bqCylZ0LZhfQWSRfn84mSrstRwufmdX8kfS5HFc+U9AdJqxdsHJLr3yNpm/o/ThAEQXXCa2hoDgLuKpx/Czg+5yF4muQqCnC27fdkd9NvA/8DkDuEycAaJK+jH+R9hyAIgq4g4ggGQdLbgY+QXEJR2gXeAjg/VzmDFFhGU2DZQvRFIe8MnGP7FdsPALNJrqdBEARdwfhxLn10gqQlJU2XdF/+f4kB6k3Jde6TNCWXvVnSryXdnfXfjinU31vS3wo6cC33a5spOyP4X+CrwOv5fCngGdtz8nm/KGFJ+0v6E2lGcGAujsjiIAi6mmFcGjqYpMgwCbgsn/dD0pLAYcCGpEHzYYUO4zu2VyM58nww67o1ONf2Ovk4pUxjysQR7AA8YfumMgYBbJ9oe2VSVPE3yt5XeM+ILA6CYNgZxoCynUkrKVBYUWliG5Kkz1O2nwamA9vafsn2FQC2XwVuZoBkYGUp07F9ENgpJ6k/h7Qk9F1gcUkNr6OBooTPoe8DRmRxEARdTTt7BMUBaz72a+OtlrP9WH79OLBcizpDrqLkYN8dSbOKBrvkVADnSyo+cwdkyI7A9iG2356T1E8GLre9J0lqYtdcbQpZhlrSpMLtHwHuy6+nApMlLSBpIjAJuL5MI4MgCIaDdjqC4oA1HycXbUn6naTbWxz9RrdZ4bntKUYeiP8MOKGh6gz8CljJ9lqkGcQZA91fpJM4gq8B50j6T+AW4NRcfoCkDwP/JHkTTQGwfYek80iy03OA/W2/1sH7B0EQ1Mp8NQaU2f7wQNck/VXS8rYfk7Q8Sdm5mUeBzQrnbweuLJyfDNxn+38L7/lk4foppH3aIWmrI7B9ZaMhuQd6g9eP7YMGuf9o4Oh23jMIgmC4GEa30KmkQfIxFFZUmrgU+K/CBvHWwCEAeQC+GE0qDo3OJZ/uRH+X/wGJyOIgCILMMHYExwDnSdoX+DOwG4Ck9YHP2f607ackHUUS7AQ4Mpe9Hfg6Sfzz5qzp9v3sIXSgpJ1Iqy5PAXuXaUx0BEEQBJnxw9QR5CWcLVuU30hhlG/7NOC0pjqPAC1bavsQ8qyhHTqRmDhVKaF9Y3d64ab6u0hy7uEaZSExEQRB1xKRxUPTLDHxBdtr593ph4ADGhckLZLrX1coC4mJIAi6mkhMMwjNEhPQJyWR5SYWpL/701EkLaKXC2UhMREEQVczn8ofvURViQkAJJ1OCoZYDfheLlsXWMH2r5tshMREEARdTSwNDcBgEhO29wHeSloy+oSkcSS10S910qiQmAiCYCSIpaGBeYPEhKSfNi7moLBzgF2ARYA1gStz/Y2AqXnDOCQmgiDoasar/NFLVJKYAD4paRWYu0ewE3C37WdtL217pVx/BrBTdokKiYkgCLqasbo0VDWOQKSE9ovm17cC/zrYDSExEQRBtzOh11KPlaSyxARpyWio+ps1nYfERBAEXcv4Hlv7L0tEFgdBEGTG6ISgo8hiSTpa0r2S7pJ0YC7fTNKzhVRphxZsbJujimdLekNGniAIgpEk9giGphFZvGg+35vkBbSa7dclLVuoe7XtHYo35yjiE4GtSDEEN0iaavvOqo0PgiCok157wJelcmQxaXP4SNuvA9hupaddZANgtu37c3q1c0jRxkEQBF3BeLn00Ut0Elm8MimI7EZJlzRlJnt/FqS7RNIauSwii4Mg6GomjCt/9BKdRBYvALxse33gR/RJpd4MvMP22iTZiV+026iILA6CYCQYq3sEnUQWPwJcmOtcBKwFSYzO9gv59TRgPklLE5HFQRB0ORFZPAADJK//f6SR/ua52oeAewEkvSVHGyNpg/weT5Ky7EySNFHS/NnW1Jo/TxAEQWXGqtZQJ3EExwBnSfoC8AJ9WXV2Bf5V0hzgH8Bk2wbmSDqAlIdzPHCa7Ts6eP8gCIJa6bGl/9J0krz+GZInUXOd7wPfH+D+acC0dhsZBEEwHAzX2r+kJYFzgZWAB4HdbD/dot4U4Bv59D9tn5HLrwSWJw22Aba2/YSkBYAzgfVIKzGfsP3gUO0Zqx1gEATBG5hvnEsfHXIwcJntScBl+bwfubM4DNiQ5H5/mKQlClX2tL1OPhru+/sCT9teBTielCBsSDqJLN5C0s2Sbpd0hqQJhbqb5ajiOyT9vlAekcVBEHQtw+g1tDNwRn59BvDRFnW2AabbfirPFqaT0vyWtXs+sGVjz3YwKuUszgloziCt/68J/BmYkq8tDvyAJD+9BvDxXN6ILN4OWB3YPecxDoIg6Ara6QiKbu752K+Nt1rO9mP59ePAci3qDBV7dXoecH+z8LCfe4/tOcCzwFJDfu4yLW4RWbwU8Krte/P5dFJiGoA9gAttP5Qb05iyRGRxEARdzbg2jqKbez5OLtqS9Lu8YtJ89HvuZWeadtea9rT9HmCTfHyy/U/bR9XI4r8DE3LmMUieQo0YgVWBJSRdKekmSXvl8ogsDoKgq5HKH0Nh+8O212xx/BL4q6Tl03tqeaCVRM+AsVe2G/8/D5xNGmj3uycv1y9G2jQelEqRxbkHmwwcL+l64HmgkWRmAmnH+iOkNa5vSlp1qPdpes+ILA6CYNgZxj2CqeTl9Px/qwfdpcDWkpbIm8RbA5dKmpCDdJE0H7ADcHsLu7uS4r6GnG2UcR9tRBZvD7wJWFTST3NQ2Sa5MVuTZgKQRvpP2n4ReFHSVcDaubx0ZDFwMsDm067prciNIAi6lmF0ozwGOE/SvqQ91t0A8irL52x/2vZTko4iBeNCEvl8StJCpA5hPlJM1u9IMj8ApwI/kTQbeIo0YB8Slegs+ipLmwFftr2DpGULfqvTgMCYyhcAAB/hSURBVKNtXy7p3aQ4gm2A+Ul5iScDd5Oij7ckdQA3AHsMFVQWHUEQBGW4YvsPdjxOv+XJi0s/b9671A49IzTRSWTxV/Ky0TjgJNuXA9i+S9JvgFmkPYVTbN8OEJHFQRB0Mz3zZG+TtmYEI0HMCIIgKEMdM4Jbnyo/I1h7yZgRBEEQ9Bw982Rvk+gIgiAIMr0mL12WsgFlD0q6LUex3ZjLjpV0t6RZki7KEcVI2rOQuH6mpNclrZOvrZftzJZ0QpnQ5yAIguGizjiC0UQ73lKbZ3GjRhDZdGBN22uRvIEOAbB9VkMIiRTt9oDtmfmek4DPAJPyMZRuRhAEwbChNo5eorLbrO3fZi0LgBmkuIBmdidJSTSi5xa1PSMHOJxJa6GlIAiCESE6gsEx8NssGdFKWOlTwCUtyj8B/Cy/fhspqKxBSEwEQdBVRM7iwdnY9rok5dD9JW3auCDp68Ac4KziDZI2BF5qxBC0Q0hMBEEwEsSMYBAKAkdPkBLVbwAgaW+SzsWeLfQsJtM3G4AUTVxcPork9UEQdBVjNWdxGdG5hSQt0nhNEj66XdK2JEXSnWy/1HTPOJJ2xjmNsqy9/ZykjbK30F60FloKgiAYEcaq11CZOILlgIuyp+cE4Gzbv8miRgsA0/O1GbY/l+/ZFHjY9v1Ntj4P/BhYkLSn0GpfIQiCYEQYq7l7h+wI8sN87Rblqwxyz5XARi3KbwTWbK+JQRAEw0OvjfTLEpHFQRAEmTHaD3QUWXy4pEcLEcTb5/L5cjL72yTdJemQgp1IXh8EQdcyVt1H25kRbG77701lx9v+TlPZx4EFbL9H0puBOyX9jJSm8kRgK1IMwQ2Sptq+s2rjgyAI6qTXHvBlmRd7IwYWyvkyFwReBZ4jktcHQdDlRBzB4AwUWXxAFp07LefUBDgfeBF4DHgI+I7tp4jk9UEQdDmSSx+9RCeRxScBKwPrkB76x+W6G5AS2b8VmAh8SdI722lURBYHQTASDNeMQNKSkqZLui//v8QA9abkOvdJmpLLFmlSeP67pP/N1/aW9LfCtU+XaU/lyGLbf7X9mu3XSYmTN8jV9wB+Y/ufuf41wPqkKOLSyesjsjgIguFmGAPKDgYusz0JuCyfN7VFSwKHARuSnq+HSVrC9vMNhees8vxn4MLCrecWrp9SpjGdRBYvX6j2L0BDU+ghYItC/Y1IietvACZJmihpfpIExdQyjQyCIBgOxrdxdMjOwBn59Rm0VmLeBphu+ynbT5Ok//tJ90taFVgWuLqTxnQSWfyTnHDGwIPAZ3P9E4HTJd1BmkGdbntWbnQkrw+CoGsZxoCy5bLsDsDjpOdsM2X2VSeTZgDFTYtd8vL9vcAXbD/MEHQSWfzJAeq/QHIhbXVtGjBtqPcMgiAYGcr3BNlxpug8c7LtkwvXfwe8pcWtXy+e2Laq7z5PJiUAa/Ar4Ge2X5H0WdJsY4uhjERkcRAEQUZtdAT5oX/yINc/POD7SH+VtLztx/Iy+xMtqj0KbFY4fztwZcHG2sAE2zcV3vPJQv1TgG8P8TGAziKL15E0o1EmqSFNvUTOYTxL0vWS1izYicjiIAi6Fmlc6aNDpgJT8usptFZivhTYOj9TlyDtz15auL47/aX+G5kgG+wE3FWmMZ1EFn8bOML2JVle4tuk3us/gJm2/0XSaqQ9gy0ljScii4Mg6GqGbZPgGOA8SfuSvH52A5C0PvA525+2/ZSko0iONgBH5pisBrsB2zfZPVDSTqRkYU8Be5dpTCdLQwYWza8XA/6SX69O+pDYvlvSSpKWA95JjiwGkNSILI6OIAiCrkDDJESdl3C2bFF+I/DpwvlpwGkD2HhDfJbtQ4BDWlQflLIdQSOy2MAP89rYvwOXSvoOaYnpA7nurcDHgKvzctE7SGtbrXbAN2y3wUEQBPOKGpZ8RiVlO4KNbT8qaVlSIpq7gV1JrkkXSNoNOBX4MGk28F1JM4HbgFtIkcZBEARdTq+pCJWjk5zFU+iLZvt5LsP2c7b3yRFvewHLAPfTRmRxSEwEQTASqI1/vUTlyGLSnsCHcrUtgPtyncVz5DCkta6rbD9HG5HFITERBMFIMFY7gk4ii18gLQFNAF6mL7Di3cAZeT/hDmBfANtzIrI4CIJuJjk3jj06iSz+A7Bei/I/AqsOYCsii4Mg6GJ6a6RflogsDoIgyPTakk9ZoiMIgiCYy9h0Hy0rMbG4pPMl3Z0T0r9f0rH5fFaWlFi8UP+QLCNxj6RtCuUhMREEQdcyVjeLy3Z/3yUlm1mNtF9wF0kbe03ba5HkTg8BkLQ6ySNoDZJ29g8kjS9ITGxHij7ePdcNgiDoCiSVPnqJIZeGJC0GbErWrMiJ518FfluoNoMUYAZJNuIc268AD0iaTV/2spCYCIKga1EdKWdGIWVmBBOBv5GSzdwi6ZQcT1DkU8Al+fVAyRQieX0QBF3OcGUt7i7KdAQTgHWBk2y/F3iRQn5NSV8nKd2dVVejIrI4CIKRYKwuDZXpCB4BHrF9XT4/n9QxIGlvYAdgz0KqtIGkJCJ5fRAEXU7MCFpi+3HgYUnvykVbAndK2hb4KrCT7ZcKt0wFJktaQNJEYBJwPZG8PgiCLkeMK330EmXjCP4NOCs/wO8H9iE92BcgqZECzLD9Odt3SDqPtAk8B9jf9msQyeuDIOh2emukXxb1reh0J5tPu6a7GxgEQVdwxfYf7Pgp/ppnlX7ejNdaPdNrRGRxEATBXHpryacs0REEQRBkei1iuCxjs/sLgiBoyfB4DUlaUtJ0Sffl/5cYoN5vJD0j6eKm8omSrstyPec2csBkJ51zc/l1klYq057oCIIgCDLDGEdwMHCZ7UnAZRRis5o4Fvhki/JvAcfbXgV4mpz3Jf//dC4/PtcbkugIgiAIMmJ86aNDdgbOyK/PAD7aqpLty4Dn+7Ux9UJbkGK6mu8v2j0f2FJlei3bo/4A9ut1W93ctrDVG7a6uW11f8662gTcWDhKtxF4pvBaxfMWdTcDLi6cL03SbWucrwDcnl/fDry9cO1PwNJDtadXZgT7DV1l1Nuq217YClvz2l632qoFFxQQ8nFy8bqk30m6vcWxc5MdAyPqJh9eQ0EQBPMA2x8e6Jqkv0pa3vZjkpYHnmjD9JPA4pIm2J5Df7mehpTPIzmf/GK5/qD0yowgCIJgNDEVmJJfTwFKq2vmGcQV9En/F+8v2t0VuDzXH5Re6QhOHrrKqLdVt72wFbbmtb1utdUNHANsJek+4MP5HEnrSzqlUUnS1cDPSZu+jxQyPn4N+GLO97IUcGouPxVYKpd/kYG9kfrR9RITQRAEwbylV2YEQRAEQUWiIwiCIBjjREcQBEEwxomOoGYkLTXSbWhG0nhJV9Rs8y2SdpK0o6S31Gm7F5FUS1b0On+XksZJ+kAdtrK9rvvuB+UY1R2BpI0l7ZNfL5MzolW19TZJH5C0aeOoaGqGpJ9L2r5UaPfgbTpO0hqd2ABwSgz0uqTFOrUFIOnTpKxzHyO5qM2Q9KkOba4r6UBJ/yZp3Q7s3CRp/4FEvEraeF7ScwMdFc3eJ+lYSatXbRfU+7u0/TpwYqd2CtT23W+Q/yb3kLRX46jDbtCfUes1JOkwYH3gXbZXlfRW4Oe2P1jB1reAT5Cyqr2Wi217pwq2RHIH+xTwPuA84Me2761g69OkbHATgNOBn9l+tl072dYvgfcC04EXG+W2D6xg6x7gA7afzOdLAdfaftfgdw5o71Dg48CFueijpN/lf1awtQrpZ/YJUtj/6cBvy/hSt7B1FPAY8BOSDMCewPK2D61gaxFSetZ9SAOw04BzbLfdsdT8u/wO8Efgwio/oyZbtX33s72fACsDM+n/d9n25wwGZzR3BDNJfww3235vLptle60Ktu4B1rL9Ss1t3Bz4KbAQcCtwsO0/VrDzLtIDZHfgGuBHtttaHpA0pVW57TNalQ9h61pgM9uv5vP5gSttV1pmyD//tW2/nM8XBGZW7ViyjXHADsBJpIfI6cB3bT/Vho1bba89VFmFtn0IOBtYnCQMdpTt2W3cX+fv8nnS93MO8DKpw7PtRdu11WS34+++pLuA1TvtoIKhGc0SE6/atqSk2iQt1IGt+4H5gI47gjw6/n8k6di/kvI9TwXWIQWGtLV8ldeWV8vH30l/VF+U9Fnbk8vasX1GfsCuaPuedtrQgtnAdXlkapLi4SxJX8zv9T9t2vsL8CbSgwhSLuxHB64+OJLWInWc2wMXAGcBGwOXk34PZXlR0p7AOaTPuTuFEXibbRoPfCS3ayXguNyuTYBpwKplbVV54A9ia5G6bNX93ScJqL2FNCsL5iGjuSM4T9IPSZobnyFNR39U0dZLwExJl1HoDCpOQf9IWkr4qO1HCuU3Svq/dgxJOp40qr0c+C/b1+dL38qj6HZs7Qh8B5gfmChpHeDIKstfJEXDPxXOG+HtVR8qzwJ3SJpOeuBuBVwv6QRo7/cg6SbgGVKE5cGFWd51ktpdNtwD+G4+TJqN7dGmjQb3kWQBjrV9baH8/Hb3oyQ9QAuRMtvvrNKwvJ8yidQZN2xdVcFULd99Sb8ifb5FgDslXU//v8sq39lgEEbt0hCApK2ArUnT2UttT69op86ptuqayuaN8PNsv2EUKmmxdvYL8gNyC9ISTmMp7Xbba9bR1k4Y6OffoJ3fg6R32r6/qWyi7Qeqtq8OJC1s+4WabBW9c95E2l9ZsuLexaeBg0jCZTOBjYA/2t6igq1avvt56WxAbP++0/cI+jOqOwIASYtSmNm0swbcZGd++qbn99j+Z0U7ywBfBdag/wir7T+sbK+W0ZqkGbY3knRLDXsq6wNfB95B/59927bqRtLNttdtKrvJ9noVbK1K2mNYzvaaeclpp4qb2G8iZY9q/l505G1VsF/1M95G2tidYXsdSauRZp8fq2Cr7u/+t2x/baiyoHNG7dKQpM8CR5DWlV8nb3IBbU+PJW1GyurzYLazgqQpFafHZwHnkpZ0PkdSAvxbBTsDjtZII/t2uUPSHsB4SZOAA4Frh7hnIM4CvgLcRvrZd4SkHYCj6OtY2t6wzA+wNYDFJBUfYotSeCi1yY9In/OHpAbNknQ20HZHQFoyuRvYBjiS5IF0V5VGqb977TiS91zVv+WXbb+slH5xAdt3Z+eEKtT23c9sRRJXK7Jdi7KgQ0ZtRwB8GVjT9t9rsHUcsHVjEzWPBH8GtD3CApayfaqkg/IU9veSbqjYroPoG61t3hitVbT1b6RR/Cukz3Yp6eFbhb/Znlrx3lb8Lykm4bYOlhbeRXoALQ7sWCh/HvhMRZtvtn29+rvEz6loaxXbH5e0c964Pxu4uqKt45ra8yCwW0Vbj0haHPgFMF3S08CfK9qq5bsv6V+BzwPvlDSrcGkRqg9egkEYzR3Bn0ibvHUwX9GTxva9kuaraKuxpPSYpI+QPGKWrGirttGa7ZdIHcHXK7alyGFKUrnNm+sXDnzLoDxMSrVXeZ3S9i+BX0p6fxUX3QH4u6SVyRuzknalugdL43vxjKQ1gceBZasYsr15xTa0svUv+eXhShHLiwG/qWiuru/+2cAlwH/TX0b5+apLv8HgjOaO4BDgWknX0bmnz435wfbTfP7/SMFIVfhPpajPLwHfIy1NfKGirdpGa3mW82WS62JxXb/KMtM+JHfW+ehbGjJ9AWHt8lVgmqTf0/93WdoNVdJXbX8b2EPS7s3XK34v9ifp4K8m6VHgAdJ3owon5/2eb5BcKhcGvlnFUP5+HQY0vI1+T/IAqxpsuDEwyfbpeZ3/baTP2i61fPfz53gW2D273S5H+s4unDfdH6rQtmAQRu1mcXYp+wNN69QVPX0WIP3Rb5yLrgZ+UHeAWSdkT4rFgN80ArnavP9W4P+Am+iL0sT2TRVs3dNJsFcLe78FXuCNv8sj2rCxo+1f1ekBVrC9EDDO9vMV7v1iq+K+ZrUdc4GkC0g+9o3P9ElSQF6VDd7aIvTrRtIBwOGkmIS5A45ucEroNUZzRzDX+6Vmu0sCb7c9a8jK/e/7HoMkoG7TF37Q6XSV6XFVr5IBbJ1O8oe/syZ7XeHG2oyk5Uh7Mm+1vZ2STtD7bZ86xK1FG4fll+8i7fc09lZ2BK633fYMQ9JM2+sMVVbWFh1G6Nf53W+yOxvY0FnKJJh3jOaloUsk7Qf8iv7LCVUeklcCO5F+HjcBT0i61nY709rGUtIHgdVJ3hOQfLzbfWDeRPrDErAi8HR+vTjwEG1EaBY6lV9J+jxwER3+vEjeSzOVApteoc/Lp+pIbZqkrW3/tuL9xSCkllQMQvoxSZqisa9yL+n3WrojaMxqJF0FrNuYVUg6HPh1hTYB/EPSxrb/kG19EPhHRVt1ROjX+d0v8jBpiSiYx4zmGUGrNUy7QnRlY3aR3TVXsH1YBz72M4CNbc/J5/MBV9veqIKtHwEX2Z6Wz7cjRW1+tg0bjSjUVmqQVX9e72hVbrvq/kVD7+bVfFRxH20EIX2MJEvQ2O/ZHfhrm516w+YNtt+n/rEXVUfe/fSs8nLkrCpLbEpR4WeQlgohDRSmtDuLzba+TIpT2Yq0Ofsp4Gzb36tgq7bvfr7/VNJM6tdU3DsKyjFqZwS2K0tOt2CCpOVJLnidetUsQdoka4y0F85lVdjI9lzXR9uXSPp2OwYaPydJb3IWdWugFOTUNrb/3GKDceEqtrK9jvVusrsiko6zvX7h0q8kVd34f1EpircxWt6I6iPUM0myGRfl84+SZhxVuAv4NkmZc/Hcpo8CbXcEpI73d8BzpIfuoa4YoU+9331Is9+HSLIo83dgJxiCUdcRSNrC9uXqHzQ0l4oujEeS/Or/YPsGSe8kacNU4RjgluyKJ5Jnx+EVbf1F0jfoG93uSXLJq8K1QLPOf6uyISluMJKWTubLbay0wajkqL8nMNH2UZJWIMk9Xz/Era1YSAWZCaUcFVUFCb9IWtNfWdI1wDKk/AttY/toSZeQROYA9rF9S8V2/ZKkp3QzHYjzZZYlBRfeTJLG/l0Htlp990tv+DdTWFZbOJ/XItERvJFRtzQk6Yi8dHN6i8t2TSH7naCUsWvDfHqd7ccr2lmS/m6CVwFHtLOun9vyNtKDeg/6logWBf7P9moV2lWbBHi+9ySSV8gWtt+d3Sx/a/t9FWxtS3L5vJ/0Wd8BfNb2pRXbNoHU4YkOpEfqpO7N9dwRb01yC16flEfgVNt/GvTG1rZq+e5nW2uSIrIb+1x/B/ayfUdVm0FrRt2MwHbDC+NINwmJqWKGMtWgAyNpNaeAr8YI++H8/1slvdX2ze22Kz/wD2r3via2AfYmyVQU11afB/6jos06JcAheYasK+kWANtPK2k/tY3t3yhJaDQ6uLurugHn78XnSW7FBq6W9H/NS2wjwLWS3mP7tjqM5d/l46Qgtzmk5ZzzJU23/dWydiQd6SR898t8Pk7SWbb3rNi0k4EvOufeUJKC+RFQW3rNIDHqOoICF/DGZY3zqSYLUYcOzJdIUgbHtbhmKugDDeAJ8yzJS+OHZR5I2X/+DEm72L6g3TYMQJ0S4AD/VAocanQsy9CmhtEgS4YrS6q6ZHgmqcNsbJzuQfqufLyCrY5REogz6e92H0n306HXlqSDgL1Io+1TgK/Y/qdSYp/7SMF+ZVlB0iG2/ztvhp8HVF3+AljIhQRMtq+sYdARtGDUdQSaN+JiHevANDZ1XWP4P2l5YxmSNhCk9IvPk1RSf0QKJCrbvguUwv6bZz1HVmjXMqROd+4GIylFYVVOILm1LivpaNI6fLtRtx8i5W3YscW1qlHPa9ou5hi+QlItsRMV2WEe2FwS+Fizx5ft15XEANvhU8BZkg4BNgcusX18B227X9I3SZ0vpKju+wepH1RkNO4R7EzykNiJvuAcSA/Ic9w/6UdZm9fb3iD7en+eNEW+vh3XyoE2rxtUGZE23BdblUm6w3bpxPZKiUHeTPoDPYX0sL3e9r4V2tVK6rnyHkG+fzVgS9Lo9jLblZQ560TST4Hv256RzzcE9rcdCdQLqL8a6nwktdZryPEWVZZFs90lSJvNDSeEq4HDbT9TvbVBK0ZdR9BANYqL5fiBC4C1SF4wC5Pc6NrJqtRq87pBpU1spZyt2zhrq0hakZSA591qM7K68aAu/L8wacS2yZA399mYqwpJ/wxliwDXuEKUbLb7E9ufHKqspK3FSUsdK9FfU6lKYve7SDOehrbNisA9pHX0SksxvUj2EhoIu3o+gkbei5Xo+13Gz30eMOqWhgo8qZRasuOkIbZPyS9/T4V8BtnGPlXuG4IvAX+Q9CfSSHki8Pm8Ttqudk4j8vQlJT2ZJ4Hl27Qxr1Qh+81ssqdOVTmMacAM6smVsG2H948Jal4OLXIWSSjxdmrIexEMgu1ReZAe2hsAtxTKbq9oaznSNPaSfL46sG9FW4uRvHNuzMdxwGIdfM4FgLXz8aYO7HyTFHy0C2np6zHgqBH+HR5CWtKbQ9pvaBxPAv9d0ebNNbZvZWCB/Hozkr/94iP5M+vmo86/o3z/H0b6M42VYzQvDdUZ/n8JWVPG9tp5RHqL7fdUsFWbMmS29wHeuMxxZhVbBZsLkDqVrtBxkfTfpEjZVenbyLarpeT8AknJ9GI616CaSfKrX4k00/glsIbt7du1NRao8+8o29uSJBFSV96LYABG89JQnUlDlrZ9XvZ2wPYcSa8NddMArGx7l8L5EfmB0jaSfkIalc6kTzraJLfGdm29mbTUtKLtz0haUdImti+u0raauZ8ULFdHSs5XgWNJa8uNUU6lFKbA6/m78DHge7a/14h1CFpS598R1J/3IhiA0dwRtEoaUjVwpU5NmTqVIdcHVnc907bTSaqm78/njwI/J42cR5oDqS8l55dI7sB1pDD9p1KSm73oc0utmrluLFDn3xHA+1xj3otgYEZzR2DbH1YhaUjVyGJq1JQB/pUUwNVPGbKirdtJSppVZzpFVrb9ifxgw/ZLUv9kvCNInQnUZ1NfCtN9SEnYj7b9QP5+/WSIe8Yydf4dQYqgXt015b0IBmY0dwQXkPTdXyyUVY0sXhnYDliBtJm6IdV/NnUqQy4N3KmUja24RlpFW/9VSQvSN1pbuWhzhKkzgfqLpFwJV9BhCtP8ADqwcP4A8K2K7ep5bN+sJAdelzZT3XkvggEYdR3BPIos/qbtn+cAls2B7wAn0See1Q51KkMe3uH9RQ4jJSVfQdJZpCCdvWu0XxnXm0D9F/moTEHKoSXxIGpN3of6IvCOvA81SdK7OtiHCvfdYWLUeQ3No8jiRmKa/wZus312uwFbBVvdmnbxp6RZyT9Im7PX1bSO3nOoL/HO/vn/osSBbR/8xrsCSeeS9qH2corteTNwbRVPvmB4GXUdQYOaI4svJo3etyIJ2f2DJL+wdgVbJ5M8TCorQ0r6g+2NlTJ3FX9BbWfuKtjcnKSFvwlp2eoW4Crb363azm5EfRnZ+uEOMtc1lb1BXiNISLrR9vpNLt23Vvk7CoaXUbc0VOAWSfvTgXR0gd1I09Dv2H5GKVvZV9oxoBqVIW1vnP/vOHNXweYVSlpK7yMtf32O9LPrqY6A5GnV4E0kpdAlB6g7FJL0QdvX5JMPAOM6bF8v0837UMEgjOYZwc9J0tF7UJCOtt2pfn/V9rTM49vAFfL5StrX9qlNZcdUWZrIchwLkfzzryZFbT7Rrp3RiKSbbLftRCBpPVLWrsVIHfrTwKdcUUSt15G0FfANUkTxb8n7ULavHMl2BUMzmmcEHUtH10mVB30JdpH0su2zACSdCCxY0dYskkfVmiRPpmck/dF21RiHrkT9lTDHkWYIlb7ntm8C1m64AndLJHYXM4WUaP580j7UQbEPNToYzR1Bwy3tGaWUdo+T8q/2ErsAUyW9Tlq6eqbi0he2vwAgaRGSt9DppBiFBeppatdwHH17BHOAB6mYSCZLcexClvhohF24Wg6HscCppD2orcj7UJJ6bh+qFxnNS0MN6ej3AD8mSUd/0/YPR7JddaCUq7jBIiR3yGtICWCq6uYcQPojXY/0cLwauNr25Z22t5tQSi859+Gdi13l4S3pN6TZ0030SXxgu1UWugBQyjRX3If6hyvkxQ6Gl1HXEUj6Yqvi/L9t/0+L66OKFp4vxQhgV/SA+TLp4X+T7TkdNrFryQ/vRhxHRw/vbnUF7lbG8j7UaGc0Lg01PGneRRp5NGIJdgSuH5EW1YztiUo5Y9/f8FipweZ36rAzCni77boCkWpNEj8GGBP7UL3IqJsRNMiukB+x/Xw+XwT4te1NR7Zl9VE1qG0sU0ccR8HWncAqJEHDkDgoSWEf6svAW2z32j5UzzEaZwQNliNJDjd4NZf1EpdJ2gW4sCYF0p6lzjiOAtvV2MSep8U+1GmMoCdfUJ7R3BGcCVwv6aJ8/lHSpnEv8VmSdstrkv5BB5HFY4Ad6jIkaVHbz5FkS4LyvImUna+n96F6kVG7NARzfcYbydevsh1JQ4KOkXSx7R0Km/Ydb9YHQTczqjuCsYCknYDGvseVHSg5Bm2Shfp+T3KzvXuk2xME84roCLoYSceQPKPOykW7AzfaPmTkWjV2aCHUdzOpU4gAqaCniI6gi5E0C1jH9uv5fDwpGXh4rQwTESAVjAVG82bxWGFxoBFJvNhgFYN6aREg9b4IkAp6kegIupv/Am6WdCVpw3JTIJKiDB8RIBWMCWJpqIvJm5X3kuSPHwRusP34iDZqDBIBUkGvEx1BFzNWsop1K2NFqC8IoiPocmKzcuQYK0J9QRAdQRcTao5BEAwHkX+1u5lF0lBaE1gLWDPnhA2CIKiNmBGMAmKzMgiCeUm4j3YxoeYYBMFwEB1BdxNqjkEQzHNiaSgIgmCME5vFQRAEY5zoCIIgCMY40REEQRCMcaIjCIIgGOP8f/melIBDCguqAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=data1[['temp','atemp', 'humidity', 'windspeed', 'count']])\n",
        "fig=plt.gcf()\n",
        "fig.set_size_inches(10,10)\n"
      ],
      "metadata": {
        "id": "Rwzsun2TA1X0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "75fa22d1-88fa-4ea2-bd45-dfa988b32628"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnames = ['temp','atemp', 'humidity', 'windspeed', 'count']\n",
        "for i in cnames:\n",
        "    print(i)\n",
        "    q75, q25 = np.percentile( data1.loc[:,i],[75,25])\n",
        "    iqr = q75-q25\n",
        "    min = q25-(iqr*1.5)\n",
        "    max = q75+(iqr*1.5)\n",
        "    print(min)\n",
        "    print(max)\n",
        "   \n",
        "    data1= data1.drop( data1[ data1.loc[:,i] < min].index)\n",
        "    data1 = data1.drop( data1[ data1.loc[:,i] > max].index)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkErHhcxFRag",
        "outputId": "d25d426d-7f10-477a-9740-a78d9181ba44"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "temp\n",
            "-4.51\n",
            "44.69\n",
            "atemp\n",
            "-4.927500000000002\n",
            "52.6525\n",
            "humidity\n",
            "2.0\n",
            "122.0\n",
            "windspeed\n",
            "-7.993100000000002\n",
            "31.992500000000003\n",
            "count\n",
            "-322.5\n",
            "649.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ibe3LNg0FRec",
        "outputId": "9f5f3fcd-e05a-4dd3-b545-a7e0f37e2766"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10352, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Train data\n",
        "data1['weekday']=label_encoder.fit_transform(data1['weekday'])\n",
        "data1['month']=label_encoder.fit_transform(data1['month'])\n",
        "data1['year']=label_encoder.fit_transform(data1['year'])"
      ],
      "metadata": {
        "id": "IJaD-MVpFRf8"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Test data\n",
        "data['weekday']=label_encoder.fit_transform(data['weekday'])\n",
        "data['month']=label_encoder.fit_transform(data['month'])\n",
        "data['year']=label_encoder.fit_transform(data['year'])"
      ],
      "metadata": {
        "id": "ByoHgefjFRiP"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "object_Variable_List = [\"hour\",'day']\n",
        "for i in object_Variable_List:\n",
        "    data1[i] = data1[i].astype(\"int64\")\n",
        "    data[i] = data[i].astype(\"int64\")\n",
        "\n"
      ],
      "metadata": {
        "id": "FFfo964_FRkz"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data1.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8PPSYNqFRnJ",
        "outputId": "9a1a175b-d7e4-417e-bd86-5ade8b56c5fa"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "season          int64\n",
              "holiday         int64\n",
              "workingday      int64\n",
              "weather         int64\n",
              "temp          float64\n",
              "atemp         float64\n",
              "humidity        int64\n",
              "windspeed     float64\n",
              "count           int64\n",
              "day             int64\n",
              "hour            int64\n",
              "year            int64\n",
              "weekday         int64\n",
              "month           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1flM_y3XFRpn",
        "outputId": "62a1d145-4aeb-48e4-ab7a-b7b15b5cc8b9"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime       object\n",
              "season          int64\n",
              "holiday         int64\n",
              "workingday      int64\n",
              "weather         int64\n",
              "temp          float64\n",
              "atemp         float64\n",
              "humidity        int64\n",
              "windspeed     float64\n",
              "day             int64\n",
              "hour            int64\n",
              "year            int64\n",
              "weekday         int64\n",
              "month           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data1.drop('count', axis=1)\n",
        "Y = data1['count']\n"
      ],
      "metadata": {
        "id": "eqKZTIIKFRsB"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check the Shape of Input and Output variables.\n",
        "\n",
        "print(X.shape)\n",
        "print(Y.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9kFKhT3FRuX",
        "outputId": "744608a7-48e9-4d0e-882d-aa73dcab32b2"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10352, 13)\n",
            "(10352,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "-TwJVeXzFRw5",
        "outputId": "c8880c75-d385-4596-b32e-817ad82e829f"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   season  holiday  workingday  weather  temp   atemp  humidity  windspeed  \\\n",
              "0       1        0           0        1  9.84  14.395        81        0.0   \n",
              "1       1        0           0        1  9.02  13.635        80        0.0   \n",
              "2       1        0           0        1  9.02  13.635        80        0.0   \n",
              "3       1        0           0        1  9.84  14.395        75        0.0   \n",
              "4       1        0           0        1  9.84  14.395        75        0.0   \n",
              "\n",
              "   day  hour  year  weekday  month  \n",
              "0    1     0     0        2      4  \n",
              "1    1     1     0        2      4  \n",
              "2    1     2     0        2      4  \n",
              "3    1     3     0        2      4  \n",
              "4    1     4     0        2      4  "
            ],
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              "      <th></th>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a5a76fa-666f-4f78-9630-a5db233c0146')\"\n",
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              "      \n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\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-9a5a76fa-666f-4f78-9630-a5db233c0146 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-9a5a76fa-666f-4f78-9630-a5db233c0146');\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": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Checking the Dependent variable.\n",
        "\n",
        "Y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCjE4JFgFRzW",
        "outputId": "a4b2b54d-99a1-40d5-8860-f6ec179408e9"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    16\n",
              "1    40\n",
              "2    32\n",
              "3    13\n",
              "4     1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing library for split Train data\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,random_state=42,test_size=0.25)"
      ],
      "metadata": {
        "id": "WFlaGItzFR2G"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models=[RandomForestRegressor(),AdaBoostRegressor(),BaggingRegressor(),KNeighborsRegressor()]\n",
        "model_names=['RandomForestRegressor','AdaBoostRegressor','BaggingRegressor','KNeighborsRegressor']\n",
        "r2score=[]\n",
        "d={}\n",
        "for model in range (len(models)):\n",
        "    clf=models[model]\n",
        "    clf.fit(X_train,Y_train)\n",
        "    test_pred=clf.predict(X_test)\n",
        "    r2score.append(np.sqrt(r2_score(test_pred,Y_test)))\n",
        "d={'Modelling Algo':model_names,'R2_error':r2score}   "
      ],
      "metadata": {
        "id": "DKoZMFijFR4Z"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JIk8Yp9VFR73",
        "outputId": "11405bc6-73d3-42e1-f039-b911bccef766"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Modelling Algo': ['RandomForestRegressor',\n",
              "  'AdaBoostRegressor',\n",
              "  'BaggingRegressor',\n",
              "  'KNeighborsRegressor'],\n",
              " 'R2_error': [0.9596347374020632,\n",
              "  0.6754851554666884,\n",
              "  0.9554178832214634,\n",
              "  0.47206151779237887]}"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rmsle_frame=pd.DataFrame(d)\n",
        "rmsle_frame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Sf2WyyxWG-8Z",
        "outputId": "717d9d4b-bc89-42cc-afa2-41a024eb2670"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Modelling Algo  R2_error\n",
              "0  RandomForestRegressor  0.959635\n",
              "1      AdaBoostRegressor  0.675485\n",
              "2       BaggingRegressor  0.955418\n",
              "3    KNeighborsRegressor  0.472062"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1b840e14-420b-48f5-a7f2-f4b43300be89\">\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>Modelling Algo</th>\n",
              "      <th>R2_error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RandomForestRegressor</td>\n",
              "      <td>0.959635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AdaBoostRegressor</td>\n",
              "      <td>0.675485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BaggingRegressor</td>\n",
              "      <td>0.955418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>KNeighborsRegressor</td>\n",
              "      <td>0.472062</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              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-1b840e14-420b-48f5-a7f2-f4b43300be89 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-1b840e14-420b-48f5-a7f2-f4b43300be89');\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": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#for random forest regresion.\n",
        "no_of_test=[500]\n",
        "params_dict={'n_estimators':no_of_test,'n_jobs':[-1],'max_features':[\"auto\",'sqrt','log2']}\n",
        "clf_rf=GridSearchCV(estimator=RandomForestRegressor(),param_grid=params_dict,scoring='neg_mean_squared_log_error')\n",
        "clf_rf.fit(X_train,Y_train)\n",
        "pred=clf_rf.predict(X_test)\n",
        "print((np.sqrt(mean_squared_log_error(pred,Y_test))))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8iSb_VLkG-_4",
        "outputId": "422ff3fa-f254-4cc0-9b4a-2c1a0fc99891"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.35561867241707246\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred=clf_rf.predict(data.drop('datetime',axis=1))\n",
        "d={'datetime':data['datetime'],'count':pred}\n",
        "ans=pd.DataFrame(d)\n",
        "ans.to_csv('answer.csv',index=False)\n",
        "\n"
      ],
      "metadata": {
        "id": "prlCAPJAG_CY"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.read_csv('answer.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "IMae4NEFG_Eg",
        "outputId": "f2c2328b-73bc-4e23-fa8a-b0f86195273f"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 datetime    count\n",
              "0     2011-01-20 00:00:00   13.324\n",
              "1     2011-01-20 01:00:00    4.726\n",
              "2     2011-01-20 02:00:00    4.154\n",
              "3     2011-01-20 03:00:00    3.574\n",
              "4     2011-01-20 04:00:00    3.292\n",
              "...                   ...      ...\n",
              "6488  2012-12-31 19:00:00  220.376\n",
              "6489  2012-12-31 20:00:00  153.728\n",
              "6490  2012-12-31 21:00:00   99.700\n",
              "6491  2012-12-31 22:00:00  106.514\n",
              "6492  2012-12-31 23:00:00   57.178\n",
              "\n",
              "[6493 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d72cce7-fe0c-415d-b61a-578efc36be7a\">\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>datetime</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-20 00:00:00</td>\n",
              "      <td>13.324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-20 01:00:00</td>\n",
              "      <td>4.726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-20 02:00:00</td>\n",
              "      <td>4.154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-20 03:00:00</td>\n",
              "      <td>3.574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-20 04:00:00</td>\n",
              "      <td>3.292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6488</th>\n",
              "      <td>2012-12-31 19:00:00</td>\n",
              "      <td>220.376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6489</th>\n",
              "      <td>2012-12-31 20:00:00</td>\n",
              "      <td>153.728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6490</th>\n",
              "      <td>2012-12-31 21:00:00</td>\n",
              "      <td>99.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6491</th>\n",
              "      <td>2012-12-31 22:00:00</td>\n",
              "      <td>106.514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>2012-12-31 23:00:00</td>\n",
              "      <td>57.178</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6493 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d72cce7-fe0c-415d-b61a-578efc36be7a')\"\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-7d72cce7-fe0c-415d-b61a-578efc36be7a 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-7d72cce7-fe0c-415d-b61a-578efc36be7a');\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": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oY544KK1G_Gx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jfzk8EaIG_Kb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}