NYC_Taxi_Trip_Time_Prediction_Capstone_Project2.ipynb

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        "<a href=\"https://colab.research.google.com/github/SRIST1608/NYC-Taxi-Trip-Time-Prediction/blob/main/NYC_Taxi_Trip_Time_Prediction_Capstone_Project2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "tOGC-qoyhJeX"
      },
      "source": [
        "# <b><u> Project Title : Taxi trip time Prediction : Predicting total ride duration of taxi trips in New York City</u></b>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y06xIdG26kRF"
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      "source": [
        "## <b> Problem Description </b>\n",
        "\n",
        "### Your task is to build a model that predicts the total ride duration of taxi trips in New York City. Your primary dataset is one released by the NYC Taxi and Limousine Commission, which includes pickup time, geo-coordinates, number of passengers, and several other variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWeU1f9bwqQq"
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      "source": [
        "## <b> Data Description </b>\n",
        "\n",
        "### The dataset is based on the 2016 NYC Yellow Cab trip record data made available in Big Query on Google Cloud Platform. The data was originally published by the NYC Taxi and Limousine Commission (TLC). The data was sampled and cleaned for the purposes of this project. Based on individual trip attributes, you should predict the duration of each trip in the test set.\n",
        "\n",
        "### <b>NYC Taxi Data.csv</b> - the training set (contains 1458644 trip records)\n",
        "\n",
        "\n",
        "### Data fields\n",
        "* #### id - a unique identifier for each trip\n",
        "* #### vendor_id - a code indicating the provider associated with the trip record\n",
        "* #### pickup_datetime - date and time when the meter was engaged\n",
        "* #### dropoff_datetime - date and time when the meter was disengaged\n",
        "* #### passenger_count - the number of passengers in the vehicle (driver entered value)\n",
        "* #### pickup_longitude - the longitude where the meter was engaged\n",
        "* #### pickup_latitude - the latitude where the meter was engaged\n",
        "* #### dropoff_longitude - the longitude where the meter was disengaged\n",
        "* #### dropoff_latitude - the latitude where the meter was disengaged\n",
        "* #### store_and_fwd_flag - This flag indicates whether the trip record was held in vehicle memory before sending to the vendor because the vehicle did not have a connection to the server - Y=store and forward; N=not a store and forward trip\n",
        "* #### trip_duration - duration of the trip in seconds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCFNAF2yMrbf"
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      "source": [
        "**Independent Variables**\n",
        "\n",
        "id — a unique identifier for each trip\n",
        "\n",
        "vendor_id — a code indicating the provider associated with the trip record\n",
        "\n",
        "pickup_datetime — date and time when the meter was engaged\n",
        "\n",
        "dropoff_datetime — date and time when the meter was disengaged\n",
        "\n",
        "passenger_count — the number of passengers in the vehicle (driver entered value)\n",
        "\n",
        "pickup_longitude — the longitude where the meter was engaged\n",
        "\n",
        "pickup_latitude — the latitude where the meter was engaged\n",
        "\n",
        "dropoff_longitude — the longitude where the meter was disengaged\n",
        "\n",
        "dropoff_latitude — the latitude where the meter was disengaged distance:distance covered speed ; speed calculated\n",
        "\n",
        "store_and_fwd_flag — This flag indicates whether the trip record was held in vehicle memory before sending to the vendor because the vehicle did not have a connection to the server — Y=store and forward; N=not a store and forward trip."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRY9m-0BMvkB"
      },
      "source": [
        "**trip_duration** = dependent variable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gek83QFBkUX-"
      },
      "source": [
        "**Importing  libraries**"
      ]
    },
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          "base_uri": "https://localhost:8080/"
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        "outputId": "8b68170a-0b97-4bc3-a7e9-3d10cef1b752"
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      "outputs": [
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ],
      "source": [
        "# Importing libraries for visualization and preidction\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime as dt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "\n",
        "import statsmodels.formula.api as sm\n",
        "from sklearn.model_selection import learning_curve\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "import warnings; warnings.simplefilter('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# connecting the drive\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "_7pTP8AFlDhp"
      },
      "outputs": [],
      "source": [
        "# Reading the data from the drive\n",
        "df=pd.read_csv(\"/content/drive/MyDrive/NYC Taxi Trip Time Prediction-Priyanka Jain/NYC Taxi Data.csv\")\n"
      ]
    },
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              "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
              "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
              "1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
              "2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
              "3  id3504673          2  2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
              "4  id2181028          2  2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
              "\n",
              "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
              "0                1        -73.982155        40.767937         -73.964630   \n",
              "1                1        -73.980415        40.738564         -73.999481   \n",
              "2                1        -73.979027        40.763939         -74.005333   \n",
              "3                1        -74.010040        40.719971         -74.012268   \n",
              "4                1        -73.973053        40.793209         -73.972923   \n",
              "\n",
              "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
              "0         40.765602                  N            455  \n",
              "1         40.731152                  N            663  \n",
              "2         40.710087                  N           2124  \n",
              "3         40.706718                  N            429  \n",
              "4         40.782520                  N            435  "
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      "source": [
        "# Head of the data\n",
        "\n",
        "df.head()"
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              "                id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
              "1458639  id2376096          2  2016-04-08 13:31:04  2016-04-08 13:44:02   \n",
              "1458640  id1049543          1  2016-01-10 07:35:15  2016-01-10 07:46:10   \n",
              "1458641  id2304944          2  2016-04-22 06:57:41  2016-04-22 07:10:25   \n",
              "1458642  id2714485          1  2016-01-05 15:56:26  2016-01-05 16:02:39   \n",
              "1458643  id1209952          1  2016-04-05 14:44:25  2016-04-05 14:47:43   \n",
              "\n",
              "         passenger_count  pickup_longitude  pickup_latitude  \\\n",
              "1458639                4        -73.982201        40.745522   \n",
              "1458640                1        -74.000946        40.747379   \n",
              "1458641                1        -73.959129        40.768799   \n",
              "1458642                1        -73.982079        40.749062   \n",
              "1458643                1        -73.979538        40.781750   \n",
              "\n",
              "         dropoff_longitude  dropoff_latitude store_and_fwd_flag  trip_duration  \n",
              "1458639         -73.994911         40.740170                  N            778  \n",
              "1458640         -73.970184         40.796547                  N            655  \n",
              "1458641         -74.004433         40.707371                  N            764  \n",
              "1458642         -73.974632         40.757107                  N            373  \n",
              "1458643         -73.972809         40.790585                  N            198  "
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              "      <td>40.768799</td>\n",
              "      <td>-74.004433</td>\n",
              "      <td>40.707371</td>\n",
              "      <td>N</td>\n",
              "      <td>764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458642</th>\n",
              "      <td>id2714485</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-01-05 15:56:26</td>\n",
              "      <td>2016-01-05 16:02:39</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.982079</td>\n",
              "      <td>40.749062</td>\n",
              "      <td>-73.974632</td>\n",
              "      <td>40.757107</td>\n",
              "      <td>N</td>\n",
              "      <td>373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458643</th>\n",
              "      <td>id1209952</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-04-05 14:44:25</td>\n",
              "      <td>2016-04-05 14:47:43</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.979538</td>\n",
              "      <td>40.781750</td>\n",
              "      <td>-73.972809</td>\n",
              "      <td>40.790585</td>\n",
              "      <td>N</td>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e434d734-526f-4789-8c49-122eff20e6e1')\"\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-e434d734-526f-4789-8c49-122eff20e6e1 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-e434d734-526f-4789-8c49-122eff20e6e1');\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": 7
        }
      ],
      "source": [
        "# Tail of the Data\n",
        "\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1C0X85WpnzQz",
        "outputId": "8f7e5b54-26d6-4734-a2b9-754d9f4a9de6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                     object\n",
              "vendor_id               int64\n",
              "pickup_datetime        object\n",
              "dropoff_datetime       object\n",
              "passenger_count         int64\n",
              "pickup_longitude      float64\n",
              "pickup_latitude       float64\n",
              "dropoff_longitude     float64\n",
              "dropoff_latitude      float64\n",
              "store_and_fwd_flag     object\n",
              "trip_duration           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Checking the data type of the dependent and independent variables\n",
        "\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Ea4P4CKhKFPc",
        "outputId": "5eb0287e-946c-4ffa-95bc-f5a7c20a98b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          vendor_id  passenger_count  pickup_longitude  pickup_latitude  \\\n",
              "count  1.458644e+06     1.458644e+06      1.458644e+06     1.458644e+06   \n",
              "mean   1.534950e+00     1.664530e+00     -7.397349e+01     4.075092e+01   \n",
              "std    4.987772e-01     1.314242e+00      7.090186e-02     3.288119e-02   \n",
              "min    1.000000e+00     0.000000e+00     -1.219333e+02     3.435970e+01   \n",
              "25%    1.000000e+00     1.000000e+00     -7.399187e+01     4.073735e+01   \n",
              "50%    2.000000e+00     1.000000e+00     -7.398174e+01     4.075410e+01   \n",
              "75%    2.000000e+00     2.000000e+00     -7.396733e+01     4.076836e+01   \n",
              "max    2.000000e+00     9.000000e+00     -6.133553e+01     5.188108e+01   \n",
              "\n",
              "       dropoff_longitude  dropoff_latitude  trip_duration  \n",
              "count       1.458644e+06      1.458644e+06   1.458644e+06  \n",
              "mean       -7.397342e+01      4.075180e+01   9.594923e+02  \n",
              "std         7.064327e-02      3.589056e-02   5.237432e+03  \n",
              "min        -1.219333e+02      3.218114e+01   1.000000e+00  \n",
              "25%        -7.399133e+01      4.073588e+01   3.970000e+02  \n",
              "50%        -7.397975e+01      4.075452e+01   6.620000e+02  \n",
              "75%        -7.396301e+01      4.076981e+01   1.075000e+03  \n",
              "max        -6.133553e+01      4.392103e+01   3.526282e+06  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3a979fd8-c38f-4272-bc40-f7fda5c5667b\">\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>vendor_id</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
              "      <th>trip_duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.534950e+00</td>\n",
              "      <td>1.664530e+00</td>\n",
              "      <td>-7.397349e+01</td>\n",
              "      <td>4.075092e+01</td>\n",
              "      <td>-7.397342e+01</td>\n",
              "      <td>4.075180e+01</td>\n",
              "      <td>9.594923e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.987772e-01</td>\n",
              "      <td>1.314242e+00</td>\n",
              "      <td>7.090186e-02</td>\n",
              "      <td>3.288119e-02</td>\n",
              "      <td>7.064327e-02</td>\n",
              "      <td>3.589056e-02</td>\n",
              "      <td>5.237432e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-1.219333e+02</td>\n",
              "      <td>3.435970e+01</td>\n",
              "      <td>-1.219333e+02</td>\n",
              "      <td>3.218114e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-7.399187e+01</td>\n",
              "      <td>4.073735e+01</td>\n",
              "      <td>-7.399133e+01</td>\n",
              "      <td>4.073588e+01</td>\n",
              "      <td>3.970000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-7.398174e+01</td>\n",
              "      <td>4.075410e+01</td>\n",
              "      <td>-7.397975e+01</td>\n",
              "      <td>4.075452e+01</td>\n",
              "      <td>6.620000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>-7.396733e+01</td>\n",
              "      <td>4.076836e+01</td>\n",
              "      <td>-7.396301e+01</td>\n",
              "      <td>4.076981e+01</td>\n",
              "      <td>1.075000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>-6.133553e+01</td>\n",
              "      <td>5.188108e+01</td>\n",
              "      <td>-6.133553e+01</td>\n",
              "      <td>4.392103e+01</td>\n",
              "      <td>3.526282e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a979fd8-c38f-4272-bc40-f7fda5c5667b')\"\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-3a979fd8-c38f-4272-bc40-f7fda5c5667b 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-3a979fd8-c38f-4272-bc40-f7fda5c5667b');\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": 9
        }
      ],
      "source": [
        "# decription of the data\n",
        "\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yFQfYmGgKtH",
        "outputId": "c4baf063-ba51-40f4-d869-3b0c48947511"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1458644, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Shape of the data\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OcyrpMmK1Df"
      },
      "source": [
        "**Checking missing value**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3SfB0Z47KiWF",
        "outputId": "980b7d59-b33c-44ca-e122-e74cf352fc65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Data Count\n",
            "Series([], dtype: float64)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Missing Data Percentage\n",
            "Series([], dtype: float64)\n"
          ]
        }
      ],
      "source": [
        "# Missing Value Count Function\n",
        "def show_missing():\n",
        "\n",
        "    missing = df.columns[df.isnull().any()].tolist()\n",
        "    \n",
        "    return missing\n",
        "\n",
        "# Missing data counts and percentage\n",
        "print('Missing Data Count')\n",
        "\n",
        "print(df[show_missing()].isnull().sum().sort_values(ascending = False))\n",
        "\n",
        "print('--'*50)\n",
        "\n",
        "print('Missing Data Percentage')\n",
        "\n",
        "print(round(df[show_missing()].isnull().sum().sort_values(ascending = False)/len(df)*100,2))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the data with respect to analyse null values in the data\n",
        "fig, ax = plt.subplots(figsize=(12,5))\n",
        "sns.heatmap(df.isnull(), cbar=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "BnYOGRiQwYUL",
        "outputId": "30c1cf51-3ee8-4797-e642-83174152c483"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff22d8bc390>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zWXwuTmQK_7A"
      },
      "source": [
        "**Here you can see there is no missing value in our dataset, So will proceed for solution and analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "k3YQQvTfn7Y8"
      },
      "outputs": [],
      "source": [
        "# Importing geopy library to get the distance\n",
        "\n",
        "from geopy.distance import great_circle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_tnlsYrxoxzt"
      },
      "source": [
        "**Calculte distance betwee pickup and dropoff points**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "StKFcbpZohEU"
      },
      "outputs": [],
      "source": [
        "#Calculte distance betwee pickup and dropoff points\n",
        "\n",
        "def distance_cal(pickup_lat,pickup_long,dropoff_lat,dropoff_long):\n",
        " \n",
        " start_coordinates=(pickup_lat,pickup_long)\n",
        " stop_coordinates=(dropoff_lat,dropoff_long)\n",
        " \n",
        " return great_circle(start_coordinates,stop_coordinates).km"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "-GMDaA7wo0b7"
      },
      "outputs": [],
      "source": [
        "# Calculating distance for each trip\n",
        "\n",
        "df['distance'] = df.apply(lambda df: distance_cal(df['pickup_latitude'],df['pickup_longitude'],df['dropoff_latitude'],df['dropoff_longitude'] ), axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQ5OeMBfpE8R",
        "outputId": "d4dd8a50-5222-43a1-fb1d-608ad13df024"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          1.498523\n",
              "1          1.805510\n",
              "2          6.385108\n",
              "3          1.485501\n",
              "4          1.188590\n",
              "             ...   \n",
              "1458639    1.225082\n",
              "1458640    6.049844\n",
              "1458641    7.824617\n",
              "1458642    1.092565\n",
              "1458643    1.134044\n",
              "Name: distance, Length: 1458644, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Printing the distance\n",
        "df['distance']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDKilNklr1xj"
      },
      "source": [
        "**converting it to datetime**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "9GepnYqGp4sm"
      },
      "outputs": [],
      "source": [
        "#convert it to datetime\n",
        "\n",
        "df['pickup_datetime']=pd.to_datetime(df['pickup_datetime'])\n",
        "df['dropoff_datetime']=pd.to_datetime(df['dropoff_datetime'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "OAs3rDeProOv"
      },
      "outputs": [],
      "source": [
        "# Extracting columns for further analysis of the data\n",
        "\n",
        "df['pickup_day_no']=df['pickup_datetime'].dt.weekday\n",
        "df['dropoff_day_no']=df['dropoff_datetime'].dt.weekday\n",
        "df['pickup_hour']=df['pickup_datetime'].dt.hour\n",
        "df['dropoff_hour']=df['dropoff_datetime'].dt.hour\n",
        "df['pickup_month']=df['pickup_datetime'].dt.month\n",
        "df['dropoff_month']=df['dropoff_datetime'].dt.month"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9N_aGMXh1pL",
        "outputId": "aa636c01-ef38-427e-943b-2bcc9f194dd9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1458644, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# shape of the data after extraction of the data\n",
        "\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKMAnt8SQj3Y"
      },
      "source": [
        "**Checking for outlier in column trip duration**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "emmP7pw3Q9a4",
        "outputId": "8b54023a-707f-49ea-e005-1ef4555cbed0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# the skewed box plot shows us the presence of outliers \n",
        "\n",
        "sns.boxplot(y=df['trip_duration'])\n",
        "\n",
        "plt.show()\n",
        "columns=['trip_duration']\n",
        "n = 1\n",
        "plt.figure(figsize=(20,15))\n",
        "\n",
        "for column in columns:\n",
        "  plt.subplot(4,4,n)\n",
        "  n = n+1\n",
        "  sns.boxplot(df[column])\n",
        "  plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NuSaB1gCRq7p"
      },
      "source": [
        "**You can see that there are some outliers in dataset so we will try to fin 0-100 percentile value  to find a the correct percentile value for removal of outliers**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pNhX_jURV-g",
        "outputId": "fda066cc-84fd-4b3b-91e9-c5cd4db916ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 percentile value is 1\n",
            "10 percentile value is 245\n",
            "20 percentile value is 348\n",
            "30 percentile value is 446\n",
            "40 percentile value is 548\n",
            "50 percentile value is 662\n",
            "60 percentile value is 798\n",
            "70 percentile value is 968\n",
            "80 percentile value is 1208\n",
            "90 percentile value is 1634\n",
            "100 percentile value is  3526282\n"
          ]
        }
      ],
      "source": [
        "#calculating 0-100th percentile to find a the correct percentile value for removal of outliers\n",
        "\n",
        "for i in range(0,100,10):\n",
        "    var =df[\"trip_duration\"].values\n",
        "    var = np.sort(var,axis = None)\n",
        "    print(\"{} percentile value is {}\".format(i,var[int(len(var)*(float(i)/100))]))\n",
        "\n",
        "# 100th percentile value\n",
        "print (\"100 percentile value is \",var[-1])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1PXzh8ASmsa"
      },
      "source": [
        "**We can see that the value of outlier is in the range from 90 to 100 percentile but we do not  know exactly so we do some more step from 90-100**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1WZ8B5xS82I",
        "outputId": "8bf9013b-2eb7-4a96-ef5a-3ae73f51d3b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "90 percentile value is 1634\n",
            "91 percentile value is 1703\n",
            "92 percentile value is 1780\n",
            "93 percentile value is 1869\n",
            "94 percentile value is 1975\n",
            "95 percentile value is 2104\n",
            "96 percentile value is 2273\n",
            "97 percentile value is 2497\n",
            "98 percentile value is 2829\n",
            "99 percentile value is 3440\n",
            "100 percentile value is  3526282\n"
          ]
        }
      ],
      "source": [
        "#calculating 90-100th percentile to find a the correct percentile value for removal of outliers\n",
        "for i in range(90,100):\n",
        "    var =df[\"trip_duration\"].values\n",
        "    var = np.sort(var,axis = None)\n",
        "    print(\"{} percentile value is {}\".format(i,var[int(len(var)*(float(i)/100))]))\n",
        "\n",
        "# 100th percentile value    \n",
        "print (\"100 percentile value is \",var[-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOnO-BHsTbmB",
        "outputId": "206d6327-b778-4d4d-bfbe-880b6905b52b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.0 percentile value is 3440\n",
            "99.1 percentile value is 3540\n",
            "99.2 percentile value is 3652\n",
            "99.3 percentile value is 3782\n",
            "99.4 percentile value is 3942\n",
            "99.5 percentile value is 4139\n",
            "99.6 percentile value is 4403\n",
            "99.7 percentile value is 4765\n",
            "99.8 percentile value is 5552\n",
            "99.9 percentile value is 85129\n",
            "100 percentile value is  3526282\n"
          ]
        }
      ],
      "source": [
        "#calculating 99-100th percentile to find a the correct percentile value for removal of outliers\n",
        "for i in np.linspace(99.0,100.0,10 ,endpoint=False):\n",
        "    var =df[\"trip_duration\"].values\n",
        "    var = np.sort(var,axis = None)\n",
        "    print(\"{} percentile value is {}\".format(i,var[int(len(var)*(float(i)/100))]))\n",
        "\n",
        "# 100th percentile value\n",
        "print (\"100 percentile value is \",var[-1])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-f_5rFsrTqga"
      },
      "source": [
        "**From here we can set the value for trip duration less than 18000 ,otherwise all the values>18000 will be treated as outlier becouse more than 5 hour of taxi trip is not feasible**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXaZNpjiT9Ap",
        "outputId": "81af5287-5944-44c8-db6f-b703ec73bef4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "trip_duration\n",
              "(1, 3601]             1446313\n",
              "(3601, 7201]            10045\n",
              "(7201, 10801]             141\n",
              "(10801, 14401]             35\n",
              "(14401, 18001]              5\n",
              "                       ...   \n",
              "(3506401, 3510001]          0\n",
              "(3510001, 3513601]          0\n",
              "(3513601, 3517201]          0\n",
              "(3517201, 3520801]          0\n",
              "(3520801, 3524401]          0\n",
              "Name: trip_duration, Length: 979, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# groupby on the trip duration\n",
        "df.trip_duration.groupby(pd.cut(df.trip_duration, np.arange(1,max(df.trip_duration),3600))).count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "AKOIQaHwhW-F"
      },
      "outputs": [],
      "source": [
        "# removing the outlier in the trip duration\n",
        "df = df[df.trip_duration <= 18000]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWbFd4_39_-_"
      },
      "source": [
        "**Oulier in distance column**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "8FlYL81r-FpH",
        "outputId": "3b7390f3-b5cb-4259-bef9-ac913a834a1a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# ploting the box plot\n",
        "sns.boxplot(y=df['distance'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lf8kxI0G-ToH"
      },
      "source": [
        "**we can see that there are some outlier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Z_83HSi-ZgT",
        "outputId": "6aaa972d-19a7-462c-959c-772a8ff52a49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 percentile value is 0.0\n",
            "10 percentile value is 0.7924562818659672\n",
            "20 percentile value is 1.089497960376577\n",
            "30 percentile value is 1.3797536152657544\n",
            "40 percentile value is 1.7060766803437428\n",
            "50 percentile value is 2.093306827438099\n",
            "60 percentile value is 2.5968141567820595\n",
            "70 percentile value is 3.3361165210061983\n",
            "80 percentile value is 4.618216034873399\n",
            "90 percentile value is 7.62749463829288\n",
            "100 percentile value is  1240.9104296218293\n"
          ]
        }
      ],
      "source": [
        "#calculating 0-100th percentile to find a the correct percentile value for removal of outliers\n",
        "for i in range(0,100,10):\n",
        "    var =df[\"distance\"].values\n",
        "    var = np.sort(var,axis = None)\n",
        "    print(\"{} percentile value is {}\".format(i,var[int(len(var)*(float(i)/100))]))\n",
        "\n",
        "# printing the 100th percentile\n",
        "print (\"100 percentile value is \",var[-1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7Z0njC_-jTB"
      },
      "source": [
        "**Will try to find 90-100 percentile value to get clear value**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ObMqSNX-r74",
        "outputId": "0b542dd2-43fc-4b21-e1b7-5bd2a6baa886"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "90 percentile value is 7.62749463829288\n",
            "91 percentile value is 8.252245356170992\n",
            "92 percentile value is 8.88433307801906\n",
            "93 percentile value is 9.454162672228154\n",
            "94 percentile value is 10.077606337837588\n",
            "95 percentile value is 11.012925939000201\n",
            "96 percentile value is 12.807934490534205\n",
            "97 percentile value is 15.403320357695033\n",
            "98 percentile value is 19.362263611693667\n",
            "99 percentile value is 20.785387862712486\n",
            "100 percentile value is  1240.9104296218293\n"
          ]
        }
      ],
      "source": [
        "#calculating 90-100th percentile to find a the correct percentile value for removal of outliers\n",
        "for i in range(90,100):\n",
        "    var =df[\"distance\"].values\n",
        "    var = np.sort(var,axis = None)\n",
        "    print(\"{} percentile value is {}\".format(i,var[int(len(var)*(float(i)/100))]))\n",
        "\n",
        "# printing the 100th percentile    \n",
        "print (\"100 percentile value is \",var[-1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HB56zRcx_NFF"
      },
      "source": [
        "**have to see the percentile from 99-100 to get more clear picture**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ek-8peD2_Wun",
        "outputId": "9c949296-21b5-49c4-8a28-5ae516d7e8a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.0 percentile value is 20.785387862712486\n",
            "99.1 percentile value is 20.919524006820765\n",
            "99.2 percentile value is 21.04985478020312\n",
            "99.3 percentile value is 21.185837864999066\n",
            "99.4 percentile value is 21.36475476479681\n",
            "99.5 percentile value is 21.555802225468828\n",
            "99.6 percentile value is 21.829646912627005\n",
            "99.7 percentile value is 22.261440544315704\n",
            "99.8 percentile value is 22.888391922719975\n",
            "99.9 percentile value is 24.7552375528992\n",
            "100 percentile value is  1240.9104296218293\n"
          ]
        }
      ],
      "source": [
        "#calculating 99-100th percentile to find a the correct percentile value for removal of outliers\n",
        "for i in np.linspace(99.0,100.0,10 ,endpoint=False):\n",
        "    var =df[\"distance\"].values\n",
        "    var = np.sort(var,axis = None)\n",
        "    print(\"{} percentile value is {}\".format(i,var[int(len(var)*(float(i)/100))]))\n",
        "\n",
        "# printing the 100th percentile    \n",
        "print (\"100 percentile value is \",var[-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEybU9RgAVmc",
        "outputId": "91b3c493-5750-4859-9e97-f6dc07e1b24b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.000000    5894\n",
              "0.000424      45\n",
              "0.000424      43\n",
              "0.000424      35\n",
              "0.000424      22\n",
              "            ... \n",
              "0.434274       1\n",
              "3.809274       1\n",
              "0.563330       1\n",
              "5.925436       1\n",
              "1.134044       1\n",
              "Name: distance, Length: 1450293, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# Doing the value counts on the distance\n",
        "df['distance'].value_counts()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15ZfE2yS_jf6"
      },
      "source": [
        "**We can take value as 30 becouse 99.9 percentile value are less than it**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "Z5Us9ftz_vFG"
      },
      "outputs": [],
      "source": [
        "# Removing the outlier from the data\n",
        "\n",
        "df=df[df['distance']<30]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ky-i-4hPAnS8"
      },
      "source": [
        "**We will only keep those observation whose distance is greater than 0 ,as equal to zero will be a false entry**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "xK9dCk8zAlip"
      },
      "outputs": [],
      "source": [
        "# removing the outlier as distance metric as zero is undesirable\n",
        "df=df[df['distance']!=0]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9UI-HkJEFf3",
        "outputId": "181a9d9a-6f01-4584-f610-669407468dad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.000424    45\n",
              "0.000424    43\n",
              "0.000424    35\n",
              "0.000424    22\n",
              "0.000424    21\n",
              "            ..\n",
              "3.343165     1\n",
              "4.218078     1\n",
              "8.860892     1\n",
              "1.498127     1\n",
              "1.134044     1\n",
              "Name: distance, Length: 1449723, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# value counts on the distance\n",
        "\n",
        "df['distance'].value_counts()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdLlWITMD2DK"
      },
      "source": [
        "**Passenger column outlier treatment**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XqOPorQ0A9O3",
        "outputId": "4780a9c1-b56a-424d-8382-99258095ffc1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1027680\n",
              "2     209112\n",
              "5      77578\n",
              "3      59559\n",
              "6      47926\n",
              "4      28200\n",
              "0         51\n",
              "7          2\n",
              "8          1\n",
              "Name: passenger_count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# Value counts on the passenge column\n",
        "df.passenger_count.value_counts()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "71MMCZyDdjAY",
        "outputId": "69462fc4-9d79-47b2-dfb6-d9f7f2c9a785"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAAE9CAYAAACx2J+XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUP0lEQVR4nO3df7Dld13f8dc7uzhsAoLdIBMX7MVeREVawIClWBp+hEaXUmeqM1gxqRVFSzeBTm2xZto63U7rtOOUpNVpCJCNRBhFHR1Jlx8SLFIFkhBIQqJzK4skhJIss0DCCt3k4x/3G7puIewy3vN97/0+HjN35pzvuefc99lP7s29z/P9fk+NMQIAAABAL2fMPQAAAAAA/z/RBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgoZ2n8slnn332WFtb26JRAAAAAJbnhhtuuGeM8ZgTt59StFlbW8v111//lzcVAAAAwMJV1ce+3HaHRwEAAAA0JNoAAAAANCTaAAAAADQk2gAAAAA0JNoAAAAANCTaAAAAADQk2gAAAAA0JNoAAAAANCTaAAAAADQk2gAAAAA0JNoAAAAANCTaAAAAADQk2gAAAAA0JNoAAAAANCTaAAAAADQk2gAAAAA0JNoAAAAANCTaAAAAADQk2gAAAAA0JNoAAAAANCTaAAAAADQk2gAAAAA0JNoAAAAANCTaAAAAADS0c+4BYFVe9rKX5ciRI9mzZ8/cozCD9fX17Nu3b+4xAAAATppow2Lcddddufe+z+eTX/Cf/dLs+Pyn5x4BAADglPnrlWXZsTNHv+375p6CFdt1+7VzjwAAAHDKnNMGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgIdEGAAAAoCHRBgAAAKAh0QYAAACgocVFm8svvzyXX3753GMAsAJ+5gMAcDrbOfcAq7axsTH3CACsiJ/5AACczha3pw0AAADA6UC0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQC2nfPOO+9LHyyLtV8m675cGxsb2bt3bzY2NuYehRU7fPhwLr744hw+fHjuUbaUaAMAAMBpaf/+/bnvvvuyf//+uUdhxQ4cOJCbb745V1999dyjbCnRBgDYVk58pd0r78th7ZfJui/XxsZGDh06lCQ5dOiQvW0W5PDhwzl48GDGGDl48OC23ttm59wDrNqdd96Zo0eP5pJLLpl7FFbs6NGjyZh7CuZwxp99Nhsbn/N9v0AbGxvZtWvX3GMAAFvgxL1r9u/fn6uuumqeYVipAwcO5IEHHkiS3H///bn66qvzqle9auaptsZX3dOmqn6iqq6vquvvvvvuVcwEAAAAD+nBvWy+0nW2r3e+8505duxYkuTYsWN5xzveMfNEW+er7mkzxrgiyRVJcu655572+yns2bMnSfKa17xm5klYtb179+beP/vi3GMwgwce/vVZ/5bH+r5fIHtXAcD2tba29hdCzdra2myzsFoveMELcu211+bYsWPZuXNnzj///LlH2jLOaQMAAMBp59JLL33I62xfF110Uc44YzNn7NixIxdeeOHME20d0QYA2Fbe/e53P+R1ti9rv0zWfbnW19e/tHfN2tpa1tfX5x2Ildm9e3cuuOCCVFUuuOCC7N69e+6RtoxoAwAAwGnp0ksvzVlnnWUvmwW66KKL8pSnPGVb72WTLPDdowCA7c8r7ctl7ZfJui/X+vp63vrWt849BjPYvXt3LrvssrnH2HL2tAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAGhItAEAAABoaOfcA6za+vr63CMAsCJ+5gMAcDpbXLTZt2/f3CMAsCJ+5gMAcDpzeBQAAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDog0AAABAQ6INAAAAQEOiDQAAAEBDO+ceAFbq/mPZdfu1c0/Biu34/KeTPHbuMQAAAE6JaMNinHPOOTly5Ej27PHH+/I8Nuvr63MPAQAAcEpEGxbjyiuvnHsEAAAAOGnOaQMAAADQkGgDAAAA0JBoAwAAANCQaAMAAADQkGgDAAAA0JBoAwAAANCQaAMAAADQkGgDAAAA0JBoAwAAANCQaAMAAADQkGgDAAAA0JBoAwAAANCQaAMAAADQkGgDAAAA0JBoAwAAANCQaAMAAADQkGgDAAAA0JBoAwAAANCQaAMAAADQkGgDAAAA0JBoAwAAANCQaAMAAADQkGgDAAAA0JBoAwAAANCQaAMAAADQUI0xTv6Tq+5O8rGtG2dlzk5yz9xDMAtrv1zWfrms/XJZ++Wy9stk3ZfL2i/Xdlr7vzrGeMyJG08p2mwXVXX9GOPcuedg9az9cln75bL2y2Xtl8vaL5N1Xy5rv1xLWHuHRwEAAAA0JNoAAAAANLTUaHPF3AMwG2u/XNZ+uaz9cln75bL2y2Tdl8vaL9e2X/tFntMGAAAAoLul7mkDAAAA0JpoAwAAANDQ4qJNVV1QVX9UVRtV9eq552E1qur1VfWpqrpl7llYrap6fFVdV1Ufqapbq+qSuWdi61XVw6vq/VX1oWndf27umVitqtpRVR+sqt+ZexZWp6oOVdXNVXVTVV0/9zysTlU9uqreUlW3V9VtVfWsuWdi61XVk6bv9wc/PltVr5x7LrZeVb1q+h3vlqp6U1U9fO6ZtsqizmlTVTuS/HGS85PckeQDSX5ojPGRWQdjy1XVc5Lcm+TqMcZ3zj0Pq1NV5yQ5Z4xxY1U9MskNSb7f9/32VlWV5Kwxxr1V9bAkv5/kkjHGH848GitSVf8syblJvn6M8aK552E1qupQknPHGPfMPQurVVUHkrxnjHFlVX1dkjPHGEfmnovVmf7WuzPJd48xPjb3PGydqtqTzd/tvmOMcbSqfjXJtWOMq+adbGssbU+bZybZGGP8yRjji0nenOTvzzwTKzDG+J9JPj33HKzeGOOuMcaN0+XPJbktyZ55p2KrjU33TlcfNn0s51WKhauqxyXZm+TKuWcBtl5VPSrJc5K8LknGGF8UbBbp+Un+t2CzGDuT7KqqnUnOTPKJmefZMkuLNnuSfPy463fEH2+wGFW1luRpSd437ySswnR4zE1JPpXkHWMM674c/yXJv0jywNyDsHIjydur6oaq+om5h2FlnpDk7iRvmA6LvLKqzpp7KFbuJUneNPcQbL0xxp1J/nOSP01yV5LPjDHePu9UW2dp0QZYqKp6RJJfT/LKMcZn556HrTfGuH+M8dQkj0vyzKpyaOQCVNWLknxqjHHD3LMwi+8ZYzw9yfcmecV0eDTb384kT0/yS2OMpyW5L4lzVy7IdEjci5P82tyzsPWq6huyecTME5J8U5Kzquql8061dZYWbe5M8vjjrj9u2gZsY9M5TX49yTVjjN+Yex5Wa9pF/rokF8w9Cyvx7CQvns5t8uYkz6uqN847EqsyvfqaMcankvxmNg+NZ/u7I8kdx+1R+ZZsRhyW43uT3DjG+D9zD8JKvCDJR8cYd48x/m+S30jyt2aeacssLdp8IMkTq+oJU419SZLfnnkmYAtNJ6R9XZLbxhi/MPc8rEZVPaaqHj1d3pXNE9DfPu9UrMIY42fGGI8bY6xl8//z7xpjbNtX3/h/quqs6YTzmQ6NeWES7xq5AGOMTyb5eFU9adr0/CTecGBZfigOjVqSP03yN6vqzOl3/edn87yV29LOuQdYpTHGsar6p0nelmRHktePMW6deSxWoKrelOS8JGdX1R1J/s0Y43XzTsWKPDvJjyS5eTq/SZL8qzHGtTPOxNY7J8mB6Z0kzkjyq2MMb/0M29tjk/zm5u/v2ZnkV8YYB+cdiRXal+Sa6YXZP0nyozPPw4pMkfb8JC+fexZWY4zxvqp6S5IbkxxL8sEkV8w71dZZ1Ft+AwAAAJwulnZ4FAAAAMBpQbQBAAAAaEi0AQAAAGhItAEAAABoSLQBAFixqnplVZ059xwAQG/ePQoAWKSq2jnGODbT1z6U5Nwxxj1zfH0A4PRgTxsA4JRU1VpV3V5V11TVbVX1lqo6s6r+dVV9oKpuqaorqqqmz7+4qj5SVR+uqjdP2/5OVd00fXywqh45bf/p6TE+XFU/d9zXu62qXltVt1bV26tq13TbM6bPvamq/lNV3TJt3zFdf/CxXj5tP6+q3lNVv53kIw/xHC+c7vehqvrl4+Z417T9d6vqm6ftV1XVDxx333uP+1rvnv59Hvz3qqq6OMk3Jbmuqq77S14eAGAbEW0AgK/Fk5L84hjj25N8Nsk/SfJfxxjPGGN8Z5JdSV40fe6rkzxtjPHXk/zktO2fJ3nFGOOpSf52kqNV9cIkT0zyzCRPTfJdVfWc6fOfmOS/jTGenORIkn8wbX9DkpdPj3P/cfP9WJLPjDGekeQZSX68qp4w3fb0JJeMMb71yz2xqnpykkuTPG+M8TeSXDLddHmSA9PzuCbJZSfx7/S0JK9M8h1JviXJs8cYlyX5RJLnjjGeexKPAQAslGgDAHwtPj7GeO90+Y1JvifJc6vqfVV1c5LnJXnydPuHk1xTVS9N8uDhSO9N8gvTXiePng5TeuH08cEkNyb5tmzGmiT56BjjpunyDUnWqurRSR45xviDafuvHDffC5NcWFU3JXlfkt3HPdb7xxgffYjn9rwkv/bgoUtjjE9P25913Nf45ek5fzXvH2PcMcZ4IMlNSdZO4j4AAEmSnXMPAACclk48Kd5I8ovZPE/Lx6vq3yZ5+HTb3iTPSfL3kvxsVT1ljPEfq+qtSb4vyXur6u8mqST/YYzx349/4KpaS/KF4zbdn809eR5KJdk3xnjbCY91XpL7TuYJnoJjmV4Iq6ozknzdcbedOLffvQCAk2ZPGwDga/HNVfWs6fI/TPL70+V7quoRSX4g+VLEePwY47ok/zLJo5I8oqr+2hjj5jHGzyf5QDb3qnlbkn883T9VtaeqvvErDTDGOJLkc1X13dOmlxx389uS/FRVPWx6rG+tqrNO8rm9K8kPVtXu6b5/Zdr+v477Gj+c5D3T5UNJvmu6/OIkDzuJr/G5JI88yXkAgIXyag8A8LX4oySvqKrXZ/OEvr+U5BuS3JLkk9kMMUmyI8kbq+pR2dz75bIxxpGq+ndV9dwkDyS5Ncn/GGN8oaq+PckfTOcwvjfJS/MXz1Vzoh9L8tqqeiDJ7yX5zLT9ymweinTjdELku5N8/8k8sTHGrVX175P8XlXdn83Dtf5Rkn1J3lBVPz093o9Od3ltkt+qqg8lOZiT25PniiQHq+oTzmsDAHwl3vIbADgl0+FKvzOdcHhWVfWIMcaD79b06iTnjDEu+Sp3AwA4LdjTBgA4ne2tqp/J5u80H8vmHjEAANuCPW0AgEWazlnzu1/mpuePMQ6veh4AgBOJNgAAAAANefcoAAAAgIZEGwAAAICGRBsAAACAhkQbAAAAgIZEGwAAAICGRBsAAACAhv4cOCWpeHezIBYAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# ploting the box plot\n",
        "\n",
        "plt.figure(figsize = (20,5))\n",
        "sns.boxplot(df.passenger_count)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "At3WJoORdpT8"
      },
      "source": [
        "**we can see that there are some outlier**\n",
        "\n",
        "There are some trips with 0 passenger count.\n",
        "\n",
        "Few trips consisted of even 7, 8 or 9 passengers. Clear outliers and pointers to data inconsistency\n",
        "\n",
        "Most of trip consist of passenger either 1 or 2\n",
        "\n",
        "So we would replace the 0 passenger count with 1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "wFtOApzbdwBz"
      },
      "outputs": [],
      "source": [
        "# Replacing the zero passenger column with one\n",
        "\n",
        "df['passenger_count'] = df.passenger_count.map(lambda x: 1 if x == 0 else x)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "khSznld0ewvf"
      },
      "source": [
        "**Also, we will remove the records with passenger count > 7, 8 or 9 as they are extreme values and looks very odd to be ocupied in a taxi.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "D4sv_qgye0Wh"
      },
      "outputs": [],
      "source": [
        "# Keeping the passenger occupancy to less tha eqaul as other are outliers\n",
        "\n",
        "df = df[df.passenger_count <= 6]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "mYSg3jwpDiPl",
        "outputId": "36393b03-744a-44ff-9dc3-88449b5ea909"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff212401cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# count plot on the passenger colunm\n",
        "\n",
        "sns.countplot(x='passenger_count',data=df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RC-_w_l5sn_Q"
      },
      "source": [
        "#EDA"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Trip duration with passenger count with catplot\n",
        "\n",
        "sns.catplot(y='trip_duration',x='passenger_count',data=df,kind='strip')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "MG4GfvGreYM8",
        "outputId": "5f4a58ca-4eea-4f04-bc1b-ecdc514d7ec5"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7ff20d930290>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "_4zY_znPj2rQ",
        "outputId": "c3ff7820-64fb-494a-a788-1be957a6c889"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# ploting the trip coounts with respect to trip duration\n",
        "\n",
        "df.trip_duration.groupby(pd.cut(df.trip_duration, np.arange(1,7200,600))).count().plot(kind='barh')\n",
        "plt.xlabel('Trip Counts')\n",
        "plt.ylabel('Trip Duration (seconds)')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-5cEXvs7Z9H"
      },
      "source": [
        "**hourly Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "C3xWivYXsNs9",
        "outputId": "f95d8cbd-30da-4e6c-e31f-ae4bdb655205"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff20d09bed0>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# ploting the subplots\n",
        "fig, axs = plt.subplots(1,2, figsize=(18, 8))\n",
        "\n",
        "# ploting the pickup days against the pickup hour \n",
        "axs[0].set_title('Pickup Days')\n",
        "sns.countplot(x=df['pickup_hour'],ax=axs[0])\n",
        "\n",
        "# ploting the Dropoff days against the Dropoff hour \n",
        "axs[1].set_title('Dropoff Days')\n",
        "sns.countplot(x=df['dropoff_hour'],ax=axs[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0CWbdp7U7ukD"
      },
      "source": [
        "**Monthly Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "QfD0o_sn7V-E",
        "outputId": "43f1f73f-7b00-4b80-a754-10fa5c3bb9b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff20e6acf90>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEHCAYAAABiAAtOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWTUlEQVR4nO3df/BddX3n8edLIi3+BEqWRUI3bs24m9JukCzSRa3KFAKrBh1/QCuklDVOBVdmXVe0M8Vimeq0akUtHSqRxKqIKGvcxdIMsqBdQb5BavihS4q4JIMkEgTUFQZ87x/3862XcPPN/cZz7+X7zfMxc+ee+z6fc8775o+8vufHPSdVhSRJXXrKpBuQJM0/hoskqXOGiySpc4aLJKlzhoskqXMLJt3Ak8VBBx1UixcvnnQbkjSnbNy48QdVtXDnuuHSLF68mKmpqUm3IUlzSpLvDap7WEyS1DnDRZLUOcNFktQ5w0WS1DnDRZLUOcNFktQ5w0WS1DnDRZLUOcNFktS5kf1CP8lhwDrgYKCAi6rqw0neA7wJ2N6GvruqrmzLvAs4A3gM+M9VdVWrrwA+DOwDfLyq3tfqzwUuBX4F2AicWlWPJPmltu0jgfuAN1TVXaP6rureMR85ZtIt7NI/vPUfJt2C9KQ3yj2XR4G3V9VS4GjgzCRL27wPVdWy9poOlqXAycCvAyuAv0qyT5J9gI8BJwBLgVP61vP+tq7nAffTCyba+/2t/qE2TpI0JiMLl6q6p6puatMPAbcDh86wyErg0qp6uKq+C2wGjmqvzVV1Z1U9Qm9PZWWSAC8HLm/LrwVO6lvX2jZ9OXBsGy9JGoOx3LgyyWLgCOAG4BjgrCSnAVP09m7upxc81/cttoWfh9HdO9VfSO9Q2A+r6tEB4w+dXqaqHk3yQBv/g06/mLQL177ktyfdwkC/fd21k25Be4mRn9BP8gzg88DZVfUgcCHwa8Ay4B7gA6PuYYbeVieZSjK1ffv23S8gSRrKSMMlyVPpBcunquoLAFV1b1U9VlU/A/6G3mEvgK3AYX2LL2q1XdXvA/ZPsmCn+uPW1eY/u41/nKq6qKqWV9XyhQuf8DgCSdIeGuXVYgEuBm6vqg/21Q+pqnvax1cDt7Tp9cCnk3wQeA6wBPgGEGBJuzJsK72T/r9bVZXkGuC19M7DrAK+2LeuVcDX2/yvVFWN6rtK881H3/6lSbcw0FkfeOWkW9CQRnnO5RjgVGBTkptb7d30rvZaRu/y5LuANwNU1a1JLgNuo3el2ZlV9RhAkrOAq+hdirymqm5t63sncGmSPwW+SS/MaO+fTLIZ2EEvkCRJYzKycKmqr9Hb69jZlTMscz5w/oD6lYOWq6o7+flhtf76T4HXzaZfSVJ3/IW+JKlzhoskqXOGiySpc4aLJKlzhoskqXNjuf2LJuP/nvcbk25hoF/9402TbkHSiLnnIknqnOEiSeqc4SJJ6pznXCTNO+e/8bWTbmGgP/rby3c/aJ5wz0WS1DnDRZLUOQ+LzeDId6ybdAsDbfzz0ybdgiTNyD0XSVLnDBdJUucMF0lS5wwXSVLnDBdJUucMF0lS5wwXSVLnDBdJUucMF0lS5wwXSVLnDBdJUucMF0lS5wwXSVLnDBdJUucMF0lS5wwXSVLnDBdJUucMF0lS5wwXSVLnRhYuSQ5Lck2S25LcmuRtrX5gkg1J7mjvB7R6klyQZHOSbyV5Qd+6VrXxdyRZ1Vc/MsmmtswFSTLTNiRJ4zHKPZdHgbdX1VLgaODMJEuBc4Crq2oJcHX7DHACsKS9VgMXQi8ogHOBFwJHAef2hcWFwJv6llvR6rvahiRpDEYWLlV1T1Xd1KYfAm4HDgVWAmvbsLXASW16JbCueq4H9k9yCHA8sKGqdlTV/cAGYEWb96yqur6qCli307oGbUOSNAZjOeeSZDFwBHADcHBV3dNmfR84uE0fCtzdt9iWVpupvmVAnRm2sXNfq5NMJZnavn377L+YJGmgkYdLkmcAnwfOrqoH++e1PY4a5fZn2kZVXVRVy6tq+cKFC0fZhiTtVUYaLkmeSi9YPlVVX2jle9shLdr7tlbfChzWt/iiVpupvmhAfaZtSJLGYJRXiwW4GLi9qj7YN2s9MH3F1yrgi33109pVY0cDD7RDW1cBxyU5oJ3IPw64qs17MMnRbVun7bSuQduQJI3BghGu+xjgVGBTkptb7d3A+4DLkpwBfA94fZt3JXAisBn4CXA6QFXtSPJe4MY27ryq2tGm3wJcAuwHfLm9mGEbkqQxGFm4VNXXgOxi9rEDxhdw5i7WtQZYM6A+BRw+oH7foG1IksbDX+hLkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOjeycEmyJsm2JLf01d6TZGuSm9vrxL5570qyOcl3khzfV1/RapuTnNNXf26SG1r9s0n2bfVfap83t/mLR/UdJUmDjXLP5RJgxYD6h6pqWXtdCZBkKXAy8Ottmb9Ksk+SfYCPAScAS4FT2liA97d1PQ+4Hzij1c8A7m/1D7VxkqQxGlm4VNV1wI4hh68ELq2qh6vqu8Bm4Kj22lxVd1bVI8ClwMokAV4OXN6WXwuc1LeutW36cuDYNl6SNCaTOOdyVpJvtcNmB7TaocDdfWO2tNqu6r8C/LCqHt2p/rh1tfkPtPFPkGR1kqkkU9u3b//Fv5kkCRh/uFwI/BqwDLgH+MCYt/84VXVRVS2vquULFy6cZCuSNK8MFS5Jrh6mtjtVdW9VPVZVPwP+ht5hL4CtwGF9Qxe12q7q9wH7J1mwU/1x62rzn93GS5LGZMZwSfLLSQ4EDkpyQJID22sxPz8MNbQkh/R9fDUwfSXZeuDkdqXXc4ElwDeAG4El7cqwfemd9F9fVQVcA7y2Lb8K+GLfula16dcCX2njJUljsmA3898MnA08B9gITJ8YfxD46EwLJvkM8FJ6wbQFOBd4aZJlQAF3tfVTVbcmuQy4DXgUOLOqHmvrOQu4CtgHWFNVt7ZNvBO4NMmfAt8ELm71i4FPJtlM74KCk3fzHSVJHZsxXKrqw8CHk7y1qj4ymxVX1SkDyhcPqE2PPx84f0D9SuDKAfU7+flhtf76T4HXzaZXSVK3drfnAkBVfSTJfwAW9y9TVetG1JckaQ4bKlySfJLeVV43A4+1cgGGiyTpCYYKF2A5sNQT45KkYQz7O5dbgH85ykYkSfPHsHsuBwG3JfkG8PB0sapeNZKuJElz2rDh8p5RNiFJml+GvVrs2lE3IkmaP4a9WuwheleHAewLPBX4cVU9a1SNSZLmrmH3XJ45Pd1uX78SOHpUTUmS5rZZ3xW5ev47cPxuB0uS9krDHhZ7Td/Hp9D73ctPR9KRJGnOG/ZqsVf2TT9K76aTKzvvRpI0Lwx7zuX0UTciSZo/hn1Y2KIkVyTZ1l6fT7Jo1M1JkuamYU/of4LeQ7ie015fajVJkp5g2HBZWFWfqKpH2+sSwIfOS5IGGjZc7kvyxiT7tNcb8bn0kqRdGDZc/gB4PfB94B56z6b//RH1JEma44a9FPk8YFVV3Q+Q5EDgL+iFjiRJjzPsnstvTgcLQFXtAI4YTUuSpLlu2HB5SpIDpj+0PZdh93okSXuZYQPiA8DXk3yufX4dcP5oWpIkzXXD/kJ/XZIp4OWt9Jqqum10bUmS5rKhD221MDFQJEm7Netb7kuStDuGiySpc4aLJKlzhoskqXOGiySpc4aLJKlzhoskqXMjC5cka9pTK2/pqx2YZEOSO9r7Aa2eJBck2ZzkW0le0LfMqjb+jiSr+upHJtnUlrkgSWbahiRpfEa553IJsGKn2jnA1VW1BLi6fQY4AVjSXquBC+Gf72F2LvBC4Cjg3L6wuBB4U99yK3azDUnSmIwsXKrqOmDHTuWVwNo2vRY4qa++rnquB/ZPcghwPLChqna0uzJvAFa0ec+qquurqoB1O61r0DYkSWMy7nMuB1fVPW36+8DBbfpQ4O6+cVtabab6lgH1mbYhSRqTiZ3Qb3scNcltJFmdZCrJ1Pbt20fZiiTtVcYdLve2Q1q0922tvhU4rG/colabqb5oQH2mbTxBVV1UVcuravnChQv3+EtJkh5v3OGyHpi+4msV8MW++mntqrGjgQfaoa2rgOOSHNBO5B8HXNXmPZjk6HaV2Gk7rWvQNiRJYzKyp0km+QzwUuCgJFvoXfX1PuCyJGcA3wNe34ZfCZwIbAZ+ApwOvccpJ3kvcGMbd157xDLAW+hdkbYf8OX2YoZtSJLGZGThUlWn7GLWsQPGFnDmLtazBlgzoD4FHD6gft+gbUiSxsdf6EuSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6N5FwSXJXkk1Jbk4y1WoHJtmQ5I72fkCrJ8kFSTYn+VaSF/StZ1Ubf0eSVX31I9v6N7dlM/5vKUl7r0nuubysqpZV1fL2+Rzg6qpaAlzdPgOcACxpr9XAhdALI+Bc4IXAUcC504HUxrypb7kVo/86kqRpT6bDYiuBtW16LXBSX31d9VwP7J/kEOB4YENV7aiq+4ENwIo271lVdX1VFbCub12SpDGYVLgU8PdJNiZZ3WoHV9U9bfr7wMFt+lDg7r5lt7TaTPUtA+pPkGR1kqkkU9u3b/9Fvo8kqc+CCW33RVW1Ncm/ADYk+Xb/zKqqJDXqJqrqIuAigOXLl498e5K0t5jInktVbW3v24Ar6J0zubcd0qK9b2vDtwKH9S2+qNVmqi8aUJckjcnYwyXJ05M8c3oaOA64BVgPTF/xtQr4YpteD5zWrho7GnigHT67CjguyQHtRP5xwFVt3oNJjm5XiZ3Wty5J0hhM4rDYwcAV7ergBcCnq+rvktwIXJbkDOB7wOvb+CuBE4HNwE+A0wGqakeS9wI3tnHnVdWONv0W4BJgP+DL7SVJGpOxh0tV3Qn8uwH1+4BjB9QLOHMX61oDrBlQnwIO/4WblSTtkSfTpciSpHnCcJEkdc5wkSR1znCRJHXOcJEkdc5wkSR1znCRJHXOcJEkdc5wkSR1znCRJHXOcJEkdc5wkSR1znCRJHXOcJEkdc5wkSR1znCRJHXOcJEkdc5wkSR1znCRJHXOcJEkdc5wkSR1znCRJHXOcJEkdc5wkSR1znCRJHXOcJEkdc5wkSR1znCRJHXOcJEkdc5wkSR1znCRJHXOcJEkdW7ehkuSFUm+k2RzknMm3Y8k7U3mZbgk2Qf4GHACsBQ4JcnSyXYlSXuPeRkuwFHA5qq6s6oeAS4FVk64J0naa6SqJt1D55K8FlhRVf+pfT4VeGFVnbXTuNXA6vbx+cB3RtjWQcAPRrj+UbP/yZnLvYP9T9qo+/9XVbVw5+KCEW7wSa+qLgIuGse2kkxV1fJxbGsU7H9y5nLvYP+TNqn+5+thsa3AYX2fF7WaJGkM5mu43AgsSfLcJPsCJwPrJ9yTJO015uVhsap6NMlZwFXAPsCaqrp1wm2N5fDbCNn/5Mzl3sH+J20i/c/LE/qSpMmar4fFJEkTZLhIkjpnuIxYkjVJtiW5ZdK9zFaSw5Jck+S2JLcmeduke5qNJL+c5BtJ/rH1/yeT7mlPJNknyTeT/I9J9zJbSe5KsinJzUmmJt3PbCXZP8nlSb6d5PYkvzXpnoaR5Pnt33z69WCSs8fag+dcRivJS4AfAeuq6vBJ9zMbSQ4BDqmqm5I8E9gInFRVt024taEkCfD0qvpRkqcCXwPeVlXXT7i1WUnyX4DlwLOq6hWT7mc2ktwFLK+qOfkjxCRrga9W1cfbladPq6ofTrqv2Wi3w9pK74fk3xvXdt1zGbGqug7YMek+9kRV3VNVN7Xph4DbgUMn29XwqudH7eNT22tO/TWVZBHwH4GPT7qXvU2SZwMvAS4GqKpH5lqwNMcC/zTOYAHDRUNKshg4Arhhsp3MTjukdDOwDdhQVXOqf+Avgf8G/GzSjeyhAv4+ycZ2u6W55LnAduAT7bDkx5M8fdJN7YGTgc+Me6OGi3YryTOAzwNnV9WDk+5nNqrqsapaRu8uDUclmTOHJpO8AthWVRsn3csv4EVV9QJ6dyg/sx0mnisWAC8ALqyqI4AfA3Pq8R3tUN6rgM+Ne9uGi2bUzlV8HvhUVX1h0v3sqXY44xpgxaR7mYVjgFe18xaXAi9P8reTbWl2qmpre98GXEHvjuVzxRZgS9/e7uX0wmYuOQG4qaruHfeGDRftUjshfjFwe1V9cNL9zFaShUn2b9P7Ab8DfHuyXQ2vqt5VVYuqajG9Qxtfqao3TritoSV5ersQhHY46Thgzlw1WVXfB+5O8vxWOhaYExez9DmFCRwSg3l6+5cnkySfAV4KHJRkC3BuVV082a6GdgxwKrCpnbcAeHdVXTnBnmbjEGBtu1rmKcBlVTXnLuedww4Gruj9jcIC4NNV9XeTbWnW3gp8qh1euhM4fcL9DK0F+u8Ab57I9r0UWZLUNQ+LSZI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SLuR5D1J/usI178wyQ3t/lUvTvK6dnv3a0a1zV30sSzJiX2fR/q9Nb8ZLtIeSNLlD5CPBTZV1RFV9VXgDOBNVfWyDrcxjGXAibsdJQ3BH1FKAyT5I2AVvbsp303vWTavAG4GXkTvlho3A39B79fnNwJ/WFUPt3uBXUbvvk7/D/jdqtrc7iy9BjiI3t12TwcOBNYD+9F75sYVwDva9PqqeseA3n4fOAl4OrCk9bAvvbspPAycWFU7kiwD/hp4GvBPwB9U1f1J/he9u1u/DNifXpjdAGzu6+PPgH8L/Crwr9v7X1bVBXv6b6q9i3su0k6SHEnvXl7Tf8n/+77Z+1bVcuBjwCXAG6rqN+gFzB/2jXug1T9K77b5AB8B1lbVbwKfAi6oqpuBPwY+W1XLqupPgCng9wYFS5/Dgde03s4HftLu3Pt14LQ2Zh3wzra9TcC5fcsvqKqjgLPp3ZLokZ36+Gwb92+A4+ndcPLcdiNTabcMF+mJXgxcUVU/aY8YWN83b/o/3ecD362q/9M+r6X3YKlpn+l7n3407m8Bn27Tn6S3B7Snrqmqh6pqO/AA8KVW3wQsbg+62r+qrt1Ff9N3uN4ILJ5hO/+zqh5uT5LcRu9+YdJuGS7S7Px4yHG1i+muPNw3/bO+zz9juBvSTo9/bDfj+7ezu7HSPzNcpCe6DjgpyX7tlvGvHDDmO/T2EJ7XPp8KXNs3/w19719v0/+b3uE2gN8Dvtpp132q6gHg/iQv3kV/gzwEPHNUPWnv4l8h0k6q6qYknwX+kd6hoBsHjPlpktOBz7Urx26kd/J82gFJvkXvL/9TWu2t9B6Z+w5+fkJ/lFYBf53kaQx3u/hrgHPa4xX+bMS9aZ7zajGpY+1qseXtPIW0V/KwmCSpc+65SE9SSY4H3r9T+btV9epJ9CPNhuEiSeqch8UkSZ0zXCRJnTNcJEmdM1wkSZ37/3/w3vY/7JZ/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# number of trips  per month basis\n",
        "\n",
        "sns.countplot(df['dropoff_month'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOKAgdIu8y1T"
      },
      "source": [
        "**Setting a time cycle of a day **"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpT0vRWo9wxy"
      },
      "source": [
        "**Based on vendor id**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "dtPKxliS9IVY",
        "outputId": "863f7c9a-54d7-47ac-b8b6-e8274e56e5ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff2204fa7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Number of trips based on vendor id\n",
        "\n",
        "sns.countplot(x='vendor_id',data=df)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Trip duration based on vendor id\n",
        "sns.catplot(x=\"vendor_id\", y=\"trip_duration\",kind=\"strip\",data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "C2EjZX_0dBRC",
        "outputId": "194c2755-0690-4452-c751-216372431ef1"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7ff20e51fed0>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojDnP3_HVzhB"
      },
      "source": [
        "**Let's visualize the relationship between Distance covered and respective trip duration.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "Xzut2a6JVnET",
        "outputId": "e1957f2a-b4f3-437b-d89c-4b677d6b34c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff2204fa090>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Share of vendors in total trips\n",
        "\n",
        "df['vendor_id'].value_counts().plot.pie(figsize = (7,9), autopct='%1.1f%%')\n",
        "plt.legend()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# function  to give  time slot according to the time of day\n",
        "\n",
        "def time_of_day(x):\n",
        "    if x in range(6,12):\n",
        "        return 'Morning'\n",
        "    elif x in range(12,16):\n",
        "        return 'Afternoon'\n",
        "    elif x in range(16,22):\n",
        "        return 'Evening'\n",
        "    else:\n",
        "        return 'Late night'"
      ],
      "metadata": {
        "id": "f-P36pXNM9YV"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ploting the pickup time and given slots \n",
        "\n",
        "new_df=df.copy()\n",
        "\n",
        "new_df['pickup_timeofday']=new_df['pickup_hour'].apply(time_of_day)\n",
        "new_df['dropoff_timeofday']=new_df['dropoff_hour'].apply(time_of_day)\n",
        "\n",
        "figure,(ax5,ax6)=plt.subplots(ncols=2,figsize=(20,10))\n",
        "ax5.set_title('Pickup Time of Day')\n",
        "ax=sns.countplot(x=\"pickup_timeofday\",data=new_df,ax=ax5)\n",
        "ax6.set_title('Dropoff Time of Day')\n",
        "ax=sns.countplot(x=\"dropoff_timeofday\",data=new_df,ax=ax6)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "cSw-m8sFNC_f",
        "outputId": "3f5b7763-5fbc-4a53-a74b-7ae4baeebfa8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ld149V81V-Pn"
      },
      "source": [
        "** Correlation heatmap**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "hAGLa0oLV8GM",
        "outputId": "468a4ddb-2afc-41c9-830c-3e30f7b7a6e6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# ploting the heatmap\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15,5))\n",
        "dataplot = sns.heatmap(df.corr(), cmap=\"YlGnBu\", annot=True, linewidths=.5)\n",
        "\n",
        "\n",
        "# displaying heatmap\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xqPS-zXSEwj9"
      },
      "source": [
        "**Done with data treatment and analysis ,will proceed to split the data in train and testthen will apply supervised ML model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NL7obz3SVE8G"
      },
      "source": [
        "#splitting data into train and test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSg7lwOWN6_b",
        "outputId": "72c5f20b-0f67-43dc-f357-2459b25e0c59"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'vendor_id', 'pickup_datetime', 'dropoff_datetime',\n",
              "       'passenger_count', 'pickup_longitude', 'pickup_latitude',\n",
              "       'dropoff_longitude', 'dropoff_latitude', 'store_and_fwd_flag',\n",
              "       'trip_duration', 'distance', 'pickup_day_no', 'dropoff_day_no',\n",
              "       'pickup_hour', 'dropoff_hour', 'pickup_month', 'dropoff_month'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "# Taking the columns of the data\n",
        "\n",
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-lVApn8EUkEp"
      },
      "source": [
        "**We will create some dummy variable for categorical variable**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "003ietTg9tgF"
      },
      "outputs": [],
      "source": [
        "# Creating the dummies\n",
        "\n",
        "x=df.drop(['trip_duration','id','pickup_datetime','dropoff_datetime'],axis=1)\n",
        "y=df['trip_duration']\n",
        "x=pd.get_dummies(x,'store_and_fwd_flag',drop_first=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "by7-rzhyWRvp",
        "outputId": "6ab02df5-67a6-4ce5-bb11-52d645fe0184"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N    0.994504\n",
              "Y    0.005496\n",
              "Name: store_and_fwd_flag, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "# Normalise the store and fwd flag\n",
        "\n",
        "df.store_and_fwd_flag.value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uPwSXDhvRIKa",
        "outputId": "f7278356-702b-4e1c-aa8d-ef5801e997e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           455\n",
              "1           663\n",
              "2          2124\n",
              "3           429\n",
              "4           435\n",
              "           ... \n",
              "1458639     778\n",
              "1458640     655\n",
              "1458641     764\n",
              "1458642     373\n",
              "1458643     198\n",
              "Name: trip_duration, Length: 1450106, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "# Normalising Predictors and creating new dataframe\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "cols = x.columns\n",
        "\n",
        "ss = StandardScaler()\n",
        "\n",
        "x = ss.fit_transform(x)\n",
        "x = pd.DataFrame(x, columns=cols)\n",
        "x.head()\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "FwJfMpdURVSW"
      },
      "outputs": [],
      "source": [
        "# getting the dummies for variables\n",
        "\n",
        "x=pd.get_dummies(x,columns=['vendor_id', \n",
        "       'passenger_count',\n",
        "       'pickup_day_no', 'dropoff_day_no', 'pickup_hour', 'dropoff_hour',\n",
        "       'pickup_month', 'dropoff_month'],drop_first=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "tCFO1Kk7WEMo"
      },
      "outputs": [],
      "source": [
        "# train test split of the data\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MNCY6ZiXBd2"
      },
      "source": [
        "# Using Linear regression ML model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "bnf73dY9b8Ap"
      },
      "outputs": [],
      "source": [
        "# Importing the various libraries\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_squared_error\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "KZXfv7WJWKSM"
      },
      "outputs": [],
      "source": [
        "# fitting the linear regression\n",
        "reg = LinearRegression().fit(x_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dmDc4qTWaTr3",
        "outputId": "ba76bef0-739c-4506-86b8-e3c4abd73c9c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.25146075e+01,  1.95045565e+01,  4.09091666e-01, -4.59484284e+01,\n",
              "        5.02896613e+02,  1.32772637e+00, -7.15244294e-01,  2.15138359e+01,\n",
              "        2.84267790e+01,  4.15005450e+01,  3.37747093e+00,  7.44849979e+00,\n",
              "        2.28591516e+02,  3.82695676e+02,  4.74484939e+02,  3.87576870e+02,\n",
              "        1.17012921e+02, -2.06874057e+02, -1.56697184e+02, -2.85140438e+02,\n",
              "       -3.67682405e+02, -3.01116062e+02, -1.06618634e+02,  1.54121510e+02,\n",
              "        5.83552407e+01,  1.97621796e+02,  4.48357050e+02,  9.01500003e+02,\n",
              "        1.67771777e+03,  2.31412587e+03,  2.54678541e+03,  2.46147678e+03,\n",
              "        2.27163399e+03,  2.11048344e+03,  1.96491267e+03,  1.80914143e+03,\n",
              "        1.63991501e+03,  1.48448377e+03,  1.25490305e+03,  9.46812748e+02,\n",
              "        6.19306309e+02,  3.24467613e+02,  1.25712636e+02,  3.72088673e+01,\n",
              "        1.90914133e+01, -1.45136805e+00, -5.64843753e+01, -6.92064058e+01,\n",
              "       -2.14807778e+02, -4.49242355e+02, -9.29808681e+02, -1.80084143e+03,\n",
              "       -2.44468789e+03, -2.57173122e+03, -2.37120502e+03, -2.14131102e+03,\n",
              "       -1.97456412e+03, -1.81358574e+03, -1.65056501e+03, -1.48543714e+03,\n",
              "       -1.32348923e+03, -1.08760856e+03, -7.95429001e+02, -4.76218910e+02,\n",
              "       -1.86385310e+02, -3.57314462e+01, -2.99506379e+00, -7.28228587e+00,\n",
              "        1.94552033e+01,  5.48156137e+01, -3.18969896e+02, -4.84350126e+02,\n",
              "       -6.27883290e+02, -5.94564511e+02, -6.29600309e+02,  3.25940414e+02,\n",
              "        4.99702313e+02,  6.77118132e+02,  6.67814564e+02,  7.05921878e+02,\n",
              "        9.45860059e+02])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "# Regression coeficients\n",
        "reg.coef_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "wXucNJVcWOyb"
      },
      "outputs": [],
      "source": [
        "# predicted values of the regression\n",
        "pred_train =reg.predict(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ploting the test vs prediction of linear regression\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "plt.subplot(1,1,1)\n",
        "sns.distplot(y_test, kde=False, color=\"orange\", label=\"Test\")\n",
        "\n",
        "plt.subplot(1,1,1)\n",
        "sns.distplot(pred_train, kde=False, color=\"red\", label=\"Prediction\")\n",
        "plt.legend()\n",
        "plt.title(\"Test VS Prediction\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "zlEESIfjstBl",
        "outputId": "a6d2d25b-6bf0-474b-febc-bf48c78bdac0"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test VS Prediction')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yV5ehOzbXQez",
        "outputId": "22c1110b-ea0b-4c4a-a794-60a0d293c911"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.42269465494088754"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "# printing mean absolute percentage error\n",
        "from sklearn.metrics import mean_absolute_percentage_error\n",
        "mean_absolute_percentage_error(pred_train,y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "p3ZyNeF0aIHp"
      },
      "outputs": [],
      "source": [
        "# predicting on the test colunm\n",
        "pred_test = reg.predict(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cCm3wQ-gdof8"
      },
      "source": [
        "**accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjvF2JLWZ3ly",
        "outputId": "cee4d5d5-47be-4615-f8fe-bf84a8c306af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6530284621303124"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "# R2 score of linear regression\n",
        "r2_score(y_train, pred_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDgXGqBvXlYj"
      },
      "source": [
        "# Random Forest Regressor (Bagging)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "HxCVODj3XWTQ"
      },
      "outputs": [],
      "source": [
        "# import random forest \n",
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bz-1VXXIXce0",
        "outputId": "0e22ce9f-ba01-4a29-b20d-a6a134a2003e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_features='sqrt', min_samples_leaf=4,\n",
              "                      min_samples_split=3, n_estimators=40, n_jobs=-1)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "# Training the random forest regresor \n",
        "\n",
        "regr1 = RandomForestRegressor(max_features='sqrt',min_samples_leaf=4,min_samples_split=3,n_estimators=40, n_jobs=-1)\n",
        "regr1.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKIBYKZ_X2W3",
        "outputId": "3d9a7f5f-ad36-44f3-d471-1b7b85070173"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.21440313087313118"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "# Predicting on the random forest regressor\n",
        "y_pred = regr1.predict(x_train)\n",
        "mean_absolute_percentage_error(y_pred, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "abFQzaG8fPuT"
      },
      "source": [
        "**accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRj24WJbYr_G",
        "outputId": "87b0f1ed-198e-4b4c-bdd7-fb0ba7535c84"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8483600472487749"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "# R2 score of the random forest\n",
        "r2_score(y_train, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ploting the test vs predicted data\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "plt.subplot(1,1,1)\n",
        "sns.distplot(y_test, kde=False, color=\"orange\", label=\"Test\")\n",
        "\n",
        "plt.subplot(1,1,1)\n",
        "sns.distplot(y_pred, kde=False, color=\"red\", label=\"Prediction\")\n",
        "plt.legend()\n",
        "plt.title(\"Test VS Prediction\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "Adq49KrVtLDS",
        "outputId": "c758c9cd-9682-45e6-fab7-3a0bbf33303a"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test VS Prediction')"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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y7bbb5oEHHnhC++222y477LDDY+/v+/SnP/3YaOD6YgQQAACge8c73pFbb701++67b1prmTdvXr7whS/kTW96U77yla9k4cKFedaznpWXvvSlT9h33rx5WbJkSd785jfnJz/5SXbZZZdceOGFecMb3pAjjjgi5557bj7+8Y8/bp/TTz89xx13XB588ME8+9nPzl//9V+v19dXrbX1+gQb2qJFi9qqGXg2W5NNmbsOU98CAMBsuPHGG/P85z9/trux2Zno+1pVV7XWFk3U3gjgxm6mPx8FAAAYLO8BBAAAGAgBEAAAYCAEQAAAYIPY3OYfmW1T+X4KgAAAwHq31VZb5e677xYCZ0hrLXfffXe22mqrJ7WfSWAAAID1bv78+VmxYkVWrlw5213ZbGy11VaZP3/+k9pHAAQAANa7pzzlKVmwYMFsd2Pw3AIKAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADMTc2e4AM2jJkonrixdv2H4AAAAbJSOAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMxFoDYFWdVlV3VdV1E2z7napqVbVzX6+q+lhVLa+qa6pq37G2x1TVd/rjmLH6flV1bd/nY1VVvb5jVV3Y219YVTvMzEsGAAAYpnUZAfxUkkNXL1bV7kkOTvIvY+XXJdmzPxYnObm33THJCUlekmT/JCeMBbqTk7xzbL9Vz3V8kotaa3smuaivAwAAMEVrDYCttaVJ7plg00lJfi9JG6sdluSMNnJZku2r6hlJDklyYWvtntbavUkuTHJo3/b01tplrbWW5Iwkh48d6/S+fPpYHQAAgCmY0nsAq+qwJLe31r652qbdktw2tr6i19ZUXzFBPUl2ba3d0ZfvTLLrVPoKAADAyNwnu0NVPTXJ72d0++cG0VprVdUm215VizO65TTPetazNlS3AAAANilTGQH8+SQLknyzqm5NMj/J16vqZ5PcnmT3sbbze21N9fkT1JPk+/0W0fSvd03Wodbaktbaotbaonnz5k3hJQEAAGz+nnQAbK1d21rbpbW2R2ttj4xu29y3tXZnkvOSHN1nAz0gyf39Ns4LkhxcVTv0yV8OTnJB3/aDqjqgz/55dJJz+1Odl2TVbKHHjNUBAACYgnX5GIjPJvlakudV1YqqOnYNzc9PcnOS5Un+KslvJklr7Z4kf5Tkyv74w15Lb/PJvs93k3yp1z+c5Jer6jtJXtvXAQAAmKK1vgewtXbUWrbvMbbckrxrknanJTltgvqyJHtNUL87yUFr6x8AAADrZkqzgAIAALDpEQABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGIi5s90BNoAlS55YW7x4w/cDAACYVUYAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgINYaAKvqtKq6q6quG6v9SVV9q6quqarPV9X2Y9veX1XLq+qmqjpkrH5ory2vquPH6guq6vJeP6uqtuj1Lfv68r59j5l60QAAAEO0LiOAn0py6Gq1C5Ps1VrbO8m3k7w/SapqYZIjk7yg7/OXVTWnquYk+USS1yVZmOSo3jZJPpLkpNbac5Lcm+TYXj82yb29flJvBwAAwBStNQC21pYmuWe12pdba4/01cuSzO/LhyU5s7X2UGvtliTLk+zfH8tbaze31n6c5Mwkh1VVJXlNknP6/qcnOXzsWKf35XOSHNTbAwAAMAUz8R7Atyf5Ul/eLcltY9tW9Npk9Z2S3DcWJlfVH3esvv3+3h4AAIApmFYArKoPJHkkyWdmpjtT7sfiqlpWVctWrlw5m10BAADYaE05AFbV25L8SpK3ttZaL9+eZPexZvN7bbL63Um2r6q5q9Ufd6y+fbve/glaa0taa4taa4vmzZs31ZcEAACwWZtSAKyqQ5P8XpI3ttYeHNt0XpIj+wyeC5LsmeSKJFcm2bPP+LlFRhPFnNeD41eTHNH3PybJuWPHOqYvH5HkK2NBEwAAgCdp7toaVNVnk7wqyc5VtSLJCRnN+rllkgv7vCyXtdaOa61dX1VnJ7kho1tD39Vae7Qf591JLkgyJ8lprbXr+1O8L8mZVfXHSb6R5NRePzXJp6tqeUaT0Bw5A68XAABgsNYaAFtrR01QPnWC2qr2JyY5cYL6+UnOn6B+c0azhK5e/1GSt6ytfwAAAKybmZgFFAAAgE2AAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMxNzZ7gCzZMmSieuLF2/YfgAAABuMEUAAAICBWGsArKrTququqrpurLZjVV1YVd/pX3fo9aqqj1XV8qq6pqr2HdvnmN7+O1V1zFh9v6q6tu/zsaqqNT0HAAAAU7MuI4CfSnLoarXjk1zUWtszyUV9PUlel2TP/lic5ORkFOaSnJDkJUn2T3LCWKA7Ock7x/Y7dC3PAQAAwBSsNQC21pYmuWe18mFJTu/Lpyc5fKx+Rhu5LMn2VfWMJIckubC1dk9r7d4kFyY5tG97emvtstZaS3LGasea6DkAAACYgqm+B3DX1todffnOJLv25d2S3DbWbkWvram+YoL6mp4DAACAKZj2JDB95K7NQF+m/BxVtbiqllXVspUrV67PrgAAAGyyphoAv99v30z/elev355k97F283ttTfX5E9TX9BxP0Fpb0lpb1FpbNG/evCm+JAAAgM3bVAPgeUlWzeR5TJJzx+pH99lAD0hyf7+N84IkB1fVDn3yl4OTXNC3/aCqDuizfx692rEmeg4AAACmYK0fBF9Vn03yqiQ7V9WKjGbz/HCSs6vq2CTfS/Krvfn5SV6fZHmSB5P8RpK01u6pqj9KcmVv94ettVUTy/xmRjONbp3kS/2RNTwHAAAAU7DWANhaO2qSTQdN0LYledckxzktyWkT1Jcl2WuC+t0TPQcAAABTM+1JYAAAANg0CIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBAzJ3tDrAO7lo69X13OXDm+gEAAGzSjAACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQMyd7Q6wkVmyZOL64sUbth8AAMCMMwIIAAAwENMKgFX136rq+qq6rqo+W1VbVdWCqrq8qpZX1VlVtUVvu2VfX9637zF2nPf3+k1VdchY/dBeW15Vx0+nrwAAAEM35QBYVbsl+a0ki1preyWZk+TIJB9JclJr7TlJ7k1ybN/l2CT39vpJvV2qamHf7wVJDk3yl1U1p6rmJPlEktclWZjkqN4WAACAKZjuLaBzk2xdVXOTPDXJHUlek+Scvv30JIf35cP6evr2g6qqev3M1tpDrbVbkixPsn9/LG+t3dxa+3GSM3tbAAAApmDKAbC1dnuSP03yLxkFv/uTXJXkvtbaI73ZiiS79eXdktzW932kt99pvL7aPpPVAQAAmILp3AK6Q0YjcguSPDPJ0zK6hXODq6rFVbWsqpatXLlyNroAAACw0ZvOLaCvTXJLa21la+3hJJ9L8vIk2/dbQpNkfpLb+/LtSXZPkr59uyR3j9dX22ey+hO01pa01ha11hbNmzdvGi8JAABg8zWdAPgvSQ6oqqf29/IdlOSGJF9NckRvc0ySc/vyeX09fftXWmut14/ss4QuSLJnkiuSXJlkzz6r6BYZTRRz3jT6CwAAMGhT/iD41trlVXVOkq8neSTJN5IsSfLFJGdW1R/32ql9l1OTfLqqlie5J6NAl9ba9VV1dkbh8ZEk72qtPZokVfXuJBdkNMPoaa2166faXwAAgKGbcgBMktbaCUlOWK18c0YzeK7e9kdJ3jLJcU5McuIE9fOTnD+dPgIAADAy3Y+BAAAAYBMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQMyd7Q6wiViyZOL64sUbth8AAMCUGQEEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIHwOYCbu7uWTm//XQ6cmX4AAACzTgDcWEz2QesAAAAzxC2gAAAAAzGtAFhV21fVOVX1raq6sapeWlU7VtWFVfWd/nWH3raq6mNVtbyqrqmqfceOc0xv/52qOmasvl9VXdv3+VhV1XT6CwAAMGTTHQH8aJJ/aK39QpIXJrkxyfFJLmqt7Znkor6eJK9Lsmd/LE5ycpJU1Y5JTkjykiT7JzlhVWjsbd45tt+h0+wvAADAYE05AFbVdkkOTHJqkrTWftxauy/JYUlO781OT3J4Xz4syRlt5LIk21fVM5IckuTC1to9rbV7k1yY5NC+7emttctaay3JGWPHAgAA4EmazgjggiQrk/x1VX2jqj5ZVU9Lsmtr7Y7e5s4ku/bl3ZLcNrb/il5bU33FBHUAAACmYDoBcG6SfZOc3Fp7UZIf5qe3eyZJ+shdm8ZzrJOqWlxVy6pq2cqVK9f30wEAAGySphMAVyRZ0Vq7vK+fk1Eg/H6/fTP96119++1Jdh/bf36vrak+f4L6E7TWlrTWFrXWFs2bN28aLwkAAGDzNeUA2Fq7M8ltVfW8XjooyQ1JzkuyaibPY5Kc25fPS3J0nw30gCT391tFL0hycFXt0Cd/OTjJBX3bD6rqgD7759FjxwIAAOBJmu4Hwb8nyWeqaoskNyf5jYxC5dlVdWyS7yX51d72/CSvT7I8yYO9bVpr91TVHyW5srf7w9baPX35N5N8KsnWSb7UHwAAAEzBtAJga+3qJIsm2HTQBG1bkndNcpzTkpw2QX1Zkr2m00cAAABGpvs5gAAAAGwiBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGYu5sd4BN3JIlT6wtXrzh+wEAAKyVEUAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAGzaaZgAABLUSURBVAZCAAQAABiIubPdgcFYvmTN2+9aumH6AQAADJYRQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCDmznYH2AwtWTJxffHiDdsPAADgcYwAAgAADIQACAAAMBDTDoBVNaeqvlFVf9/XF1TV5VW1vKrOqqoten3Lvr68b99j7Bjv7/WbquqQsfqhvba8qo6fbl8BAACGbCZGAP9LkhvH1j+S5KTW2nOS3Jvk2F4/Nsm9vX5Sb5eqWpjkyCQvSHJokr/soXJOkk8keV2ShUmO6m0BAACYgmkFwKqan+Q/JPlkX68kr0lyTm9yepLD+/JhfT19+0G9/WFJzmytPdRauyXJ8iT798fy1trNrbUfJzmztwUAAGAKpjsC+OdJfi/JT/r6Tknua6090tdXJNmtL++W5LYk6dvv7+0fq6+2z2R1AAAApmDKAbCqfiXJXa21q2awP1Pty+KqWlZVy1auXDnb3QEAANgoTWcE8OVJ3lhVt2Z0e+Zrknw0yfZVterzBecnub0v355k9yTp27dLcvd4fbV9Jqs/QWttSWttUWtt0bx586bxkgAAADZfUw6ArbX3t9bmt9b2yGgSl6+01t6a5KtJjujNjklybl8+r6+nb/9Ka631+pF9ltAFSfZMckWSK5Ps2WcV3aI/x3lT7S8AAMDQzV17kyftfUnOrKo/TvKNJKf2+qlJPl1Vy5Pck1GgS2vt+qo6O8kNSR5J8q7W2qNJUlXvTnJBkjlJTmutXb8e+gsAADAIMxIAW2sXJ7m4L9+c0Qyeq7f5UZK3TLL/iUlOnKB+fpLzZ6KPAAAAQzcTnwMIAADAJkAABAAAGIj18R5ANid3LZ36vrscOHP9AAAAps0IIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBAmAWUDWfJkonrixdv2H4AAMBAGQEEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIGYO9sdgCxZ8sTa4sUbvh8AALCZMwIIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQMyd6o5VtXuSM5LsmqQlWdJa+2hV7ZjkrCR7JLk1ya+21u6tqkry0SSvT/Jgkre11r7ej3VMkv/eD/3HrbXTe32/JJ9KsnWS85P8l9Zam2qf2cDuWjqNnRfPWDcAAICR6YwAPpLkd1prC5MckORdVbUwyfFJLmqt7Znkor6eJK9Lsmd/LE5ycpL0wHhCkpck2T/JCVW1Q9/n5CTvHNvv0Gn0FwAAYNCmPALYWrsjyR19+YGqujHJbkkOS/Kq3uz0JBcneV+vn9FH8C6rqu2r6hm97YWttXuSpKouTHJoVV2c5Omttct6/Ywkhyf50lT7zCZkyZKJ64uNDAIAwFTNyHsAq2qPJC9KcnmSXXs4TJI7M7pFNBmFw9vGdlvRa2uqr5igDgAAwBRMOwBW1TZJ/jbJf22t/WB8Wx/tW+/v2auqxVW1rKqWrVy5cn0/HQAAwCZpWgGwqp6SUfj7TGvtc738/X5rZ/rXu3r99iS7j+0+v9fWVJ8/Qf0JWmtLWmuLWmuL5s2bN52XBAAAsNmacgDss3qemuTG1tqfjW06L8kxffmYJOeO1Y+ukQOS3N9vFb0gycFVtUOf/OXgJBf0bT+oqgP6cx09diwAAACepClPApPk5Un+U5Jrq+rqXvv9JB9OcnZVHZvke0l+tW87P6OPgFie0cdA/EaStNbuqao/SnJlb/eHqyaESfKb+enHQHwpJoABAACYsunMAvq/k9Qkmw+aoH1L8q5JjnVaktMmqC9LstdU+wgAAMBPzcgsoAAAAGz8BEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABmI6HwQPG96SJRPXFy/esP0AAIBNkBFAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAbCx0Cwcbpr6ZNrv3xs+Tk+EgIAACZiBBAAAGAgjACyeThzbMRwl/7Vh8MDAMDjGAEEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAizgLL5WrJk4rrZQQEAGCgjgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBAmASG4TE5DAAAAyUAsvm5a+nU9lvevz5HEAQAYPPkFlAAAICBMAIIq5zZRw53Gau5LRQAgM2IEUAAAICBMAIIa2LCGAAANiNGAAEAAAZCAAQAABgIt4DCVLg1FACATZAACKub6ucIJkkEQAAANl4CIMwkI4MAAGzEBEDYECYKhkIhAAAbmElgAAAABsIIIMykJ/P+wT9ere0uB46+GhkEAGA9EQBhY+N9hAAArCcCIGws1jZ6uPqIYZIc2UcNnyMcAgCwdht9AKyqQ5N8NMmcJJ9srX14lrsEG48zeyjcZbW60UIAACawUQfAqpqT5BNJfjnJiiRXVtV5rbUbZrdnsJFZffRwotHCyax67+HqhEgAgM3ORh0Ak+yfZHlr7eYkqaozkxyWRACEmTLZradPJkRO5shJwuUqbl0FANigNvYAuFuS28bWVyR5ySz1ZWacOQN/VMOmYq0/7xvoelhbEH0yhFYAYBO2sQfAdVJVi5Os+qvs36rqptnszyR2TvKvs92JgXMONg4b/jz8P5+ZwYP93zN4rFnjWtg4OA+zzznYODgPs8852DjM5Hn4uck2bOwB8PYku4+tz++1x2mtLUkyydz5G4eqWtZaWzTb/Rgy52Dj4DzMPudg4+A8zD7nYOPgPMw+52DjsKHOw8+s7yeYpiuT7FlVC6pqiyRHJjlvlvsEAACwSdqoRwBba49U1buTXJDRx0Cc1lq7fpa7BQAAsEnaqANgkrTWzk9y/mz3YwZs1LeoDoRzsHFwHmafc7BxcB5mn3OwcXAeZp9zsHHYIOehWmsb4nkAAACYZRv7ewABAACYIQLgelZVh1bVTVW1vKqOn+3+bG6qaveq+mpV3VBV11fVf+n1Havqwqr6Tv+6Q69XVX2sn49rqmrfsWMd09t/p6qOma3XtKmqqjlV9Y2q+vu+vqCqLu/f67P6RE6pqi37+vK+fY+xY7y/12+qqkNm55Vsmqpq+6o6p6q+VVU3VtVLXQcbXlX9t/5v0XVV9dmq2sq1sP5V1WlVdVdVXTdWm7Gf/6rar6qu7ft8rKpqw77Cjd8k5+BP+r9J11TV56tq+7FtE/6MT/Z302TXEY830XkY2/Y7VdWqaue+7lpYDyY7B1X1nn49XF9V/2OsvuGvhdaax3p6ZDRxzXeTPDvJFkm+mWThbPdrc3okeUaSffvytkm+nWRhkv+R5PhePz7JR/ry65N8KUklOSDJ5b2+Y5Kb+9cd+vIOs/36NqVHkt9O8r+S/H1fPzvJkX35lCT/uS//ZpJT+vKRSc7qywv7NbJlkgX92pkz269rU3kkOT3JO/ryFkm2dx1s8HOwW5Jbkmzd189O8jbXwgb53h+YZN8k143VZuznP8kVvW31fV832695Y3tMcg4OTjK3L39k7BxM+DOeNfzdNNl15LH289Dru2c0qeL3kuzca66FDXQOkrw6yT8m2bKv79K/zsq1YARw/do/yfLW2s2ttR8nOTPJYbPcp81Ka+2O1trX+/IDSW7M6I+wwzL6gzj96+F9+bAkZ7SRy5JsX1XPSHJIkgtba/e01u5NcmGSQzfgS9mkVdX8JP8hySf7eiV5TZJzepPVz8Gqc3NOkoN6+8OSnNlae6i1dkuS5RldQ6xFVW2X0S+cU5Oktfbj1tp9cR3MhrlJtq6quUmemuSOuBbWu9ba0iT3rFaekZ//vu3prbXL2ugvrjPGjkU30TlorX25tfZIX70so89zTib/GZ/w76a1/E5hzCTXQpKclOT3koxP/uFaWA8mOQf/OcmHW2sP9TZ39fqsXAsC4Pq1W5LbxtZX9BrrQb996kVJLk+ya2vtjr7pziS79uXJzolzNT1/ntEvlp/09Z2S3Df2i3/8+/nY97pvv7+3dw6mbkGSlUn+uka34X6yqp4W18EG1Vq7PcmfJvmXjILf/UmuimthtszUz/9ufXn1Ok/O2zMaMUqe/DlY0+8U1qKqDktye2vtm6ttci1sOM9N8op+6+Y/VdWLe31WrgUBkM1CVW2T5G+T/NfW2g/Gt/X/pTLd7XpSVb+S5K7W2lWz3ZcBm5vR7SYnt9ZelOSHGd3y9hjXwfrX32N2WEaB/JlJnhYjqBsFP/+zq6o+kOSRJJ+Z7b4MTVU9NcnvJ/mD2e7LwM3N6JbaA5L8bpKzZ/P9kwLg+nV7RvdcrzK/15hBVfWUjMLfZ1prn+vl7/dbFdK/rhpqn+ycOFdT9/Ikb6yqWzO6ReE1ST6a0a0kqz5rdPz7+dj3um/fLsndcQ6mY0WSFa21y/v6ORkFQtfBhvXaJLe01la21h5O8rmMrg/XwuyYqZ//2/PTWxfH66yDqnpbkl9J8tYexJMnfw7uzuTXEWv28xn9p9Q3++/p+Um+XlU/G9fChrQiyef67bZXZHTH1M6ZpWtBAFy/rkyyZ5+tZ4uM3uR/3iz3abPS//fk1CQ3ttb+bGzTeUlWzVp1TJJzx+pH95mvDkhyf79F6IIkB1fVDv1/8Q/uNdaitfb+1tr81toeGf2Mf6W19tYkX01yRG+2+jlYdW6O6O1brx9Zo5kRFyTZM6M3m7MWrbU7k9xWVc/rpYOS3BDXwYb2L0kOqKqn9n+bVp0H18LsmJGf/77tB1V1QD+vR48dizWoqkMzenvAG1trD45tmuxnfMK/m/p1Mdl1xBq01q5tre3SWtuj/55ekdHkeXfGtbAhfSGjiWBSVc/NaGKXf81sXQvrMlOMx7RmAnp9RjNTfjfJB2a7P5vbI8kvZXRbzzVJru6P12d0j/RFSb6T0axLO/b2leQT/Xxcm2TR2LHentGbb5cn+Y3Zfm2b4iPJq/LTWUCf3f8RW57k/8tPZ77aqq8v79ufPbb/B/q5uSlmFnuy3/t9kizr18IXMpq5zXWw4c/Dh5J8K8l1ST6d0cxuroX1/33/bEbvu3w4oz9wj53Jn/8ki/o5/W6Sv0hSs/2aN7bHJOdgeUbvY1r1+/mUsfYT/oxnkr+bJruOPNZ+Hlbbfmt+Oguoa2EDnYOMAt/f9O/d15O8Zqz9Br8Wqh8IAACAzZxbQAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQgM1OVW1fVb+5hu2XzsBzvK2q/mK6xxk73u+vtj7tPgLA6gRAADZH2yd5QgCsqrlJ0lp72Ybu0KrnXoPHBcDZ6CMAmz8BEIDN0YeT/HxVXV1VV1bVJVV1XpIbkqSq/q1/fVVVLa2qL1bVTVV1SlVN+ruxqn6jqr5dVVckeflY/VNVdcTY+vjxV3/uL1TVVVV1fVUt7rUPJ9m69/czqx2jqupPquq6qrq2qn5t7NgXV9U5VfWtqvpMVdUMfg8B2Ayt7X8jAWBTdHySvVpr+1TVq5J8sa/fMkHb/ZMsTPK9JP+Q5M1Jzlm9UVU9I8mHkuyX5P4kX03yjXXoy76rPffbW2v3VNXWSa6sqr9trR1fVe9ure0zwf5vTrJPkhcm2bnvs7Rve1GSFyT5P0n+OaNQ+r/XoU8ADJQRQACG4IpJwt+qbTe31h5N8tkkvzRJu5ckubi1trK19uMkZ03xuX+rqr6Z5LIkuyfZcy37/1KSz7bWHm2tfT/JPyV58dixV7TWfpLk6iR7rGOfABgoI4AADMEP17CtrWV9XTyS/p+q/RbSLSZ67j4a+dokL22tPVhVFyfZagrPt8pDY8uPxu91ANbCCCAAm6MHkmy7jm33r6oFPbj9Wia/hfLyJK+sqp2q6ilJ3jK27daMbg1Nkjcmecokx9guyb09/P1CkgPGtj3cj7u6S5L8WlXNqap5SQ5McsW6vDAAWJ0ACMBmp7V2d5J/rqrrkvzJWppfmeQvktyY5JYkn5/kmHck+WCSr2X0frsbxzb/VUbh8JtJXprJRxz/Icncqroxo4lqLhvbtiTJNasmgRnz+STXJPlmkq8k+b3W2p1reU0AMKFqbSp3ugDApq/fkvne1tqvzHZfAGBDMAIIAAAwEEYAAWA1VXV5ki1XK/+n1tq1s9EfAJgpAiAAAMBAuAUUAABgIARAAACAgRAAAQCA/7/9OhAAAAAAEORvPchlERMCCAAAMCGAAAAAEwHUIomB5tTuhgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlT3G8IcYv5R"
      },
      "source": [
        "#XGboost ML model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "VQwUByx9Y7bO"
      },
      "outputs": [],
      "source": [
        "# importing the xg boost \n",
        "import xgboost as xgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPdFbcSSZBWZ",
        "outputId": "30f3cdbc-d6f9-4154-a681-a5b21054a603"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[17:33:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(colsample_bytree=0.8, n_estimators=1000, reg_alpha=200,\n",
              "             reg_lambda=200, subsample=0.8)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "# Training the model\n",
        "\n",
        "xgb_model = xgb.XGBRegressor(\n",
        " learning_rate =0.1,\n",
        " n_estimators=1000,\n",
        " max_depth=3,\n",
        "\n",
        " subsample=0.8,\n",
        " reg_alpha=200, reg_lambda=200,\n",
        " colsample_bytree=0.8,)\n",
        "xgb_model.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted = xgb_model.predict(x_train)"
      ],
      "metadata": {
        "id": "wZNMnbPWzmgl"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted_test = xgb_model.predict(x_test)"
      ],
      "metadata": {
        "id": "2bKxQkQ10yIp"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Accuracy**"
      ],
      "metadata": {
        "id": "Eho0qIbw0ly8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_train, y_predicted)"
      ],
      "metadata": {
        "id": "0GnQ9m12zCNM",
        "outputId": "3ca9b4a0-0a70-4873-ad0e-ddfdaf0d58f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7757716832774739"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RuAJ28x8ZGeD",
        "outputId": "75918f2c-ba52-405a-961f-1a0f137a2b52"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7723563950058541"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "r2_score(y_test, y_predicted_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ploting the test vs prediction of xgboost model\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "plt.subplot(1,1,1)\n",
        "sns.distplot(y_test, kde=False, color=\"blue\", label=\"Test\")\n",
        "\n",
        "plt.subplot(1,1,1)\n",
        "sns.distplot(y_predicted, kde=False, color=\"red\", label=\"Prediction\")\n",
        "plt.legend()\n",
        "plt.title(\"Test VS Prediction\")"
      ],
      "metadata": {
        "id": "Q9Rl31IYtW0o",
        "outputId": "51f4c5de-198e-4910-d2c6-732123d038a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        }
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test VS Prediction')"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "phC2X7IArFbZ"
      },
      "execution_count": null,
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