NYC_Taxi_Trip_Time_Prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/anishjohnson/NYC_Taxi_Trip_Time_Prediction/blob/main/NYC_Taxi_Trip_Time_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "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>\n",
        "\n",
        "## <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.\n",
        "\n",
        "## <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": "uIeMIxr9EJTg"
      },
      "source": [
        "# **Import libraries and data.** ⬇️⬇️⬇️"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4mC-K0_IESxj",
        "outputId": "759eacba-9dbb-447b-a486-22a2487dcac3"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting haversine\n",
            "  Downloading haversine-2.5.1-py2.py3-none-any.whl (6.1 kB)\n",
            "Installing collected packages: haversine\n",
            "Successfully installed haversine-2.5.1\n"
          ]
        }
      ],
      "source": [
        "# import the libraries.\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error\n",
        "\n",
        "import xgboost as xgb\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import HistGradientBoostingRegressor\n",
        "\n",
        "import time\n",
        "import datetime as dt\n",
        "from seaborn import palettes\n",
        "\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# We will use haverseine to calculate the distance using the longitudinal and lattitue data.\n",
        "!pip install haversine\n",
        "from haversine import haversine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAhk8GrjEcdo",
        "outputId": "2177632e-b018-473f-85ff-ca8e3afbdf17"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "# Mount the drive.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "\n",
        "# Load the data.\n",
        "df = pd.read_csv('/content/drive/MyDrive/Capstone data files/NYC Taxi Data.csv')\n",
        "\n",
        "# Create a copy of data to be on the safer side.\n",
        "nyc_df = df.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Br-RCkRZErMg"
      },
      "source": [
        "# **Exploratory Data Analysis.** 📊📈📉"
      ]
    },
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      "cell_type": "code",
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        "colab": {
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              "          id  vendor_id  ... store_and_fwd_flag trip_duration\n",
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              "    [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-7849158e-c24a-476c-aee4-2056dae7847b 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-7849158e-c24a-476c-aee4-2056dae7847b');\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",
              "  "
            ],
            "text/plain": [
              "                id  vendor_id  ... store_and_fwd_flag trip_duration\n",
              "1458639  id2376096          2  ...                  N           778\n",
              "1458640  id1049543          1  ...                  N           655\n",
              "1458641  id2304944          2  ...                  N           764\n",
              "1458642  id2714485          1  ...                  N           373\n",
              "1458643  id1209952          1  ...                  N           198\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "nyc_df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGPHsCRYE0oi",
        "outputId": "de504fab-5809-4533-cf9c-5e7e025a2373"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The dataset contains 1458644 rows and 11 columns.\n"
          ]
        }
      ],
      "source": [
        "# Check the shape of our data.\n",
        "shape = nyc_df.shape\n",
        "print(f'The dataset contains {shape[0]} rows and {shape[1]} columns.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LkcVnBS1E98S",
        "outputId": "a744a2f7-d931-41ff-9b37-77710bcc3e4d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                    0\n",
              "vendor_id             0\n",
              "pickup_datetime       0\n",
              "dropoff_datetime      0\n",
              "passenger_count       0\n",
              "pickup_longitude      0\n",
              "pickup_latitude       0\n",
              "dropoff_longitude     0\n",
              "dropoff_latitude      0\n",
              "store_and_fwd_flag    0\n",
              "trip_duration         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Check if it contains null values.\n",
        "nyc_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RV4NexExFFTb",
        "outputId": "e3ae5a29-79fb-49e9-8f43-9ff4522535ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No duplicates exist we are good to go.👌\n"
          ]
        }
      ],
      "source": [
        "# Only originals needed, remove the duplicates.\n",
        "duplicates = nyc_df.duplicated().sum()\n",
        "\n",
        "if duplicates == 0:\n",
        "  print('No duplicates exist we are good to go.👌')\n",
        "else:\n",
        "  print(duplicates,'exist remove them.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IvVFOG-vFNdT",
        "outputId": "3d99860f-0da1-4209-9e0d-47720f53a47f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1458644 entries, 0 to 1458643\n",
            "Data columns (total 11 columns):\n",
            " #   Column              Non-Null Count    Dtype  \n",
            "---  ------              --------------    -----  \n",
            " 0   id                  1458644 non-null  object \n",
            " 1   vendor_id           1458644 non-null  int64  \n",
            " 2   pickup_datetime     1458644 non-null  object \n",
            " 3   dropoff_datetime    1458644 non-null  object \n",
            " 4   passenger_count     1458644 non-null  int64  \n",
            " 5   pickup_longitude    1458644 non-null  float64\n",
            " 6   pickup_latitude     1458644 non-null  float64\n",
            " 7   dropoff_longitude   1458644 non-null  float64\n",
            " 8   dropoff_latitude    1458644 non-null  float64\n",
            " 9   store_and_fwd_flag  1458644 non-null  object \n",
            " 10  trip_duration       1458644 non-null  int64  \n",
            "dtypes: float64(4), int64(3), object(4)\n",
            "memory usage: 122.4+ MB\n"
          ]
        }
      ],
      "source": [
        "# Reveal information about the data.\n",
        "nyc_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abAndTttFP45",
        "outputId": "b8f51d7f-fbe8-4e01-994e-7c0b766b9ab0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f7443845-c542-4cf7-9941-8f0c9a544170\">\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-f7443845-c542-4cf7-9941-8f0c9a544170')\"\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-f7443845-c542-4cf7-9941-8f0c9a544170 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-f7443845-c542-4cf7-9941-8f0c9a544170');\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",
              "  "
            ],
            "text/plain": [
              "          vendor_id  passenger_count  ...  dropoff_latitude  trip_duration\n",
              "count  1.458644e+06     1.458644e+06  ...      1.458644e+06   1.458644e+06\n",
              "mean   1.534950e+00     1.664530e+00  ...      4.075180e+01   9.594923e+02\n",
              "std    4.987772e-01     1.314242e+00  ...      3.589056e-02   5.237432e+03\n",
              "min    1.000000e+00     0.000000e+00  ...      3.218114e+01   1.000000e+00\n",
              "25%    1.000000e+00     1.000000e+00  ...      4.073588e+01   3.970000e+02\n",
              "50%    2.000000e+00     1.000000e+00  ...      4.075452e+01   6.620000e+02\n",
              "75%    2.000000e+00     2.000000e+00  ...      4.076981e+01   1.075000e+03\n",
              "max    2.000000e+00     9.000000e+00  ...      4.392103e+01   3.526282e+06\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Get some statiscis.\n",
        "nyc_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4w8hDNNFZ7v"
      },
      "source": [
        "***Points to be noted:***\n",
        "* *id can be dropped - as every ride would have different id it would'nt give any useful insight.*\n",
        "* *pickup_datetime and dropoff_time need to be changed to datetime format - currently they are in string (object) format.*\n",
        "* *extract data from datetime values.*\n",
        "* *duration is given in seconds lets convert it into minutes.*\n",
        "* *store_and_fwd_flag is a categorical variable that needs to be converted.*\n",
        "* *vendor_id consists of two values 1 and 2.*\n",
        "* *passenger_count ranges from 0-9, the difference between the 75th percentile and the max value shows the precense of outliers.*\n",
        "* *trip_duration_minutes also contains outliers.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4RDP3ovJFfJD"
      },
      "outputs": [],
      "source": [
        "# Change format for pickup_datetime and dropoff_time.\n",
        "nyc_df['pickup_datetime'] = pd.to_datetime(nyc_df['pickup_datetime'])\n",
        "nyc_df['dropoff_datetime'] = pd.to_datetime(nyc_df['dropoff_datetime'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "821I-nHwVy22"
      },
      "outputs": [],
      "source": [
        "# Retrive day and month data.\n",
        "nyc_df['day'] = nyc_df.pickup_datetime.dt.weekday\n",
        "nyc_df['month'] = nyc_df.pickup_datetime.dt.month\n",
        "\n",
        "# Retrive pickup and dropoff hrs.\n",
        "nyc_df['pickup_hour'] = nyc_df.pickup_datetime.dt.hour\n",
        "nyc_df['dropoff_hour'] = nyc_df.dropoff_datetime.dt.hour"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VEaGlYgjWRG2"
      },
      "outputs": [],
      "source": [
        "# Now lets drop pickup_datetime and dropoff_datetime as we have extracted data from it.\n",
        "nyc_df.drop(['pickup_datetime', 'dropoff_datetime'], inplace=True, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HWJ4r5tjFfFS"
      },
      "outputs": [],
      "source": [
        "# Convert the data from seconds to mins.\n",
        "nyc_df['trip_duration_minutes']=nyc_df['trip_duration']/60\n",
        "\n",
        "# Drop duration in seconds.\n",
        "nyc_df.drop('trip_duration', inplace=True, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "714KNbkuFhew",
        "outputId": "82580e00-35cf-4261-8952-c66e43564547"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1458644 entries, 0 to 1458643\n",
            "Data columns (total 13 columns):\n",
            " #   Column                 Non-Null Count    Dtype  \n",
            "---  ------                 --------------    -----  \n",
            " 0   id                     1458644 non-null  object \n",
            " 1   vendor_id              1458644 non-null  int64  \n",
            " 2   passenger_count        1458644 non-null  int64  \n",
            " 3   pickup_longitude       1458644 non-null  float64\n",
            " 4   pickup_latitude        1458644 non-null  float64\n",
            " 5   dropoff_longitude      1458644 non-null  float64\n",
            " 6   dropoff_latitude       1458644 non-null  float64\n",
            " 7   store_and_fwd_flag     1458644 non-null  object \n",
            " 8   day                    1458644 non-null  int64  \n",
            " 9   month                  1458644 non-null  int64  \n",
            " 10  pickup_hour            1458644 non-null  int64  \n",
            " 11  dropoff_hour           1458644 non-null  int64  \n",
            " 12  trip_duration_minutes  1458644 non-null  float64\n",
            "dtypes: float64(5), int64(6), object(2)\n",
            "memory usage: 144.7+ MB\n"
          ]
        }
      ],
      "source": [
        "# Recheck if formats been changed.\n",
        "nyc_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Us-MXlhFlKc"
      },
      "source": [
        "***Now lets bring in the charts and diagrams for better understanding.***  *😊*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKjAvFtGFx-h"
      },
      "source": [
        "## **Dependent Variable.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nm1uzMSAKCsf",
        "outputId": "6f136e85-842b-4b3a-c010-f8da8c3111d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.458644e+06\n",
              "mean     1.599154e+01\n",
              "std      8.729053e+01\n",
              "min      1.666667e-02\n",
              "25%      6.616667e+00\n",
              "50%      1.103333e+01\n",
              "75%      1.791667e+01\n",
              "max      5.877137e+04\n",
              "Name: trip_duration_minutes, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "nyc_df['trip_duration_minutes'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U5iZ4h4RFhaf",
        "outputId": "5e521e97-de54-4503-b12c-e53a58d6a762"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Check out the trip_duration.\n",
        "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(25,5))\n",
        "# plt.figure(figsize=(10,5))\n",
        "sns.distplot(nyc_df['trip_duration_minutes'], color='orange', ax=ax1)\n",
        "sns.boxplot(nyc_df['trip_duration_minutes'], color='orange', ax=ax2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deV6gy9qF8Cn"
      },
      "source": [
        "* ***trip_duration_minutes is positively skewed.***\n",
        "* ***also it seems there are few extreme values that we will need to check.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6UDNbSfAlRr",
        "outputId": "9f13a8ec-ea89-4fe5-abf8-56003a6d8849"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ba3cd22d-5f8d-40a9-8685-770275e2772e\">\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>id</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>store_and_fwd_flag</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>pickup_hour</th>\n",
              "      <th>dropoff_hour</th>\n",
              "      <th>trip_duration_minutes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>355003</th>\n",
              "      <td>id1864733</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.789650</td>\n",
              "      <td>40.643559</td>\n",
              "      <td>-73.956810</td>\n",
              "      <td>40.773087</td>\n",
              "      <td>N</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>32328.933333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>680594</th>\n",
              "      <td>id0369307</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-73.921677</td>\n",
              "      <td>40.735252</td>\n",
              "      <td>-73.984749</td>\n",
              "      <td>40.759979</td>\n",
              "      <td>N</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>22</td>\n",
              "      <td>15</td>\n",
              "      <td>34159.633333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>924150</th>\n",
              "      <td>id1325766</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.983788</td>\n",
              "      <td>40.742325</td>\n",
              "      <td>-73.985489</td>\n",
              "      <td>40.727676</td>\n",
              "      <td>N</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>37126.866667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>978383</th>\n",
              "      <td>id0053347</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.783905</td>\n",
              "      <td>40.648632</td>\n",
              "      <td>-73.978271</td>\n",
              "      <td>40.750202</td>\n",
              "      <td>N</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>58771.366667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba3cd22d-5f8d-40a9-8685-770275e2772e')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      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-ba3cd22d-5f8d-40a9-8685-770275e2772e 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-ba3cd22d-5f8d-40a9-8685-770275e2772e');\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",
              "  "
            ],
            "text/plain": [
              "               id  vendor_id  ...  dropoff_hour  trip_duration_minutes\n",
              "355003  id1864733          1  ...            11           32328.933333\n",
              "680594  id0369307          1  ...            15           34159.633333\n",
              "924150  id1325766          1  ...             1           37126.866667\n",
              "978383  id0053347          1  ...            18           58771.366667\n",
              "\n",
              "[4 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "nyc_df[nyc_df['trip_duration_minutes'] > 10000]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUK6Rt29BIQV"
      },
      "source": [
        "* ***Now that we look at the extreme values these are more than 20 days, which was either due to some technical error or a vaccation was planned.***\n",
        "* ***Since there are only four values and these could only cause problems for our model lets remove these values.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xs0Hb-oyCSHE"
      },
      "outputs": [],
      "source": [
        "# Get rid of the extreme values.\n",
        "nyc_df = nyc_df[nyc_df['trip_duration_minutes'] < 10000]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVw3GUzoJRyg",
        "outputId": "6ad3a85f-24a2-4d13-af5d-5af1693c2952"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Now lets check for outliers in the dependent variables.\n",
        "fig = plt.figure(figsize=(25,5))\n",
        "sns.boxplot(nyc_df['trip_duration_minutes'], color='orange')\n",
        "plt.title('trip_duration after removing extreme values.')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7DtrQpxjYS5U"
      },
      "source": [
        "* ***there are few values which maybe outliers, this could be bad for our model.***\n",
        "* ***lets look into this ahead in our analysis.***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2F2xYpzyF2BI"
      },
      "source": [
        "## **Independent Variables.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzySFX7hTeKP",
        "outputId": "54cb7dbe-b62c-4065-dc1c-903d079b4994"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th>id</th>\n",
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              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
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              "    [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-95aba8ee-55cd-4eef-98c1-04cbefc164bc 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-95aba8ee-55cd-4eef-98c1-04cbefc164bc');\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",
              "  "
            ],
            "text/plain": [
              "          id  vendor_id  ...  dropoff_hour  trip_duration_minutes\n",
              "0  id2875421          2  ...            17               7.583333\n",
              "\n",
              "[1 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "nyc_df.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6kiFGQ6KFVpi"
      },
      "outputs": [],
      "source": [
        "independent_variables = ['vendor_id', 'passenger_count', 'pickup_longitude', 'pickup_latitude', 'dropoff_longitude',\n",
        "                         'dropoff_latitude', 'store_and_fwd_flag', 'day', 'month', 'pickup_hour', 'dropoff_hour']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SD3L0V6eIkDT"
      },
      "source": [
        "**vendor_id.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pE8hYkjcJ0QF",
        "outputId": "b92d1597-112b-4242-f98d-20a493d52fc7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4/DrGhdiSFUcR4mTqATM9Pv+9qoM4jWzym8jj8ydjzC+doDqLEBF6ArhDrmVlDilUk3h8/sYY+0sHK44iRHXNBCbJlVejJ4VqAo/P3x7jSP5ZqrMIUUNvA2Nl1aroSKFGyePz9wH8QJrqLEJEaQFwaSDHe0h1ELuSQo2Cx+fvifEibKQ6ixAmWQpkB3K8e1UHsSMp1Bry+PxdgM+AZoqjCGG2tcDQQI53q+ogdiPTpmrA4/N3AuYjZSqcqTOw0OPzn6E6iN3ICLWawgegvgBaq84iRIxtAwYGcrybVAexCynUavD4/K0xylTOyzdJgksjNaUO6Q3rkNawLukN6pDaoA71khNJdGkkuDQSXRqaBqVlUFJWRmlZiJLSEAeOFLHr4FHy8guP/XnwiCz/abKfgEGBHG+e6iB2IIUaIY/P3xz4HOikOovdJLg0OqWn0LWVm64t3bRtUp/0hnVIbVCXpqcl43Jppn2vwuJS8sLluvNgIT/tOsSqbUFWbQ2y+5DMCKqhFcDgQI73oOogVieFGoHw8nufAV0UR7G8iuXZvVUjurVqyFktGlI3KUF1NHYGC1m1LcjqbUGjZLcF2Z0vJRuhL4BhgRzvEdVBrEwKtQrhy5UsAHqqzmJVrRvX48LO6VzYOZ0+nsaWKM9IbT9whM/W5/HxmjwWb9jD0ZIy1ZGs7F3gskCOV/6RTkEKtRIenz8Jo0wHqc5iNZmt3VyYYZRo5xYNVccxxeGjJSz6eQ8fr93FgrV57D1cpDqSFf0jkOOdojqEVUmhVsLj8z8N3Kg6hxVoGpx7RioXd2nOBZ3TSG9YV3WkmCotC/HdlgPMX7OL91ZuZ+t+2dKt4I5Ajnea6hBWJIV6Ch6f/wbgWdU5VGtyWjJj+7ZhXP+2tG5cX3UcJUrLQny2Po+Xv97E5z/uRn5lKAN+E8jxvqk6iNVIoZ6Ex+c/G+MgVNyuZ9qnXWPGZ7Uju1tz6iTaZ59orG3eW8CcbzYxd+kW9hcUq46j0hHg/ECO92vVQaxEzpQ6gcfnbwG8QRyWaf3kBK7u35YP/nAO824awKierWq1TEtLS+nZsyeXXHIJAKFQiD//+c906tSJzp07M3369JM+LiEhgR49etCjRw9Gjhx57PO5ubn079+fjh07cuWVV1JUZOwTnTFjBl27dmX48OHHPrdw4UJuu+22KjO2bVofX3Znvr7nAqZdkUmvtnG7jEM94I3wDBgRJiPUCsILRH8GnK04Sq2ql5TApHPac8M5HWhYT91FWadNm8ayZcs4ePAg77//Pi+++CKffvopM2fOxOVykZeXR1rarxf1SklJ4dChXy+QdMUVVzB69GjGjh3LjTfeSGZmJjfddBNZWVksXryYhx56iMzMTC655BKGDRvGq6++SpMmTaqd+9tN+3n0w3V8vXFfjX5um/sA8MoC1QYZoR5vBnFUpokujQlnt+OLuwdzx9AzlZbp1q1b8fv9TJo06djnnn76ae677z5cLuNlerIyPZVQKMSCBQsYM2YMANdeey1vv/32sa8VFxdTUFBAUlISs2fPJjs7u0ZlCtCrXWNem3w2M6/rS4ZDZjxUQzYgl6gOk0IN8/j8k4HJqnPUlpGZLfnkjvN44NKupDZQf8T+1ltv5dFHHz1WngAbNmxg7ty59OnTh+zsbH766aeTPrawsJA+ffqQlZV1rDT37t1Lo0aNSExMBKB169Zs27YNgFtuuYWsrCw2b97MwIEDefHFF7n55puj/hkGn5nG+1MG8eTYHrRtElcH8P4aPu4Q96RQAY/PPwBjdOp453VKxT91ENOv6km7pqepjgPA+++/T1paGr179z7u80ePHqVu3bosW7aMG264geuvv/6kj9+0aRPLli1jzpw53HrrrWzYsKHS73fNNdewYsUKZs+ezRNPPMHUqVP54IMPGDNmDLfddhtlZTWft+5yaVzaoxXzbz+PBy7tQrOUuNgVnwi8Gr4MUFyL+0L1+PypwDwcfhCqbZP6zJ7Yn5eu70eXlm7VcY6zaNEi3n33XTweD2PHjmXBggWMHz+e1q1bM3r0aAAuu+wyVq48+aXkW7VqBUCHDh0YPHgwK1asoGnTphw4cICSEmOxlK1btx67X7nt27fzzTffMGrUKB5//HHmzp1Lo0aN+OSTT6L+mZITXUw428Pndw3hxvM6YOJyBVbVDvi36hCqxX2hAk8BLVSHiKXrBnr4363nMOgMax6Qffjhh9m6dSuBQIDXXnuN888/n9mzZzNq1Cg+/fRTAD7//HM6dfr1ujT79+/n6FHjfPw9e/awaNEiMjIy0DSNIUOGMG/ePABeeuklLr300uMee++99/LAAw8AcOTIETRNw+VyUVBQYNrPdlqdRHzZnXnzpgF0TEsx7XktapTH55+qOoRKcV2oHp9/DDBGdY5YadukPnN/l8VfRnShfnKi6jjV5vP5eOONN+jWrRv33HMPzz//PADLli07dvBq7dq19OnTh8zMTIYMGYLP5yMjIwOARx55hGnTptGxY0f27t3LxIkTjz33ihUrAOjVqxcA48aNo1u3bixatIhhw4aZ/rP0aNsY/5RB3HTe6U4frf7N4/P3rvpuzhS306bC8+d+wKEX17tuoIe7Lj7TlkXqdN9t3s+d81byc55jr4W3AegVj8v9xfMIdQYOLFO7j0rjQRyMVk/H2JUWd+JyhOrx+S8DHHce8uherXhwVFcpUhv5dtN+bpy9nDxnrss6NJDj/Vh1iNoUd4Xq8fmbAGuAdNVZzOLS4J7hnbnhnA6qo4ga2HWwkMmzlvH91qDqKGb7EegWyPHGzTqI8bjJPx0HlWnDuon8+7d9pUxtLL1hXeb+7mwu69mq6jvbSyfgbtUhalNcjVA9Pv8IjFXHHaFDs9N47to+nJ7q+Ok4cePZLzby8AdrnbRE4BGgSyDHm6s6SG2ImxFq+CyOZ1TnMMt5nVJ5++aBUqYOM/ncDvz72r40qOOY/eD1iJOzECGOChV4BIdM4L/hnA78+7d9lS5mImJnyFlpvH3zQNo3s8apwSbwenz+UapD1Ia42OT3+PwZwErA9isl33dJBtcPaq86hqgF+w4Xcc0LS/hhuyOmc24GOgdyvOadhmZB8TJCfRgHlOmDo7pKmcaRJqclM+eGLHq2ccQi1m2B+1SHiDXHj1A9Pv9AYKHqHNHQNHj08u78pk8b1VGEAvmFxVw/cylLA/tVR4lWMZAZyPGuVR0kVuJhhPqI6gDR0DR47DeZUqZxrEHdJF66vh99PbZfHS8J+KfqELHk6EL1+PwjgYGqc0Tjr6O6cXmv1qpjCMXqJxvzjXvYf/N/iMfnP191iFhxbKF6fP4E4CHVOaKhj+zCuP5tVccQFlE+Uu3S0vaXWXHsvlTHFipwLdBFdYiaumNoJ347wKM6hrAYd70kXp7Y3+5Tqs7z+PznqA4RC44sVI/PXxe4X3WOmhqZ2ZIp55+hOoawqCanJfP8hD52n/zvyFGqIwsVmArYcsdjt1ZuHh3TXXUMYXGnp6Uw/aqeaPZd/u9Cj8+fpTqE2RxXqB6fvxHgU52jJlJT6vDshN7UTbL9lFlRC4aclYZv2FmqY0TDcaNUxxUqxqWgbTe/JDnBxTPX9KaFu57qKMJGfnfe6XZepSrb4/P3UR3CTI4q1PCR/d+rzlETD4/uRq92tnsfEBaQM7qbnadT3as6gJkcVajACIzL2drKxEHtuby3LXf5Cguok5TAM9f0Jq1BHdVRamKkx+fPVB3CLE4r1CmqA1RX//ZNuCfb1vvBhAWkN6zLv8b3tus1qhwzSnVMoXp8/i6Arc7AqJeUwN/GZJKY4Jj/BqFQr3aN+d25p6uOUROjPT6/I+YJOuk32XajU1/2WbRtWl91DOEgt154Bh3TbLfouAZcrzqEGRxRqOGpUuNV56iOszs05Zos2+3uFRZXJymBx36TacdN/wnhg8q25ohCxXh3s825ePWSEnjk8u64bPiqF9bXo00jO276twSGqQ4RLdsXqsfndwE3q85RHbKpL2LNppv+tt/st32hAsMB21xDWTb1RW2w6ab/CI/P30x1iGg4oVBvUR0gUrKpL2pTjzaNmHyubcYaYCxAbatjISeydaF6fP404CLVOSI16Zz2sqkvatWU88+gWUqy6hjVcZ3qANGwdaECo7DJz9C4fhI32Gu0IBzgtDqJdlsKsrudz++3RRlVYrTqAJG6eUhHGtZNUh1DxKGr+rWlTRNbLbpj24NTti3U8NxTW5wZ1dJdVw5ECWWSE13ccdGZqmNUx1XhReJtx7aFirEQii2GfLdd1Ik6ssapUGhkZksyWtjmWlSNMH6/bcfOhXq56gCROCMthdFy1VKhmMulcfcwW41Sh6sOUBO2LFSPz38aMFR1jkjcPexMEmSalLCAwWem0b99E9UxIjXM4/Pb7hfHloWK8e5l+b3sPds04qKM5qpjCHHMH+1zyZTmgO3WSbVrodri6P7EQe1VRxDiOL3aNaanfVb3t925/bYrVI/PXwfwqs5RldSUOgztIqNTYT3jz7bNjBMp1FpwEdBAdYiqjO3XhuREO/7zCqe7pFsLGtW3xQSZAR6f3/K/6xXZ8Tfe8qNTl2ZMphbCiuokJXBlnzaqY0QiCbhAdYjqsGOhnqs6QFUuykinZSPLHzMTcWxc/7Zo9jiGbqvNflsVqsfnbwp0Vp2jKuPlrChhce2ansZ5nVJVx4jExaoDVIetChUYhHH9Gctq3+w0Bp5u6yUdRZywyenQHo/Pb5u5XnYr1HNUB6jK+Ky2st6psIXBZ6bRurEtdk3ZZrNfCtVkI7q3VB1BiIgkuDQu6d5CdYxI9FcdIFJ2K9RLgcuAvwELgUK1cY7Xo00j0hracpEcEacu7JyuOkIkbHPGlBYKhVRnqDGPz58E9ADOrnBTtmPozqFncsv5HVV9eyGqrbQsRN+/zmff4SLVUSpTCjQI5HiPqA5SlUTVASKmu9MxjvAvRQ8eBgjkeIuBpeHbdACPz9+S4wu2N1CnNiJe0DmtNr6NEKZJcGlccFYary/fqjpKZRKArhi/55Zmn0I1JvS/AJSiu1cCX4Vvi9GDG8vvFMjxbgfeCN/w+PzJQE+Mch0Q/tP09fRaN65HZ/usNynEMRdmpFu9UMHYEpVCNVH5dWYSMAqyJ/B7AHR3Hr8U7FcYo9gjAIEcbxGwJHz7O4DH52/N8aPYXkBUVzKzyb4oIX5lUMdm1El0cbSkTHWUythiP6qdCrVvJV9LwzhgdWn47yXo7u+BxZSXrB4MlN85kOPdCrwevpUvuNKb40u2WofrpVCFXZ1WJ5EBpzfj0/V5qqNUpofqAJGwx0Ep3Z0M5BPdKHInx49il6EHTzlLwOPzt+WXch2A8R960hUlGtRJZPm9F8liKMK25izZxJ/eWq06RmXyAXcgx2vpwrLLCLUbUW6SYyxYe1n4BlCM7l5BxZLVg5vL7xzI8W4GNgNzATw+fz1+PYptDjDwjGZSpsLWzjvT8gdUGwAdgA2qg1TGLoVa2eZ+TSUB/cK3PwCgu7dT8WAXfIsePAoQnrKxMHwDwOPze4CzR3RveSfGflghbKlVo3qkptRh96GjqqNUJhMpVFN0r6Xv0xLj4n/lFwAsQnd/y/Gj2GOHQwM53gAQACbWUj4hYqZrK7cd9qO+qTpEZexSqB5F3zcZyArfbgNAd2+l4sEuWIEelNGpsL1urRtavVC7qg5QFbsUqpVWw20NXBG+QeP2R6mlEweEiKVurdyqI1SlleoAVbHLkRQrFerxWmRKmQpH6Gr9QrX83ETrF6rubghY93+6ZU/VCYQwRQu3cWDKwqRQTWDd0SlAS1vMNxYiIhYfpdb1+PyWvga2HQrV2le7a2GLM+KEiEi31pZfj8LSo1Q7FKp1R6gNmkO9xqpTCGGaTmmWv2pzc9UBKiOFGo0Gsjq/cJZ0t+UXSJcRapSsu8nfwNL/t0JUW1oDSx+UAhmhRs3CI1RbXI9HiIilNbD8CFUKNUrWHaGmyAhVOEu95AQa1rX0+T6W/qWzdqHqbo0YrK5vmgaWfrMUokZSrT1KtfQvnbULFVKx8mmdUqjCgdIbWvdXDmimOkBlrF6olp5lTIoUqnAei+9HjXZd5JiyeqFaO5+MUMkP5GoAABQESURBVIUDWXyEaukdvNYuLKvnk0n9woEa1T/plX6sQgo1CgmqA1QqwdIvPCFqJDHB0rUghRoF6+bTNNCsG0+Imkp0aaojVMbShWrpcFh5hOqy+j+dEDXTtZX7S+A/qnOcQr7qAJWxeitYdwioWbfrhYhGX0+TtYEc7z9U57Aj6xaWwcKtZenLgwsRjTLVAexKRqg1VVaiOoEQsbHk2d/xwV03qI5xCs+gB29WHeJUpFBrqqxUdQIhYqOsWMO6W4eWHj1bt7AMVv1PNcgoVTiRtQcLxaoDVMbqhWrtfEWHVScQwnzFBaoTVMbSoxhrF5bVR6iH8lQnEMJ8h3apTlAZGaFGwdqH0vN3qE4ghPms/bqWEWoU9qkOUClrv5MLUTP5ln5dywg1CpZ+qyR/p+oEQpjP2iPUvaoDVMbqhboHKw/xpVCF05SVwmFLHxvYpjpAZaxdqHqwDLDu/66138mFqL6CPVafNiWFGiXrDgNlH6pwGmvvPwUp1KhZdxgoI1ThNNZ+TZdg5S1W7FGo1h2hBrfK2VLCWQ5sUp2gMjvCuwEtyw6Fat23zJKjsHud6hRCmGf7d6oTVMbSm/tgj0K17ggVrP4CFKJ6dlj69SyFagLrjlDB6i9AISJXfMTqW1xSqCaQEaoQtWHXapkyFSU7FKq1R6g7V8mBKeEM1h8cSKGawNoj1JJCq28mCRGZ7StUJ6iKFGrU9OARLD73zAbv7EJUzfrHA6RQTbJSdYBKWf+FKETlrH9AqgzYqjpEVexSqN+rDlCpwELVCYSIzuavrX5Aam14a9XS7FKo1h4C5q2FfbmqUwhRc+v/qzpBVb5RHSASdilUa49QAX78QHUCIWpuveVfv0tUB4iEXQp1LXBUdYhKWf8FKcTJ7VwFwS2qU1RFCtU0erAEWKM6RqU2LYIjB1SnEKL6rD8YKABWqw4RCXsUqmGZ6gCVKiuFn+erTiFE9Vm/UL8ND6osz06F+pXqAFWy/o59IY53cDts/1Z1iqrY4oAU2KtQv1YdoEo/z4fSItUphIjcjx+qThAJW+w/BXsV6jpgv+oQlSoMQu6XqlMIEbl176lOEAkZoZpOD4awwz/sty+pTiBEZPZvgg0LVKeoSh56MKA6RKTsU6gG6+9HXfe+sV9KCKtb/iKEQqpTVMU2m/tgv0JdrDpAlcpKZZQqrK+kEL6dpTpFJKy/VVqB3Qr1C+CQ6hBVWj4TSotVpxDi1Na8AwV7VaeIhIxQY0YPHgUsP2mO/J0yhUpY29LnVSeIxGHAVisP2atQDW+pDhARe7xgRTzauRK22GJL+r92WGGqIjsWqh+w/mTP3C9g93rVKYT4taX/Vp0gUvNUB6gu+xWqHjwIWH6uBwBLn1OdQIjjFR6AlXNVp4jEEcB2+83sV6gGe2z2fzsLDlr+qg0iniyeAcUFqlNE4kP0oPUPQJ/AroX6DsYlEayt5Ch8lqM6hRCG/J3w1VOqU0TqDdUBasKehaoHd2GHc/sBVsyWfanCGr74m11Gp0WALc6JPZE9C9Vgj83+UBkseFB1ChHv9m005kfbw3z0YFB1iJqQQq0Na9+FrdZezlU43IIHocwWS4qCDY/ul7NvoerBDcAq1TEiNl9XnUDEqx3fw2rb7JIswThGYkv2LVSDfUapgS/h509UpxDxaP79qhNUx6fowX2qQ9SU3Qv1TdUBqmW+bqfNLuEEGz+DDbZ6I7fNUPpk7F2oevB77LQazc6V8NU/VacQ8aLoMLx3q+oU1VGGnbY6T8LehWp4UnWAavn0rzKNStSO+Trsz1WdojreRw/mqQ4RDScU6uuAfVZ0LjkKb98km/4itgIL4ZtnVaeorhmqA0TL/oWqB4sB25z+AcC25bLpL2Kn6DC8c4vqFNW1Fj1o++uw279QDc8AhapDVIts+otYsd+mPsA/VAcwgzMKVQ/uAV5RHaNaZNNfxII9N/UPAra4HktVnFGoBnsdnALZ9BfmsuemPsCLdlxZ6mScU6h6cBV2WSe1ogUP2mX1dGF1799mx039UhxwMKqccwrVYL9RamkRzL1a1k0V0Vk03S4LR5/ojfBp5I7gtEJ9H7Dff86hPHjtaii21eVzhFX89BHMv091ippy1ILBzipUPViGXTcftq+Ad225/0uotOdHmDcRQiHVSWriI/TgCtUhzOSsQjX8G+Ooof2smgcLn1CdQtjFkQPw6lg4as+XOw4bnYITC1UP5mO3if4VfXI/rP9AdQphdWUlMO862Gu/PVxhS9CDn6oOYTbnFarhYcCe5wSHQvDmDbB7neokwso+vg822G9SSwV/VR0gFpxZqMalpm27l56j+TB7DBzYrDqJsKKv/mn3+cvz0YO2vGZUVZxZqIbngdWqQ9RYcAu8NBIO2mfdF1ELlj4PH/5JdYpolAC2WlOwOpxbqHqwFLhddYyo7M+FWSONaVVCrHgZ/HeoThGtf6EHf1AdIlacW6gAevBjwK86RlT2/CSlKuC7OfDuFNUporUXO++Ki4CzC9VwJ8Zmhn3lrYWZw2XzP14tn2kspGPPuaYV3Yce3K86RCw5v1D14DrgadUxorbnJ3hxuByoijdL/gXv/UF1CjOswlhm09GcX6gGHbD/O+P+XKNU89aqTiJqw+ePwgd/VJ3CLLeGj2s4WnwUqnFZ2gdUxzBFcAu8cJFM/ney4gKYd72xCLkzvIketPWk2UjFR6Ea/gn8qDqEKY7mw2tXyWmqThTcCv/OhtW2vppyRYUYxzHiQvwUqnHtqbtUxzBNKGRc6uKNicaIRtjfliXw3BDY8Z3qJGaahh603SKtNRU/hQqgB9/F7tOoTrRqnrFfVdZTtbcVs2HmJU6bHrcNeEh1iNoUX4VqmIQxH845tq+AZwfLyv92VFYC/7sH3rnZWGzcWf6AHjysOkRt0kL2n9tWfbp7DPC66himS0iGIX+GAbeAK1F1GlGVfblGkW5apDpJLDyHHpysOkRti8cRKujBecAc1TFMV1oE8/8CLwyttUtUezweunXrRo8ePejTpw8A9957L927d6dHjx4MHTqU7dtPfkJCQkICPXr0oEePHowcOfLY53Nzc+nfvz8dO3bkyiuvpKjIGLnNmDGDrl27Mnz48GOfW7hwIbfddluMf0qThcpgyTPw9ACnlukawBGTZ6srPkeoALq7EcZk49aqo8REYh0Y/KeYj1Y9Hg/Lli2jWbNmxz538OBBGjZsCMD06dNZs2YN//rXv3712JSUFA4d+vXFLq+44gpGjx7N2LFjufHGG8nMzOSmm24iKyuLxYsX89BDD5GZmckll1zCsGHDePXVV2nSpEnMfkZT7cs1rswQWKg6SawUAn3Rg/ZdmCgK8TlCBdCDB4DrAGe+o5QcrfXRarnyMgU4fPgwmqZF/NhQKMSCBQsYM2YMANdeey1vv/32sa8VFxdTUFBAUlISs2fPJjs72x5lWnFU6twyBbg9XssU4rlQAfTgfOBx1TFiattyeOYcWPh34wCIyTRNY+jQofTu3Ztnn3322Of//Oc/06ZNG1555RUeeODk51QUFhbSp08fsrKyjpXm3r17adSoEYmJxqi6devWbNtmzGC45ZZbyMrKYvPmzQwcOJAXX3yRm2++2fSfyXT7cuGlEfDB3U6f4vYmetD+p3lHIX43+cvp7iTgS6C/6igx17wbXKhDxwtNe8pt27bRqlUr8vLyuOiii5gxYwbnnnvusa8//PDDFBYWcv/995/ysRs3buT888/nk08+we12k5WVxc8//wzAli1byM7OZvXq4wc9DzzwAN27d8flcjFr1izatGnD448/jstloTFCYRAWTzcWg3b+FW23AJlOX/ykKhZ69SliTPgfCxxQHSXmdq6C2Zcb8x23LjXlKVu1agVAWloal112Gd98c/zUrauvvpo33jj5WT/lj+3QoQODBw9mxYoVNG3alAMHDlBSYoymt27deux+5bZv384333zDqFGjePzxx5k7dy6NGjXik08+MeVnilpJISyeAU9mwhePxUOZlgLj4r1MQQrVoAcDwETVMWpN4Et4/kKYOz6q/auHDx8mPz//2McfffQRXbt25aeffjp2n3feeYezzjrrV4/dv38/R48eBWDPnj0sWrSIjIwMNE1jyJAhzJs3D4CXXnqJSy+99LjH3nvvvcd2Ixw5cgRN03C5XBQUKN6cLisxFoGe3gs++j84Ejf98gB60NE7hiMlhVpOD76Jcb5//Fj7HjyVZSxcHNxa7Yfv2rWLQYMGkZmZSb9+/fB6vQwbNgyfz0fXrl3p3r07H330EU8++SQAy5YtY9KkSca3XruWPn36kJmZyZAhQ/D5fGRkZADwyCOPMG3aNDp27MjevXuZOPGX97oVK4zLuPfq1QuAcePG0a1bNxYtWsSwYcOi+ueIytr3jANO79wSb2etfQ48qDqEVcg+1Ip0dx2MF4jz96eeKLEO9JoAfSdB6q9HlOIkSouMIv3qn8bBv/izF2O/aVy9g1RGCvVEursZsBg4Q3UUZTznGMV6lhcSklSnsZ7gFmMV/eUvweHdqtOoUgKMRA/KOpIVSKGejO7uAHwFpKmOolSD5tDrWuh9LTRsVfX9nSxUBhs/M646uv4D4+/x7Tr04EzVIaxGCvVUdHcf4DPgNMVJ1HMlwJle6P1baH+OsWZAvMjfYaxNuvQF2LdRdRqr+BN68GHVIaxICrUyujsbeBeQlUbK1WkIZ1wEZ2ZDx4ugXiPVicy3c5UxCv3xA9j2reo0VjMdPRiX5+lHQgq1Krr7euAF1TEsyZUAbQcY5XrmcGjSXnWimiktgsAiWP9fo0iDW1Qnsqq5wFXoQSmNU5BCjYTu/gvGhf5EZVLPAs8gaNkDWvY0/m7FZQSP7Ift3xkr429bbuwbPZqvOpXVzQe86EHHLdpqJinUSOnu5zAWpxaRSqwLzbtCix7qSrZieW5fYXx8YFPtfX9n+BYYjB6Ud50qSKFGSncnAO8AXtVRbC2xjjFjoEELYxZBg+aQ0vyXjxs0h5R0SKp/6ilbZaVQVmyUZf7OX26HdkL+LuNA0qFdcHC78aeIxgZgAHrQUddmiRUp1OrQ3acBnwJ9VUeJG5rLGNFqLuPUzhismCVOaRcwED24QXUQu5BCrS7dnYqxOtWZqqMIEUP5wHnowRWqg9iJnMtfXXpwN3AOEJfnGoq4UACMkjKtPinUmjBKdQhgkfXihDDNAeAi9OAC1UHsSAq1powjnsNx4tVTRbzaibGZv1h1ELuSQo2GMSdvLBDXl30QjpALDEIPrlQdxM7koJRZdLcO/EV1DCFqYDVwMXrw5Nf7FhGTQjWT7v49MAMZ+Qv7+AK4DD24T3UQJ5BffDPpwaeAqwA5PU/YwSsYB6CkTE0iI9RY0N0XAm8BKaqjCHEKD6IH71UdwmmkUGPFWE/1v0Cq6ihCVFAM/A49+KLqIE4km/yxogeXAT0x9lEJYQX7gGwp09iRQo0l4+Jl5wMPAHF/zQyh1GcYF9STk1FiSDb5a4vuHoJxEKCF6igirpRgrOX7MHpQ3tRjTAq1NhkLq8wCFF5AXsSRXGAcevBr1UHihWzy1yZjDYDhwF0YBweEiJU5QA8p09olI1RVdHc/4DXAphdiEhaVD9yCHpylOkg8kkJVSXe7geeA36iOIhxhKcZF9GRBaEWkUK1Ad/8OeAKopzqKsKUQ8ChwL3pQdiUpJIVqFbr7DOCfwEWqowhb2QJcJ9OhrEEK1Wp09xUYo9WWqqMISysAHgH+hh48ojqMMEihWpHubgDcD0wBLHhhe6FQCGM+sy984oiwEClUK9Pd3YF/YFzDSoglwB/Qg0tUBxEnJ4VqB7r7cozNu9NVRxFKbAV8wBz0oPzCWpgUql3o7mTgFuBeoJHiNKJ2HAH+BjyCHixQHUZUTQrVbnR3U4xLrdwIJClOI2LnVeCP6MEtqoOIyEmh2pXubgPcCtwANFCcRpjnY0CXK4/akxSq3RlnW00GpgKtFacRNVOIceT+7+jB1arDiJqTQnUK3Z0EXAncAfRQnEZEZhfwFPB0eOEcYXNSqE5kXNPqDmSZQKtaiXHyxqvowaOqwwjzSKE6me7uilGs44BkxWniXQjwA0+gBxeoDiNiQwo1HujuFhhTrsYBHrVh4k4BMBN4Ej34o+IsIsakUOON7u6LsVzgGGQt1lgpxjhaPxd4Gz14UHEeUUukUOOZ7u6NUa6/ATooTmN3pcACjBJ9Cz24T3EeoYAUqjDo7p78Uq4dFaexiwJgPvAe8C56ME9xHqGYFKr4Nd3dA6NYRwNnKU5jNduA9zFK9BP0YKHiPMJCpFBF5XR3GtAfyArf+hI/Z2aVAeuAbzAuL/IVenCF2kjCyqRQRfXobhfQhV8KNgvoDGgqY5kkgFGcSzFK9Fv0YL7SRMJWpFBF9IzTX/vyS8F2wzgN1sqXKc/jl/I0bnK2koiSFKqIDeNU2DYYU7Mq3loBzYF0wB2D71wE7AC2V7jt+NXHchRexIAUqlBHd9fll3JNxzibSwvfXBU+1ir5fAnGOfFGYerBvbX7QwjxCylUIYQwiZX3cQkhhK1IoQohhEmkUIUQwiRSqEIIYRIpVCGEMIkUqhBCmEQKVQghTCKFKoQQJpFCFUIIk0ihCiGESaRQhRDCJFKoQghhEilUIYQwiRSqEEKYRApVCCFMIoUqhBAmkUIVQgiTSKEKIYRJpFCFEMIkUqhCCGESKVQhhDCJFKoQQphEClUIIUwihSqEECaRQhVCCJNIoQohhEmkUIUQwiRSqEIIYRIpVCGEMIkUqhBCmEQKVQghTCKFKoQQJpFCFUIIk0ihCiGESaRQhRDCJFKoQghhEilUIYQwyf8HduVXDTmDz3oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Calculate the trips done by each vendor.\n",
        "one = len(nyc_df[nyc_df['vendor_id'] == 1])\n",
        "two = len(nyc_df[nyc_df['vendor_id'] == 2])\n",
        "v_id = [one, two]\n",
        "v_name = ['vendor_id_1', 'vendor_id_2']\n",
        "\n",
        "# Plot it.\n",
        "fig = plt.figure(figsize=(6,6))\n",
        "plt.pie(v_id, autopct='%1.2f%%', explode=(0.01,0.01))\n",
        "plt.title('vendor_id')\n",
        "plt.legend(labels=v_name)\n",
        "\n",
        "#draw circle\n",
        "centre_circle = plt.Circle((0,0),0.70,fc='white')\n",
        "fig = plt.gcf()\n",
        "fig.gca().add_artist(centre_circle)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# Groupby trip duration\n",
        "durationByvendor = nyc_df.groupby('vendor_id', as_index=False)['trip_duration_minutes'].sum()\n",
        "sns.barplot(data=durationByvendor, x='vendor_id', y='trip_duration_minutes', palette='gist_heat')\n",
        "plt.title('vendor_id by trip_duration')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-3AqBuvRMXl"
      },
      "source": [
        "* ***53.50% of the trips are completed by the vendor 2 and 46.50% of the trips are completed by vendor 1.***\n",
        "* ***also maximum duration is covered by vendor 2***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iy2NHO1aXxH8"
      },
      "source": [
        "."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwKGuHVyXyxI"
      },
      "source": [
        "**passenger_count.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zEq95fbH4ab",
        "outputId": "4a8de541-263e-4912-fbf6-b3ddad26b8d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.458640e+06\n",
              "mean     1.664531e+00\n",
              "std      1.314244e+00\n",
              "min      0.000000e+00\n",
              "25%      1.000000e+00\n",
              "50%      1.000000e+00\n",
              "75%      2.000000e+00\n",
              "max      9.000000e+00\n",
              "Name: passenger_count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "nyc_df['passenger_count'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zTYDJ3IZCrR",
        "outputId": "92f515e9-29bb-4a63-a5aa-978037b83501"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Passenger count.\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.countplot(nyc_df['passenger_count'], palette='autumn')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ma19IlJ9Zb93"
      },
      "source": [
        "* ***some values are zero which mean either the trip was cancled or there was an error in the data entry.***\n",
        "* ***7, 8, 9 are extreme cases considering the capacity of a car, so we will get rid of them.***\n",
        "* ***most of the trips (75%) contain atmost 1 or 2 passengers.***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCv9vZHj_973"
      },
      "source": [
        "**Remove unwanted values from passenger_count.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SfTNUx85CKFk"
      },
      "outputs": [],
      "source": [
        "# Remove the extreme values from passenger_count.\n",
        "nyc_df = nyc_df[nyc_df['passenger_count'] <= 6]  # This will remove passenger values with 7,8,9 values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CjZwS1DICKBD"
      },
      "outputs": [],
      "source": [
        "# Remove values which are zero.\n",
        "nyc_df = nyc_df[nyc_df.passenger_count != 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "At5meMK9DBDI",
        "outputId": "271f75ed-b8a8-4f5e-f60f-7135e63526c4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot it.\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.countplot(nyc_df['passenger_count'], palette='autumn')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3_neOweoF9JP"
      },
      "source": [
        "***Looks good*** *👍*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-RxmiHQgdFmN"
      },
      "source": [
        "."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wNJTjDoLdG1Y"
      },
      "source": [
        "**pickup/dropoff latitude and longitude.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vq4t6N0omf35",
        "outputId": "37eb51ee-27e3-4bf2-854c-e1cb63d59045"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5))\n",
        "sns.scatterplot(nyc_df['pickup_longitude'], nyc_df['pickup_latitude'], ax=ax1)\n",
        "sns.scatterplot(nyc_df['dropoff_longitude'], nyc_df['dropoff_latitude'], ax=ax2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5-dOadrJn8D"
      },
      "source": [
        "***Lets check whats with those extreme values.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hc3Z88Q9JOaB",
        "outputId": "e2a769fe-0a25-4ae9-e8c8-4df371155386"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-968841a3-922a-4877-bd64-946624c1df39\">\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>id</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>store_and_fwd_flag</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>pickup_hour</th>\n",
              "      <th>dropoff_hour</th>\n",
              "      <th>trip_duration_minutes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1068810</th>\n",
              "      <td>id3777240</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>-121.933235</td>\n",
              "      <td>37.389297</td>\n",
              "      <td>-121.933205</td>\n",
              "      <td>37.389103</td>\n",
              "      <td>N</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18.416667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1184454</th>\n",
              "      <td>id2854272</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>-121.933342</td>\n",
              "      <td>37.389381</td>\n",
              "      <td>-121.933304</td>\n",
              "      <td>37.389511</td>\n",
              "      <td>N</td>\n",
              "      <td>4</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-968841a3-922a-4877-bd64-946624c1df39')\"\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "                id  vendor_id  ...  dropoff_hour  trip_duration_minutes\n",
              "1068810  id3777240          2  ...            18              18.416667\n",
              "1184454  id2854272          2  ...            13               8.316667\n",
              "\n",
              "[2 rows x 13 columns]"
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        "nyc_df[nyc_df['pickup_longitude'] < -120]"
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "               id  vendor_id  ...  dropoff_hour  trip_duration_minutes\n",
              "184925  id2306955          1  ...            19                   13.2\n",
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              "[1 rows x 13 columns]"
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        "nyc_df[nyc_df['pickup_latitude'] > 50]"
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d0abb45-dded-422d-b524-2a2a56146a75')\"\n",
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              "          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",
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              "                id  vendor_id  ...  dropoff_hour  trip_duration_minutes\n",
              "1068810  id3777240          2  ...            18              18.416667\n",
              "1184454  id2854272          2  ...            13               8.316667\n",
              "\n",
              "[2 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
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      ],
      "source": [
        "nyc_df[nyc_df['dropoff_longitude'] < -120]"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
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        {
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              "      <td>4</td>\n",
              "      <td>-75.354332</td>\n",
              "      <td>34.712234</td>\n",
              "      <td>-73.834923</td>\n",
              "      <td>32.181141</td>\n",
              "      <td>N</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>14.583333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e6e7088-e077-4532-8ce6-115f9f2f90fa')\"\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-8e6e7088-e077-4532-8ce6-115f9f2f90fa 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-8e6e7088-e077-4532-8ce6-115f9f2f90fa');\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",
              "  "
            ],
            "text/plain": [
              "               id  vendor_id  ...  dropoff_hour  trip_duration_minutes\n",
              "275644  id0978162          1  ...            16              14.583333\n",
              "\n",
              "[1 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "nyc_df[nyc_df['dropoff_latitude'] < 34]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61sCFP4CN-T7"
      },
      "source": [
        "***Lets drop these values.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xQxQ6FfNOCX-"
      },
      "outputs": [],
      "source": [
        "nyc_df = nyc_df[nyc_df.id != 'id3777240']\n",
        "nyc_df = nyc_df[nyc_df.id != 'id2854272']\n",
        "nyc_df = nyc_df[nyc_df.id != 'id2306955']\n",
        "nyc_df = nyc_df[nyc_df.id != 'id0978162']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7c5XcEp-RqVh",
        "outputId": "83e65046-372e-4be2-d189-e7f15477a5c9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5))\n",
        "sns.scatterplot(nyc_df['pickup_longitude'], nyc_df['pickup_latitude'], ax=ax1)\n",
        "sns.scatterplot(nyc_df['dropoff_longitude'], nyc_df['dropoff_latitude'], ax=ax2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GbWfw9CjBAH3"
      },
      "source": [
        "***It looks better now.***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-TKktihmgbA"
      },
      "source": [
        "."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f0Kz1BQbmhj3"
      },
      "source": [
        "**store_and_fwd_flag.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nY4AZgjjmnkT",
        "outputId": "919c7dd3-9356-4e47-9127-f87b26971c33"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     1458571\n",
              "unique          2\n",
              "top             N\n",
              "freq      1450529\n",
              "Name: store_and_fwd_flag, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "nyc_df['store_and_fwd_flag'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YVUnEsQAmnfh",
        "outputId": "2b5de3a8-a1b7-489e-da1b-5a06c9c4507a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot it.\n",
        "fig = plt.figure(figsize=(7,5))\n",
        "plot = sns.countplot(nyc_df['store_and_fwd_flag'], palette='gist_heat')\n",
        "\n",
        "total = len(nyc_df)\n",
        "for p in plot.patches:\n",
        "    percentage = f'{100 * p.get_height() / total:.1f}%\\n'\n",
        "    x = p.get_x() + p.get_width() / 2\n",
        "    y = p.get_height()\n",
        "    plot.annotate(percentage, (x, y), ha='center', va='center')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zlOLu-yFt5Hg"
      },
      "source": [
        "* ***99.4% of the data values are N and only 0.6% 0f values are Y, which means most of the data was uploaded directly without storing it and forwarding.***\n",
        "* ***this ia a categorical feature which we will be converting into numeric by getting dummies.***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMDjn19Hx-cz"
      },
      "source": [
        "."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkZYsJhZx_Tw"
      },
      "source": [
        "**day/month\tand pickup_hour/dropoff_hour**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWhErvBNyPTE",
        "outputId": "98301a87-aa13-44e8-9c3d-d509440c8eac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RRERERPqLx2M5crzRu+Lw5MrDMidHjjf2anxSdCjjU6OZ4NuqdHxqDDnJkYQGadWh9K2e5oxaoSgiIsNOSU0Tj60u4NUtxZ3Crcz4CB5YmMcNl4wmSFtx9omgwACmjY1n2th4vn11PvWuNj4pqma9bwWjo6K+vW9ZbTOvbS3mta3FAIxPjWZeXiJzchO5/KIEbb0hIiInFQNv+ALETcYYD5AIVHbR95+AbSfDRICOz40xTwJv93O9IiIiIiLtahtb2883PBkc7i930tTqPuvYkMAA8lKi2oPDCWkxjEuNJjFKN2WL/ylQFBGRYeNYXTO/W+PgpU1HaXF72ttHx4axbGEeN07L0Jl+/SwqNIiFE1JYOCEFgPLaZjY4qthQUMkGRzVV9a72vt4P1k6eXH+QkMAALhsbx7y8JObmJjIpPVarR0VERq4/A/OBNcaYfCAE74rDrizmtO1OjTFp1toy349fBnb3V6EiIiIiMnK1uT0crGpoP+Nwn++xtLa5V+NHx4Yx/uSKw7QYJqRGc1FipG6Cl0FLgaKIiAx5VfUuVqwt5PmPD+NqOxUkpsSEsnR+LjfNGKMtIPwkNTaMG6dlcOO0DKy17D/mbD9/8ZOi4+1357W4PXxcdJyPi47z//19P7HhwczOSWBuXiJzcxMZmxDp53ciIiL9wRjzInAVkGiMKQZ+BKwEVvq2Pm0BllhrrTFmNPCUtfZa39hIYBFwz2kv+wtjzCV4tzw91MV1EREREZFzUlXvaj/j8OSWpQUV9bR0+B6qO+HBgYxLjWbCybMOfVuWxkYED0DlIn1HZyiKiMiQdaKhhcfXFfHsh4c6bRuRGBXCfVfl8q+XZxIWrCBxsHK1ufn0SA0bCqpY76hiV3EN3Ry/yJj4cObmJjI3N4nZOQmMigwZ2GJFRIaJwXiG4mChOaOIiIjI0Nbm9hBgDAEXsOORq81NYUVD+5ale8u8AWLHHZd6MjYhoj0wPBkgZsZHXFBNIgOppzmjAkURERlyapta+eP6IlZuPES9q629fVREMPdcmcOtV4wlIkSL8Iea2sZWPiqqYn1BFRsdVRyq7vpgcmNgcnosc3ITmZebyGVjRyk4FhHpJQWK3dOcUURERGRoentnKU+uK2JHcS0hgQEsujiFZQtzGZ8a0+0Yay3H6lzsLa9rX3m4r8xJYWU9bd3d7dxBdGgQ49OifWcdxjA+LZr8lGiiQvV9lAxtChTR5FBEZDiod7Xx9IaDPLm+iLrmU0FiTFgQd83L5t/mXqQPbsPI0eON3vMXHVV86KjiRGNrl/3CggOYkRXPvLxE5uQmMiE1Rnf+iYh0Q4Fi9zRnFBERERl6fr/WwS/e3X9Ge0RIIC/eNYupY+JoanFz4Fjn7Ur3lTup6eZ7ho4CDFyUGNl+xuHJ8DA9Lhxj9N2DDD8KFNHkUERkKGtsaeO5jw7z+AeFnUKlqNAgbp+TxR3zsokN177zw5nHY/msrI71BVVscFSy+dCJbs8pSIgMYU6u9+zFuXmJjI4LH+BqRUQGLwWK3dOcUURERGRoKa1pYt4v1uDuZkVhbHgQCZGhHKxuoDcxyKiI4E4rDiekxpCXEqVdkWRE6WnOqGUcIiIyaDW3unnhkyOsWOugqr6lvT08OJAls7O453PZOktvhAgIMExKj2VSeiz3XZVDc6ubzYeOs6HAu4JxT2lde9/qhhb+sqOUv+woBSA7MZK5ed6AcVZOAjFhCp9FREREREREhrq/7ijtNkwEqG1qo7ap7Yz24EBDTlKULzyMbl99mBQdqlWHIj1QoCgiIoOOq83NK5uP8tgaB8fqTh16HRoUwC2zxnLPlTkkRYf6sULxt7DgQOblJTEvLwmA6noXHxZWtweMJTVN7X2LqhooqmrguY8OExhgmJoRy9y8JObmJnJpZhzBgQH+ehsiIiIiIiIicp5KapvO2mdURDCTM+KYkOo77zAtmuzEKEKC9F2AyLlSoCgiIoNGq9vDa1uLeWy1o1MgFBIYwOKZY/jm/FxSYsL8WKEMVglRofzz1NH889TRWGs5VN3IhoJK1hdU8VFhNU6X945Et8ey7UgN247U8NtVBUSGBDIrO4E5uYnMy0skNzlKdyOKiIiIiIiIDGIVzmaeWn+Qlz452mO/AAP/ePBK3ZQu0kcUKIqIiN+1uT38eXspv11VwJHjje3tQQGGr00fw9IFuaTrHDzpJWMMFyVGclFiJLdckUWb28POktr21YvbDp+gzbclSkOLm1X7Kli1rwKAlJjQ9nBxTk4iyb0MsJtb3QQFGIK02lFERERERESkX5TVNvH4B0W8uOkIrjbPWfv/0+Q0hYkifUiBooiI+I3HY/nrzlJ+s6qAosqG9vYAA1+5LIMHFuSRmRDhxwplOAgKDOCyzFFcljmKBxbm0eBq45OD1WwoqGaDo5IDx+rb+x6rc/HGthLe2FYCwLiU6PbzFy/Pjici5NRHJ2str28r4an1RewrdxIUYLh6QgrfXpTH+NSYAX+fIiIiIiIiIsPR0eONrPigkNe2FNPiPhUkxoYHs+jiZN7ZWUZTa+eAcXxqND+5ftJAlyoyrBlruz+0dDiZPn263bJli7/LEBERvEHi3/eU8+j7BzqFOcbAl6aO5lsL88hOivJjhTKSVNQ1s8FR1b6CscLp6rJfcKDhssxRzM1NZG5eIu/vPcbv1hSe0S8iJJCX7p7FlIy4/i5dROScGWO2Wmun+7uOwUhzRhEREZHBpaiynt+vLeTNT0twe07lGAmRIdw5L5tbrhhLVGgQZbVNvPDxEbYdOUFoUACfn5jKDZemExYc6MfqRYamnuaM/RooGmNWAtcBFdbaSR3alwH3A27gHWvt97oYewhw+vq0nXwDxph44GUgCzgE3GStPXG2WjQ5FBHxP2stq/ZW8Mh7B/isrK7TtWsnp/Ltq/PJT4n2U3Ui3v+NFlTUs76gio2OKj4uqqaxxX3OrzNt7Chev292P1QoInJhFCh2T3NGERERkcFhf7mT361x8PbOUjrkiCRHh3LPlTksnjmm0w5CItJ3epoz9ve/dc8AjwHPdShmPnA9MNVa6zLGJPcwfr61tuq0tv8EVllrHzbG/Kfv5//o27JFRKQvWWtZV1DFI+8dYMfRmk7Xrp6QwoOL8pg4OtZP1YmcYowhPyWa/JRo7ph7ES1tHj49coKNjirWO6rYcbSm02SmO1sPn+Do8UbGxGvLXhEREREREZHe2F1Sy2OrHby7p7xTe3pcOPdelcPXpmVo1aGIH/VroGitXWeMyTqt+T7gYWuty9en4hxf9nrgKt/zZ4G1KFAUERm0Piys4pF/HGDL4c6Lya8al8SDV+czdYy2hZTBKyQogMuzE7g8O4GHrhlHbVMrHxdV8+h7B9hX7uxx7InGFgWKIiIiIiIiImfx6ZETLF/tYPW+zlHB2IQI7r8qlxsuTSckKMBP1YnISf5YF5wPzDPG/AxoBr5rrd3cRT8L/MMYY4HHrbVP+NpTrLVlvuflQEp3v8gYczdwN0BmZmZf1S8iIr2w+dBxHvnHAT4qqu7UPic3gYcW5TNtbLyfKhM5f7HhwXx+YipV9S5+8ObubvsFBcDY+MgBrExERERERERkaPmkqJrlqx1scHTepDA3OYql83O5bkoaQYEKEkUGC38EikFAPDALmAG8YozJtmce5jjXWlvi2xL1PWPMPmvtuo4drLXWFzh2yRdCPgHe8zD69F2IiEiXth+t4Vf/2M/6gs4fBmdmxfPQNfnMyk7wU2UifedLU0fz8//ZR11zW5fXMxMiiQnXeQ4iIiIiIiIiHVlr2eCoYvkqB5sOHe90bUJaDMsW5PKFiakEBBg/VSgi3fHHN13FwBu+AHGTMcYDJAKVHTtZa0t8jxXGmDeBmcA64JgxJs1aW2aMSQPOdctUERHpB7tLann0vQOsOm17ikvGxPGda/KZm5uIMfowKMNDdFgwK26exl3PbaGxxX3G9aLKBpavdvDAwjw/VCciIiIiIiIyuFhrWb2vguWrHWw/WtPp2pSMWJYtyOPqCcn67khkEPNHoPhnYD6wxhiTD4QAnZaxGGMigQBrrdP3/Brgv32X/wIsAR72Pb41UIWLiMiZ9pc7efS9A2ccmD0pPYaHFuUzf5w+DMrwNCc3kdXfuYo/bTrC9qM1hAcHkJUYyR/XH6TNY3nkvQMkRYeyeKa2XRcREREREZGRyeOx/H1POctXO/isrK7TteljR7FsYR6fy9NN6CJDQb8GisaYF4GrgERjTDHwI2AlsNIYsxtoAZb4ti4dDTxlrb0W77mIb/r+iAQBf7LWvut72YfxbpN6B3AYuKk/34OIiHStsLKeX79fwNs7S+m4afW4lGgeXJTP5yem6MOgDHupsWE8tCi/U9uE1Bi+/fJ2AH7w5i4SIkO4ZmKqP8oTERERERER8Qu3x/L2zlIeW+2goKK+07U5uQksnZ/HrOx4fXckMoT0a6BorV3czaWbu+hbClzre14ETO3mNauBhX1Vo4iInJvD1Q38ZlUBf/60BE+HIDEnKZJvX53PFyenaZ97GdFuuDSdqnoXP31nLx4Ly178lBfuvJzpWfH+Lk1ERERERESkX7W6Pbz5aQkr1hZysKqh07X545JYuiCPaWNH+ak6EbkQ/tjyVEREhqDiE408ttrBq1uLcXdIEscmRPCthXlcf0k6gQoSRQC4c142FU4XT6wrwtXm4fZnNvPafbPJT4n2d2kiIiIiIiIifc7V5ubVLcWsWFtISU1Tp2vXXJzCsgV5TM6I9VN1ItIXFCiKiEiPymubeWxNAS9vPkqr+1SQmB4XzgMLc/nKZRkEBwb4sUKRwek/vzCeSqeLNz8toa65jSUrN/H6fbMZHRfu79JERAYVY8xK4Dqgwlo7qUP7MuB+wA28Y639XhdjDwFOX582a+10X3s88DKQBRwCbrLWnujXNyIiIiIyAjW1uHlx0xEeX1fIsTpXe7sxcN2U0dw/P4fxqTF+rFBE+ooCRRER6VKFs5kVawt54ZMjtLR52ttTY8JYuiCXm6aPISRIQaJIdwICDL+4cQrVDS2sO1BJWW0zt67cxGv3XkFcRIi/yxMRGUyeAR4DnjvZYIyZD1wPTLXWuowxyT2Mn2+trTqt7T+BVdbah40x/+n7+T/6tmwRERGRkave1cbzHx/mqfVFVNW3tLcHBhhuuCSdb87PIScpyo8VikhfU6AoIiKdHG9o4fEPCnn2o0M0t54KEhOjQrl/fg6LZ2YSFhzovwJFhpDgwABW/OtlfOPJj9lRXIujop47nt3C83dcTniI/j0SEQGw1q4zxmSd1nwf8LC11uXrU3GOL3s9cJXv+bPAWhQoioiIiFyw2qZWnv3wECs3HqSmsbW9PTjQcOO0DO67MpfMhAg/Vigi/UWBooiIAFDb2MqT64t4euNBGlrc7e3xkSHce2U2t8zKUgAich4iQ4NYedsMbvzDRxysamDr4RMse3Ebf7h5GkHaLlhEpDv5wDxjzM+AZuC71trNXfSzwD+MMRZ43Fr7hK89xVpb5nteDqR09UuMMXcDdwNkZmb2Zf0iIiIiw8rxhhZWbjjIsx8ewulqa28PCQpg8Ywx3HNljo74EBnmFCiKiIxwzuZWVm44xFMbinA2n/pAGBsezN2fy2bJ7CyiQvWfC5ELkRAVynO3z+QrKz6k0uni/b0V/ODN3Tz81ckYY/xdnojIYBQExAOzgBnAK8aYbGutPa3fXGttiW9L1PeMMfustes6drDWWl/geAZfAPkEwPTp07vsIyIiIjKSVTibeWr9QZ7/+DCNHW5ADw8O5OZZmdw1L5vkmDA/VigiA0XfEIuIjFANrjae/egQT6wr6rRFRXRoELfPvYg75l1ETFiw/woUGWbGxEfw7L/N5F8e/winq42XtxwlOSaU71wzzt+liYgMRsXAG74AcZMxxgMkApUdO1lrS3yPFcaYN4GZwDrgmDEmzVpbZoxJA851y1QRERGREa2stonHPyjixU1HcLWdOhInKjSIJbPHcsfcbOIjQ/xYoYgMNAWKIiIjTHOrm+c/PsyKtYVUN5w6NDsiJJDbZmdx9+eyiYvQB0KR/nDx6BieuHU6S1ZuosXtYflqB8nRodxyRZa/SxMRGaPw0QsAACAASURBVGz+DMwH1hhj8oEQoKpjB2NMJBBgrXX6nl8D/Lfv8l+AJcDDvse3BqpwERERkaHs6PFGVnxQyGtbimlxnwoSY8ODuX3ORdw2O4vYCN2ALjISKVAUERkhXG1uXtp0lMfWOKh0utrbw4IDuPWKLO75XDYJUaF+rFBkZLgiJ4Fff/0S7v/TNqyFH/5lDwlRoVw7Oc3fpYmI+IUx5kXgKiDRGFMM/AhYCaw0xuwGWoAlvq1LRwNPWWuvxXsu4pu+raODgD9Za9/1vezDeLdJvQM4DNw0kO9JREREZKgpqqzn92sLefPTEtyeUzvBJ0SGcOe8bG65YqyOxBEZ4fQXQERkmGtp8/Dq1qM8ttpBWW1ze3tIYADfuDyTb16Vo73uRQbYtZPT+N9fmsgP39qDtfDtl7YzKiKEK3IS/F2aiMiAs9Yu7ubSzV30LQWu9T0vAqZ285rVwMK+qlFERERkuNpf7uR3axy8vbOUDjkiydGh3HNlDotnjiEiRDGCiChQFBEZttrcHt74tITfriqg+ERTe3twoOGm6WNYuiCXtNhwP1YoMrLdekUWFXUuHlvjoMXt4e7ntvDyPVdw8egYf5cmIiIiIiIiw9zukloeW+3g3T3lndrT48K596ocvjYtg7DgQD9VJyKDkQJFEZFhxu2x/HVHKb9ZVcDBqob29sAAw1cvS2fZgjzGxEf4sUIROek71+RT6XTx8pajOF1tLHl6E2/cN1v/joqIiIiIiEi/+PTICZavdrB6X0Wn9rEJEdx/VS43XJpOSFCAn6oTkcFMgaKIyDDh8Vj+Z3c5v37/AAUV9e3txsANl6TzwMI8LkqM9GOFInI6Yww/+/IkqhtcvL+3gkqniyUrN/HqvVfoTFMRERERERHpM58UVbN8tYMNjqpO7bnJUSydn8t1U9IIClSQKCLdU6AoIjLEWWt577NjPPp+AXvL6jpd++KUNB68Oo/c5Gg/VSciZxMUGMDyxZfxr099zLYjNRRVNXD7M5v5012ziNSB9yIiIiIiInKerLVscFSxfJWDTYeOd7o2IS2GZQty+cLEVAICjJ8qFJGhRN9SiYgMUdZa1h6o5NH3DrCzuLbTtWsuTuHBRflMSNNZbCJDQXhIICtvm8GNf/gIR0U9O4prue+FbfxxyXSCdYeoiIiIiIiInANrLav3VbB8tYPtR2s6XZuSEcuyBXlcPSEZYxQkikjvKVAUERlirLVsdFTzyHv72Xak84fCBeOTefDqfCZnxPqpOhE5X3ERITx3+0y+8vsPKa9rZt2BSv7jtZ388mtTdbeoiIiIiIiInJXHY/n7nnKWr3bw2Wm7WE0fO4plC/P4XF6igkQROS/nHCgaY0YBY6y1O/uhHhER6cEnRdX86r0DbDrYeZuKeXmJPLgon8syR/mpMhHpC6PjwnnujpncuOJD6prbeOPTEpKiQ/mvayf4uzQRkXOieaOIiIjIwHF7LG/vLOWx1Q4KKuo7XZuTm8DS+XnMyo5XkCgiF6RXgaIxZi3wJV//rUCFMWajtfahfqxNRER8th05wSP/OHDGwdmXXxTPQ4vyuTw7wU+ViUhfy0+J5o+3zeDmpz7B1ebh8XVFJEWHcue8bH+XJiLSI80bRURERAZWq9vDm5+WsGJtIQerGjpdmz8uiaUL8pg2Vjefi0jf6O0KxVhrbZ0x5k7gOWvtj4wxutNURKQP1Da18szGQ/x1Zyl1Ta1MSIvhttlZzB+fzK7iWh55bz9r9ld2GnNZZhzfuWYcs3MSdHeZyDA0Iyue5Ysv5d7nt+Kx8NN39pIUHcr1l6T7uzQRkZ5o3igiIiIyAFxtbl7dUsyKtYWU1DR1unbNxSksW5Cn43BEpM/1NlAMMsakATcBP+jHekRERpTqehdfe/wjiipP3UVW4azkgwOV5CZF4qjsfHfZlIxYHlyUz1X5SQoSRYa5ayam8rMvT+a/3tgFwHdf3UF8ZAjz8pL8XJmISLc0bxQRERHpR00tbl7cdITH1xVyrM7V3m4MXDdlNPfPz2F8aowfKxSR4ay3geJ/A38HNlprNxtjsoGCsw0yxqwErgMqrLWTOrQvA+4H3MA71trvnTZuDPAckAJY4Alr7W98134M3AWcXK7zfWvt33r5PkREBpVfvLu/U5jYUccwcXxqNA8tymfRxSkKEkVGkMUzM6moc/Ho+wdodVvu/b9beenuK3SnqYgMVuc1bxQRERGRntW72nj+48M8tb6IqvqW9vbAAMMNl6Tzzfk55CRF+bFCERkJehUoWmtfBV7t8HMR8NVeDH0GeAxvOAiAMWY+cD0w1VrrMsYkdzGuDfiOtXabMSYa2GqMec9a+5nv+qPW2l/2pnYRkcGqqaWNP28v7rFPXHgwP/vyZP5pUioBAQoSRUaiBxbmUuFs5oVPjtDQ4ua2pzfx+n2zyUqM9HdpIiKdXMC8UURERES6UNvUynMfHuKPGw9S09ja3h4caLhxWgb3XZlLZkKEHysUkZGkV4GiMSYfWAGkWGsnGWOmAF+y1v60p3HW2nXGmKzTmu8DHrbWunx9KroYVwaU+Z47jTF7gXTgs9P7iogMFZVOFzuLa9hRXMuu4hq2H63B1WZ7HDM7J4EvTkkboApFZDAyxvDf10+iur6Fd/eUU93Qwq0rN/HafVeQHB3m7/JERNqd77xRRERERDo73tDC0xsP8szGQzhdbe3tIUEBLJ4xhnuuzGF0XLgfKxSRkai3W54+Cfw78DiAtXanMeZPwPlMDPOBecaYnwHNwHettZu76+wLJC8FPunQvNQYcyuwBe9KxhPdjL0buBsgMzPzPEoVETk/tY2t7CypYWdxLTuLvY9ltc3n/DoZ8brLTES829j8+uuXcOvKTWw6eJwjxxv5t6c389Lds4gOC/Z3eSIiJ/XlvFFERERkxKlwNvPU+oM8//FhGlvc7e3hwYHcPCuTu+ZlkxyjG0tFxD96GyhGWGs3nXZuV1t3nXvxO+OBWcAM4BVjTLa19oxlOsaYKOB14NvW2jpf8wrgJ3jPVvwJ8Cvg9q5+kbX2CeAJgOnTp/e8DEhE5Dw1uNrYU1rXafXhoerGs47LTozEY223fQ1w0/SMPq5WRIaqsOBAnrx1Ov/y+EfsK3eyp7SOe5/fysrbZhAaFOjv8kREoG/njSIiIiIjRlltE49/UMSLm47gavO0t0eFBrFk9ljumJtNfGSIHysUEel9oFhljMnBG+JhjLkR35ak56EYeMMXIG4yxniARKCyYydjTDDeMPEFa+0bJ9uttcc69HkSePs86xAROWeuNjd7y5ztqw53FtfgqKjHc5ZbFtLjwpk6JpbJ6XFMzYhlUkYsMWHB1DS28PUnPmZfufOMMf917Xhyk6P76Z2IyFAUGx7Ms7fP5Cu//5CSmiY2Oqr5zis7+O3XL9U5qyIyGPTlvFFERERk2Dt6vJEVHxTy2pZiWtyngsTY8GBun3MRt83OIjZCu9KIyODQ20Dxfrwr/cYbY0qAg8C/nufv/DMwH1jjO2MjBKjq2MF4b2n9I7DXWvvIadfSfGcsAnwZ2H2edYiI9KjN7eHAsXp2lXhXHu4srmF/uZNWd8/pYWJUKFMzYpmSEceUjFgmZ8SSGBXaZd+4iBBeu282z398mL/uKMXZ3MaEtGiWXJHF7NzE/nhbIjLEpcSE8dwdM7lxxYecaGzl7Z1lJEWH8sPrLua0VUEiIgOtL+eNIiIiIsNWUWU9v19byJufluDucJd6QmQId87L5pYrxhIV2tuv7kVEBkZv/ypZa+3VxphIIMBa6zTGXHS2QcaYF4GrgERjTDHwI2AlsNIYsxtoAZZYa60xZjTwlLX2WmAOcAuwyxiz3fdy37fW/g34hTHmErx3vR4C7untmxUR6Y7HYzlY3dBh5WEte0praW719DguNjyYKRmx3uAwPY6pY2JJjQk7py/1o0KDuPfKHO69MudC34aIjBA5SVGsvG0G33jyE5pa3Ty98RDJ0WHcd5X+joiIX53XvFFERERkpNhf7uR3axy8vbO0025XydGh3HNlDotnjiEiREGiiAxOvf3r9DpwmbW2oUPba8C0ngZZaxd3c+nmLvqWAtf6nm/Ae3xYV695S28KFhHpjrWW4hNN3uCwpIadR2vZXVKL09XzET8RIYFMSo9lSnosU8Z4ty7NjI/QiiAR8YtLM0fx+5sv485nt+D2WH7+7j6SokO5cZrOXhURvzmveaOIiIjIcLe7pJbHVjt4d095p/b0uHDuvSqHr03LICw40E/ViYj0To+BojFmPDARiDXGfKXDpRggrD8LExHpKxV1ze3nHe4ormVXSS3HG1p6HBMSGMCE0TFMzYhlcnosU8fEkZMURaDOKBORQWT+uGR+/tUpfPfVHQD8x+s7SYgMYf74ZD9XJiIjyYXMG40xK4HrgApr7aQO7cvwbqHqBt6x1n7vtHFjgOeAFLy71zxhrf2N79qPgbuASl/3k7vdiIiIiAyoT4+cYPlqB6v3VXRqH5sQwf1X5XLDpemEBAX4qToRkXNzthWK4/BO7uKAf+7Q7sQ7QRMRGVRqGlvY6QsNdxz1bl9aXtfc45jAAEN+SrRv5WEsUzPiyE+J1gc6ERkSbpyWQaXTxc/f3YfbY/nmC9t44a7LuSxzlL9LE5GR40Lmjc8Aj+ENBwEwxswHrgemWmtdxpiu7pJoA75jrd1mjIkGthpj3rPWfua7/qi19pfn9W5ERERELtAnRdUsX+1gg6OqU3tuchRL5+dy3ZQ0ggL1vZOIDC09BorW2reAt4wxV1hrPxqgmkREeqXe1caeEu95hzuKa9hVUsvh6sazjstOimRqRlz72YcXp8USHqJtJURk6Lr3ymwqnM08vfEQTa1ubn9mM6/dO5vc5Ch/lyYiI8CFzButteuMMVmnNd8HPGytdfn6VHQxrgwo8z13GmP2AunAZ6f3FRERERkI1lo2OKpYvsrBpkPHO12bkBbDsgW5fGFiKgHa/UpEhqjenqF4tzHmjDtLrbW393E9IiJdam51s7eszrd1qXf7UkdlPdb2PC5jVDhTM+KY7AsPJ6fHEh0WPDBFi4gMEGMM/+uLF1NV38Jfd5RS09jKkpWbeP2+2aTGapd6ERkwfTVvzAfmGWN+BjQD37XWbu6usy+QvBT4pEPzUmPMrcAWvCsZT3Qz9m7gboDMzMxzLFNERETEGySu3lfB8tUOth+t6XRtakYsyxbksXBCMsYoSBSRoa23geLbHZ6HAV8GSvu+HBERaHV7OHDMya7iWnb4wsP95U7aPD2nh0nRoUzNiGWKb/Xh5PRYEqJCB6hqERH/Cggw/PJrUzje4GKjo5qSmiaWrNzEK/deQWy4bqQQkQHRV/PGICAemAXMAF4xxmRbe+atZMaYKOB14NvW2jpf8wrgJ3jPVvwJ8Cugy1DTWvsE8ATA9OnTz3KrmoiIiMgpHo/l73vKWb7awWdldZ2uzcgaxbIFeczLS1SQKCLDRq8CRWvt6x1/Nsa8CGzol4pEZETxeCxFVQ3sLK5pX3m4p7QOV5unx3Gx4cFMyYhtX304NSNOq3BEZMQLDQrkDzdP4+tPfMye0jr2H3Ny13NbeO72mYQFa2tnEelffThvLAbe8AWIm4wxHiARqDzt9YPxhokvWGvf6FDHsQ59nqRz0CkiIiJyQdwey9s7S3lstYOCivpO1+bkJrB0fh6zsuMVJIrIsNPbFYqnywOS+7IQERn+rLUUn2jynnfoO/dwd0kd9a62HsdFhgQyKT3Wd+ZhHFMz4hgTH64PZiIiXYgOC+aZf5vJV1d8yJHjjWw6eJxvv7Sd3/3rZQTqrA4RGVjnO2/8MzAfWGOMyQdCgKqOHYz3g+Afgb3W2kdOu5bmO2MRvKskd59HDSIiIiKdtLo9vPlpCSvWFnKwqqHTtfnjkli6II9pY0f5qToRkf7Xq0DRGOPEu12M8T2WA//Rj3WJyDBwrK65fdXhjuJadhXXcKKxtccxIUEBXJwW02nr0uykKH0JLiJyDpKiQ3nu9pnc+IcPqapv4d095fzwrd389IZJuhlDRPrN+cwbfasYrwISjTHFwI+AlcBKY8xuoAVYYq21xpjRwFPW2muBOcAtwC5jzHbfy33fWvs34BfGmEt8NRwC7unTNyoiIiIjiqvNzatbilmxtpCSmqZO1z4/MYWl8/OYnBHrp+pERAZOb7c8je7vQkRkaDvR0MLOklp2Hq3xPhbXcKzO1eOYwADDuJRopo6JZXK6NzwclxpNcGDAAFUtIjJ8ZSVG8vRtM/n6Ex/R0OLmhU+OkBwdxreuzvN3aSIyTJ3PvNFau7ibSzd30bcUuNb3fAPe4LKr17zlXOsQEREROV1Ti5sXNx3h8XWFnb7jMgaumzKa++fnMD41xo8ViogMrF5veWqMSQfGdhxjrV3XH0WJyOBW72pjV3Etu0q8Kw93Ftdw9HhTj2OMgZykKKac3Lp0TBwXp8XoTC8RkX40OSOWP9wyjduf2Uyr2/Lo+wdIig7lG5dn+rs0ERmmNG8UERGRoa7e1cbzHx/mqfVFVNW3tLcHBhhuuCSdb87PIScpyo8Vioj4R2+3PP058C/AZ4Db12wBTQxFhrnmVjefldV5Vx4W17KzpJbCynqs7XncmPhw33mH3tWHk9JjiA4LHpiiRUSk3by8JH75tal86yXvjoD/z593kRAVwucnpvq5MhEZbjRvFBERkaGstqmV5z48xB83HqSmw5E9wYGGG6dlcN+VuWQmRPixQhER/+rtCsUbgHHW2p73LxSRIa3V7WF/uZNdvi1Ldxyt5cAxJ22entPDlJhQ73mH6d6Vh1PSYxkVGTJAVYuIyNlcf0k6lU4XP31nLx4LD7z4Kc/feTkzsuL9XZqIDC+aN4qIiMiQc7yhhac3HuSZjYdwutra20OCAlg8Ywz3XJnD6LhwP1YoIjI49DZQLAKCAU0MRYYJt8dSVFnvXXVY7D338LPSOlxtnh7HxUUEt688nJLhPfcwJSZsgKoWEZHzdee8bCqdLh5fV4SrzcMdz2zm1XtnMy5VR2WLSJ/RvFFERESGjApnM0+tP8jzHx+mscXd3h4eHMjNszK5a142yfrOS0SkXW8DxUZguzFmFR0mh9baB/qlKhHp0uHqBkpONJEWF85FiZG9Hmet5ejxJnYU13jDw+JadpfU0tDhw1JXokKDmJQe0x4cTs2II2NUOMaYC30rIiLiB//xhfFUOl288WkJdc1tLFm5ide/OZt03W0rIn1D80YREREZ9Mpqm3j8gyJe3HSk0431UaFBLJk9ljvmZhOvnbdERM7Q20DxL75/RMQPiirr+a83dvHJwePtbTOz4vk/X5lEbvKZK0vKa5vbg8MdxTXsKqnttPd7V0KDApg4+lR4OCUjluzEKAICFB6KiAwXAQGGn984heqGFj44UEl5XTNLVm7itXuvIC5CE2YRuWCaN4qIiMigdfR4Iys+KOS1LcW0uE8FibHhwdw+5yJum51FbESwHysUERncjLU9n402XEyfPt1u2bLF32WInLOKuma+uHwDlc4zd45KiAzh+TtncqzO5du61Lt9aUUXfTsKCjCMS41u37p0ckYs+SnRBAcG9NfbEBGRQaTB1cY3nvyYHcW1AFyWGccLd84iPCTQz5WJSH8zxmy11k73dx2DkeaMIiIiw9PBqgZ+t8bBm5+W4Pac+i48ITKEO+dlc8sVY4kK7e26GxGR4a2nOWOPfymNMa9Ya28yxuwCzkgerbVT+qhGEenGyo2HugwTAaobWvin32zocbwxkJsUxWTflqVTMmKZkBZDWLC+NBYRGakiQ4NYedsMbvzDRxysamDbkRqW/mkbj98yjSDdXCIi50jzRhERERmMDhxz8thqB2/vLKVDjkhydCj3XJnD4pljiAhRkCgi0ltn+4v5Ld/jdf1diIh07f29x86pf2Z8RPt5h5MzYpmUHqu7rERE5AwJUaE8d/tMvrriQyqcLlbtq+D7b+7i51+dorNyReRcad4oIiIig8bukloeW+3g3T3lndrT48K596ocvjYtQzfai4ichx5TBmttme/xMIAxJuZsY0Skb7V0OBy6K2FBASxdkNt+9qHOwBIRkd4aEx/Bs7fP5KY/fITT1cYrW4pJjg7ju58f5+/SRGQI0bxRREREBoNPj5xg+WoHq/dVdGofmxDB/VflcsOl6YQEaUcWEZHz1atJnjHmHuB/A82c2sLGAtn9VJeI+EzLjOPI8cZury+amMrSBXkDWJGIiAwnE9JieOLW6SxZuYkWt4fH1jhIig5lyewsf5cmIkOM5o0iIiLiD58UVbN8tYMNjqpO7bnJUSydn8t1U9J0tIOISB/o7V2j3wUmWWurztpTRPpUQED3284FGsMdcy8awGpERGQ4uiIngV9//RLu/9M2rIUf/3UPiVGhfHFKmr9LE5GhRfNGERER6VMtbR481p6xRam1lg2OKpavcrDp0PFO1yakxbBsQS5fmJja4/dqIiJybnobKBYC3S+R6oYxZiXeczQqrLWTOrQvA+4H3MA71trvdTH2C8BvgEDgKWvtw772i4CXgARgK3CLtbblXGsTGQpWrC3k9W0lXV6LCAnk4a9O4ZIxcQNclYiIDEfXTk7jv780kf/11h6shQdf3s6oyGBm5yT6uzQRGTrOa94oIiIicrrtR2v47aoC1u6vwGNhcnos91yZzRcnp7F6XwXLVzvYfrSm05ipGbEsW5DHwgnJOhdeRKQfGGvt2TsZcynwNPAJ4DrZbq194CzjPgfUA8+dDBSNMfOBHwBftNa6jDHJ1tqK08YFAgeARUAxsBlYbK39zBjzCvCGtfYlY8wfgB3W2hVnew/Tp0+3W7ZsOet7FRks/vTJEb7/5i4AAgMMj9w0laYWNyU1TaTFhvPFKWnEhgf7uUoRERlufvWP/Sxf7QAgKjSIl++ZxcTRsX6uSkT6ijFmq7V2ej+99nnNGwcLzRlFREQGh48Kq1ny9CZa2jxnXEuJCeVYnatT24ysUSxbkMe8vEQFiSIiF6inOWNvVyg+DqwGdgFn/iXvhrV2nTEm67Tm+4CHrbUuX5+K08cBMwGHtbYIwBjzEnC9MWYvsAD4hq/fs8CPgbMGiiJDyV93lPKDP+9q//kXX53C9Zek+7EiEREZKR5alE+l08VLm49S72rjtqc388Z9sxkTH+Hv0kRk8DuveaOIiIjISdZafvjW7i7DRKBTmDgnN4Gl8/OYlR2vIFFEZAD0NlAMttY+1Ee/Mx+YZ4z5GdAMfNdau/m0PunA0Q4/FwOX493mtMZa29ahvduUxRhzN3A3QGZmZt9UL9LP1u6v4KFXtnNy8fAPr7uYr07L8G9RIiIyYhhj+OkNk6iqb+H9vceodLq4deUmXrv3ChKiQv1dnogMbn05bxQREZERaE9pHQUV9T32yUuO4uGvTmHa2FEDVJWIiAAE9LLf/xhj7jbGpBlj4k/+c56/MwiIB2YB/w68YvrpFhJr7RPW2unW2ulJSUn98StE+tSWQ8e59/mttLq9aeK3FuZx+9yL/FyViIiMNEGBASxffGn7BP1gVQO3P7OZBlfbWUaKyAjXl/NGERERGUGstXxWWsfj64rO2vfayWkKE0VE/KC3KxQX+x7/q0ObBbLP43cW4z0D0QKbjDEeIBH4/9m77/iuqvuP46+TTUISIAlZEPaGJEgIimgRxYEBW7WuyhCclU5bu1vb/jrUqq21bhDESt0KcaA4UZBNwgx7ZkMgi8zv+f2RLzGQEEJIcjPez8eDR/I999xv3nk8bPM993Pv5+TUmHMI6FnjdQ/32GGgizHGy/2U4olxkTZvS3o+t81bTUl5VUuHGWN78+PLBjicSkREOqpOPp7MmZ7Ad59ewY7sQlIOHuOe/67j+WkJ+Hg19J40EelgmnLdKCIiIh3AjqwCFqdmkJyazu6cogadMyC8czOnEhGRujToapC1tk8d/6oXhcaYiWfxM98GLnGfNxDwAXJPmbMaGGCM6WOM8QFuAha5i5CfAte7500H3jmLny3SKu3NLWLa3FUUlFQ9+fGdkdH8Pmmo+r+LiIijuvj7MH9mIpHBfgB8sT2HX7yRistlHU4mIq1RY9eNxpi5xphsY8ymU8Z/YIzZZozZbIx56DTnXmmMSTPG7DTG/LLGeB9jzEr3+CvudaWIiIi0Antyi/j3xzu44rEvmPjYFzz+8Y6Tioke9VwOCw/yZeLQ8BZIKSIip2qq28sfrGvQGLMQWAEMMsYcNMbMAuYCfd2Lxf8B06211hgTZYx5D8D99OFsYAmwFXjVWrvZ/ba/AH5qjNlJ1Z6Kc5rodxBxROaxEm6ds5LcwqpNpS8b0p2Hro/Fo75PTyIiIi0kqksn5s9MJMivqrHFW+sP8fcPtjmcSkTaqDrXjcA84MqaA8aYS4BrgDhr7TDgH6eeZIzxBP4DXAUMBW42xgyt8bMes9b2B/KAWU3xC4iIiEjjHDhSzFOf7eLqx5dxyT8+45GPtpOWVVB93NfLgyuHRfDELSP57GeXMLCOpxCDO3nz9K2j8PXybMnoIiLi1tCWp2dSZ+XDWntzXePArXXMTQcm1Xj9HvBeHfN2A4mNiynSuuQVlTF1zkoO5h0HYEyfbjxxy3l4e6qVnIiItB4DwwOZO2M033t+JaUVLp79YjfdA325/SJ1MRSRs3K6deMXxpjepwzfA/zdWlvqnpNdx6mJwE73GhFjzP+Aa4wxW4EJwC3uefOBB4CnzjG/iIiInIX0o8d5193ONOXgsVrHfTw9uHhgKEmxUVw2NJzOvt9cql40exyLNqTz0dYsyipcjO7dlZsSYwjt7NuSv4KIiNTQVAVF9b0SOUuFpRXMmLeaHdmFAAyPDuL56Qn4eesuKxERaX0Selfd9HLXgjW4LPzfu1sJ7ezLt0dGOx1NRNqOs1k3DgQuMsb8BSgBfmatXX3KnGjgQI3XB4ExjzxfjwAAIABJREFUVHWyOerufHNiXP9nJSIi0gKy80t4d2MGyakZrN2XV+u4l4dh3ICqIuLEoeEEd/Ku8338vD25YXRPbhjds7kji4hIAzVVQVFEzkJJeSV3vriGlANHAegbFsD82xIJ9Kv7Q5SIiEhrMHFoOH/9zgh++eZGAH72WgrdAny4eGCYw8lEpB3yAroB5wOjgVeNMX2ttU16M6sx5k7gToCYmJimfGsREZEOI7ewlPc3ZZKcks6qvUc49a+1h4Gx/UJJio3kimERdA3Q1sYiIm1RUxUU9zbR+4i0exWVLn64cD3Ldx0GICrYj5dmjSFELRtERKQNuCkxhuyCUh79aDsVLsvdL63lf3eeT2yPLk5HE5HWb+9ZzD0IvOkuIK4yxriAUCCnxpxDQM3HFnq4xw4DXYwxXu6nFE+M12KtfRZ4FiAhIUGdd0RERBoor6iMJZszSU7NYPmuXFyn/BU1pmprn6tjo7hqeIRalYqItAMNLigaY8YCvWueY6190f312iZPJtIOuVyWX765kQ+3ZAEQEuDDgtvHENWlk8PJREREGu4HE/qTU1DKgq/3UVxWyW0vrOb1e8bSJzTA6Wgi4rAmXDe+DVwCfGqMGQj4ALmnzFkNDDDG9KGqYHgTcIu11hpjPgWuB/4HTAfeadQvJCIiItWOHS/nQ3cR8auduVScWkUEEnp1JSk2kkkjIuke5OdAShERaS4NKigaYxYA/YANQKV72AIvNlMukXbHWstf3tvK62sPAhDo68X8mYn0C+vscDIREZGzY4zhgSnDqlsbHS4qY9rclbxxz1i6B+qigUhH1dh1ozFmITAeCDXGHAT+AMwF5hpjNgFlwHR3oTAKeN5aO8laW2GMmQ0sATyBudbaze63/QXwP2PM/wHrgTlN+KuKiIh0GIWlFSzdkkVyajpfbM+lrNJVa05czy5MdhcRddO8iEj71dAnFBOAoU29X4VIR/KfT3cy58s9APh6efD89ASGRwc7nEpERKRxPD0Mj90Yz5GiVazcc4QDR44zY+5qXrnrfO0JLNJxNWrdaK29+TSHbq1jbjowqcbr94D36pi3G0g8mxwiIiJSpbisgo+3ZvNuagafpmVTWlG7iDgsKoik2CiSYiPp2c3fgZQiItLSGlpQ3AREABnNmEWk3VqwYi//+HA7AF4ehqduPY8xfUOcDSUiInKO/Lw9eXZaAjc+s4JtmQVsycjnrgVreeG20fh6eTodT0RantaNIiIibVRJeSWfpWWzODWDT7Zmc7y8stacwRGBXD0ikqS4KG13ICLSATW0oBgKbDHGrAJKTwxaa6c0SyqRduSdDYf4/aKqzkvGwCM3xDFhcLjDqURERJpGcCdv5s9M5Nonl3Po6HGW7zrMT19N4d83jcTDwzgdT0RaltaNIiIibUhpRSXLtueSnJrOR1uyKCqrXUTsFxZQ/STigPBAB1KKiEhr0dCC4gPNGUKkvfpkWxb3vZrCiaZPf5wyjGvio50NJSIi0sTCg/x4cVYi1z+1nLzict5NzSCssy9/mDwUY1RUFOlAHnA6gIiIiNSvvNLFVztzSU7NYMnmTApKKmrN6RXiT1JsJEmxUQyOCNRnehERARpYULTWfm6MCQdGu4dWWWuzmy+WSNu3as8R7nlpHRWuqmrifRMHMu2C3s6GEhERaSb9wjozd8ZobnluJcfLK5m3fC9hgb7ce0l/p6OJSAvRulFERKR1qqh08fXuI7y7MZ0PNmWSV1xea050l07VRcTh0UEqIoqISC0NKigaY24AHgY+Awzwb2PMz621rzdjNpE2a9OhY8yat7p60+pZ4/owe4IuqIqISPs2MqYrT956HnfMX0OFy/LwkjTCAn25IaGn09FEpAVo3SgiItJ6VLosq/ceITm1qoiYW1hWa05EkB+TRkSSFBfJyJ5dVEQUEZF6NbTl6W+A0SfuLjXGhAFLAS0MRU6xO6eQ6XNXUVBa1TLi+lE9+M2kIfpQJiIiHcIlg7rz4HWx3PdaCgC/enMjIQE+XDpE+weLdABaN4qIiDjI5bKsP5DH4pQM3tuYQXZBaa05oZ19uXpEBElxUYyK6ap9z0VEpMEaWlD0OKVVzWHAoxnyiLRp6UePM3XOKg4XVd31dfnQcP5+7Qh9OBMRkQ7lulE9yCks5e/vb6PSZbn35XX89/bzGdWrq9PRRKR5ad0oIiLSwqy1pB48RnJqOu+mZpB+rKTWnG4BPlw5PIKk2EjG9AnBU9epRESkERpaUPzAGLMEWOh+fSPwXvNEEmmbDheWMnXOSg4dPQ7A2H4hPH7zSLw8dQ1FREQ6nrsu7kt2filzv9pDSbmLWfNX8/rdF9C/e6DT0USk+WjdKCIi0gKstWzJyCc5NYN3UzPYf6S41pzgTt5cMSycpNgoxvYL0fUpERE5Zw0qKFprf26MuQ640D30rLX2reaLJdK2FJSUM+OF1ezKKQIgrkcwz05LwM/b0+FkIiIizjDG8Nurh5BbWMqilHSOFpczbc4q3vz+hUQE+zkdT0SagdaNIiIizSsts6D6ScTduUW1jgf6ejFxWDiTY6O4sH8oPl4qIoqISNNp6BOKWGvfAN5oxiwibVJJeSW3z1/DxkPHABjQvTPzbkuks2+D/+clIiLSLnl4GP7x3TiOFJXx5c5c0o+VMH3uKl696wKC/b2djicizUDrRhERkaa1K6eQ5JQMklPT2ZFdWOu4v48nlw0JJyk2kosHhunmdhERaTb1VjyMMV9aa8cZYwoAW/MQYK21Qc2aTqSVK690MfvldazccwSA6C6dWDBrDF0DfBxOJiIi0jr4eHnw9NRR3PTsCjYdyictq4A7XlzDi7MSdbFDpJ3QulFERKRp7TtcRHJqBsmpGWzNyK913M/bgwmDu5MUG8Ulg7rTyUefq0VEpPnVW1C01o5zf9VmNyKncLksv3g9laVbswEI7ezLf28fozZuIiIip+js68ULMxK5/unl7DtczKq9R/jR/9bz5PdG4elhnI4nIudI60YREZFzdzCvmHfdRcQTXbBq8vHyYPzAMK6OjeSyIeEEqDOWiIi0sAb95THGLLDWTj3TmEhHYa3lT8lbeHP9IQAC/bx4cWYivUMDHE4mIiLSOoUF+vLizESue2o5uYVlLNmcxe/e2cRfvj0cY1RUFGkPtG4UERE5O5nHSnh3Y1U70/X7j9Y67u1puGhAGEmxkVw2NJwgP20bICIizmnorSzDar4wxngBo5o+jkjb8K+PdzBv+V6gqs3ECzNGMzRKnZxERETq0yskgHm3JXLjMysoKqvk5ZX76R7oy48vG+h0NBFpGlo3ioiInEFOQSnvb8ogOSWD1fuOYO3Jxz09DGP7hTA5NoorhkVo73EREWk1zrSH4q+AXwOdjDEnGnYboAx49kxvboyZCyQB2dba4e6xB4A7gBz3tF9ba9875bxBwCs1hvoCv7fW/rMh54s0pxe+2sM/l+4Aqu4Ue/rWUST07uZwKhERkbZheHQwz0xN4LZ5qyivtPxz6Q7CAn353pheTkcTkUY613WjiIhIe3ekqIwPNmWSnJrO17sP4zqliOhhYEyfEJLiIrlyWAQhnX2dCSoiIlKPM+2h+Dfgb8aYv1lrf9WI958HPAG8eMr4Y9baf9Tzc9OAeABjjCdwCHiroeeLNJc31x3kj4u3AGAMPHpDPOMHdXc4lYiISNsybkAoj9wQzw8Xrgfgd29vIrSzL1cMi3A4mYg0RhOsG0VERNqdY8XlLNmcyeLUdJbvOkzlqVVEYHTvriTFRnHViAi6B/o5kFJERKThGtTy1Fr7K2NMV2AA4Fdj/IsznPeFMab3uQQELgV2WWv3neP7iJyTj7Zk8fPXU6tf/9+3hzM5LsrBRCIiIm3XlLgocgpK+XPyFlwWfrBwPS/NGkNiHz31L9JWNXbdKCIi0l4UlJTz0ZYsklMzWLYjh/LK2kXEkTFdSIqN4uoRkUQEq4goIiJtR4MKisaY24EfAT2ADcD5wApgQiN/7mxjzDRgDXCftTavnrk3AQsbc74x5k7gToCYmJhGRhWBFbsOc+/L66rvJrv/ykFqzSYiInKOZo3rQ3ZBCc98vpuyChe3z1/Na3ePZVBEoNPRRKQRmmHdKCIi0uoVlVbw8bZsklPS+Wx7DmUVrlpzRkQHkxQbydWxkfTo6u9AShERkXNn7Kk7/9Y1yZiNwGjga2ttvDFmMPBXa+21DTi3N5BcYw/FcCAXsMCfgUhr7czTnOsDpAPDrLVZZ3t+TQkJCXbNmjVnmiZSS+rBo9zy3EoKSysAuOvivvzyqsEYYxxOJiIi0vZZa7nvtRTeXHcIgIggP974/liiu3RyOJlI+2SMWWutTWim9270urE10JpRREQaqqS8kk+3ZZOcmsHH27IoKa9dRBwcEcjkuKonEXuHBjiQUkRE5OzVt2Zs0BOKQIm1tsQYgzHG11q7zRgzqDFhThQG3cGeA5LrmX4VsK7mOWd5vsg52ZldwPS5q6qLiTcm9FQxUUREpAkZY3jwuliOFJXxWVoOmfklTJuzktfvHkvXAB+n44nI2TnrdaMxZi6QBGTXuAn1AeAOIMc97dfW2vdOOW8Q8EqNob7A7621/2zI+SIiImertKKSz9NySE7NYOnWLIrLKmvN6d+9M0mxkSTFRtG/e2cHUoqIiDSfhhYUDxpjugBvAx8ZY/KARu1paIyJtNZmuF9+B9hUz/SbOaXd6VmeL9JoB/OKufX5VeQVlwMwaUQEf712hIqJIiIiTczb04Mnv3ceNz+3kpQDR9mVU8TM+at5+fbz6eTj6XQ8EWm4xqwb5wFPAC+eMv6YtfYfpzvJWpsGxAMYYzyBQ8BbDT1fRESkIcoqXHy1M5fFqel8tDmLAvcN5zX1CQ2oLiIODO+s60YiItJuNaigaK39jvvbB4wxnwLBwAdnOs8YsxAYD4QaYw4CfwDGG2PiqWpZuhe4yz03CnjeWjvJ/ToAmHjieA0P1XW+SFPKKShl6pxVZOaXAHDRgFAeuzEeTw99KBQREWkO/j5evDBjNNc/tZzduUWs33+Ue19exzNTR+Ht6eF0PBFpgMasG621X7i3yTgXlwK7rLWNuulVRESkpopKFyt2HyY5JYMPNmdy7Hh5rTk9unYiKTaKpNhIhkUFqYgoIiIdwhkLiu67PTdbawcDWGs/b+ibW2tvrmN4zmnmpgOTarwuAkLqmDe1oT9fpDGOHS9n+txV7MktAmBkTBeevnUUvl56QkJERKQ5dQvwYf7MRK57ajnZBaV8si2bX7+5kYeuj9VFGpFW7lzWjacx2xgzDVgD3Getzatn7k2c0tnmLM8XEZEOrtJlWbnnMO+mZvDBpkwOF5XVmhMZ7MfVIyJJiosirkewPp+KiEiHc8aCorW20hiTZoyJsdbub4lQIk45XlbJ7fNXsyUjH4BB4YG8MGM0Ab4N7Q4sIiIi56JnN3/mz0zkhqdXUFBawWtrDxIW6Mv9Vw52OpqI1KOJ141PAX+mqivNn4FHgJl1TTTG+ABTgF818vw7gTsBYmJizjG2iIi0JS6XZe3+PJJT0nlvUyY5BaW15nQP9GXSiEgmx0UysmdXPNS5SkREOrCGVkm6ApuNMauAohOD1topzZJKxAFlFS7u+e9aVu+tunk5pps/C2Yl0sXfx+FkIiIiHcuQyCCem57AtLmrKKtw8eRnu+ge6MuMC/s4HU1E6tck60ZrbdaJ740xzwHJ9Uy/ClhX85yzOd9a+yzwLEBCQoI9m5wiItL2WGvZcOAoyakZvLcxg4xjJbXmhAT4cNWICJJioxjdu5u2vxEREXFraEHxd82aQsRhlS7Lfa+l8FlaDlB1B9pLs8bQPcjP4WQiIiId0/l9Q/jXjfF8/+V1WAt/TN5CaKAvSbFRTkcTkdNrknWjMSbSWpvhfvkdYFM902/mlHanZ3m+iIi0c9ZaNh3KJ3ljOu+mZnAw73itOV38vblyWFUR8fy+3fDSHt4iIiK1NKig2AT7X4i0WtZa/rBoE4tT0gEI7uTNglljiAnxdziZiIhIx3bViEj+dM1wfvf2JqyFn76SQjd/H8b2D3U6mojUoTHrRmPMQmA8EGqMOQj8ARhvjImnqmXpXuAu99wo4Hlr7ST36wBg4onjNTxU1/kiItJxWGvZlllAcmpVEXHv4eJacwL9vLh8aARJcZGM6x+Kt4qIIiIi9WpQQdEYU0DVYgzAB/AGiqy1Qc0VTKSlPPLhdl76umqbl07enrxw22gGRQQ6nEpEREQApp7fi5z8Eh7/ZCdllS7uXLCW/915PsOjg52OJiKnaMy60Vp7cx3Dc04zNx2YVON1ERBSx7ypZxFbRETakZ3ZBSxOySA5NZ1dOUW1jgf4eDJxaDhJsVFcNDAUXy9PB1KKiIi0TQ19QrG6umKMMcA1wPnNFUqkpTz3xW6e+HQnAN6ehmenjeK8mK4OpxIREZGafjJxIDmFpSxcdYDC0gpmvLCaN+8Zq24CIq2M1o0iIuKEvblFJKemk5yawbbMglrHO3l7MmFIdybHRjJ+UHf8vFVEFBERaYyG7qFYzVprgbeNMX8Aftn0kURaxqurD/CX97YC4GHgXzeN5KIBYQ6nEhERkVMZY/jzNcPJKShj6dYscgtLmTZ3Ja/fM5bQzr5OxxOROmjdKCIizenAkWLe3Vj1JOKmQ/m1jvt4eXDJoDCSYqO4dEh3/H3O+hKoiIiInKKhLU+vrfHSA0gASpolkUgL+GBTBr98M7X69d+uHcGkEZEOJhIREZH6eHl68MQtI7n1+ZWs2ZfH3sPFzJy3moV3nE+Ary4QibQGWjeKiEhzyjh2nHdTM1icmkHKgaO1jnt7Gi4eEEZSXCSXDQkn0M/bgZQiIiLtV0Ovvkyu8X0FVRvbX9PkaURawJc7cvnhwg243Lu7/HrSYG4cHeNsKBERETkjP29Pnp+ewHefXsGO7EJSDx7j7pfWMmf6aHy8PJyOJyJaN4qISBPLzi/hvY0ZJKdmsGZfXq3jXh6GC/uHkhQbyeXDIgjupCKiiIhIc2noHoq3NXcQkZawfn8edy5YQ1mlC4Dvj+/HnRf3cziViIiINFQXfx/mz0zkuqeWk3GshGU7crn/9RQevSEeDw/jdDyRDk3rRhEROZOd2QW8tf4QR4rK6BMawLXn9ajVwv5wYSnvb8okOTWdlXuOYO3J7+Fh4IJ+ISTFRnHlsAi6Bvi04G8gIiLScdVbUDTG/Buwpzturf1hkycSaSZpmQXMeGE1xWWVANwyJoafXzHI4VQiIiJytqK6dOLFmYlc//QKjh0v5+0N6YQF+vKbq4c6HU2kQ9K6UUREzsRayyMfbueJT3eeNP7oR9v5543xnN83hCWbM0lOzWD5rsNUuk7+s2IMJPbuRlJsJFcOjyQsUPtoi4iItLQzPaG4xv31QmAo8Ir79XeBLc0VSqSpHThSzNQ5Kzl2vByApNhI/nzNcIzRkwwiIiJt0YDwQObOSOCW51ZSWuHiuWV76B7oxx0X93U6mkhHpHWjiIjU650N6bWKiQAl5S7ueWkdHh6mVhERYFSvriTFRjJpRCThQX4tEVVEREROo96CorV2PoAx5h5gnLW2wv36aWBZ88cTOXfZ+SV87/mVZBeUAvCtgWE8ekM8nmqLJiIi0qaN6tWNJ245j7sWrMFl4S/vbSU00IfvjOzhdDSRDkXrRhEROZO5X+057TELJxUT43oEkxQbxaTYSKK7dGqBdCIiItIQDdpDEegKBAFH3K87u8dEWrVjxeVMm7uK/UeKAUjo1ZWnbx2Fj5eHw8lERESkKUwcGs5fvzOCX765EYCfv5ZKtwBfvjUwzOFkIh2S1o0iIlKLtZaNh47VO8ffx5PZE/qTNCKKmBD/FkomIiIiZ6OhVZW/A+uNMfOMMfOBdcBfmy+WyLkrLqvgtnmr2JZZAMCQyCDmzBhNJx9Ph5OJiIhIU7opMYb7Jg4EoMJlueeltaQcOOpwKpEOSetGERGpJS2zAI8zbDkzunc3vj++v4qJIiIirViDCorW2heAMcBbwJvABSfa2gAYY4Y1TzyRximtqOSuBWtZt7/qYmLvEH9enJlIcCdvh5OJiIhIc5g9oT/TLugFQHFZJbfNW82e3CKHU4l0LFo3iohITQeOFPPTVzdw1ePL6twfsaarR0S2UCoRERFprIa2PMVamwm8c5rDC4DzmiSRyDmqdFl++koKy3bkAhAe5MuCWWMIC/R1OJmIiIg0F2MMf5g8jNzCUt7bmMmRojKmzV3JG/eMpXugn9PxRDoMrRtFRCSnoJT/fLqT/67cR3nlN4VEDwN11RVjewQzJT6qBROKiIhIYzTVRnL19y0QaSHWWn779kbe3ZgBQBd/b16aNYae3dQyQ0REpL3z9DA8ekM8Y/p0A+DAkeNMn7ua/JJyh5OJiJvWjSIi7VhBSTmPfpjGtx7+lHnL91YXE6O7dOKR78bx/o8uZvygME50P/X18uCm0T1ZMGsMft7ankZERKS1a/ATimdQf98CkRby4AdpLFx1AIAAH0/m3ZbIgPBAh1OJiIhIS/Hz9uS56Qnc8PQKtmUWsDUjnztfXMP8mYn4eulClYjDtG4UEWmHSsoreenrffzn053kFX9zI1dIgA+zJ/TnljEx1Z/D5t2WyOHCUvKKy4gI7kRn36a6NCkiIiLNTX+1pd146rNdPP35LgB8PD14bloC8T27OJxKREREWlqQnzfzZyZy7ZPLOXT0OF/vPsJPX0nh8ZtH4umhB6REREREmkJFpYs31x3in0u3k36spHq8s68Xd1zUl1kX9amzYBjS2ZeQztqWRkREpK1pqoJiWRO9j0ijLFy1nwc/2AZU9eT/9y0jGds/1OFUIiIi4pTwID9enJXI9U8tJ6+4nHc3ZhDa2YcHpgzDGBUVRRyidaOISDtgrWXJ5kweXpLGrpyi6nEfTw+mXtCL74/vp4KhiIhIO9TggqIx5lpgHFVtar601r514pi19vzTnDMXSAKyrbXD3WMPAHcAOe5pv7bWvlfHuXuBAqASqLDWJrjHuwGvAL2BvcAN1tq8hv4e0v4kp6bz67c2Vr9+8LpYrhgW4WAiERERaQ36hXVm7ozR3PLcSo6XVzJ/xT66B/lx7yX9nY4m0m41Zt0oIiJtx/KduTz4wTZSDh6rHvMwcP2oHvzosoFEd+nkYDoRERFpTh4NmWSMeRK4G9gIbALuMsb8pwGnzgOurGP8MWttvPtfrWJiDZe45yTUGPsl8LG1dgDwsfu1dFCfb8/hJ69swLp3Y/ld0lC+m9DT2VAiIiLSaoyM6cqTt56Hl7vV6cNL0nh19QGHU4m0T+ewbhQRkVYu9eBRps5ZyS3PrzypmHjlsAg+/MnFPHR9nIqJIiIi7VxDn1CcAAyxtqpsY4yZD2w+00nW2i+MMb0bna5u1wDj3d/PBz4DftHEP0PagLX7jnD3grWUV1ZVE384oT+zxvVxOJWIiIi0NpcM6s6D18Vy32spAPzqrY2EdPbh0iHhDicTaXcatW4UEZHWa1dOIY98mMZ7GzNPGr+gbwj3XzmIkTFdHUomIiIiLa1BTygCO4GYGq97uscaa7YxJtUYM9cYc7pPHhb40Biz1hhzZ43xcGtthvv7TOC0V4KMMXcaY9YYY9bk5OScbpq0QVsz8rnthdUcL68EYNoFvfjJxIEOpxIREZHW6rpRPfjVVYMBqHRZ7n15HWv3qWu+SBNr1LrRvS7MNsZsqjH2gDHmkDFmg/vfpNOcu9cYs9E9Z02N8W7GmI+MMTvcX3XFW0TkLGQcO84v30jl8se+OKmYOCI6mAWzEnn5jjEqJoqIiHQwDS0oBgJbjTGfGWM+BbYAQcaYRcaYRWf5M58C+gHxQAbwyGnmjbPWngdcBdxrjLn41AnuO1/t6X6QtfZZa22CtTYhLCzsLGNKa7U3t4ipc1aRX1IBwDXxUTwweRjGGIeTiYiISGt258V9q7sZlJS7mDV/NTuzCxxOJdKuNHbdOA9tlSEi0irkFZXx1/e28q2HP+N/qw9Q6aq67NY3NIAnv3cei2ZfyEUDwnQNRkREpANqaMvT3zfVD7TWZp343hjzHJB8mnmH3F+zjTFvAYnAF0CWMSbSWpthjIkEspsqm7R+mcdKuHXOSnILSwGYMLg7//huHB4e+iArIiIi9TPG8JtJQ8gtLOWdDekcLS5n2pxVvPH9sUQGa88fkSbQqHWjtsoQEXFeUWkFL3y1h2c+301BaUX1eESQHz++bADXj+qBl2dDn0sQERGR9qhBBUVr7edN9QNPFAPdL78DbKpjTgDgYa0tcH9/OfAn9+FFwHTg7+6v7zRVNmnd8orKmDpnJQfzjgOQ2KcbT37vPLz1gVZEREQayMPD8PD1cRwpKmPZjlzSj5Uwfe4qXrtrLMH+3k7HE2nTmnLd6DbbGDMNWAPcZ62tq0/xia0yLPCMtfZZ93iDt8oQEenIyipc/G/1fh7/eGf1zdsAwZ28ufeSfky7oDd+3p4OJhQREZHWot5KjDHmS/fXAmNMfo1/BcaY/DO9uTFmIbACGGSMOWiMmQU85N7jIhW4BPiJe26UMeZEG5tw4EtjTAqwCnjXWvuB+9jfgYnGmB3AZe7X0s4VlVYwY95qdmQXAjAsKojnpyfoQ62IiIicNR8vD566dRQjooMB2J5VyO0vrqbEvTeziJydc103nkaLbJVhjLnTGLPGGLMmJyenkVFFRNqeSpflrfUHufTRz/j9O5uri4mdvD2ZfUl/vrj/Eu68uJ+uu4iIiEg1U7W2av8SEhLsmjVrnI4hjVBaUcnMeav5audhoKpv/6t3X0BoZ1+Hk4mIiEhblltYynVPLWff4WIALh8azpPfO0/tvKRdM8asPWWvwVbB3fI02Vo7/GyOnTLvAaDQWvsPY0waML7GVhmfWWsH1Xe+1owi0hFYa/lkWzYPL0ljW+Y3e0l7eRhuGRM0jO3HAAAgAElEQVTD7An96R7o52BCERERcVJ9a8YzXi0xxngaY7Y1fSyRM6uodPGjhRuqi4mRwX4suH2MiokiIiJyzkI7+/LizERCO/sA8OGWLH73zmY6yg13Ik2pqdeN7iLgCafdKsMYE3jie6q2yjgx78RWGaCtMkREAFi99wjffXoFs+avqS4mGgPfjo/ik/vG86drhquYKCIiIqd1xj0UrbWVxpg0Y0yMtXZ/S4QSgaq75n715kY+2JwJQLcAHxbMGkN0l04OJxMREZH2oldIAPNuS+TGZ1ZQVFbJwlX76R7oy08mDnQ6mkibci7rRvdWGeOBUGPMQeAPwHhjTDxVrUr3Ane550YBz1trJ1G1VcZbxhioWtu+fMpWGa+6t93YB9xwjr+iiEibtTUjn4eXpPHJtuyTxicM7s7PLh/E0Kggh5KJiIhIW3LGgqJbV2CzMWYVUHRi0Fo7pVlSSYdnreUv727ltbUHAejs68X82xLp372zw8lERESkvRkeHcwzUxO4bd4qyist//p4B2GBvtx6fi+no4m0NY1aN1prb65jeM5p5qYDk9zf7wbiTjPvMHBpw2KLiLRP+w8X8+hHabyTkk7NBgwJvbryi6sGM7p3N+fCiYiISJvT0IKiH5BU47UBHmz6OCJVnvxsF89/uQcAHy8Pnp+ewIgewQ6nEhERkfZq3IBQHrkhnh8uXA/A79/ZRGhnH6K7+LPncBFhnX1J7NMNTw/jcFKRVk3rRhGRViC7oIR/f7yThav2U+H6ppI4OCKQ+68cxCWDuuN+ultERESkwRpaUPSy1n5ec8AYo76T0iwWfL2Ph5ekAeDpYXjylvM4v2+Iw6lERESkvZsSF0VuQSl/St6Cy8I9L62j5m6KPbp24sHrYrmwf6hjGUVaOa0bRUQcdOx4Oc9+sYu5X+7leHll9XjPbp24b+IgpsRF4aGbo0RERKSR6i0oGmPuAb4P9DXGpNY4FAh81ZzBpGN6Z8Mhfv/OpurX//huLJcNDXcwkYiIiHQkM8f1Yd+RIuYv33dSMRHgYN5xbpu3mjfvGcvwaHVOEDlB60YREWeVlFcyf/lenvxsF8eOl1ePh3b25YeX9uem0TH4eHk4mFBERETagzM9ofgy8D7wN+CXNcYLrLVHmi2VdEifbsvmvldTqvv6/2HyUL4zsoezoURERKTDCfH3Oe2xsgoXT322i/9877wWTCTS6mndKCLigIpKF6+tPci/lu4gM7+kejzQ14u7x/fjtgt74+/T0OZkIiIiIvWr91OFtfYYcAy4uWXiSEe1as8R7n5pbXVv/x9fNoDbLuzjcCoRERHpiL7YkVvv8c/SslsoiUjboHWjiEjLcrks72/K5JEP09idW1Q97uvlwYyxvbn7W/3oGnD6G6REREREGkO3KYnjNh06xqx5qymtcAEwY2xvfnTpAIdTiYiISEdVaU9tdnqyorJK7lqwhilx0Vw6pDt+3p4tlExEREQ6Mmsty3bk8vCSNDYeOlY97ulhuCGhBz+8dACRwdq6VkRERJqHCoriqN05hUyfu4qC0goArh0Zze+ThmKMNgkXERERZ4ztF8L6/UfrnbNkcxZLNmcR4OPJ5cMimBIXxbgBoXh7an8iERERaXrr9+fx0AdprNh9+KTxq0dE8tPLB9IvrLNDyURERKSjUEFRHJNx7DhT56zicFEZAJcNCefB62Px8FAxUURERJwz9fzeLFixj/ySilrHDBDdtRMH844DVU8rvrX+EG+tP0RXf2+uGhHJlLgoEnt302caEREROWc7swt4eEkaSzZnnTR+0YBQ7r9iMCN6BDuUTERERDoaFRTFEUeKyrj1+ZUcOlp1Me78vt144paRuqtfREREHBcR7MeLs8Yw++V11YVDgOBO3vz1OyOYNCKCbZkFLEpJZ3FKevWcvOJyXl65n5dX7iciyI+k2EimxEcxIjpY3RdERETkrBw6epx/frSdN9YdxFWjG3tczy784opBjO0f6lw4ERER6ZCMPcMeMe1FQkKCXbNmjdMxBCgoKed7z68k9WBVv/8R0cG8fMcYAv28HU4mIiIi8o1Kl2XZjhz25hYRFuhX536J1lrW7T/K4pR0klMzyC0srfU+vUP8mRIXxZT4KPp3D2yp+CJ1MsastdYmOJ2jNdKaUURag8OFpTz52S4WrNhHWaWrerxfWAA/v2IwVwwL141KIiIi0mzqWzOqoCgtqqS8khkvrOLr3UeAqg/Er951ASGdfR1OJiIiInJuKipdfL37CItSDvH+pkwK6miZOiQyiClxUUyOi6RHV38HUkpHp4Li6WnNKCJOKiytYM6yPTy3bDeFpd98hogK9uPHEwdy7chovNTVSURERJpZfWtGtTyVFlNR6WL2y+uri4nRXTqxYNYYFRNFRESkXfDy9GDcgFDGDQjlz98ezudpOSxKSWfp1ixKyqueMNiakc/WjHwe/GAbo3p1ZUpcFJNGRBIWqM9DIiIiHVFpRSUvr9zPE5/s5HBRWfV4V39v7r2kP7ee36tWhwQRERERJ6igKC3C5bLc/0YqS7dWbSIeEuDDglmJRHXp5HAyERERkabn6+XJ5cMiuHxYBEWlFSzdmsWiDel8vj2HCvdGSGv35bF2Xx5/XLyZC/uHMjkuiiuHRxCkNvAiIiLtXqXL8tb6Qzz20XYOHf1mz2Z/H09uv6gvd1zUR1vDiIiISKuigqI0O2stf0rewpvrDgEQ6OvF/JmJ9A3r7HAyERERkeYX4OvFNfHRXBMfTV5RGe9vymRRyiFW7jmCteCysGxHLst25PLbtzYxflAY18RH17lno4iIiLRt1lo+2pLFPz5MY3tWYfW4t6fhe2N6MXtCf0LVyUlERERaIRUUpdk9/vFO5i3fC4CvlwdzZoxmeHSws6FEREREHNA1wIdbxsRwy5gYMo+VkJyazuKUdFIOHgOgrNLFh1uy+HBLFgE+VU85TomLYtyAULy1b5KIiEib9vXuwzz4wTbW7z9aPWYMXDuyBz++bAA9u2l/ZREREWm9VFCUZjXvqz08tnQ7AF4ehqduPY/EPt0cTiUiIiLivIhgP26/qC+3X9SXvblFLE5JZ1FKOjuyq55WKCqr5K31h3hr/SG6+ntz1YhIpsRFkdi7Gx4exuH0IiIi0lCbDh3j4SVpfL4956TxiUPD+dnlgxgUEehQMhEREZGGU0FRms1b6w/ywOItQNUdd4/cEMeEweEOpxIRERFpfXqHBvCDSwcwe0J/tmUWsCil6snFg3lVeyrlFZfz8sr9vLxyP+FBvkyOjWJKfBQjooMxRsVFERGR1mhvbhGPfLSdxSnpJ40n9unGL64czKheXR1KJiIiInL2mrWgaIyZCyQB2dba4e6xB4A7gBO3Zf3aWvveKef1BF4EwgELPGut/VdDzxfnLd2Sxc9eS61+/adrhnNNfLSDiURERERaP2MMQyKDGBIZxP1XDGLd/qMsTkknOTWD3MJSALLyS3n+yz08/+Ueeof4MyWuqrjYv7uebhAREWkNsvJLePzjHbyy+gAVLls9PjQyiPuvHMS3BobphiARERFpc4y19syzGvvmxlwMFAIvnlJQLLTW/qOe8yKBSGvtOmNMILAW+La1dktDzq9LQkKCXbNmTSN/EzkbK3YdZvoLqyircAHws8sHMnvCAIdTiYiIiLRdFZUuvt59hEUph3h/UyYFJRW15gyJDGJKXBST4yLp0VV7MEndjDFrrbUJTudojbRmFJFzday4nKe/2MULX+2hpNxVPd47xJ+fXj6IpBGRalsuIiIirVp9a8ZmfULRWvuFMaZ3I87LADLc3xcYY7YC0cCWJg0oTW7jwWPc8eKa6mLi7eP6cO8l/R1OJSIiItK2eXl6MG5AKOMGhPLnbw/n87QcFqWks3RrVvUFy60Z+WzNyOfBD7YxqldXpsRFMWlEJGGBvg6nFzk9dbURkfbgeFklLyzfw9Of7SK/xk0/3QN9+dFlA7ghoSfenh4OJhQRERE5d07toTjbGDMNWAPcZ63NO91Ed0FyJLCyMedLy9mZXcj0F1ZRWFr14fm7o3rwm6uHqI2HiIiISBPy9fLk8mERXD4sgqLSCpZuzWLRhnQ+355T3VZt7b481u7L44+LN3Nh/1Amx0VxxbAIgjt5O5xepJZ5wBNUFQdreuwMXWkqqFoLVne1McZ8ZK3d0sDzRUTOWXmli1dWH+Dxj3eQXVBaPR7k58U94/szY2xvOvl4OphQREREpOk4UVB8CvgzVXeR/hl4BJhZ10RjTGfgDeDH1tr8Rpx/J3AnQExMTNP9BlLLoaPHmTpnJUeKygC4Ylg4f7t2hIqJIiIiIs0owNeLa+KjuSY+mqPFZby/KZNFG9L5es9hrAWXhWU7clm2I5ffvrWJ8YPCmBIfxaWDw3WBU1oFdbURkbbI5bIkb8zgkQ/T2He4uHrcz9uD2y7sw90X9yPYXzfxiIiISPvSrHsoQvUThskn2tecxTFvIBlYYq199Gzf+1TaD6P55BaWcsPTK9idWwTAhf1DmDN9NH7eukglIiIi4oSs/BKSUzNYlJJOyoGjtY4H+HgycWg4U+KjuGhAmNqwdSCtcQ/FU9d17palM4B8Gt7V5gtguLU2/2zOP+Um1FH79u07919IRNotay2fb8/hoQ/S2JKRXz3u5WG4cXRPfnjpAMKD/BxMKCIiInJu6lsztnhB0RgT6b6bFGPMT4Ax1tqbTjnHAPOBI9baH59y7Izn10UFxeaRX1LOzc9+zeb0qg/ScT278N/bx9DZ16luuiIiIiJS077DRSxOSeedDensyC6sdbyLvzdXDY9kSlwUY/p0w8NDHSbaszZSUAwHcvmmK02ktba+rjafA3+x1r55tufXpDWjiNRn7b4jPPhBGqv2HDlpfEpcFD+dOJDeoQEOJRMRERFpOvWtGZu16mOMWQiMB0KNMQeBPwDjjTHxVC3u9gJ3uedGAc9baycBFwJTgY3GmA3ut/u1tfY94KG6zpeWV1Jeye3z1lQXEwd078y8GaNVTBQRERFpRXqFBDB7wgDuvaQ/2zILWJSSzuKUdA7mHQfgaHE5C1ftZ+Gq/YQH+ZIUG8WUuChiewSrfb04wlqbdeJ7Y8xzVHWuqcXd1eYN4L8niolnc76ISEOkZRbw8JI0lm7NOml8/KAwfnb5IIZHBzuUTERERKRlNWvlx1p7cx3Dc04zNx2Y5P7+S6DOqxfW2qlNFlAarbzSxff/u45Ve6vuzOvRtRMLZo2ha4CPw8lEREREpC7GGIZEBjEkMoj7rxjEuv1HWZySTnJqBrmFpQBk5Zcy58s9zPlyD71D/JkcV1VcHBAe6HB66UhqdqUBvgNsqmOOoWptufXULTIacr6IyJkcOFLMY0u389b6Q9Rs7nVeTBfuv3Iw5/cNcS6ciIiIiAOaveVpa6H2NU3H5bL85NUNvLMhHYDQzr68fvcFau8hIiIi0gZVuixf7z7MOxsO8f6mTApKKmrNGRwRyJT4KCbHRtGzm78DKaWptLaWpzW72gBZuLvaACd1pbHWZtTsamOMGQcsAzYCLvfb/dpa+54xZkFd558pi9aMIgKQW1jKE5/s5L8r91Fe+c01s4Hhnfn5FYO5bEh3PcEvIiIi7Zajeyi2FlocNg1rLX9YtJkXV+wDIMjPi1fuuoAhkUEOJxMRERGRc1VaUcnnaTksSkln6dYsSspdteaM6tWVKXFRTBoRSVigrwMp5Vy0toJia6I1o0jHVlBSznPL9vD8st0Ul1VWj0d36cRPJw7k2yOj8dQ+wyIiItLOObaHorQ/j320vbqY6OftwQu3jVYxUURERKSd8PXy5PJhEVw+LIKi0gqWbs1i0YZ0vtiRU/2Uxtp9eazdl8cfF2/mwv6hTI6N4orhEQR38nY4vYiIyNkrKa/kpa/38Z9Pd5JXXF49HhLgww8m9OfmMTH4enk6mFBERESkdVBBURrs+WW7efyTnQB4exqemZrAqF7dHE4lIiIiIs0hwNeLa+KjuSY+mqPFZby/KZNFG9L5es9hrAWXhWU7clm2I5ffvr2Jbw0KY0pcFJcNCaeTjy68iohI61ZR6eLNdYf459LtpB8rqR7v7OvFnRf3Zea4PnT21WUzERERkRP0yUga5LU1B/i/d7cCYAw8dmM83xoY5nAqEREREWkJXfx9uDkxhpsTY8jKLyE5NYNFKemkHDgKQFmli4+2ZPHRliz8fTy5fGg4U+KjGNc/DB8vD4fTi4iIfMNay5LNmTy8JI1dOUXV4z5eHkw7vxffv6Q/3QJ8HEwoIiIi0jqpoChntGRzJr94I7X69V++PYKk2CgHE4mIiIiIU8KD/Jg1rg+zxvVh3+EiFqeksyglne1ZhQAUl1Xy9oZ03t6QThd/b64aHsmUuCgS+3TT3lMiIuKo5TtzeXBJWvUNMQAeBq4f1YMfXTaQ6C6dHEwnIiIi0rqpoCj1+mpnLj94eT2uqi1z+MWVg7llTIyzoURERESkVegVEsDsCQOYPWEA2zLzWbShqrh4MO84AEeLy1m4aj8LV+0nPMiXpNgopsRFEdsjGGNUXBQRkZax8eAxHlqyjWU7ck8av3JYBD+7YiD9uwc6lExERESk7VBBUU5rw4Gj3PHiGsoqXQDc9a2+3DO+n8OpRERERKQ1GhwRxOArg/j5FYNYf+Aoizakk5yaQW5hKQBZ+aXM+XIPc77cQ+8QfybHVRUXB4TrIq6IiDSPXTmFPPrhdt7dmHHS+AV9Q/jFVYOJ79nFoWQiIiIibY+x1jqdoUUkJCTYNWvWOB2jzdiRVcB3n1nB0eJyAG4a3ZO/XTtCd5KLiIiISINVuixf7z7MOxsO8f6mTApKKmrNGRwRyJT4KCbHRtGzm78DKTsWY8xaa22C0zlaI60ZRdqPjGPH+dfSHby29iCVrm+ue42IDub+Kwcxrn+orm+IiIiI1KG+NaMKilLLgSPFXP/0crLyq+4mv3pEJI/fPFJ73oiIiIhIo5VWVPLF9lwWpaSzdEsWx8sra80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\n",
            "text/plain": [
              "<Figure size 1800x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Now lets get some visuals from the above data.\n",
        "fig, ax = plt.subplots(2,2, figsize=(25,8))\n",
        "fig.tight_layout(h_pad=4, w_pad=4)\n",
        "sns.pointplot(data=nyc_df, x='day', y='trip_duration_minutes', ax=ax[0][0], ci=None)\n",
        "sns.pointplot(data=nyc_df, x='month', y='trip_duration_minutes', ax=ax[0][1], ci=None)\n",
        "sns.pointplot(data=nyc_df, x='pickup_hour', y='trip_duration_minutes', ax=ax[1][0], ci=None)\n",
        "sns.pointplot(data=nyc_df, x='dropoff_hour', y='trip_duration_minutes', ax=ax[1][1], ci=None)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYJijDqY8ouX"
      },
      "source": [
        "* ***trip duration decreases as the weekend approches, it makes sense as most of the people either stay at home or go for vacations.***\n",
        "* ***trip duration increases after Feburary, this might be due to the people returning after holidays***.\n",
        "* ***pickup and dropoff hours are almost the same, most of the comute happen from 6am to 7pm after which it gradually decreases.***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ot9CJUmEwYhM"
      },
      "source": [
        "."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ry9xK3wnIA3F"
      },
      "source": [
        "# **Data Cleaning And Feature Engineering.** 🚧🏗️👷‍♂️"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxeFXZk3Kip8"
      },
      "source": [
        "***First lets remove outliers from our dependent variable.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R__hwVfVKpRH"
      },
      "outputs": [],
      "source": [
        "# Create a function that returns upper and lower bounds for removing outliers.\n",
        "def outlier_treatment(feature):\n",
        "  sorted(feature)\n",
        "  Q1,Q3 = np.percentile(feature , [25,75])\n",
        "  IQR = Q3 - Q1\n",
        "  lower_range = Q1 - (1.5 * IQR)\n",
        "  upper_range = Q3 + (1.5 * IQR)\n",
        "  return lower_range,upper_range"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tc-yk8F5KuLY",
        "outputId": "cda17cf2-a297-4ff2-db95-cf6eef8b6e28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-10.333333333333336 34.866666666666674\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-441e099f-bcd4-4c39-b689-b10f8e978aa4\">\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>id</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>store_and_fwd_flag</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>pickup_hour</th>\n",
              "      <th>dropoff_hour</th>\n",
              "      <th>trip_duration_minutes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id3858529</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.979027</td>\n",
              "      <td>40.763939</td>\n",
              "      <td>-74.005333</td>\n",
              "      <td>40.710087</td>\n",
              "      <td>N</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>35.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>id3379579</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.991165</td>\n",
              "      <td>40.755562</td>\n",
              "      <td>-73.999290</td>\n",
              "      <td>40.725353</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>38.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>id3321406</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.955231</td>\n",
              "      <td>40.777134</td>\n",
              "      <td>-73.788750</td>\n",
              "      <td>40.641472</td>\n",
              "      <td>N</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>41.416667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>id1410897</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.982117</td>\n",
              "      <td>40.756351</td>\n",
              "      <td>-73.865692</td>\n",
              "      <td>40.770988</td>\n",
              "      <td>N</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>38.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>id0023813</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.990311</td>\n",
              "      <td>40.756580</td>\n",
              "      <td>-73.870918</td>\n",
              "      <td>40.773930</td>\n",
              "      <td>N</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>37.816667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458571</th>\n",
              "      <td>id1440046</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-73.778946</td>\n",
              "      <td>40.647343</td>\n",
              "      <td>-73.952103</td>\n",
              "      <td>40.781166</td>\n",
              "      <td>N</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>22</td>\n",
              "      <td>23</td>\n",
              "      <td>40.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458584</th>\n",
              "      <td>id1607338</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.786697</td>\n",
              "      <td>40.641628</td>\n",
              "      <td>-73.996880</td>\n",
              "      <td>40.732506</td>\n",
              "      <td>N</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>21</td>\n",
              "      <td>38.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458589</th>\n",
              "      <td>id2071901</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-73.782280</td>\n",
              "      <td>40.644783</td>\n",
              "      <td>-73.904305</td>\n",
              "      <td>40.738285</td>\n",
              "      <td>N</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>34.883333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458600</th>\n",
              "      <td>id0995846</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>-73.789543</td>\n",
              "      <td>40.647099</td>\n",
              "      <td>-73.960320</td>\n",
              "      <td>40.798180</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>63.683333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458621</th>\n",
              "      <td>id3267199</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.959534</td>\n",
              "      <td>40.782749</td>\n",
              "      <td>-73.990959</td>\n",
              "      <td>40.751091</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>39.250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74212 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-441e099f-bcd4-4c39-b689-b10f8e978aa4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-441e099f-bcd4-4c39-b689-b10f8e978aa4 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-441e099f-bcd4-4c39-b689-b10f8e978aa4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                id  vendor_id  ...  dropoff_hour  trip_duration_minutes\n",
              "2        id3858529          2  ...            12              35.400000\n",
              "16       id3379579          2  ...            18              38.600000\n",
              "24       id3321406          2  ...             8              41.416667\n",
              "29       id1410897          1  ...            14              38.850000\n",
              "33       id0023813          1  ...            15              37.816667\n",
              "...            ...        ...  ...           ...                    ...\n",
              "1458571  id1440046          1  ...            23              40.900000\n",
              "1458584  id1607338          2  ...            21              38.916667\n",
              "1458589  id2071901          1  ...            17              34.883333\n",
              "1458600  id0995846          2  ...            18              63.683333\n",
              "1458621  id3267199          2  ...            15              39.250000\n",
              "\n",
              "[74212 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "# Get upper/lower bounds for trip_duration.\n",
        "lowerbound,upperbound = outlier_treatment(nyc_df['trip_duration_minutes'])\n",
        "print(lowerbound,upperbound)\n",
        "\n",
        "nyc_df[(nyc_df['trip_duration_minutes'] < lowerbound) | (nyc_df['trip_duration_minutes'] > upperbound)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YqNlMsTiKuLY"
      },
      "outputs": [],
      "source": [
        "nyc_df.drop(nyc_df[ (nyc_df['trip_duration_minutes'] > upperbound) | (nyc_df['trip_duration_minutes'] < lowerbound) ].index , inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aEbWWwV9LbH6"
      },
      "source": [
        "."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hulmy1AkIw_y"
      },
      "source": [
        "**Lets calculate distance travelled using pickup and dropoff data.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vHC42YTsIQL7"
      },
      "outputs": [],
      "source": [
        "# Function to calculate distance using the haversine function as we are provided with gps data.\n",
        "def calc_distance(df):\n",
        "    pickup = (df['pickup_latitude'], df['pickup_longitude'])\n",
        "    drop = (df['dropoff_latitude'], df['dropoff_longitude'])\n",
        "    return haversine(pickup, drop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TrqxO4cPJL3P"
      },
      "outputs": [],
      "source": [
        "# Get distances.\n",
        "nyc_df['distance'] = nyc_df.apply(lambda x: calc_distance(x), axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_NDi64_ykLV",
        "outputId": "d54c03b0-50b4-4df2-826d-0f8b232ca2a4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Get plots.\n",
        "fig = plt.figure(figsize=(25,5))\n",
        "sns.boxplot(nyc_df['distance'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SSEhZ93cafXu"
      },
      "source": [
        "***There are too many outliers lets remove them.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-la07lKJJvw",
        "outputId": "7052086d-fee8-409f-f3e8-5a654a3d1fa3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-2.233443060186282 6.917934441398998\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a4a9ba0d-7e51-4003-975f-7094c37c0207\">\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>id</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>store_and_fwd_flag</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>pickup_hour</th>\n",
              "      <th>dropoff_hour</th>\n",
              "      <th>trip_duration_minutes</th>\n",
              "      <th>distance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>id0718344</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-74.016327</td>\n",
              "      <td>40.704990</td>\n",
              "      <td>-73.982109</td>\n",
              "      <td>40.773750</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>24.633333</td>\n",
              "      <td>8.171214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>id2067879</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.996819</td>\n",
              "      <td>40.752861</td>\n",
              "      <td>-73.809006</td>\n",
              "      <td>40.816875</td>\n",
              "      <td>N</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>25.950000</td>\n",
              "      <td>17.340841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>id0526266</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.934883</td>\n",
              "      <td>40.801010</td>\n",
              "      <td>-73.983002</td>\n",
              "      <td>40.722569</td>\n",
              "      <td>N</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>13.650000</td>\n",
              "      <td>9.617850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>id3913101</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-74.001411</td>\n",
              "      <td>40.761433</td>\n",
              "      <td>-73.924629</td>\n",
              "      <td>40.864029</td>\n",
              "      <td>N</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>16.716667</td>\n",
              "      <td>13.111154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>id0283075</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.782478</td>\n",
              "      <td>40.644646</td>\n",
              "      <td>-73.981125</td>\n",
              "      <td>40.720886</td>\n",
              "      <td>N</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>29.700000</td>\n",
              "      <td>18.773512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1458538</th>\n",
              "      <td>id1302341</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.973053</td>\n",
              "      <td>40.793495</td>\n",
              "      <td>-73.872581</td>\n",
              "      <td>40.774479</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>25.516667</td>\n",
              "      <td>8.719416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458546</th>\n",
              "      <td>id2085200</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.995178</td>\n",
              "      <td>40.749668</td>\n",
              "      <td>-73.960800</td>\n",
              "      <td>40.688099</td>\n",
              "      <td>N</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>29.850000</td>\n",
              "      <td>7.434015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458547</th>\n",
              "      <td>id2525150</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.978241</td>\n",
              "      <td>40.744911</td>\n",
              "      <td>-73.870483</td>\n",
              "      <td>40.773777</td>\n",
              "      <td>N</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>23.150000</td>\n",
              "      <td>9.626801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458553</th>\n",
              "      <td>id2796154</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.985817</td>\n",
              "      <td>40.727772</td>\n",
              "      <td>-74.021095</td>\n",
              "      <td>40.628605</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>21.500000</td>\n",
              "      <td>11.421131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458641</th>\n",
              "      <td>id2304944</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.959129</td>\n",
              "      <td>40.768799</td>\n",
              "      <td>-74.004433</td>\n",
              "      <td>40.707371</td>\n",
              "      <td>N</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>12.733333</td>\n",
              "      <td>7.824617</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>107687 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4a9ba0d-7e51-4003-975f-7094c37c0207')\"\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-a4a9ba0d-7e51-4003-975f-7094c37c0207 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-a4a9ba0d-7e51-4003-975f-7094c37c0207');\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",
              "  "
            ],
            "text/plain": [
              "                id  vendor_id  ...  trip_duration_minutes   distance\n",
              "45       id0718344          2  ...              24.633333   8.171214\n",
              "50       id2067879          2  ...              25.950000  17.340841\n",
              "54       id0526266          2  ...              13.650000   9.617850\n",
              "67       id3913101          1  ...              16.716667  13.111154\n",
              "86       id0283075          1  ...              29.700000  18.773512\n",
              "...            ...        ...  ...                    ...        ...\n",
              "1458538  id1302341          1  ...              25.516667   8.719416\n",
              "1458546  id2085200          1  ...              29.850000   7.434015\n",
              "1458547  id2525150          1  ...              23.150000   9.626801\n",
              "1458553  id2796154          1  ...              21.500000  11.421131\n",
              "1458641  id2304944          2  ...              12.733333   7.824617\n",
              "\n",
              "[107687 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "# Get upper/lower bounds for trip_duration.\n",
        "lowerbound,upperbound = outlier_treatment(nyc_df['distance'])\n",
        "print(lowerbound,upperbound)\n",
        "\n",
        "nyc_df[(nyc_df['distance'] < lowerbound) | (nyc_df['distance'] > upperbound)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HbO1JM34J9GO"
      },
      "outputs": [],
      "source": [
        "nyc_df.drop(nyc_df[ (nyc_df['distance'] > upperbound) | (nyc_df['distance'] < lowerbound) ].index , inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Glnm_qxZ-QPf"
      },
      "source": [
        "***Lets check the relation between distance and the dependent variable.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNy-jgS480PG",
        "outputId": "e8c2903f-5510-484c-faec-dde2b60af75f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.lineplot(nyc_df['trip_duration_minutes'], nyc_df['distance'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FuuAWWluMK59"
      },
      "source": [
        "."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h0yk886jAzyl"
      },
      "source": [
        "**Get dummies for store_and_fwd_flag, day, month.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WHjk1uGdAx-q"
      },
      "outputs": [],
      "source": [
        "nyc_df = pd.get_dummies(nyc_df, columns=['store_and_fwd_flag'], drop_first=True)\n",
        "nyc_df = pd.get_dummies(nyc_df, columns=['day', 'month'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02nG_lOQSV_2",
        "outputId": "ae63cac4-adac-4c62-e187-6dce0216062e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9049719a-595b-4768-8ef6-981b2f4222c6\">\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>id</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>pickup_hour</th>\n",
              "      <th>dropoff_hour</th>\n",
              "      <th>trip_duration_minutes</th>\n",
              "      <th>distance</th>\n",
              "      <th>store_and_fwd_flag_Y</th>\n",
              "      <th>day_0</th>\n",
              "      <th>day_1</th>\n",
              "      <th>day_2</th>\n",
              "      <th>day_3</th>\n",
              "      <th>day_4</th>\n",
              "      <th>day_5</th>\n",
              "      <th>day_6</th>\n",
              "      <th>month_1</th>\n",
              "      <th>month_2</th>\n",
              "      <th>month_3</th>\n",
              "      <th>month_4</th>\n",
              "      <th>month_5</th>\n",
              "      <th>month_6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id2875421</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.982155</td>\n",
              "      <td>40.767937</td>\n",
              "      <td>-73.964630</td>\n",
              "      <td>40.765602</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>7.583333</td>\n",
              "      <td>1.498523</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9049719a-595b-4768-8ef6-981b2f4222c6 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-9049719a-595b-4768-8ef6-981b2f4222c6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          id  vendor_id  passenger_count  ...  month_4  month_5  month_6\n",
              "0  id2875421          2                1  ...        0        0        0\n",
              "1  id2377394          1                1  ...        0        0        1\n",
              "3  id3504673          2                1  ...        1        0        0\n",
              "4  id2181028          2                1  ...        0        0        0\n",
              "5  id0801584          2                6  ...        0        0        0\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "# Check the data.\n",
        "nyc_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXXkEsz4hjuq"
      },
      "source": [
        "."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i68bBc3AS0bE"
      },
      "source": [
        "**Transform pickup/ dropoff hours.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FMKVFGVJVmjE"
      },
      "outputs": [],
      "source": [
        "# Since there are total 24 different values in these two columns it would be better to categorize them and get dummies for them.\n",
        "\n",
        "# Function to convert pickup/dropoff hours to category.\n",
        "def timezone(x):\n",
        "  if x>=(0) and x <(5):\n",
        "    return 'mid_night'\n",
        "  elif x>=(5) and x <(12):\n",
        "    return 'morning'\n",
        "  elif x>=(12) and x <(16):\n",
        "    return 'afternoon'\n",
        "  elif x>=(16) and x <(19):\n",
        "    return 'evening'\n",
        "  elif x>=(19) and x <(25):\n",
        "    return 'night'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WBHUYfv2u45R"
      },
      "outputs": [],
      "source": [
        "# Apply the above function.\n",
        "nyc_df['pickup_time'] = nyc_df['pickup_hour'].apply(lambda x :timezone(x) )\n",
        "nyc_df['dropoff_time'] = nyc_df['dropoff_hour'].apply(lambda x :timezone(x) )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MGnrtbxi76BL",
        "outputId": "2e494ac5-b9f2-4a34-8b75-148aa3464131"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\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-981102d2-e00a-4ebf-8b85-3714a04a6e63 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-981102d2-e00a-4ebf-8b85-3714a04a6e63');\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",
              "  "
            ],
            "text/plain": [
              "          id  vendor_id  passenger_count  ...  month_6  pickup_time  dropoff_time\n",
              "0  id2875421          2                1  ...        0      evening       evening\n",
              "1  id2377394          1                1  ...        1    mid_night     mid_night\n",
              "3  id3504673          2                1  ...        0        night         night\n",
              "4  id2181028          2                1  ...        0    afternoon     afternoon\n",
              "5  id0801584          2                6  ...        0        night         night\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "nyc_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tk1n5_WB8AAF",
        "outputId": "ee893c3e-2f00-4f6c-df33-7c768f3550c8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot it.\n",
        "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(20,5))\n",
        "fig.tight_layout(h_pad=4, w_pad=4)\n",
        "sns.barplot(data=nyc_df, x='pickup_time', y='trip_duration_minutes', ax=ax1, ci=None)\n",
        "sns.barplot(data=nyc_df, x='dropoff_time', y='trip_duration_minutes', ax=ax2, ci=None)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHxJ6v1q7Tc3"
      },
      "source": [
        "* ***most of the pickups and dropoffs happen in the evenings and afternoons, which makes sense as most people comute during these timimgs.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kAVjU9uyJJIg"
      },
      "outputs": [],
      "source": [
        "# Now lets get dummies for these variables.\n",
        "nyc_df = pd.get_dummies(nyc_df, columns=['pickup_time', 'dropoff_time'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1TMFEsWU5ED",
        "outputId": "2dcc1198-011e-4b79-fc1d-6059384e638c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-45aafd00-98f9-4b24-b642-a9f4ca80baf5\">\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>id</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>pickup_hour</th>\n",
              "      <th>dropoff_hour</th>\n",
              "      <th>trip_duration_minutes</th>\n",
              "      <th>distance</th>\n",
              "      <th>store_and_fwd_flag_Y</th>\n",
              "      <th>day_0</th>\n",
              "      <th>day_1</th>\n",
              "      <th>day_2</th>\n",
              "      <th>day_3</th>\n",
              "      <th>day_4</th>\n",
              "      <th>day_5</th>\n",
              "      <th>day_6</th>\n",
              "      <th>month_1</th>\n",
              "      <th>month_2</th>\n",
              "      <th>month_3</th>\n",
              "      <th>month_4</th>\n",
              "      <th>month_5</th>\n",
              "      <th>month_6</th>\n",
              "      <th>pickup_time_afternoon</th>\n",
              "      <th>pickup_time_evening</th>\n",
              "      <th>pickup_time_mid_night</th>\n",
              "      <th>pickup_time_morning</th>\n",
              "      <th>pickup_time_night</th>\n",
              "      <th>dropoff_time_afternoon</th>\n",
              "      <th>dropoff_time_evening</th>\n",
              "      <th>dropoff_time_mid_night</th>\n",
              "      <th>dropoff_time_morning</th>\n",
              "      <th>dropoff_time_night</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id2875421</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.982155</td>\n",
              "      <td>40.767937</td>\n",
              "      <td>-73.964630</td>\n",
              "      <td>40.765602</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>7.583333</td>\n",
              "      <td>1.498523</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id2377394</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.980415</td>\n",
              "      <td>40.738564</td>\n",
              "      <td>-73.999481</td>\n",
              "      <td>40.731152</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11.050000</td>\n",
              "      <td>1.805510</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id3504673</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-74.010040</td>\n",
              "      <td>40.719971</td>\n",
              "      <td>-74.012268</td>\n",
              "      <td>40.706718</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>7.150000</td>\n",
              "      <td>1.485500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id2181028</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.973053</td>\n",
              "      <td>40.793209</td>\n",
              "      <td>-73.972923</td>\n",
              "      <td>40.782520</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>7.250000</td>\n",
              "      <td>1.188590</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>id0801584</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>-73.982857</td>\n",
              "      <td>40.742195</td>\n",
              "      <td>-73.992081</td>\n",
              "      <td>40.749184</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>7.383333</td>\n",
              "      <td>1.098944</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45aafd00-98f9-4b24-b642-a9f4ca80baf5')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-45aafd00-98f9-4b24-b642-a9f4ca80baf5 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-45aafd00-98f9-4b24-b642-a9f4ca80baf5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          id  vendor_id  ...  dropoff_time_morning  dropoff_time_night\n",
              "0  id2875421          2  ...                     0                   0\n",
              "1  id2377394          1  ...                     0                   0\n",
              "3  id3504673          2  ...                     0                   1\n",
              "4  id2181028          2  ...                     0                   0\n",
              "5  id0801584          2  ...                     0                   1\n",
              "\n",
              "[5 rows x 35 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "nyc_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oB7JihkbVkcK"
      },
      "source": [
        "***Now than we are done with the cleaning as well as the feature engineering of our data lets do some feature selection before proceeding with the model building.***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_134hbr0QxF8"
      },
      "source": [
        "# **Data preparation.** 🧑‍🍳👩‍🍳🧑‍🍳"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ddIspsHtR1wK"
      },
      "outputs": [],
      "source": [
        "# Lets drop id before going further as its not useful.\n",
        "nyc_df.drop('id', inplace=True, axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qj9Cut1gRHGl"
      },
      "source": [
        "***Split the data first.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sYMZ2ZL-RGi7"
      },
      "outputs": [],
      "source": [
        "# Get X and y values.\n",
        "X = nyc_df.drop('trip_duration_minutes', axis=1)\n",
        "            \n",
        "y = nyc_df['trip_duration_minutes']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o0Sgss06RkmL"
      },
      "outputs": [],
      "source": [
        "# Train Test Split.\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2,random_state =5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fs5q5fnBRx0a"
      },
      "outputs": [],
      "source": [
        "# scale the data.\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ond0jferPAx2"
      },
      "source": [
        "## **Correlation between variables.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbljWemG1tsZ"
      },
      "source": [
        "***Correlation of features with the dependent variable.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3nElcT5I5Yit",
        "outputId": "f0448034-14db-4d31-a9bd-29a2a1d54dd4"
      },
      "outputs": [
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 5760x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "corr_data = X.corrwith(y)\n",
        "plt.figure(figsize=(80,5))\n",
        "plt.plot(corr_data)\n",
        "plt.title('Correlation of features with the dependent variable.')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXvYISgx11iX"
      },
      "source": [
        "***Check for multicollinearity among the variables.***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1q5og2hixK5i",
        "outputId": "05cacf75-6474-434e-8c72-2f6c8aa742cb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Multicollinearity.\n",
        "fig = plt.figure(figsize=(18,12))\n",
        "sns.heatmap(nyc_df.corr(), cmap='Paired')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Now that we have completed preparing our data lets build few models using it.***"
      ],
      "metadata": {
        "id": "aBN3oOB-KZKp"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vrfIRnMmYunR"
      },
      "source": [
        "# **Model Selection:** ✅✅✅"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AioblOa_Y2-R"
      },
      "source": [
        "**Models we will be building:**\n",
        "* **Linear Regression**\n",
        "* **Decision Tree Regressorr**\n",
        "* **XG Boost Regressor**\n",
        "* **Hist Gradient Boosting Regressor**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_lTycHgh9jsc"
      },
      "outputs": [],
      "source": [
        "# Defining the models to be used.\n",
        "models = [['Linear Regression', LinearRegression()],\n",
        "          ['Decision Tree Regressor', DecisionTreeRegressor()],\n",
        "          ['XG Boost Regressor', xgb.XGBRegressor()],\n",
        "          ['Hist Gradient Boosting Regressor', HistGradientBoostingRegressor()],\n",
        "          ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--k4uce4-fcR",
        "outputId": "8616cad2-120f-4b0e-bcd8-27bbaf8f5abe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[06:37:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        }
      ],
      "source": [
        "# Creating a function to train and test models.\n",
        "model_data = []\n",
        "for name,current_model in models:\n",
        "  current_model_data = {}\n",
        "  current_model.random_state = 78\n",
        "  current_model_data[\"Name\"] = name\n",
        "  start = time.time()\n",
        "  current_model.fit(X_train,y_train)\n",
        "  end = time.time()\n",
        "  current_model_data[\"Train_Time\"] = end - start\n",
        "  current_model_data[\"Train_R2_Score\"] = r2_score(y_train,current_model.predict(X_train))\n",
        "  current_model_data[\"Test_R2_Score\"] = r2_score(y_test,current_model.predict(X_test))\n",
        "  current_model_data[\"Test_RMSE_Score\"] = np.sqrt(mean_squared_error(y_test,current_model.predict(X_test)))\n",
        "  model_data.append(current_model_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGKGslq0-1Ko",
        "outputId": "f015ce6c-464b-49ae-de36-a1dddd17cb59"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-324a580a-d00f-448d-bd70-9e1d2c874e11\">\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>Name</th>\n",
              "      <th>Train_Time</th>\n",
              "      <th>Train_R2_Score</th>\n",
              "      <th>Test_R2_Score</th>\n",
              "      <th>Test_RMSE_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>1.580218</td>\n",
              "      <td>0.527756</td>\n",
              "      <td>0.528722</td>\n",
              "      <td>4.568891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
              "      <td>25.717400</td>\n",
              "      <td>0.999988</td>\n",
              "      <td>0.422492</td>\n",
              "      <td>5.057680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XG Boost Regressor</td>\n",
              "      <td>140.886116</td>\n",
              "      <td>0.619757</td>\n",
              "      <td>0.620201</td>\n",
              "      <td>4.101558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hist Gradient Boosting Regressor</td>\n",
              "      <td>22.419768</td>\n",
              "      <td>0.687188</td>\n",
              "      <td>0.686527</td>\n",
              "      <td>3.726250</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-324a580a-d00f-448d-bd70-9e1d2c874e11')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "          const element = document.querySelector('#df-324a580a-d00f-448d-bd70-9e1d2c874e11');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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            ],
            "text/plain": [
              "                               Name  Train_Time  ...  Test_R2_Score  Test_RMSE_Score\n",
              "0                 Linear Regression    1.580218  ...       0.528722         4.568891\n",
              "1           Decision Tree Regressor   25.717400  ...       0.422492         5.057680\n",
              "2                XG Boost Regressor  140.886116  ...       0.620201         4.101558\n",
              "3  Hist Gradient Boosting Regressor   22.419768  ...       0.686527         3.726250\n",
              "\n",
              "[4 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "results_df = df = pd.DataFrame(model_data)\n",
        "results_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SaHup1zMoV3p"
      },
      "source": [
        "**Hist Gradient Boosting Regressor performs the best with a Test R2 as 0.686527 and Test_RMSE of 3.726250.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONZYvOk_oOwv"
      },
      "source": [
        "# **Hyperparameter Tuning:**  🪛🔩⚒️"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0C8Oi7Lh03Of"
      },
      "source": [
        "**We will tune the parameters of Hist Gradient Boosting Regressor for better results.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xA-1cCXloVCy"
      },
      "outputs": [],
      "source": [
        "# Maximum depth of trees\n",
        "max_depth = [6,8,10]\n",
        "\n",
        "# Learning Rate\n",
        "learning_rate = [0.2,0.4,0.6]\n",
        "\n",
        "# Minimum sample leafs.\n",
        "min_samples_leaf = [50,60]\n",
        "\n",
        "# Maximum itteration.\n",
        "max_iter = [400,500]\n",
        "\n",
        "# Hyperparameter Dict\n",
        "param_dict = {\n",
        "              'max_depth' : max_depth,\n",
        "              'learning_rate' : learning_rate,\n",
        "              'min_samples_leaf' : min_samples_leaf,\n",
        "              'max_iter': max_iter\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDSrLGEWoyKe",
        "outputId": "c49d97c2-dcdd-4f6e-86ae-d56734a2ca91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  53.6s\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  53.6s\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  55.8s\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  53.7s\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  53.7s\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  54.1s\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  53.6s\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  53.9s\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  53.6s\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  53.6s\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  51.8s\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  52.7s\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  52.7s\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  52.9s\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  51.8s\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  51.6s\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  51.4s\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  51.1s\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  52.3s\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  51.6s\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=500, min_samples_leaf=50; total time= 1.2min\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=8, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  51.6s\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  51.0s\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  50.5s\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  50.9s\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  51.1s\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  50.9s\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  51.1s\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  50.8s\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  50.7s\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  51.4s\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=500, min_samples_leaf=50; total time= 1.1min\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.2, max_depth=10, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  51.2s\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  51.3s\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  51.0s\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  51.6s\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  51.7s\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  53.6s\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  51.5s\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  52.3s\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  51.5s\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  52.2s\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.1min\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.1min\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.1min\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.1min\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.4, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  49.3s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  49.2s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  49.1s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  49.0s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  49.3s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  49.4s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  49.7s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  49.3s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  49.2s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  49.1s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=500, min_samples_leaf=50; total time=  59.3s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=500, min_samples_leaf=50; total time=  55.3s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=500, min_samples_leaf=50; total time=  59.5s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=500, min_samples_leaf=50; total time=  59.0s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=500, min_samples_leaf=50; total time=  59.3s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=500, min_samples_leaf=60; total time=  59.3s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=500, min_samples_leaf=60; total time=  59.6s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=500, min_samples_leaf=60; total time=  59.3s\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.4, max_depth=8, max_iter=500, min_samples_leaf=60; total time=  59.6s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  48.2s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  48.7s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  49.3s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  48.6s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  48.8s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  48.8s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  49.1s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  49.1s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  48.7s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  48.4s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=500, min_samples_leaf=50; total time=  59.3s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=500, min_samples_leaf=50; total time=  59.5s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=500, min_samples_leaf=50; total time=  58.6s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=500, min_samples_leaf=50; total time=  58.9s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=500, min_samples_leaf=50; total time=  59.1s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=500, min_samples_leaf=60; total time=  59.2s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=500, min_samples_leaf=60; total time=  59.3s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=500, min_samples_leaf=60; total time=  57.1s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=500, min_samples_leaf=60; total time=  51.0s\n",
            "[CV] END learning_rate=0.4, max_depth=10, max_iter=500, min_samples_leaf=60; total time=  59.9s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  50.9s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  51.0s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  51.1s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  50.8s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=400, min_samples_leaf=50; total time=  53.1s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  51.6s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  51.6s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  51.1s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  50.8s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=400, min_samples_leaf=60; total time=  45.5s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=500, min_samples_leaf=50; total time=  44.4s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=500, min_samples_leaf=50; total time= 1.0min\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.1min\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=500, min_samples_leaf=60; total time=  53.6s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=500, min_samples_leaf=60; total time=  56.0s\n",
            "[CV] END learning_rate=0.6, max_depth=6, max_iter=500, min_samples_leaf=60; total time= 1.0min\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  48.4s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  42.6s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  41.1s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  40.1s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=400, min_samples_leaf=50; total time=  48.0s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  48.0s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  37.1s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  48.9s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  48.8s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=400, min_samples_leaf=60; total time=  43.4s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=500, min_samples_leaf=50; total time=  49.7s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=500, min_samples_leaf=50; total time=  48.3s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=500, min_samples_leaf=50; total time=  43.3s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=500, min_samples_leaf=50; total time=  44.6s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=500, min_samples_leaf=50; total time=  45.1s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=500, min_samples_leaf=60; total time=  47.8s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=500, min_samples_leaf=60; total time=  41.1s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=500, min_samples_leaf=60; total time=  46.3s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=500, min_samples_leaf=60; total time=  57.2s\n",
            "[CV] END learning_rate=0.6, max_depth=8, max_iter=500, min_samples_leaf=60; total time=  50.8s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  45.5s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  48.0s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  41.3s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  47.9s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=400, min_samples_leaf=50; total time=  30.6s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  47.9s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  47.8s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  36.5s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  44.1s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=400, min_samples_leaf=60; total time=  47.2s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=500, min_samples_leaf=50; total time=  44.4s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=500, min_samples_leaf=50; total time=  43.8s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=500, min_samples_leaf=50; total time=  39.3s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=500, min_samples_leaf=50; total time=  44.8s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=500, min_samples_leaf=50; total time=  54.7s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=500, min_samples_leaf=60; total time=  42.2s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=500, min_samples_leaf=60; total time=  44.7s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=500, min_samples_leaf=60; total time=  52.6s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=500, min_samples_leaf=60; total time=  47.8s\n",
            "[CV] END learning_rate=0.6, max_depth=10, max_iter=500, min_samples_leaf=60; total time=  40.9s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=HistGradientBoostingRegressor(),\n",
              "             param_grid={'learning_rate': [0.2, 0.4, 0.6],\n",
              "                         'max_depth': [6, 8, 10], 'max_iter': [400, 500],\n",
              "                         'min_samples_leaf': [50, 60]},\n",
              "             scoring='r2', verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "# Define model.\n",
        "# min_samples_leaf = 60, max_iter = 500, learning_rate = 0.2\n",
        "hbg_reg = HistGradientBoostingRegressor()\n",
        "\n",
        "# Grid search\n",
        "hbg_grid = GridSearchCV(estimator=hbg_reg,\n",
        "                       param_grid = param_dict,\n",
        "                       cv = 5, verbose=2, scoring='r2')\n",
        "\n",
        "hbg_grid.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7M3AUrdUe5Lc",
        "outputId": "8b0efa6c-dbe7-47d2-9c7e-a877855da51b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.4,\n",
              " 'max_depth': 10,\n",
              " 'max_iter': 500,\n",
              " 'min_samples_leaf': 50}"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "hbg_grid.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oevTEkLAe5BU",
        "outputId": "991467fc-55f2-43b7-a3ec-d847e341abdc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HistGradientBoostingRegressor(learning_rate=0.4, max_depth=10, max_iter=500,\n",
              "                              min_samples_leaf=50)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "hbg_grid.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UHA1ndqBQt5T"
      },
      "outputs": [],
      "source": [
        "hgb_optimal_model = hbg_grid.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3leWza5re5vo"
      },
      "outputs": [],
      "source": [
        "# Making predictions on train and test data\n",
        "train_class_preds = hgb_optimal_model.predict(X_train)\n",
        "test_class_preds = hgb_optimal_model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqUVeo98zQAm",
        "outputId": "2bff12a3-88b8-4fcd-f8b9-3f8ce1e4339f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE : 10.493231548665367\n",
            "RMSE : 3.2393257861267006\n",
            "R2 : 0.7615026638712817\n",
            "Adjusted R2 :  0.7614949576253467\n"
          ]
        }
      ],
      "source": [
        "#Train set metrics\n",
        "hgb_MSE_train  = mean_squared_error((y_train), (train_class_preds))\n",
        "print(\"MSE :\" , hgb_MSE_train)\n",
        "\n",
        "hgb_RMSE_train = np.sqrt(hgb_MSE_train)\n",
        "\n",
        "print(\"RMSE :\" ,hgb_RMSE_train)\n",
        "\n",
        "hgb_r2_train = r2_score((y_train), (train_class_preds))\n",
        "print(\"R2 :\" ,hgb_r2_train)\n",
        "hgb_ar2_train = 1-(1-r2_score((y_train), (train_class_preds)))*((X_train.shape[0]-1)/(X_train.shape[0]-X_train.shape[1]-1))\n",
        "print(\"Adjusted R2 : \",hgb_ar2_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xQDKEg8zS0f",
        "outputId": "f2a06166-97e5-4d51-ed0e-752e0b4d8ecb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE : 11.339631625692881\n",
            "RMSE : 3.367436951999678\n",
            "R2 : 0.7439913368965054\n",
            "Adjusted R2 :  0.7439582454245472\n"
          ]
        }
      ],
      "source": [
        "#Test set metrics\n",
        "hgb_MSE_test  = mean_squared_error((y_test), (test_class_preds))\n",
        "print(\"MSE :\" , hgb_MSE_test)\n",
        "\n",
        "hgb_RMSE_test = np.sqrt(hgb_MSE_test)\n",
        "\n",
        "print(\"RMSE :\" ,hgb_RMSE_test)\n",
        "\n",
        "hgb_r2_test = r2_score((y_test), (test_class_preds))\n",
        "print(\"R2 :\" ,hgb_r2_test)\n",
        "hgb_ar2_test = 1-(1-r2_score((y_test), (test_class_preds)))*((X_test.shape[0]-1)/(X_test.shape[0]-X_test.shape[1]-1))\n",
        "print(\"Adjusted R2 : \",hgb_ar2_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evHf22-N1Yup"
      },
      "source": [
        "**We can observe improvement in the R2 and RMSE after hyperparameter tuning.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "Mi9lCiZz4ykX",
        "outputId": "17d3d442-158a-4157-bf02-4cad4c857ed5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        " plt.figure(figsize=(20,5))\n",
        "plt.plot((test_class_preds)[:200])\n",
        "plt.plot(np.array(y_test)[:200])\n",
        "plt.legend([\"Predicted\",\"Actual\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Since the data is very large we will consider the first 200 values to compare the actual and predicted trip_durations.**\n",
        "* **As we can see the model has done a pretty good job in predicting the durations.**\n",
        "* **Hence it would be safe to say that Hist Gradient Boosting Regressor can be used for future predictions.**\n"
      ],
      "metadata": {
        "id": "4LzcxE97KGaH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conclusions:**\n",
        "* **Distance calculated using the haversine function plays an important role in predicting the trip durations.**\n",
        "\n",
        "* **Rest of the features showed moderate to very little linear correlation with the dependent variable.**\n",
        "\n",
        "* **The best algorithm in this case is Hist Gradient Boosting Regressor.**\n",
        "\n",
        "* **The untuned model was able to explain 68% of the variance on the test set, while the tuned model explained 74% of variance on the test set which is a good improvement**.\n",
        "\n",
        "* **The least RMSE on test set by the Hist Gradient Boosting Regressor was 3.367436 which is comparatively lower when compared with rest of the models.**"
      ],
      "metadata": {
        "id": "EVQDW0VyoTuK"
      }
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "uIeMIxr9EJTg",
        "Br-RCkRZErMg",
        "fKjAvFtGFx-h",
        "2F2xYpzyF2BI",
        "ry9xK3wnIA3F",
        "_134hbr0QxF8",
        "ond0jferPAx2",
        "vrfIRnMmYunR",
        "ONZYvOk_oOwv"
      ],
      "name": "NYC_Taxi_Trip_Time_Prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOUK1A444exoK2JgQoTGsv5",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}