nyc_taxi_data_analysis.ipynb

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  "metadata": {
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "colab": {
      "name": "nyc-taxi-data-analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "S02dVMydx0Ym"
      ],
      "machine_shape": "hm"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4MUz8VwvRf3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "from __future__ import division\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import urllib.request\n",
        "import zipfile\n",
        "import random\n",
        "import itertools\n",
        "import math\n",
        "import os\n",
        "import datetime\n",
        "import sys\n",
        "import random"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXsQLZE8vRgA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# !pip3 install pyshp\n",
        "# !pip3 install descartes\n",
        "# !pip3 install xgboost\n",
        "# !pip3 install bayesian_optimization\n",
        "\n",
        "\n",
        "import shapefile\n",
        "from shapely.geometry import Polygon\n",
        "from descartes.patch import PolygonPatch\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "import socket\n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from bayes_opt import bayesian_optimization\n",
        "\n",
        "from random import randint\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "import statsmodels.api as sm\n",
        "import sklearn.model_selection as cv\n",
        "from scipy import stats\n",
        "from sklearn import linear_model\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import SGDRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrT6Txyr0pca",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth \n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "#Authenticate and create the PyDrive client\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o82XU4ga1U17",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "link = 'https://drive.google.com/open?id=1cQyoWmsTiiWCnlzHeiJ5dtPIRETiHPuo'\n",
        "fluff, id = link.split('=')\n",
        "downloaded = drive.CreateFile({'id':id})\n",
        "downloaded.GetContentFile('jan-2017-2018-2019-combined.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOfJ0lsUvRgI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Function for cross-validation over a grid of parameters\n",
        "\n",
        "def cv_optimize(clf, parameters, X, y, n_jobs=1, n_folds=5, score_func=None, verbose=0):\n",
        "    if score_func:\n",
        "        gs = GridSearchCV(clf, param_grid=parameters, cv=n_folds, n_jobs=n_jobs, scoring=score_func, verbose=verbose)\n",
        "    else:\n",
        "        gs = GridSearchCV(clf, param_grid=parameters, n_jobs=n_jobs, cv=n_folds, verbose=verbose)\n",
        "    gs.fit(X, y)\n",
        "    print(\"BEST\", gs.best_params_, gs.best_score_, gs.cv_results_, gs.scorer_)\n",
        "    print(\"Best score: \", gs.best_score_)\n",
        "    best = gs.best_estimator_\n",
        "    return best"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KBm0gKu_vRgM",
        "colab_type": "text"
      },
      "source": [
        "## NYC Taxi Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aw6DSu2dvRgN",
        "colab_type": "text"
      },
      "source": [
        "#### Download the Trip Record Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSRN4M3MvRgQ",
        "colab_type": "code",
        "outputId": "9496d9ed-02e2-40c3-8c88-2757c3ba4048",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "# For downloading TLC data for multiple months in the same year\n",
        "'''\n",
        "for month in range(1,2):\n",
        "    urllib.request.urlretrieve(\"https://s3.amazonaws.com/nyc-tlc/trip+data/\"+ \\\n",
        "                               \"yellow_tripdata_2018-{0:0=2d}.csv\".format(month), \n",
        "                               \"nyc.2018-{0:0=2d}.csv\".format(month))\n",
        "'''\n",
        "\n",
        "# For downloading TLC data for a single month in multiple years\n",
        "'''\n",
        "month = 1\n",
        "for year in range(2017, 2020):\n",
        "    urllib.request.urlretrieve(\"https://s3.amazonaws.com/nyc-tlc/trip+data/\"+ \\\n",
        "                               \"yellow_tripdata_{0:4d}-{1:0=2d}.csv\".format(year, month), \n",
        "                               \"nyc.{0:4d}-{1:0=2d}.csv\".format(year, month))\n",
        "'''"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nmonth = 1\\nfor year in range(2017, 2020):\\n    urllib.request.urlretrieve(\"https://s3.amazonaws.com/nyc-tlc/trip+data/\"+                                \"yellow_tripdata_{0:4d}-{1:0=2d}.csv\".format(year, month), \\n                               \"nyc.{0:4d}-{1:0=2d}.csv\".format(year, month))\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1hfUCMlvRgW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import dask.dataframe as dd\n",
        "\n",
        "nyc_df = dd.read_csv('jan-2017-2018-2019-combined.csv', parse_dates=['tpep_pickup_datetime', 'tpep_dropoff_datetime'])\n",
        "nyc_df = nyc_df.compute()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8d-NzcATZPc9",
        "colab_type": "code",
        "outputId": "bfd5ebfa-dc12-498c-9a73-c353576ef9a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        }
      },
      "source": [
        "nyc_df = nyc_df.set_index('tpep_pickup_datetime')\n",
        "nyc_df.describe()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>VendorID</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "      <td>2.613779e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.578038e+00</td>\n",
              "      <td>1.603406e+00</td>\n",
              "      <td>2.806822e+00</td>\n",
              "      <td>1.045081e+00</td>\n",
              "      <td>1.646335e+02</td>\n",
              "      <td>1.626697e+02</td>\n",
              "      <td>1.315090e+00</td>\n",
              "      <td>1.234102e+01</td>\n",
              "      <td>3.252228e-01</td>\n",
              "      <td>4.973188e-01</td>\n",
              "      <td>1.796207e+00</td>\n",
              "      <td>2.970778e-01</td>\n",
              "      <td>2.995632e-01</td>\n",
              "      <td>1.556036e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.114147e-01</td>\n",
              "      <td>1.253912e+00</td>\n",
              "      <td>3.724228e+01</td>\n",
              "      <td>5.444552e-01</td>\n",
              "      <td>6.647977e+01</td>\n",
              "      <td>7.046584e+01</td>\n",
              "      <td>4.832841e-01</td>\n",
              "      <td>2.152400e+02</td>\n",
              "      <td>4.650344e-01</td>\n",
              "      <td>4.848126e-02</td>\n",
              "      <td>2.522775e+00</td>\n",
              "      <td>1.792202e+00</td>\n",
              "      <td>1.561743e-02</td>\n",
              "      <td>2.154229e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-4.500000e+02</td>\n",
              "      <td>-6.000000e+01</td>\n",
              "      <td>-5.000000e-01</td>\n",
              "      <td>-8.880000e+01</td>\n",
              "      <td>-7.000000e+01</td>\n",
              "      <td>-3.000000e-01</td>\n",
              "      <td>-4.503000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>9.200000e-01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.140000e+02</td>\n",
              "      <td>1.130000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>8.300000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.570000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.620000e+02</td>\n",
              "      <td>1.620000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>1.360000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>1.130000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.870000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.340000e+02</td>\n",
              "      <td>2.340000e+02</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.350000e+01</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>2.320000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>1.660000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>1.894838e+05</td>\n",
              "      <td>9.900000e+01</td>\n",
              "      <td>2.650000e+02</td>\n",
              "      <td>2.650000e+02</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>6.259008e+05</td>\n",
              "      <td>5.353800e+02</td>\n",
              "      <td>6.080000e+01</td>\n",
              "      <td>9.999900e+02</td>\n",
              "      <td>3.288000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.259016e+05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           VendorID  passenger_count  ...  improvement_surcharge  total_amount\n",
              "count  2.613779e+07     2.613779e+07  ...           2.613779e+07  2.613779e+07\n",
              "mean   1.578038e+00     1.603406e+00  ...           2.995632e-01  1.556036e+01\n",
              "std    5.114147e-01     1.253912e+00  ...           1.561743e-02  2.154229e+02\n",
              "min    1.000000e+00     0.000000e+00  ...          -3.000000e-01 -4.503000e+02\n",
              "25%    1.000000e+00     1.000000e+00  ...           3.000000e-01  8.300000e+00\n",
              "50%    2.000000e+00     1.000000e+00  ...           3.000000e-01  1.130000e+01\n",
              "75%    2.000000e+00     2.000000e+00  ...           3.000000e-01  1.660000e+01\n",
              "max    4.000000e+00     9.000000e+00  ...           1.000000e+00  6.259016e+05\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WL1HTy1KvRga",
        "colab_type": "text"
      },
      "source": [
        "#### Remove the rows that don't belong to the choosen time frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73Hl8pawvRgb",
        "colab_type": "code",
        "outputId": "81ffde48-8972-4af6-fd1c-99f5a5eace9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        }
      },
      "source": [
        "# Initially\n",
        "# nyc_df = nyc_df.loc[(nyc_df.index.month==1) & ((nyc_df.index.year==2017) | (nyc_df.index.year==2018))]\n",
        "\n",
        "# New\n",
        "nyc_df = nyc_df.loc[(nyc_df.index.month==1) & ((nyc_df.index.year==2017) | (nyc_df.index.year==2018) | (nyc_df.index.year==2019))]\n",
        "nyc_df.describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>VendorID</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "      <td>2.613691e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.578024e+00</td>\n",
              "      <td>1.603394e+00</td>\n",
              "      <td>2.806808e+00</td>\n",
              "      <td>1.045080e+00</td>\n",
              "      <td>1.646339e+02</td>\n",
              "      <td>1.626699e+02</td>\n",
              "      <td>1.315085e+00</td>\n",
              "      <td>1.234096e+01</td>\n",
              "      <td>3.252198e-01</td>\n",
              "      <td>4.973189e-01</td>\n",
              "      <td>1.796216e+00</td>\n",
              "      <td>2.970779e-01</td>\n",
              "      <td>2.995632e-01</td>\n",
              "      <td>1.556031e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.114175e-01</td>\n",
              "      <td>1.253901e+00</td>\n",
              "      <td>3.724290e+01</td>\n",
              "      <td>5.444568e-01</td>\n",
              "      <td>6.647967e+01</td>\n",
              "      <td>7.046575e+01</td>\n",
              "      <td>4.832829e-01</td>\n",
              "      <td>2.152436e+02</td>\n",
              "      <td>4.650397e-01</td>\n",
              "      <td>4.848100e-02</td>\n",
              "      <td>2.522774e+00</td>\n",
              "      <td>1.792212e+00</td>\n",
              "      <td>1.561714e-02</td>\n",
              "      <td>2.154265e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-4.500000e+02</td>\n",
              "      <td>-6.000000e+01</td>\n",
              "      <td>-5.000000e-01</td>\n",
              "      <td>-8.880000e+01</td>\n",
              "      <td>-7.000000e+01</td>\n",
              "      <td>-3.000000e-01</td>\n",
              "      <td>-4.503000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>9.200000e-01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.140000e+02</td>\n",
              "      <td>1.130000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>8.300000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.570000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.620000e+02</td>\n",
              "      <td>1.620000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>1.360000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>1.130000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.870000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.340000e+02</td>\n",
              "      <td>2.340000e+02</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.350000e+01</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>2.320000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>1.660000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>1.894838e+05</td>\n",
              "      <td>9.900000e+01</td>\n",
              "      <td>2.650000e+02</td>\n",
              "      <td>2.650000e+02</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>6.259008e+05</td>\n",
              "      <td>5.353800e+02</td>\n",
              "      <td>6.080000e+01</td>\n",
              "      <td>9.999900e+02</td>\n",
              "      <td>3.288000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.259016e+05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           VendorID  passenger_count  ...  improvement_surcharge  total_amount\n",
              "count  2.613691e+07     2.613691e+07  ...           2.613691e+07  2.613691e+07\n",
              "mean   1.578024e+00     1.603394e+00  ...           2.995632e-01  1.556031e+01\n",
              "std    5.114175e-01     1.253901e+00  ...           1.561714e-02  2.154265e+02\n",
              "min    1.000000e+00     0.000000e+00  ...          -3.000000e-01 -4.503000e+02\n",
              "25%    1.000000e+00     1.000000e+00  ...           3.000000e-01  8.300000e+00\n",
              "50%    2.000000e+00     1.000000e+00  ...           3.000000e-01  1.130000e+01\n",
              "75%    2.000000e+00     2.000000e+00  ...           3.000000e-01  1.660000e+01\n",
              "max    4.000000e+00     9.000000e+00  ...           1.000000e+00  6.259016e+05\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GsyeA7q8vRgg",
        "colab_type": "text"
      },
      "source": [
        "#### Check for null values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGkRcu4OvRgh",
        "colab_type": "code",
        "outputId": "88f946f1-d6db-4032-e140-4e36935f9c30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "source": [
        "nyc_df.isnull().sum()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VendorID                 0\n",
              "tpep_dropoff_datetime    0\n",
              "passenger_count          0\n",
              "trip_distance            0\n",
              "RatecodeID               0\n",
              "store_and_fwd_flag       0\n",
              "PULocationID             0\n",
              "DOLocationID             0\n",
              "payment_type             0\n",
              "fare_amount              0\n",
              "extra                    0\n",
              "mta_tax                  0\n",
              "tip_amount               0\n",
              "tolls_amount             0\n",
              "improvement_surcharge    0\n",
              "total_amount             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Un1MCtGDvRgm",
        "colab_type": "text"
      },
      "source": [
        "#### Sort the data based on the date information and reindex it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRgkiaqovRgn",
        "colab_type": "code",
        "outputId": "a04f580b-f9aa-4fa8-bacc-ff89985624bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "nyc_df = nyc_df.sample(frac = 0.8) \n",
        "nyc_df.sort_values(by=['tpep_pickup_datetime'], inplace=True)\n",
        "nyc_df.reset_index(inplace=True)\n",
        "nyc_df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>tpep_pickup_datetime</th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-01-01 00:00:02</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:03:50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>5.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-01-01 00:00:02</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-01-01 00:39:22</td>\n",
              "      <td>4</td>\n",
              "      <td>7.75</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.66</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>27.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017-01-01 00:00:03</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:06:58</td>\n",
              "      <td>1</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>162</td>\n",
              "      <td>161</td>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>8.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-01-01 00:00:05</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:08:33</td>\n",
              "      <td>2</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>48</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>8.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-01-01 00:00:05</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-01-01 00:15:36</td>\n",
              "      <td>1</td>\n",
              "      <td>8.47</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>138</td>\n",
              "      <td>262</td>\n",
              "      <td>1</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>7.71</td>\n",
              "      <td>5.54</td>\n",
              "      <td>0.3</td>\n",
              "      <td>38.55</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  tpep_pickup_datetime  VendorID  ... improvement_surcharge  total_amount\n",
              "0  2017-01-01 00:00:02         1  ...                   0.3          5.30\n",
              "1  2017-01-01 00:00:02         2  ...                   0.3         27.96\n",
              "2  2017-01-01 00:00:03         1  ...                   0.3          8.75\n",
              "3  2017-01-01 00:00:05         1  ...                   0.3          8.30\n",
              "4  2017-01-01 00:00:05         2  ...                   0.3         38.55\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6IJYU6PNXrp",
        "colab_type": "code",
        "outputId": "7732cdd5-dc58-49b7-e400-e4806c4903ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        }
      },
      "source": [
        "nyc_df.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>VendorID</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "      <td>2.090953e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.578032e+00</td>\n",
              "      <td>1.603460e+00</td>\n",
              "      <td>2.808369e+00</td>\n",
              "      <td>1.045100e+00</td>\n",
              "      <td>1.646264e+02</td>\n",
              "      <td>1.626683e+02</td>\n",
              "      <td>1.315080e+00</td>\n",
              "      <td>1.230089e+01</td>\n",
              "      <td>3.252131e-01</td>\n",
              "      <td>4.973209e-01</td>\n",
              "      <td>1.796282e+00</td>\n",
              "      <td>2.971372e-01</td>\n",
              "      <td>2.995632e-01</td>\n",
              "      <td>1.552036e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.113715e-01</td>\n",
              "      <td>1.253947e+00</td>\n",
              "      <td>4.159849e+01</td>\n",
              "      <td>5.441982e-01</td>\n",
              "      <td>6.647869e+01</td>\n",
              "      <td>7.047746e+01</td>\n",
              "      <td>4.832929e-01</td>\n",
              "      <td>1.430484e+02</td>\n",
              "      <td>4.681149e-01</td>\n",
              "      <td>4.958176e-02</td>\n",
              "      <td>2.522253e+00</td>\n",
              "      <td>1.692989e+00</td>\n",
              "      <td>1.561950e-02</td>\n",
              "      <td>1.433353e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-4.500000e+02</td>\n",
              "      <td>-6.000000e+01</td>\n",
              "      <td>-5.000000e-01</td>\n",
              "      <td>-8.880000e+01</td>\n",
              "      <td>-7.000000e+01</td>\n",
              "      <td>-3.000000e-01</td>\n",
              "      <td>-4.503000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>9.200000e-01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.140000e+02</td>\n",
              "      <td>1.130000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>8.300000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.560000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.620000e+02</td>\n",
              "      <td>1.620000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>1.360000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>1.130000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.870000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.340000e+02</td>\n",
              "      <td>2.340000e+02</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.350000e+01</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>2.320000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>1.660000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>1.894838e+05</td>\n",
              "      <td>9.900000e+01</td>\n",
              "      <td>2.650000e+02</td>\n",
              "      <td>2.650000e+02</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>5.385792e+05</td>\n",
              "      <td>5.353800e+02</td>\n",
              "      <td>6.080000e+01</td>\n",
              "      <td>9.999900e+02</td>\n",
              "      <td>9.507000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>5.385800e+05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           VendorID  passenger_count  ...  improvement_surcharge  total_amount\n",
              "count  2.090953e+07     2.090953e+07  ...           2.090953e+07  2.090953e+07\n",
              "mean   1.578032e+00     1.603460e+00  ...           2.995632e-01  1.552036e+01\n",
              "std    5.113715e-01     1.253947e+00  ...           1.561950e-02  1.433353e+02\n",
              "min    1.000000e+00     0.000000e+00  ...          -3.000000e-01 -4.503000e+02\n",
              "25%    1.000000e+00     1.000000e+00  ...           3.000000e-01  8.300000e+00\n",
              "50%    2.000000e+00     1.000000e+00  ...           3.000000e-01  1.130000e+01\n",
              "75%    2.000000e+00     2.000000e+00  ...           3.000000e-01  1.660000e+01\n",
              "max    4.000000e+00     9.000000e+00  ...           1.000000e+00  5.385800e+05\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lINdUWF5K803"
      },
      "source": [
        "#### Check for anomalies in the dataset and drop them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNMYjeDwvRgs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "duration = nyc_df.tpep_dropoff_datetime - nyc_df.tpep_pickup_datetime\n",
        "speed = (nyc_df.trip_distance/duration.dt.seconds)*60\n",
        "\n",
        "nyc_df = nyc_df.loc[(duration > pd.to_timedelta('00:12:00')) & ((speed < 90.0) | (speed > 0))]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9B0mE5QnFxu",
        "colab_type": "code",
        "outputId": "d86c5101-9afd-479b-89a9-7f0e24af0b84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "nyc_df = nyc_df[nyc_df['total_amount'] >= 0]\n",
        "nyc_df = nyc_df[nyc_df['extra'] >= 0]\n",
        "nyc_df = nyc_df[nyc_df['mta_tax'] >= 0]\n",
        "nyc_df = nyc_df[nyc_df['fare_amount'] >= 0]\n",
        "nyc_df = nyc_df[nyc_df['tolls_amount'] >= 0]\n",
        "nyc_df = nyc_df[nyc_df['improvement_surcharge'] >= 0]\n",
        "\n",
        "nyc_df.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>VendorID</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "      <td>8.682070e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.583269e+00</td>\n",
              "      <td>1.615280e+00</td>\n",
              "      <td>5.061918e+00</td>\n",
              "      <td>1.078206e+00</td>\n",
              "      <td>1.606148e+02</td>\n",
              "      <td>1.566089e+02</td>\n",
              "      <td>1.279747e+00</td>\n",
              "      <td>1.962680e+01</td>\n",
              "      <td>3.389453e-01</td>\n",
              "      <td>4.970130e-01</td>\n",
              "      <td>2.810089e+00</td>\n",
              "      <td>6.924363e-01</td>\n",
              "      <td>2.999491e-01</td>\n",
              "      <td>2.427078e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.098916e-01</td>\n",
              "      <td>1.257658e+00</td>\n",
              "      <td>4.736217e+00</td>\n",
              "      <td>4.447453e-01</td>\n",
              "      <td>6.468259e+01</td>\n",
              "      <td>7.231309e+01</td>\n",
              "      <td>4.641323e-01</td>\n",
              "      <td>1.832236e+02</td>\n",
              "      <td>5.332803e-01</td>\n",
              "      <td>3.855071e-02</td>\n",
              "      <td>3.227811e+00</td>\n",
              "      <td>2.451206e+00</td>\n",
              "      <td>3.970005e-03</td>\n",
              "      <td>1.834992e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.100000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.290000e+02</td>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.150000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>1.430000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.200000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.610000e+02</td>\n",
              "      <td>1.610000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>2.360000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>1.830000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>6.080000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.300000e+02</td>\n",
              "      <td>2.310000e+02</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.250000e+01</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>3.760000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>2.680000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>2.677000e+02</td>\n",
              "      <td>9.900000e+01</td>\n",
              "      <td>2.650000e+02</td>\n",
              "      <td>2.650000e+02</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>5.385792e+05</td>\n",
              "      <td>2.500000e+01</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>4.448000e+02</td>\n",
              "      <td>9.507000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>5.385800e+05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           VendorID  passenger_count  ...  improvement_surcharge  total_amount\n",
              "count  8.682070e+06     8.682070e+06  ...           8.682070e+06  8.682070e+06\n",
              "mean   1.583269e+00     1.615280e+00  ...           2.999491e-01  2.427078e+01\n",
              "std    5.098916e-01     1.257658e+00  ...           3.970005e-03  1.834992e+02\n",
              "min    1.000000e+00     0.000000e+00  ...           0.000000e+00  0.000000e+00\n",
              "25%    1.000000e+00     1.000000e+00  ...           3.000000e-01  1.430000e+01\n",
              "50%    2.000000e+00     1.000000e+00  ...           3.000000e-01  1.830000e+01\n",
              "75%    2.000000e+00     2.000000e+00  ...           3.000000e-01  2.680000e+01\n",
              "max    4.000000e+00     9.000000e+00  ...           1.000000e+00  5.385800e+05\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izkCfEmbN_Hf",
        "colab_type": "code",
        "outputId": "684e0f6d-6364-44ea-9655-e6ecf8471372",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "nyc_df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>tpep_pickup_datetime</th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-01-01 00:00:02</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-01-01 00:39:22</td>\n",
              "      <td>4</td>\n",
              "      <td>7.75</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.66</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>27.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-01-01 00:00:05</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-01-01 00:15:36</td>\n",
              "      <td>1</td>\n",
              "      <td>8.47</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>138</td>\n",
              "      <td>262</td>\n",
              "      <td>1</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>7.71</td>\n",
              "      <td>5.54</td>\n",
              "      <td>0.3</td>\n",
              "      <td>38.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2017-01-01 00:00:06</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:23:37</td>\n",
              "      <td>2</td>\n",
              "      <td>12.60</td>\n",
              "      <td>5</td>\n",
              "      <td>N</td>\n",
              "      <td>161</td>\n",
              "      <td>265</td>\n",
              "      <td>1</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>70.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2017-01-01 00:00:10</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:24:52</td>\n",
              "      <td>1</td>\n",
              "      <td>6.60</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>232</td>\n",
              "      <td>2</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>24.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2017-01-01 00:00:12</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:12:53</td>\n",
              "      <td>1</td>\n",
              "      <td>1.70</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>125</td>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.3</td>\n",
              "      <td>10.80</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tpep_pickup_datetime  VendorID  ... improvement_surcharge  total_amount\n",
              "1   2017-01-01 00:00:02         2  ...                   0.3         27.96\n",
              "4   2017-01-01 00:00:05         2  ...                   0.3         38.55\n",
              "7   2017-01-01 00:00:06         1  ...                   0.3         70.30\n",
              "9   2017-01-01 00:00:10         1  ...                   0.3         24.30\n",
              "11  2017-01-01 00:00:12         1  ...                   0.3         10.80\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MYw0HbkDvRgw",
        "colab_type": "text"
      },
      "source": [
        "## NYC Taxi Zones"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfb0fYEevRgz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# Download the location Data\n",
        "urllib.request.urlretrieve(\"https://s3.amazonaws.com/nyc-tlc/misc/taxi_zones.zip\", \"taxi_zones.zip\")\n",
        "with zipfile.ZipFile(\"taxi_zones.zip\",\"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"./datasets/taxi_zones/shape\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cKC5Q7ZvRg2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_lat_lon(sf):\n",
        "    content = []\n",
        "    for sr in sf.shapeRecords():\n",
        "        shape = sr.shape\n",
        "        rec = sr.record\n",
        "        loc_id = rec[shp_dic['LocationID']]\n",
        "        \n",
        "        x = (shape.bbox[0]+shape.bbox[2])/2\n",
        "        y = (shape.bbox[1]+shape.bbox[3])/2\n",
        "        \n",
        "        content.append((loc_id, x, y))\n",
        "    return pd.DataFrame(content, columns=[\"LocationID\", \"longitude\", \"latitude\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYJ-aHI1vRg6",
        "colab_type": "text"
      },
      "source": [
        "#### Convert shape file to data frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEOsIEz3vRg7",
        "colab_type": "code",
        "outputId": "c44f54a1-9cbc-41ab-b9a6-b147ea887570",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "sf = shapefile.Reader(\"datasets/taxi_zones/shape/taxi_zones.shp\")\n",
        "fields_name = [field[0] for field in sf.fields[1:]]\n",
        "shp_dic = dict(zip(fields_name, list(range(len(fields_name)))))\n",
        "attributes = sf.records()\n",
        "shp_attr = [dict(zip(fields_name, attr)) for attr in attributes]\n",
        "\n",
        "loc_df = pd.DataFrame(shp_attr).join(get_lat_lon(sf).set_index(\"LocationID\"), on=\"LocationID\")\n",
        "loc_df.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>OBJECTID</th>\n",
              "      <th>Shape_Leng</th>\n",
              "      <th>Shape_Area</th>\n",
              "      <th>zone</th>\n",
              "      <th>LocationID</th>\n",
              "      <th>borough</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.116357</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>Newark Airport</td>\n",
              "      <td>1</td>\n",
              "      <td>EWR</td>\n",
              "      <td>9.366817e+05</td>\n",
              "      <td>190522.130278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.433470</td>\n",
              "      <td>0.004866</td>\n",
              "      <td>Jamaica Bay</td>\n",
              "      <td>2</td>\n",
              "      <td>Queens</td>\n",
              "      <td>1.033536e+06</td>\n",
              "      <td>161853.982300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.084341</td>\n",
              "      <td>0.000314</td>\n",
              "      <td>Allerton/Pelham Gardens</td>\n",
              "      <td>3</td>\n",
              "      <td>Bronx</td>\n",
              "      <td>1.027136e+06</td>\n",
              "      <td>254730.010849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.043567</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>Alphabet City</td>\n",
              "      <td>4</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>9.904240e+05</td>\n",
              "      <td>203100.040432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.092146</td>\n",
              "      <td>0.000498</td>\n",
              "      <td>Arden Heights</td>\n",
              "      <td>5</td>\n",
              "      <td>Staten Island</td>\n",
              "      <td>9.321332e+05</td>\n",
              "      <td>139954.541936</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   OBJECTID  Shape_Leng  Shape_Area  ...        borough     longitude       latitude\n",
              "0         1    0.116357    0.000782  ...            EWR  9.366817e+05  190522.130278\n",
              "1         2    0.433470    0.004866  ...         Queens  1.033536e+06  161853.982300\n",
              "2         3    0.084341    0.000314  ...          Bronx  1.027136e+06  254730.010849\n",
              "3         4    0.043567    0.000112  ...      Manhattan  9.904240e+05  203100.040432\n",
              "4         5    0.092146    0.000498  ...  Staten Island  9.321332e+05  139954.541936\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Z5bn07JvRhC",
        "colab_type": "text"
      },
      "source": [
        "#### Remove location id's that are outside of the Manhattan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0k0TTeUMvRhD",
        "colab_type": "code",
        "outputId": "40723a9e-1738-4fde-f65c-519f86fcdea5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "loc_df = loc_df[loc_df.borough == \"Manhattan\"]\n",
        "loc_df.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>OBJECTID</th>\n",
              "      <th>Shape_Leng</th>\n",
              "      <th>Shape_Area</th>\n",
              "      <th>zone</th>\n",
              "      <th>LocationID</th>\n",
              "      <th>borough</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.043567</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>Alphabet City</td>\n",
              "      <td>4</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>990424.019570</td>\n",
              "      <td>203100.040432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>0.036661</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>Battery Park</td>\n",
              "      <td>12</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>979889.680922</td>\n",
              "      <td>195215.447490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0.050281</td>\n",
              "      <td>0.000149</td>\n",
              "      <td>Battery Park City</td>\n",
              "      <td>13</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>979844.241304</td>\n",
              "      <td>198508.159318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>0.047000</td>\n",
              "      <td>0.000061</td>\n",
              "      <td>Bloomingdale</td>\n",
              "      <td>24</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>993795.402611</td>\n",
              "      <td>231438.438453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>41</td>\n",
              "      <td>0.052793</td>\n",
              "      <td>0.000143</td>\n",
              "      <td>Central Harlem</td>\n",
              "      <td>41</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>997895.956595</td>\n",
              "      <td>232264.009901</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    OBJECTID  Shape_Leng  Shape_Area  ...    borough      longitude       latitude\n",
              "3          4    0.043567    0.000112  ...  Manhattan  990424.019570  203100.040432\n",
              "11        12    0.036661    0.000042  ...  Manhattan  979889.680922  195215.447490\n",
              "12        13    0.050281    0.000149  ...  Manhattan  979844.241304  198508.159318\n",
              "23        24    0.047000    0.000061  ...  Manhattan  993795.402611  231438.438453\n",
              "40        41    0.052793    0.000143  ...  Manhattan  997895.956595  232264.009901\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xo-qppykvRhH",
        "colab_type": "text"
      },
      "source": [
        "#### Convert location ids that belong to Manhattan into a list"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8uMMOV4vRhI",
        "colab_type": "code",
        "outputId": "b28683b8-598c-423e-fde2-6882363ebb16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "manhattan_loc_id_list = loc_df[\"LocationID\"].tolist()\n",
        "print(manhattan_loc_id_list)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4, 12, 13, 24, 41, 42, 43, 45, 48, 50, 68, 74, 75, 79, 87, 88, 90, 100, 103, 103, 103, 103, 103, 103, 103, 103, 103, 107, 113, 114, 116, 120, 125, 127, 128, 137, 140, 141, 142, 143, 144, 148, 151, 152, 153, 158, 161, 162, 163, 164, 166, 170, 186, 194, 202, 209, 211, 224, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 243, 244, 246, 249, 261, 262, 263]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtglTENMvRhM",
        "colab_type": "text"
      },
      "source": [
        "## Intersect NYC Taxi and NYC Taxi Zone Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXW3FaLFvRhN",
        "colab_type": "text"
      },
      "source": [
        "#### Remove taxi rides that didn't originate from Manhattan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ut45vCRvRhO",
        "colab_type": "code",
        "outputId": "4e0082bf-8f5b-455a-c356-550e5486fd47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "nyc_df = nyc_df[nyc_df['PULocationID'].isin(manhattan_loc_id_list)]\n",
        "\n",
        "nyc_df.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>tpep_pickup_datetime</th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-01-01 00:00:02</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-01-01 00:39:22</td>\n",
              "      <td>4</td>\n",
              "      <td>7.75</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>27.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2017-01-01 00:00:06</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:23:37</td>\n",
              "      <td>2</td>\n",
              "      <td>12.60</td>\n",
              "      <td>5</td>\n",
              "      <td>N</td>\n",
              "      <td>161</td>\n",
              "      <td>265</td>\n",
              "      <td>1</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>70.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2017-01-01 00:00:10</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:24:52</td>\n",
              "      <td>1</td>\n",
              "      <td>6.60</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>232</td>\n",
              "      <td>2</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>24.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2017-01-01 00:00:12</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:12:53</td>\n",
              "      <td>1</td>\n",
              "      <td>1.70</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>125</td>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>10.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2017-01-01 00:00:13</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-01-01 00:15:39</td>\n",
              "      <td>1</td>\n",
              "      <td>4.68</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>140</td>\n",
              "      <td>223</td>\n",
              "      <td>1</td>\n",
              "      <td>16.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.45</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>22.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tpep_pickup_datetime  VendorID  ... improvement_surcharge  total_amount\n",
              "1   2017-01-01 00:00:02         2  ...                   0.3         27.96\n",
              "7   2017-01-01 00:00:06         1  ...                   0.3         70.30\n",
              "9   2017-01-01 00:00:10         1  ...                   0.3         24.30\n",
              "11  2017-01-01 00:00:12         1  ...                   0.3         10.80\n",
              "13  2017-01-01 00:00:13         2  ...                   0.3         22.25\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nTu-N46WvRhS",
        "colab_type": "text"
      },
      "source": [
        "## Weather Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vX8ZYkEbvRhT",
        "colab_type": "text"
      },
      "source": [
        "#### Import the weather dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mox9jl8b7vYX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "link = 'https://drive.google.com/open?id=1IbU83VjSs-VIVnI_6ov1mv3JKOkaIVU8'\n",
        "fluff, id = link.split('=')\n",
        "downloaded = drive.CreateFile({'id':id})\n",
        "downloaded.GetContentFile('./datasets/weather_data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0kEP6DVvRhV",
        "colab_type": "code",
        "outputId": "1e908a73-b04b-4b5f-eaeb-c74d5691fdde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "weather_df = pd.read_csv(\"./datasets/weather_data.csv\")\n",
        "weather_df.describe()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>temp_high</th>\n",
              "      <th>temp_avg</th>\n",
              "      <th>temp_low</th>\n",
              "      <th>dew_high</th>\n",
              "      <th>dew_avg</th>\n",
              "      <th>dew_low</th>\n",
              "      <th>humidity_high</th>\n",
              "      <th>humidity_avg</th>\n",
              "      <th>humidity_low</th>\n",
              "      <th>sea_level_press_high</th>\n",
              "      <th>sea_level_press_avg</th>\n",
              "      <th>sea_level_press_low</th>\n",
              "      <th>visibility_high</th>\n",
              "      <th>visibility_avg</th>\n",
              "      <th>visibility_low</th>\n",
              "      <th>wind_high</th>\n",
              "      <th>wind_avg</th>\n",
              "      <th>wind_high.1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1399.000000</td>\n",
              "      <td>1399.000000</td>\n",
              "      <td>1399.000000</td>\n",
              "      <td>1399.000000</td>\n",
              "      <td>1399.000000</td>\n",
              "      <td>1399.000000</td>\n",
              "      <td>1373.000000</td>\n",
              "      <td>1373.000000</td>\n",
              "      <td>1373.000000</td>\n",
              "      <td>1399.000000</td>\n",
              "      <td>1399.000000</td>\n",
              "      <td>1399.000000</td>\n",
              "      <td>1368.000000</td>\n",
              "      <td>1368.000000</td>\n",
              "      <td>1368.000000</td>\n",
              "      <td>1365.000000</td>\n",
              "      <td>1365.000000</td>\n",
              "      <td>1365.000000</td>\n",
              "      <td>1242.000000</td>\n",
              "      <td>1242.000000</td>\n",
              "      <td>1224.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2017.436026</td>\n",
              "      <td>6.333810</td>\n",
              "      <td>15.673338</td>\n",
              "      <td>17.787706</td>\n",
              "      <td>14.033595</td>\n",
              "      <td>10.021444</td>\n",
              "      <td>9.314639</td>\n",
              "      <td>6.021850</td>\n",
              "      <td>2.324836</td>\n",
              "      <td>79.463188</td>\n",
              "      <td>62.634739</td>\n",
              "      <td>45.288063</td>\n",
              "      <td>1020.815058</td>\n",
              "      <td>1017.002924</td>\n",
              "      <td>1013.330409</td>\n",
              "      <td>15.936996</td>\n",
              "      <td>13.950183</td>\n",
              "      <td>9.967033</td>\n",
              "      <td>21.516908</td>\n",
              "      <td>7.925121</td>\n",
              "      <td>35.241830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.100144</td>\n",
              "      <td>3.365345</td>\n",
              "      <td>8.832640</td>\n",
              "      <td>10.035985</td>\n",
              "      <td>9.574623</td>\n",
              "      <td>9.304704</td>\n",
              "      <td>10.092072</td>\n",
              "      <td>10.621686</td>\n",
              "      <td>11.212795</td>\n",
              "      <td>16.034027</td>\n",
              "      <td>14.659179</td>\n",
              "      <td>16.013873</td>\n",
              "      <td>6.982799</td>\n",
              "      <td>7.385705</td>\n",
              "      <td>7.991430</td>\n",
              "      <td>0.433716</td>\n",
              "      <td>2.970605</td>\n",
              "      <td>6.169022</td>\n",
              "      <td>7.009614</td>\n",
              "      <td>3.909331</td>\n",
              "      <td>10.639811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-11.000000</td>\n",
              "      <td>-14.000000</td>\n",
              "      <td>-18.000000</td>\n",
              "      <td>-24.000000</td>\n",
              "      <td>-27.000000</td>\n",
              "      <td>-28.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>998.000000</td>\n",
              "      <td>994.000000</td>\n",
              "      <td>979.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>1016.000000</td>\n",
              "      <td>1012.000000</td>\n",
              "      <td>1009.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2017.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>1021.000000</td>\n",
              "      <td>1017.000000</td>\n",
              "      <td>1014.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>34.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2018.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1022.000000</td>\n",
              "      <td>1019.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2019.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>1044.000000</td>\n",
              "      <td>1041.000000</td>\n",
              "      <td>1036.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>77.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              year        month  ...     wind_avg  wind_high.1\n",
              "count  1399.000000  1399.000000  ...  1242.000000  1224.000000\n",
              "mean   2017.436026     6.333810  ...     7.925121    35.241830\n",
              "std       1.100144     3.365345  ...     3.909331    10.639811\n",
              "min    2016.000000     1.000000  ...     0.000000    11.000000\n",
              "25%    2016.000000     3.000000  ...     5.000000    27.000000\n",
              "50%    2017.000000     6.000000  ...     7.000000    34.000000\n",
              "75%    2018.000000     9.000000  ...    10.000000    40.000000\n",
              "max    2019.000000    12.000000  ...    29.000000    77.000000\n",
              "\n",
              "[8 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fam685yZvRha",
        "colab_type": "text"
      },
      "source": [
        "#### Check the types of the columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDFroUqDvRhc",
        "colab_type": "code",
        "outputId": "e875dfba-0490-4f84-98d7-5edf47ca9da5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "weather_df.dtypes"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year                      int64\n",
              "month                     int64\n",
              "day                       int64\n",
              "temp_high                 int64\n",
              "temp_avg                  int64\n",
              "temp_low                  int64\n",
              "dew_high                float64\n",
              "dew_avg                 float64\n",
              "dew_low                 float64\n",
              "humidity_high             int64\n",
              "humidity_avg              int64\n",
              "humidity_low              int64\n",
              "sea_level_press_high    float64\n",
              "sea_level_press_avg     float64\n",
              "sea_level_press_low     float64\n",
              "visibility_high         float64\n",
              "visibility_avg          float64\n",
              "visibility_low          float64\n",
              "wind_high               float64\n",
              "wind_avg                float64\n",
              "wind_high.1             float64\n",
              "precipitation            object\n",
              "events                   object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tB3AkTjLvRhi",
        "colab_type": "text"
      },
      "source": [
        "#### Convert events column into multiple columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tYCOWqZvRhk",
        "colab_type": "code",
        "outputId": "7ed470aa-b89e-494e-f4e6-aeea87fe633a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "# weather_df['event_rain'] = 0\n",
        "# weather_df['event_fog'] = 0\n",
        "# weather_df['event_snow'] = 0\n",
        "\n",
        "# for index, row in weather_df.iterrows():\n",
        "#     if(isinstance(weather_df.events[index], str)):\n",
        "#         if(\"Rain\" in weather_df.events[index]):\n",
        "#             weather_df.event_rain[index] = 1\n",
        "\n",
        "#         if(\"Fog\" in weather_df.events[index]):\n",
        "#             weather_df.event_fog[index] = 1\n",
        "\n",
        "#         if(\"Snow\" in weather_df.events[index]):\n",
        "#             weather_df.event_snow[index] = 1\n",
        "\n",
        "# weather_df = weather_df.drop(\"events\", axis = 1)\n",
        "\n",
        "weather_df.head()\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>temp_high</th>\n",
              "      <th>temp_avg</th>\n",
              "      <th>temp_low</th>\n",
              "      <th>dew_high</th>\n",
              "      <th>dew_avg</th>\n",
              "      <th>dew_low</th>\n",
              "      <th>humidity_high</th>\n",
              "      <th>humidity_avg</th>\n",
              "      <th>humidity_low</th>\n",
              "      <th>sea_level_press_high</th>\n",
              "      <th>sea_level_press_avg</th>\n",
              "      <th>sea_level_press_low</th>\n",
              "      <th>visibility_high</th>\n",
              "      <th>visibility_avg</th>\n",
              "      <th>visibility_low</th>\n",
              "      <th>wind_high</th>\n",
              "      <th>wind_avg</th>\n",
              "      <th>wind_high.1</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>events</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>59</td>\n",
              "      <td>52</td>\n",
              "      <td>44</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>52</td>\n",
              "      <td>46</td>\n",
              "      <td>40</td>\n",
              "      <td>1019.0</td>\n",
              "      <td>1016.0</td>\n",
              "      <td>1014.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>56</td>\n",
              "      <td>47</td>\n",
              "      <td>37</td>\n",
              "      <td>1014.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>-4</td>\n",
              "      <td>-10</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>59</td>\n",
              "      <td>44</td>\n",
              "      <td>28</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>-2</td>\n",
              "      <td>-7</td>\n",
              "      <td>-12</td>\n",
              "      <td>-17.0</td>\n",
              "      <td>-19.0</td>\n",
              "      <td>-21.0</td>\n",
              "      <td>58</td>\n",
              "      <td>41</td>\n",
              "      <td>24</td>\n",
              "      <td>1037.0</td>\n",
              "      <td>1035.0</td>\n",
              "      <td>1031.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  month  day  temp_high  ...  wind_avg  wind_high.1  precipitation  events\n",
              "0  2016      1    1          6  ...      12.0         42.0              0     NaN\n",
              "1  2016      1    2          4  ...      11.0         37.0              0     NaN\n",
              "2  2016      1    3          7  ...      13.0         42.0              0     NaN\n",
              "3  2016      1    4          2  ...      14.0         50.0              0     NaN\n",
              "4  2016      1    5         -2  ...       8.0         48.0              0     NaN\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wIoNXRxLvRho",
        "colab_type": "text"
      },
      "source": [
        "#### Add primary key to be used in the NYC taxi data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSbdGUsjvRhp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# weather_df.insert(0, \"primary_key\", \"\") \n",
        "\n",
        "# for index, row in weather_df.iterrows():\n",
        "#     key = str(row['year'])\n",
        "    \n",
        "#     if (row['month'] == 0) or (row['month'] == 1) or (row['month'] == 2) or (row['month'] == 3) or (row['month'] == 4) or (row['month'] == 5) or (row['month'] == 6) or (row['month'] == 7) or (row['month'] == 8) or (row['month'] == 9):\n",
        "#         key = key + \"-0\" + str(row['month'])\n",
        "#     else:\n",
        "#         key = key + \"-\" + str(row['month'])\n",
        "        \n",
        "#     if (row['day'] == 0) or (row['day'] == 1) or (row['day'] == 2) or (row['day'] == 3) or (row['day'] == 4) or (row['day'] == 5) or (row['day'] == 6) or (row['day'] == 7) or (row['day'] == 8) or (row['day'] == 9):\n",
        "#         key = key + \"-0\" + str(row['day'])\n",
        "#     else:\n",
        "#         key = key + \"-\" + str(row['day'])\n",
        "        \n",
        "#     weather_df.primary_key[index] = key\n",
        "\n",
        "weather_df = weather_df.assign(primary_key = weather_df.year.astype(str) + '-' + weather_df.month.astype(str) + '-' + weather_df.day.astype(str))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMqz3zQQvRhs",
        "colab_type": "text"
      },
      "source": [
        "#### Sort the data based on the date information and reindex it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "IoeJKo_jvRht",
        "colab_type": "code",
        "outputId": "c09164ec-c0a4-4c57-ad23-25301b86209b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "weather_df['primary_key'] = pd.to_datetime(weather_df['primary_key'])\n",
        "weather_df = weather_df.set_index('primary_key')\n",
        "weather_df.precipitation = weather_df.precipitation.replace('T', np.nan)\n",
        "\n",
        "\n",
        "# weather_df.reset_index(inplace=True)\n",
        "weather_df.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>temp_high</th>\n",
              "      <th>temp_avg</th>\n",
              "      <th>temp_low</th>\n",
              "      <th>dew_high</th>\n",
              "      <th>dew_avg</th>\n",
              "      <th>dew_low</th>\n",
              "      <th>humidity_high</th>\n",
              "      <th>humidity_avg</th>\n",
              "      <th>humidity_low</th>\n",
              "      <th>sea_level_press_high</th>\n",
              "      <th>sea_level_press_avg</th>\n",
              "      <th>sea_level_press_low</th>\n",
              "      <th>visibility_high</th>\n",
              "      <th>visibility_avg</th>\n",
              "      <th>visibility_low</th>\n",
              "      <th>wind_high</th>\n",
              "      <th>wind_avg</th>\n",
              "      <th>wind_high.1</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>events</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>primary_key</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-01-01</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>59</td>\n",
              "      <td>52</td>\n",
              "      <td>44</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-02</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>52</td>\n",
              "      <td>46</td>\n",
              "      <td>40</td>\n",
              "      <td>1019.0</td>\n",
              "      <td>1016.0</td>\n",
              "      <td>1014.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-03</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>56</td>\n",
              "      <td>47</td>\n",
              "      <td>37</td>\n",
              "      <td>1014.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-04</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>-4</td>\n",
              "      <td>-10</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>59</td>\n",
              "      <td>44</td>\n",
              "      <td>28</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-05</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>-2</td>\n",
              "      <td>-7</td>\n",
              "      <td>-12</td>\n",
              "      <td>-17.0</td>\n",
              "      <td>-19.0</td>\n",
              "      <td>-21.0</td>\n",
              "      <td>58</td>\n",
              "      <td>41</td>\n",
              "      <td>24</td>\n",
              "      <td>1037.0</td>\n",
              "      <td>1035.0</td>\n",
              "      <td>1031.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             year  month  day  ...  wind_high.1  precipitation  events\n",
              "primary_key                    ...                                    \n",
              "2016-01-01   2016      1    1  ...         42.0              0     NaN\n",
              "2016-01-02   2016      1    2  ...         37.0              0     NaN\n",
              "2016-01-03   2016      1    3  ...         42.0              0     NaN\n",
              "2016-01-04   2016      1    4  ...         50.0              0     NaN\n",
              "2016-01-05   2016      1    5  ...         48.0              0     NaN\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lqog2eqy4-VT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "42ca3c79-a0fb-42ce-dce0-99ca2507e96d"
      },
      "source": [
        "weather_df.dtypes"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year                      int64\n",
              "month                     int64\n",
              "day                       int64\n",
              "temp_high                 int64\n",
              "temp_avg                  int64\n",
              "temp_low                  int64\n",
              "dew_high                float64\n",
              "dew_avg                 float64\n",
              "dew_low                 float64\n",
              "humidity_high             int64\n",
              "humidity_avg              int64\n",
              "humidity_low              int64\n",
              "sea_level_press_high    float64\n",
              "sea_level_press_avg     float64\n",
              "sea_level_press_low     float64\n",
              "visibility_high         float64\n",
              "visibility_avg          float64\n",
              "visibility_low          float64\n",
              "wind_high               float64\n",
              "wind_avg                float64\n",
              "wind_high.1             float64\n",
              "precipitation            object\n",
              "events                   object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lEkgYZw5LSv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "weather_df['precipitation'] = weather_df['precipitation'].astype(str).astype(float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZT5dDgHgvRhw",
        "colab_type": "text"
      },
      "source": [
        "#### Check for missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ziV6iQeQvRhx",
        "colab_type": "code",
        "outputId": "55dc27ae-c678-48ed-b7b3-d51dabe0ce6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "weather_df.isnull().sum()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year                      0\n",
              "month                     0\n",
              "day                       0\n",
              "temp_high                 0\n",
              "temp_avg                  0\n",
              "temp_low                  0\n",
              "dew_high                 26\n",
              "dew_avg                  26\n",
              "dew_low                  26\n",
              "humidity_high             0\n",
              "humidity_avg              0\n",
              "humidity_low              0\n",
              "sea_level_press_high     31\n",
              "sea_level_press_avg      31\n",
              "sea_level_press_low      31\n",
              "visibility_high          34\n",
              "visibility_avg           34\n",
              "visibility_low           34\n",
              "wind_high               157\n",
              "wind_avg                157\n",
              "wind_high.1             175\n",
              "precipitation           121\n",
              "events                  841\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpjI8DV56e_a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "ecfac33a-afaa-4727-a8ea-ed4bd1d54097"
      },
      "source": [
        "weather_df.dtypes"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year                      int64\n",
              "month                     int64\n",
              "day                       int64\n",
              "temp_high                 int64\n",
              "temp_avg                  int64\n",
              "temp_low                  int64\n",
              "dew_high                float64\n",
              "dew_avg                 float64\n",
              "dew_low                 float64\n",
              "humidity_high             int64\n",
              "humidity_avg              int64\n",
              "humidity_low              int64\n",
              "sea_level_press_high    float64\n",
              "sea_level_press_avg     float64\n",
              "sea_level_press_low     float64\n",
              "visibility_high         float64\n",
              "visibility_avg          float64\n",
              "visibility_low          float64\n",
              "wind_high               float64\n",
              "wind_avg                float64\n",
              "wind_high.1             float64\n",
              "precipitation           float64\n",
              "events                   object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lRolFcRvRh4",
        "colab_type": "text"
      },
      "source": [
        "#### Fill rows with missing values using interpolation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubOvTY63vRh5",
        "colab_type": "code",
        "outputId": "dbeeb8c8-3fb5-4e76-af9b-27bc916a8ce2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "weather_df = weather_df.interpolate(method=\"linear\")\n",
        "weather_df.isnull().sum()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year                      0\n",
              "month                     0\n",
              "day                       0\n",
              "temp_high                 0\n",
              "temp_avg                  0\n",
              "temp_low                  0\n",
              "dew_high                  0\n",
              "dew_avg                   0\n",
              "dew_low                   0\n",
              "humidity_high             0\n",
              "humidity_avg              0\n",
              "humidity_low              0\n",
              "sea_level_press_high      0\n",
              "sea_level_press_avg       0\n",
              "sea_level_press_low       0\n",
              "visibility_high           0\n",
              "visibility_avg            0\n",
              "visibility_low            0\n",
              "wind_high                 0\n",
              "wind_avg                  0\n",
              "wind_high.1               0\n",
              "precipitation             0\n",
              "events                  841\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87_Grzd0refM",
        "colab_type": "text"
      },
      "source": [
        "# Quick Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVAsRkKtretz",
        "colab_type": "code",
        "outputId": "b066d867-e3e3-45c2-c562-2a2e17550c6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "nyc_df.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\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>tpep_pickup_datetime</th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-01-01 00:00:02</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-01-01 00:39:22</td>\n",
              "      <td>4</td>\n",
              "      <td>7.75</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>27.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2017-01-01 00:00:06</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:23:37</td>\n",
              "      <td>2</td>\n",
              "      <td>12.60</td>\n",
              "      <td>5</td>\n",
              "      <td>N</td>\n",
              "      <td>161</td>\n",
              "      <td>265</td>\n",
              "      <td>1</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>70.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2017-01-01 00:00:10</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:24:52</td>\n",
              "      <td>1</td>\n",
              "      <td>6.60</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>232</td>\n",
              "      <td>2</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>24.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2017-01-01 00:00:12</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:12:53</td>\n",
              "      <td>1</td>\n",
              "      <td>1.70</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>125</td>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>10.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2017-01-01 00:00:13</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-01-01 00:15:39</td>\n",
              "      <td>1</td>\n",
              "      <td>4.68</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>140</td>\n",
              "      <td>223</td>\n",
              "      <td>1</td>\n",
              "      <td>16.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.45</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>22.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tpep_pickup_datetime  VendorID  ... improvement_surcharge  total_amount\n",
              "1   2017-01-01 00:00:02         2  ...                   0.3         27.96\n",
              "7   2017-01-01 00:00:06         1  ...                   0.3         70.30\n",
              "9   2017-01-01 00:00:10         1  ...                   0.3         24.30\n",
              "11  2017-01-01 00:00:12         1  ...                   0.3         10.80\n",
              "13  2017-01-01 00:00:13         2  ...                   0.3         22.25\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Qh_bDUtsAod",
        "colab_type": "code",
        "outputId": "9eeeb761-f90b-42f2-e433-209eb0cf1cb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "nyc_df.tail()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20909519</th>\n",
              "      <td>2019-01-31 23:59:51</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-02-01 00:28:56</td>\n",
              "      <td>6</td>\n",
              "      <td>11.23</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>125</td>\n",
              "      <td>247</td>\n",
              "      <td>1</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>7.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>42.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20909520</th>\n",
              "      <td>2019-01-31 23:59:51</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-01 00:23:33</td>\n",
              "      <td>2</td>\n",
              "      <td>6.50</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>4</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>21.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>3.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>26.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20909521</th>\n",
              "      <td>2019-01-31 23:59:51</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-01 00:30:54</td>\n",
              "      <td>1</td>\n",
              "      <td>6.90</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>158</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>25.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>5.35</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>32.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20909522</th>\n",
              "      <td>2019-01-31 23:59:52</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-01 00:34:16</td>\n",
              "      <td>1</td>\n",
              "      <td>9.80</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>164</td>\n",
              "      <td>160</td>\n",
              "      <td>2</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>34.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20909526</th>\n",
              "      <td>2019-01-31 23:59:54</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-02-01 00:12:36</td>\n",
              "      <td>1</td>\n",
              "      <td>4.03</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>79</td>\n",
              "      <td>146</td>\n",
              "      <td>2</td>\n",
              "      <td>13.5</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>14.80</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         tpep_pickup_datetime  VendorID  ... improvement_surcharge  total_amount\n",
              "20909519  2019-01-31 23:59:51         2  ...                   0.3         42.36\n",
              "20909520  2019-01-31 23:59:51         1  ...                   0.3         26.80\n",
              "20909521  2019-01-31 23:59:51         1  ...                   0.3         32.15\n",
              "20909522  2019-01-31 23:59:52         1  ...                   0.3         34.30\n",
              "20909526  2019-01-31 23:59:54         2  ...                   0.3         14.80\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1L-nH_JXtIU",
        "colab_type": "code",
        "outputId": "130f3e9f-5566-4311-83c7-26f8e53d221d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "nyc_df['tpep_pickup_datetime'].dt.to_period('D')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1           2017-01-01\n",
              "7           2017-01-01\n",
              "9           2017-01-01\n",
              "11          2017-01-01\n",
              "13          2017-01-01\n",
              "               ...    \n",
              "20909519    2019-01-31\n",
              "20909520    2019-01-31\n",
              "20909521    2019-01-31\n",
              "20909522    2019-01-31\n",
              "20909526    2019-01-31\n",
              "Name: tpep_pickup_datetime, Length: 7354109, dtype: period[D]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rr1D0cRYAyq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# nyc_df['tpep_pickup_datetime'].dt.to_period('D').to_datetime()\n",
        "nyc_df = nyc_df.set_index(nyc_df['tpep_pickup_datetime'].dt.strftime(\"%Y-%m-%d\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJ4_4_ukz5kW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nR6lPnktz_vt",
        "colab_type": "code",
        "outputId": "095e692e-7a49-4a34-c654-70dd4aba0df5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "source": [
        "weather_df"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>temp_high</th>\n",
              "      <th>temp_avg</th>\n",
              "      <th>temp_low</th>\n",
              "      <th>dew_high</th>\n",
              "      <th>dew_avg</th>\n",
              "      <th>dew_low</th>\n",
              "      <th>humidity_high</th>\n",
              "      <th>humidity_avg</th>\n",
              "      <th>humidity_low</th>\n",
              "      <th>sea_level_press_high</th>\n",
              "      <th>sea_level_press_avg</th>\n",
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              "      <th>visibility_avg</th>\n",
              "      <th>visibility_low</th>\n",
              "      <th>wind_high</th>\n",
              "      <th>wind_avg</th>\n",
              "      <th>wind_high.1</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>events</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>primary_key</th>\n",
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              "    <tr>\n",
              "      <th>2016-01-01</th>\n",
              "      <td>2016</td>\n",
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              "      <td>1</td>\n",
              "      <td>6</td>\n",
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              "      <td>-3.0</td>\n",
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              "      <td>-9.0</td>\n",
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              "      <td>44</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-02</th>\n",
              "      <td>2016</td>\n",
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              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-8.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>52</td>\n",
              "      <td>46</td>\n",
              "      <td>40</td>\n",
              "      <td>1019.0</td>\n",
              "      <td>1016.0</td>\n",
              "      <td>1014.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-03</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
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              "      <td>37</td>\n",
              "      <td>1014.0</td>\n",
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              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-04</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>-4</td>\n",
              "      <td>-10</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-13.0</td>\n",
              "      <td>-18.0</td>\n",
              "      <td>59</td>\n",
              "      <td>44</td>\n",
              "      <td>28</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-05</th>\n",
              "      <td>2016</td>\n",
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              "      <td>-19.0</td>\n",
              "      <td>-21.0</td>\n",
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              "      <td>41</td>\n",
              "      <td>24</td>\n",
              "      <td>1037.0</td>\n",
              "      <td>1035.0</td>\n",
              "      <td>1031.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-01</th>\n",
              "      <td>2019</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>90</td>\n",
              "      <td>61</td>\n",
              "      <td>32</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>1014.0</td>\n",
              "      <td>998.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>2.79</td>\n",
              "      <td>Rain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-02</th>\n",
              "      <td>2019</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>66</td>\n",
              "      <td>55</td>\n",
              "      <td>44</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>1021.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-03</th>\n",
              "      <td>2019</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>58</td>\n",
              "      <td>48</td>\n",
              "      <td>38</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-04</th>\n",
              "      <td>2019</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>70</td>\n",
              "      <td>53</td>\n",
              "      <td>35</td>\n",
              "      <td>1029.0</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-05</th>\n",
              "      <td>2019</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>77</td>\n",
              "      <td>74</td>\n",
              "      <td>69</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>1023.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1399 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             year  month  day  ...  wind_high.1  precipitation  events\n",
              "primary_key                    ...                                    \n",
              "2016-01-01   2016      1    1  ...         42.0           0.00     NaN\n",
              "2016-01-02   2016      1    2  ...         37.0           0.00     NaN\n",
              "2016-01-03   2016      1    3  ...         42.0           0.00     NaN\n",
              "2016-01-04   2016      1    4  ...         50.0           0.00     NaN\n",
              "2016-01-05   2016      1    5  ...         48.0           0.00     NaN\n",
              "...           ...    ...  ...  ...          ...            ...     ...\n",
              "2019-11-01   2019     11    1  ...         56.0           2.79    Rain\n",
              "2019-11-02   2019     11    2  ...         27.0           0.00     NaN\n",
              "2019-11-03   2019     11    3  ...         29.0           0.00     NaN\n",
              "2019-11-04   2019     11    4  ...         34.0           0.00     NaN\n",
              "2019-11-05   2019     11    5  ...         34.0           0.00     NaN\n",
              "\n",
              "[1399 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cDpLfQQvRh8",
        "colab_type": "text"
      },
      "source": [
        "## Add Weather Data to NYC Taxi Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4H6rNtTzvRh9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# nyc_df['rain'] = 0\n",
        "# nyc_df['temperature_avg'] = 0\n",
        "# nyc_df['humidity_avg'] = 0\n",
        "# nyc_df.insert(0, \"day\", 0) \n",
        "\n",
        "# for index, row in nyc_df.iterrows():\n",
        "#     if(index%10000 == 0):\n",
        "#       print(index)\n",
        "#     string_key = nyc_df.tpep_pickup_datetime[index].strftime(\"%Y-%m-%d\")\n",
        "    \n",
        "#     row_array = weather_df.loc[string_key]\n",
        "    \n",
        "#     nyc_df.rain[index] = row_array[\"precipitation\"]\n",
        "#     nyc_df.temperature_avg[index] = row_array[\"temp_avg\"]\n",
        "#     nyc_df.humidity_avg[index] = row_array[\"humidity_avg\"]\n",
        "#     nyc_df.day[index] = int(nyc_df.tpep_pickup_datetime[index].strftime(\"%d\"))\n",
        "\n",
        "nyc_df = pd.merge(nyc_df, weather_df[['precipitation','temp_avg','humidity_avg']], how='inner', left_index=True, right_index=True)\n",
        "nyc_df['dayOfMonth'] = nyc_df.tpep_pickup_datetime.dt.day\n",
        "nyc_df['dayOfWeek'] = nyc_df.tpep_pickup_datetime.dt.dayofweek"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvDsK5frvRiA",
        "colab_type": "text"
      },
      "source": [
        "## Check for missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkQ0MSR3vRiB",
        "colab_type": "code",
        "outputId": "f899a971-da3c-41d6-f3f2-82a8e7e7f2b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        }
      },
      "source": [
        "nyc_df.isnull().sum()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tpep_pickup_datetime     0\n",
              "VendorID                 0\n",
              "tpep_dropoff_datetime    0\n",
              "passenger_count          0\n",
              "trip_distance            0\n",
              "RatecodeID               0\n",
              "store_and_fwd_flag       0\n",
              "PULocationID             0\n",
              "DOLocationID             0\n",
              "payment_type             0\n",
              "fare_amount              0\n",
              "extra                    0\n",
              "mta_tax                  0\n",
              "tip_amount               0\n",
              "tolls_amount             0\n",
              "improvement_surcharge    0\n",
              "total_amount             0\n",
              "precipitation            0\n",
              "temp_avg                 0\n",
              "humidity_avg             0\n",
              "dayOfMonth               0\n",
              "dayOfWeek                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "koulQixhvRiG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#nyc_df.tpep_pickup_datetime.count()\n",
        "#nyc_df.tpep_pickup_datetime[0].strftime(\"%Y-%m-%d\")\n",
        "#weather_df.primary_key[0].strftime(\"%Y-%m-%d\")\n",
        "#weather_df.dtypes\n",
        "#weather_df.loc[\"2016-01-10\"]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S02dVMydx0Ym",
        "colab_type": "text"
      },
      "source": [
        "## Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQSvRuuQxpis",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## THE FOLLOWING FUNCTIONS WERE NOT WRITTEN BY US. FULL CREDITS GO TO THE AUTHOR OF THIS BLOG: https://chih-ling-hsu.github.io/2018/05/14/NYC\n",
        "  \n",
        "def get_boundaries(sf):\n",
        "    lat, lon = [], []\n",
        "    for shape in list(sf.iterShapes()):\n",
        "        lat.extend([shape.bbox[0], shape.bbox[2]])\n",
        "        lon.extend([shape.bbox[1], shape.bbox[3]])\n",
        "\n",
        "    margin = 0.01 # buffer to add to the range\n",
        "    lat_min = min(lat) - margin\n",
        "    lat_max = max(lat) + margin\n",
        "    lon_min = min(lon) - margin\n",
        "    lon_max = max(lon) + margin\n",
        "\n",
        "    return lat_min, lat_max, lon_min, lon_max\n",
        "\n",
        "def draw_zone_map(ax, sf, heat={}, text=[], arrows=[]):\n",
        "    continent = [235/256, 151/256, 78/256]\n",
        "    ocean = (89/256, 171/256, 227/256)\n",
        "    theta = np.linspace(0, 2*np.pi, len(text)+1).tolist()\n",
        "    ax.set_facecolor(ocean)\n",
        "    \n",
        "    # colorbar\n",
        "    if len(heat) != 0:\n",
        "        norm = mpl.colors.Normalize(vmin=min(heat.values()),vmax=max(heat.values())) #norm = mpl.colors.LogNorm(vmin=1,vmax=max(heat))\n",
        "        cm=plt.get_cmap('Reds')\n",
        "        sm = plt.cm.ScalarMappable(cmap=cm, norm=norm)\n",
        "        sm.set_array([])\n",
        "        plt.colorbar(sm, ticks=np.linspace(min(heat.values()),max(heat.values()),8),\n",
        "                     boundaries=np.arange(min(heat.values())-10,max(heat.values())+10,.1))\n",
        "    \n",
        "    for sr in sf.shapeRecords():\n",
        "        shape = sr.shape\n",
        "        rec = sr.record\n",
        "        loc_id = rec[shp_dic['LocationID']]\n",
        "        zone = rec[shp_dic['zone']]\n",
        "        \n",
        "        if len(heat) == 0:\n",
        "            col = continent\n",
        "        else:\n",
        "            if loc_id not in heat:\n",
        "                R,G,B,A = cm(norm(0))\n",
        "            else:\n",
        "                R,G,B,A = cm(norm(heat[loc_id]))\n",
        "            col = [R,G,B]\n",
        "\n",
        "        # check number of parts (could use MultiPolygon class of shapely?)\n",
        "        nparts = len(shape.parts) # total parts\n",
        "        if nparts == 1:\n",
        "            polygon = Polygon(shape.points)\n",
        "            patch = PolygonPatch(polygon, facecolor=col, alpha=1.0, zorder=2)\n",
        "            ax.add_patch(patch)\n",
        "        else: # loop over parts of each shape, plot separately\n",
        "            for ip in range(nparts): # loop over parts, plot separately\n",
        "                i0 = shape.parts[ip]\n",
        "                if ip < nparts-1:\n",
        "                    i1 = shape.parts[ip+1]-1\n",
        "                else:\n",
        "                    i1 = len(shape.points)\n",
        "\n",
        "                polygon = Polygon(shape.points[i0:i1+1])\n",
        "                patch = PolygonPatch(polygon, facecolor=col, alpha=1.0, zorder=2)\n",
        "                ax.add_patch(patch)\n",
        "        \n",
        "        x = (shape.bbox[0]+shape.bbox[2])/2\n",
        "        y = (shape.bbox[1]+shape.bbox[3])/2\n",
        "        if (len(text) == 0 and rec[shp_dic['Shape_Area']] > 0.0001):\n",
        "            # plt.text(x, y, str(loc_id), horizontalalignment='center', verticalalignment='center') \n",
        "            continue           \n",
        "        elif len(text) != 0 and loc_id in text:\n",
        "            #plt.text(x+0.01, y-0.01, str(loc_id), fontsize=12, color=\"white\", bbox=dict(facecolor='black', alpha=0.5))\n",
        "            eta_x = 0.05*np.cos(theta[text.index(loc_id)])\n",
        "            eta_y = 0.05*np.sin(theta[text.index(loc_id)])\n",
        "            ax.annotate(\"[{}] {}\".format(loc_id, zone), xy=(x, y), xytext=(x+eta_x, y+eta_y),\n",
        "                        bbox=dict(facecolor='black', alpha=0.5), color=\"white\", fontsize=12,\n",
        "                        arrowprops=dict(facecolor='black', width=3, shrink=0.05))\n",
        "    if len(arrows)!=0:\n",
        "        for arr in arrows:\n",
        "            ax.annotate('', xy = arr['dest'], xytext = arr['src'], size = arr['cnt'],\n",
        "                    arrowprops=dict(arrowstyle=\"fancy\", fc=\"0.6\", ec=\"none\"))\n",
        "    \n",
        "    # display\n",
        "    limits = get_boundaries(sf)\n",
        "    plt.xlim(limits[0], limits[1])\n",
        "    plt.ylim(limits[2], limits[3])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8scLvNzyQ1p",
        "colab_type": "code",
        "outputId": "cea3b308-52cd-45fb-e56a-0cc15c1c995e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        }
      },
      "source": [
        "figure = plt.figure(figsize=(12,10))\n",
        "ax = plt.axes()\n",
        "ax.set_title(\"Pickup Frequency by New York Zones\")\n",
        "PUcount = dict(nyc_df['PULocationID'].value_counts().sort_values())\n",
        "draw_zone_map(ax, sf, heat=PUcount)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ae83GvvrPZr/v0i+G7kvvNuqjdi7/pIMvVi+tu11/Ltevd441mJhRRERE5h/9QhcRWVxG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\n7giPUSsSIhFEICuVjQLW8wOW3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XDiw+TSBg1A5gtHaRnMCjPXC3XnJnadnBrshhbqke1V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\nZGTgwW/LBZQlWfZQzAzLZNcHaFkhLJETbQuhBekFnkUv6LUGx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gAAIABJREFU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x+LPnkWsm8tolRfobmh/UuXvt0P5+cPR1NOJ99Q1Hz25sn340l46y5m3naoNEqLWZsyPxFZ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\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p5kLNYmxzS2dDDrXQtFcECpCd7/cii3UvnaXDmsESbd3+hXO80ifiGmPyoo32H5HcSTjk\nMeMrha+gwqqY3Eo1VV8duLBWDsSOjDgAgRFs581m8Q2HgURrmjEg5JElm2c5F273WEPpAIzM6zvY\nponinHQ4HI5x4sReh8Oxwo1HPL71mpDLaorM2OGDdoii4Cn7Q2/tjxFZcwNhcwVZfa3veaZtgZlM\ng8ZQjPRSef6g0KPLDu01DofjguWjTyTcfnyGy669kyOf//OV13tX3IrXWUIGuOKMCO0T1xE+8QAq\nHezSHYTfWcY7+yTNhZPMbVKIzQCLUkaM4d99os3TnenrzDpREV57dYjOnagfeTzlAw/GvOC4z5Gy\n4rtvivjN+3os9gwLESudgOuxFdsLJ2u/b8nWHVdiBTcldmi4yr9jElPkoHr0Mk07wd7EK0PZ39xB\nV+gLowo/VvSyAvNuNYo4M3RSk/soDa3E4CuxLmU1urC7GSoXfJdTK/ja4mBOLhgFlYtTibb7Zift\nPbEd2rVw/Otnl7G5W1JrTU/bDnZECMQK0OuFRyvCFU92uT5KEaDpatlR/MW48fI4jtYe7AOdu1mV\nQGmTD1v1oZVCZUrjAzZDa3tt0WY1k3eYz+crrNi7D+u4nvVZ6dOGy+x1OBzjxom9DocDgNuOe3z/\nLSXmIzXS0LzhbyA2nyAzhm6aEumUH7w14oMPxTzddj96HA7H9jzvmM+V8z7RA4+veT05cimVxScG\ntunOX4rRGaXHvjDy8spf/gTNf/w6UqPw1w2NTlCcMyU++VTC734pntpbt2fbho8/mXLnJT5vuj6i\nm8InT6f8xn1d3nlHmdtPBnxPBn/81Zi33BxxpKRsHm0u7ia5uKsB8hvvUNlMR4V9PdF5pEFmvwOg\n7wbdGPxcFI08ReStOhfj2OCJobqJqAWGjNFFWyWQ6p3lt/bTzh3PMz54yJ66Da3gC51U00wMUS6G\nT6PQsZ/0F8EDK04NSgEonOi2uB59xfJWi+YJ9njfKwpBuV/M1FrTSiE1xkYqBGpNFMNe00yKwm2T\ncZxVAqGZ6LG5e+NM005XXdIGCNXgeYeeopVkxNp26BwWFmPrNp0LR4unCJXdFwdBkZWeGtvxMU1M\n2eo6doD7XnYcBE7sdTgchArecnOJIyW16fCsvcQToRoIZWO48+KAW475fHUx40+/GvPgAWVUOhyO\nySfy4K23WA9RcOaxlde1UkhUxm+f39BG+xHxkcsoPfTpHS1T6RT/2cdpHbuEWdNduUmL8WiqEr/1\n913uOzN9sQ39GOB3/qGHAb7uEp+3PC8i9OBvn0j58OMJd18e8o8u8vnDr/TwVInF2CCYFQFM9Ym7\n/gAnq2JzUTXTmkG3voFSzEc2O7KdwlJiUBiq64YVC1a0HbUekoIdZyn3U4wgDvZxSHnZV/jKCr5p\nYl3R7sZyLTovPNbL1hZDVFihfznWK6IubEzP7I8gydMfKDbxVoW5xkHFF5YTTZxZkbdY75lAreSk\n7heptsXIqhOkpvkCgtDNoLLD005rTSePGhMRAgVlz3Yone9ZB3V5k3lHntBJzb4K7ntJJ9UrQu+o\nn8dXB5fbC3Z0Qyc1BOH07weHw+HYLU7sdTgucJTAd94UUfE3z+Hav3Wxom/FN8xFiqvmPM51DX/y\n1ZjPPpOO5Ubc4XAcHi6dUfQWz3Ps3j9aE9eQnLwa0h4qS9ZMb4DWiWtBZ/SuuYPk+JVUvvJxVDw4\nkmEzSg99iuax15PgEWKF3VQ8nmnrqRd6Cy6vKX7/gR6ZMbzs0oB7boyIPOHJpibTVujqpFZEq/lC\nK4O5YPcxBd42AoGvFLPhqrtxObEFo6qBECjJC1rtYLl5pNBuCT2hlez/l1WgFHPBao5vLbQdqYeV\nwkUONsYgNVaA9ZQVQjOz6sjdbG8YY8gQ0Hb/2/xk+7tICXhwIILVerxczGylq07TuZ3kToyBOCOP\nYJmcY0vEFoxrJnqkYnurMRiArAro60e3RZ6dprzJXXPJg542ZEbYY91/X+imhkqgdph/bo/Q1MAe\nJZtsScm3zvNpi3IQnLvX4XCMHyf2OhwXKAuR8PJTAS+7LCBQMB9Nzs8METsc7kRZsRDBW26OqN8Q\n8T8eifnbJxJbZdrhcFzwXDyjCFrnNuTypscvX1OsbeX1maPo0gyzZx/GKJ929RjNF30LaukMla98\nEhW3h1rlWQV8AAAgAElEQVSu0hnRo5+ndeVt+LqNAjram7j4mSMlYSYQKoHQTg1nOzZD9o3Xh7zy\n8pDfuq/LZ55ON7S7uKr4yTsrPNHM+C+f63LLMZ/jZcW3XhPSy6x7q5VYUTXT4Hs2AyFD7TrzdliU\nUtTC1VzNZmIzff08jmH0+QlJuvv9Z52Wu57NjihyfJspLB3CHN9C4O1mNme3eE2w7kqwIm9qrFi7\nIt5i90khxE2CgDsKIsJcrpzF2hYwPLh1ObBFb0kh0PYyTeRtv3+11iwmNgaj5NvjZzMBO8oLf23m\nVlVK4YmmnRpmD4OjVHaXx6yw1+CDKN5nF2mmL8pBYPNuKYfD4dgZTux1OC4wrpxVfPPVIdcf8YiU\nFQImtYqwiK1YfqLikWrDt18b8dprQj70tYRPPZVMnLDicDj2l7MdQ3LR7IbX07kTVM8+uuH1zsIp\nou55K0jqlNryaTIvoF09xvLzX8Xcp/770MuWbgvShLYKqRIzJz1uWChxsiL7em2KPDhRUZyoKI6U\nhONloZPaG+27Tm3trfqBW0t85unmhtfPdDRfei7lxqM+v/CyKh4wE1j37HNdTaCsmAirVcSVCL3M\n7Lu4qJSioiDUmuXYFqrayT2zx3hutYuPn2qNfwCiosqdz91DluPbSXXe0WtQWCdnIOMreDfprO6/\ng/3dEynoZpBqc+CjwfoRESJli+UNn50rQ0UVFIX81sdEFB1N3b7oh0mhWLdeRh6wY93hIjAbbN/h\nYYrA4h0QKNjv2N5epummtgC0/aTTxbStr2MHTND10nHh4MReh+MC4XhZqN8Qcd2CRy0USt503fz5\nSjhaFjJt+MYrAl5zVUimDW//y9ZBr5rD4TggHl7M8GvzGFGIsXeX2g+RIBqY12uCiKBzZs1rXpYQ\nxi0yvzrUMrNyjdYLX7PyPDGG8xIym7WIlOHuywP+25fiXXyqzYk8uG7BYy4fifHaq0NmAiHVdvh6\nyRM8ZZ1/hUstzgy+su60qi9oY/NuAX7ni4PjKxIN7/lsl9ddG3L3FQHV0A7pFeB42Xa+nawqLq6q\nlYzako8tanRAw2f93NXaSiEF2ommMoL6onamEW9AcmEoGUOxt91QynN8l2NDqm1xr2n6zu8nznKh\nzYdA9rbo3TRwkHJv4WJtJWblOjQplHyhF2/uwO3Hvq8ZVtMs+0Ir0ZQ8Tayhk48wE6C6zwXytqLI\nHo51UbxPVqRPg41oWEpgflBFwhwBlhNDLWBHgr6nhG6292pvmgvacbb6nVML7HfSJOyLUZiutXU4\nHNOCE3sdjkPOkZLw2qtDXnTSz/Nwp0vkXY+nhONlj2as+eSA4ccOh+PCoZvB2XZC7cjFeOefQUcV\nkuOXg1I0T1yHl8X43SWC5TP5KEmNkQEFw3SKCUto2DKGwADN61/C559JuG7Bs2KiCE8tp1Cr8sBz\nKX/wlZ0JvQLU8iHAhWv2xiMeb7w+5PPPZtx7OuVVVwTccZGPJ5KLqrAQbcxWrKz7dddfXEobszIc\n/mxnVTYq+zYDuSiKaYA/fDDm8abmu26KOBIpglzZ9ZWQZJqbj3osx4bQM1TyzZoZDiy3soh2SLVh\nOTHEPT2Uiw1WTTeFULSbAkOFs628o9bjoxDAF5M8xzdgYkfybEaqDa3UUPEZanj+YSe/5BBnmvCA\ntseMD4sJZNpM1PFUOHA7GVSH2TTGoI0MZbgLFbRFWEzstbrkbR39sN+sOHm1db5vVryvGJWxHGtq\n4eCNNB8pluKdx1J4e7hJ1hfS88QKvCJWoFYYRKbzOrEqyTscDsd4cGKvw3GIuesynzdcF1H27fDb\nSflRuluMMbRTw58/kmw/scPhONR86HHNPbd/0+oLS2dBZ3izR9AGOvo4nRPXIlkKno8ZcCPox21U\nltC89W5m7/vQwOUYP2T5ztchwNPPdvmt+3q8+kqfb76mxImqx7s/3eEr57KRbtWumlPcfXnA7Sdt\nVaFCPHnbXzQJFNxzY8SpmselMx53Xx4Q5O7c0JM1Au529E8TZ9b1WwmEN98Q8ksf72CA776pxO0n\nfd7+oeaaQmWfOp1ypqN52wvKzEeyEglwpGQzfO89nXLVvOGiimddxROQW+krm3G6HBvOx4bZcPtI\nBckLC52PDSJW8I7UaO7ggkAJvf0ex7wJKi/c1kqto3vGZ0W0n3S0saJ9pJzQW6BEKHmGZmKYl513\nSOxqHZRCsO7e2Qlz91Z8oZloqkMUajMMX8xRRJgJ8iKAE+QcXe/ktQXmNl83JUIttILvVqMfKj4s\nJ/YcHPXeQY15+/RHUmxWSK/4Pky0jRqZNorvH4fD4RgnTux1OA4pL7/M5/XXRRwtq0NXkTvR8ExH\n81RrMm6mHQ7HwfF3T2nuuXn1uanUmA8UqxqIx7muJojb+K02YbKxCJsAtaUnWVq4gu6Vz6f0yN+v\nzg/I5k7Qvu7Olde+6aoSD55v8ycPp/zJw012cps2GwrvfHGFkgfeSt6j8GxHU78hZDYUjpYVJQ/K\nvsIYgzarDtSd3EwbY4g1VDzrVDteUbznVTN86qmE20/6nG7pNUJvwcOLmnd/psPbbi8xGyrmQiuo\nHisrXnyxz4PnMxZ7KdfOe3hKJiLPU4kwG9poieUESp6mvE2uQi0QDNbpl2TQyQyVHSzbm5Ah3QWF\n47mXapqpIdRm4kf5GGNYjm3xtZ0I7oeZkif0MoZ3sI4ZrW38QcZaJ3w3s1nhB5FVXVAIgMNkZosI\no/yKnKRih4nWtBL7vbMqfg63fp4ItcA6YSUdfF3085iLTmqojljprJh6N6MjtN4Yl1EJ7PfkIPFZ\nRAiVdXVHTt1wOBwOYOvRig6HY0o5VpZDK/QCLMaav3zUuXodDod1Zr3zr5s8fD4lzTLmI58N95cC\nUW9poNBboIxmZulJ4ouvIz5+BQBGFMvX3kn7llfwWNfeQT501l57/ufbV2XAnfhx3nyDLZ5W9hVh\nnqEuIhwtKe6+POSOkwEL0aogJ2KLae5GoEs0YMyKIHK87BEouHbBZjD89wd7m7b9jhsiaqEd3ns+\nj5lQIhwrKW5c8JmPFE82NSofTpsNa5nbQ0SEaqCYCYRuBos9veIAG4SvhEAJnljBd6tpt6I4/FKt\niTPNua7mfE+j9cF2UEa+ohZYd/dSbHb8+faDTmrQxjA7hENzmjB5jMputr11mQqxZt+PqV6mWYyN\njQgAmimc79mokJ6G5THHlWutaSf2HDrbzViK7Tm12ecuhvZ3hkj58gX0BFynhqXYFme7Gc3ECtuz\noTAXDS/0Fvh5gWZbbG/wtqz6EOvRr4NFvvtWgxtSrWnmn+Vcd3XCXqZXjqduZnPqZ0NhPlLbxmaU\nPLFu7V2eE9oYeplhsWfXpZOONj9tzEpc0sjbbqSpHVOFyOF6OKYC1/flcBxC7r48oOLLoRR6M2PI\nNHza5fU6HI6cTgr/9xd7/PSdlcG/QUUQvf01w097VJdP07r2xUjSZfnUbfizCySZ5n+7t8f33qS5\n45KID3+tx31nspHX0xN43XUhcWZ4YR7dsL6gmadkJbt33LRTY2+Y+9TwdmJYKNnn92/ymd72ghJX\nzVtBeH3Oo4gwH0HgKRJtb7iTzLCUGObCwS6s3WKMrS8/7LxXnGbGsBTbYmXbtVW7uJ8RERSG5bxP\nMvKtU3gxNsyEmuAAnY/9Ob6t1DAzomtvP0i1oadhJpy+YmyFA19jO6IybUiN/f962cf6yK1o5ykr\nUqpcRN2uU8dXgi/bF9saJ+1E09M2KiHyFXFmI7WUwExghbalIQukbYXWdjndFJBVV6cSoZsaWsWl\n3GhCz7qJ+8+psi804+0FOiWQToHWq3VeeNKMtyBcoIRIWQd9bUDUTegpWklGrIVoY9T9lghsGCVS\nuL+7fXm7M4GilRqWYm07CEUIV4qNjvb5PGUzm2MNpREPP2NsUc1Oaqzb21ihuac3fo6tSLSNWFk3\n9zXP1n+qtSODpuCAdDgcU4MTex2OQ8htx31KB1UhZ49pJYaPPZls6RhwOBwXHk+37U3WQigM0q+M\n8iHb3nYWxC3C9lk6N9/F33ytx9NPdLnnphI/clvIf/l8zAsuCrnlqEfjy6OPLqjfEPLSS0OyPrdP\nrCFUZtc37tuhc4G0tO6mvXCEfeLJhJlA+LEXlXn3Zzqc762u4xeey7j5mP3JuBxrqnmsRL/reCYQ\nMmNQ2HzVpVizFBtmxyz4GmNdqRqGqhZf3HxHuYCwlBcrmwm2HpZt35IdC1dlPy/S5luRyniGTgrN\nZOdZwOOi+P4sT+DvBGPs/goVByqKD2K9kKuNPb4Kc6hZN63kkSC+ygVdsW5SpRSZ1qQGMm33Rzcz\nwOo5pTDMbRM+Wg2ExdiQ6L3vQFiO7frWAlk550LP5of3I1ihPkKTGNvJUfG3L5K4IvD2Ddsv+RuL\noAWhrOyH1NjtFicAGowVfiMPENl2uwjDZ/YeFInWNGObm9u/7cdF2bci/WaCb8kXOqkZWVz28iKV\nWmt6Wb5fZbBYHWuDMTATDi4qNwqhZ5dVGkLhMMaQahvXkxn7PPKEsrd6vPa6GakWFmONwnYA2r+S\nd8rYeUn+SPP5HOn7oi2cxhp7jTDGPgpR2eRtu9o5ex0Ox3hxYq/DccgQYDaSPa2Ge1AYY+hmhr96\nzEU4OByOjfzuP/T44eeXOBaudWUqoFOeZ6b5zLbz0MqjHczw8LmUP/hKigEuqfa46/KIr79M846/\nbrETjeyOkz53XhwwHwlKFKnWJBm0Ek0nL/6zlzm3aT4cV6m1au9CSXG+ZzM4v+vmiBNVtUa0un7B\n41uuDummhpIvVHyxLixs5ET/CJL+/8+Gdr5LsXX4jkPM1rnQC7YIz/I27uFuaosXlT0o5bmU8xF0\nUp0Liptn16q8YI5mZ5lnoacI+za15EXxQm2LjsU9zWywvQg2bjKtaWeGqj+ZMU+t1Ao/5RGdhONA\nGyv6FEJuqs2Kq6//nJC+v56CQEApVqINhtmnnlJ4AOs+p9aadmbP1/UUHReC7YwIcjdwM4GFPXT3\nFkLvbLj9iLHQs47crlgJS9g8W1hrbd2U2argVfKsiLzVcgpXqIcVgwvxNzHQS62zE7AOS5MNtOib\nvPDYJGq9cWaz05PcVVryhZK3NxnbIkLFZ0XwnQvXdm5FCrqpIdVCMMI5WfaFpdiwmNj9Wg6sY3fQ\ntXpmjB1fkRJ62fbu8k5qXcbGGELPbgNfbfyAM6GQ5MdnIdTarGqzKtwC/VeI9d/jxXps9yl7Pc1k\nHpEOh2NacWKvw3HIWOllnsCbuN0Sa3h0UXOm434MORyOjXzh/2fv3WNkye77vu/vnHr0c3rm3n2/\n+BDJ3eVDSy5FkXrQlCjKiWQLUWJZiqIkBiTrAYSRZECJk1iAYcNBbMQGYjiWYUGCECWOoZX1ogSD\noCgHsvWiZJHiQ3ztklySqyW5u7z3zvT0q+rU75c/flU9PXOnZ/pR3V0193yAxe7eO9NVXV11uup7\nvuf7/WqGj77o8OZ7AvSikz9vB4R+ows5fvHChykXxLjZeQDv/2KG3/rsSX7tU59O0Y0IT99iOAaW\nDZG5v23wA6+Nca1hpg+7gTEIDBBbfejsXyI+rkvKMldA2YsIX3dfgMAQPvyCw+GMq/dtDwRohYQk\nE6SZFq+1IxUGIIxrDTNXpN4LMXXSriv4ulwktQR0cqdgmpwvJovo8vKEBe2AENnTj9nNwCA0jH6i\nzq55sQ7FUuQyb5YDQ+jlx+9wjptuk+iydapk5F7KupS6G21fBBfRzwO5A94SwRogIhV0be7o2/R+\nFed168zyhCJHFFDnKotmL2e5g3hTLCP0AuqeBwSRMbCkguVxymjn2cvM6vgduRN5LDYq2i67bL9g\nVvxtWMqPlTo8u9GJA7NYLi8CZEJIK2jrdXlkQyHo74WrH5dFISK0A+BYirH6RCg1xiCyjKET9JZw\nkVjSgsziv7dFEeUwYaB5waXKeSTGwdmlLmcIjcG2FmEUgrLnilLFL13PlceLvR7PFeSqfp3cmjDe\n92zJ7R8ej+dK8a8+McGrDyyagTqJAF1GDQHE2Nuye5ks0riDo6CDsNnCL318jD98/vTPCICf+9hq\nY0/DAu9+UwO96PwluMYYtAwQsQqXLEAnLH/CLmUgmvPQWhSt3ZwwnrgnwN98QwO/9vQEN8aCD3zJ\n4U33BOiEhKETCASxISDUfMMbE8bBnIIgYwz2QsZhgmmkwyrvazaKYTb+oBsAh4kK5d38mInItCCu\nG9FcIbXIru27+bEOBF1mf9Z9uS4mF0KGDuinQNPy1Hm8aWJrwML58RQ0NzS5sCxFfENssFXxu4By\nJ3c7vH1yYJsw61ru0JwckyJXVkQng7Z1rhwvKfQCem63ZpY+hHlEzdjpcn7OPbxRIfCuuWx/3j40\nrGCcqUg++3nm6SywwG0RFFXAQD/ndmi2un+Ury45SoDDFOiFJ4Jv0wKHrBNuy6w+2dXKgcgCk0yj\ndOYRW0LC6+dLlwkRQF7t9Xg8JVKN0c3j8XguIWXB2Ak+/tXlS5E8Hs+dw8gBP/NnY9xMTpfvCAR8\nZplmv3UXju56Jf5ouI/3Pq9/9+iBwX/7eIR//q5OKY6etz0Qohffnm15lsAY7Ec0FTaXbfG+iKLQ\nLLpAtCQiHMQG7UBdvj/6RAP/xWsivPtNDUSWkPBJORtD80I7kYq84wtajowx6EUquqzyvsZ55ELT\n4rac2+K1M1Ynb5Hnmwlycf3iD1B/36Bhdcn3yJ3eP0vLlfMsAxGhHRp0QsIoA44SXrtFflGagUEn\nBCa5W7rMc21V+nmp0S6yjB0LDieaqLlhE+WlFJm1aZ5f60Qzb3shcK1htyb0DlN14O6F65X9Fq7b\nUaYO6W6o41w71BUBm5poICKERidU6oQx+Zi6qYHnAogIe7kT+njmuJncpT2sQ5sd8mgP4MLxtPg6\nrlL/h4EWe3o8Hk9ZeLHX47liRKb6hRPLIiK4NWG855nE3wZ5PFvE0O2FXnXg80eMX/zzMfqOMMw0\nAoaMRRacBFsKgKx9gPc9m+BnP5rgvZ9L8cLA4S33R3jrgxFujLJShL4/fD7FH3/J4aURazbuBcKa\nyQVfl4uX26YQSMZO8GDX4p2PRDB0kgF/nPDUtZewljV1Ai1nuwgVVfV9DRYUF9XVyBg5QTvAXJHL\nGINupGL0YaKidm/JLNxmYNANtezpOJXpkvkiv3WThIbQiwgs6lJ2WxJ8Q2PQCwkZY5qDvCuSTJ3Y\nvXC72y1E3n7KCAzQCy+fINg0DavZv0OnE9yW1I29Lffh2DFujDNMuLwIgW5I2I8InQ0LvGdpLCD6\nVQ1mhmPJ4zC2DxGhGxEy0XOhoBUgHw+rfxdu8/K0yQUfO5G6y0cV8o8YEp/Z6/F4SsXHOHg8V4xO\nfpN2lXCi+YYf+HLNLBoeT02JDHBfx+Bvf30LAPD/fSHBrz6d1Goi6UMvZBggAjjBBABEMOreCxLG\nJN5DFrcBAH/55RF+4xmNaPj5jyb4n98W4GMvpvgXH57Mf/ElmGTAL358gvd/IcXfeVvr0p9X8ZKn\nJV6zzBZDEQGdkOaWk536PSKEJBg6THMU55EyEFpCmgmuNYxGIkhRRkUYpLr0P2GgnzJ6kVlIvDHG\nYC/SqIqhU2fwRXBeUHRRFENBYAzageYpN+xyQm+aF+UV35suz62cbWbfNIY0x3eQqtO2ZRnxlhyc\ngOx0OXsmgoHTeIIyBE0RjT0YpgJrzi9/KiYdGDrWNW052y4Dvf4v/pmjRAu8QtIJuTIyhZkZR6lO\ngrVDg8iUF6+wq5iQWfdmXI2P91L67mTSbVfYPNLhOBUERjPF01wUzRg4p8escsRWnfEXRTlEFYty\nIMCLvVeZCsQlee48vNjr8VwxOiGhBhPvCzN19X6mXkKTx1NXIgP81FuaeKBjIPly+Hc8HOLupsG/\n/Mi4VtdhNhu2mt9oD/fun/vzzx0zfu4jI3x5sN6bvLtJ+P7HY/z+Xzh86AUHFuBdLwthsZjwERgD\nAiMyJ9ELnLd+s3ZIYcKCTGjhZefWELJLZgKL8Ta0hP1YXXhNKxhlKsqJ6OuknMfYEi2V42gLITsR\nGDCaFygaxUsu+ggeWXNhTMV5MDOOU91GaPThX0UzgdmyokFE6ERahDdwgoQZ7WCzhWCHqX6eu3IR\nigj6iSAgrCxui6homzGQsBbuieg5mZ7JGb1d5N1+EVwZmDxeRKBC5uk5oQyxoaXiMBzn12S+jH+R\nCaQ6QESIrWCYAXHFn3iZNT+c8+/cXedoh4YQGb0+QQwC0AkIYQVzjs8jshqFcVbIFREkrHnzAgBS\nnTHAkBd7PR5PuVT8q8/j8SzLQWPxh/864AQ4TgT/0bt6PZ6NYwl495NNPNi1+sAJfWC+Fhu89jrh\nR59o4F9+uD6CL4NwhCY+8aUjPHvo8H2PNQEAn73lQCL4J386wePXT4t6H3ph/XWd3/U1EV53PcAr\nexZ//dEI//azCV69b9EIFh+cRQShNXOF1PE4W6qMU+TyPNJZV28Y6Q9rfAJjlBF6MeHGmGGNLvXl\nXPxdhsAYWMO47CgTaav6Jl15+pDP6NxWQLW7L9HIEqzR1SyHCdCLNuM6K+IiWjssaDtOVXBpL/k0\n4liQZCraFENRIfA2rQpVxhgMU817bofq9GVoREJ3wyL6pumEWqYIAL34tJDlmHDsBDEz7ALvcewY\no0zPu12eC5uiYQmTc0S/KpEy4zjR+chehcT2Vr6CI8hXkdTp3LD598eEgWZedlisUhHRmIxlV4Fs\nGgJd2YJtj8ezG7zY6/FcMa41zFKCQpUREdwaM379Ge/q9Xg2DQH44a9t4BU9q3l3maCZu+2ICNca\nwGuvB/ixJxr4F382ron/hMAg/OKfayTD5w6HuL9F+OOvnEiNZZc+NgPgTfcGaAaEbmTgWPA9r4kh\nABY1L6bMABEuGsqXHeUXSa1Mcufvfnw6mqERGDTyO8ZrDQOWk+KlVXAs5y6vPwuRTvjFl/7k6ogI\nWFZ/L5vA5rEOx6ngMBG0Q0ZkyxUlDDQGoJ8KmlYjK7Yt5jjB0u5lzkUbA0yd7xpfcLsTuxUaTMaM\n45QR5UJwlcSdddgLgaP0tHOZiBBaIMgERylwcMGFk2SMkdNxoU6OzWUxueg3yoB2hT56ZkbKeYEc\naexAs2JiuxMAImjXTOgF8slSA4ydficL1A3fCYGwqjkUtMtpRo/HcxXxYq/Hc8W4v20QXRFrrxPg\n2HlXr8ezDb7/sRivvW7RCoC/OGY0LOF6k08Jvt0Q+Jp9CyLUMi7mi33GF/ub3cbYAb/7xRTf9GCI\nyOiy6LuWDH6dZOqyvvABm2ipCDiRyyPj2uHlbl1D660eYWYQ0aXCt4gu73cb7lYi0pz7RbrBtukO\nLIqSxo4xcDoB0C4xyNMYg06kot8gVQd1J9xuviph+QK8SZ5f0GssdiwOGqbSrs5VscYgIP3sevHp\nz6wdEg4TwcSdzn52zBhnOqmjOd5anlYVJ+mmaAWEQcpob7kAcB4Txxg6/QwaARDban4Gw1TzvOt4\n7RwlWrRZ5JEbIhgI0gw7zUO+CJ/Ze8Wp4XXkqT9e7PV4rhj3d8yViXG4NWb8xjOJv/XxeLbAqw8s\nupFBYIC9iPDeZxN868MR7m5yvpQfOEwYv/ypiXfaz2EvInzvoxH+9CsO7/1cgm95OMQ7H4mwFxNa\nS2SSpqylUfMo2uUJKoouItAxLr/pW8etuyiFj3pW3FB3rU7wJZlM80ghmy8PCwlIMzmVW+tYy96y\nvCSOZ44xgbEXbs8h2ggMAiM4TgU3J4xeyduOrIqGh4kW03XD5aM5lkVExWVgebE3YSBc0phXR7Fq\nEdqB5i6fzc02RGgFwCBlWGJMWEXy4vruhjrO182tuSqhAUCEScaIS3bIL4NjRj/V/26VXIJXNikL\nGHLh91DViSydmiBrBIRhymhVRPT3eDyeTePFXo/nCkEAHuyYhZcKV5mUBcPMu3o9nm3w3a+KcK2p\nDeCGCNcbBv/pyyP85mcSfMcrIvRizb40pCVmnvP5zleGePO9IV5/V4BxJvjtZ1M8f8w4aCx3u6X5\nupc9ZGuma/HfZ+mEhHBGACrE1EGqZTuG1Olr8qWjuhR+818eSabbSlmQZir6FWeUiEyX24fm/KX5\nZWMATAQ4nDAEJ0eyOEaN3HVn8+OlWbqCbqQt9dsgMBrr0E8Fh6mgEzLCErdtjEEvYgwdcJQKWlZW\nLkxbhJSBgWMAtHSpXmwJI+dnmwD93AJSl+hedHq8iAwwJELf6TneCQ3CCouLm2Ra1OY0LmEXFNnI\nIaHysQgiWmQYm3q6egGNxDg+4+YuRP+qOv29s9fj8ZSNF3s9nivE9abeJlRxOdiyHE4E//YzPqvX\n49k0D3YMvv3l0aliGGsI1xoGf/VrIvzq0xO8+d4Qv/1sgptjwfMDL/aeR8MC3/hAiHZI6MWa1fvd\nrzJgCKKSnyuNMdM8zsLlC6hoKgCOU9weTSACMgTHp6Xh00Msn/r5E+FTFWGDE5G4EENjs5xInOSb\n6CcMS5RnhgLhjhx3gdHYjEag8RQ2f2/zxJi9CBg5oJ8CDcNobmlNcBGjMs70840No7XBWIeEeWOl\nTEUMzLUFoxhmCdTOXvo+1ZXi/J1HbFDqeVJXiqK2bMHiujIphN6zE3BVJeWTErO6UkxsOD6ZlCui\nHKqW3+zxeDybwou9Hs8V4o33BIhrfHNWwKJZWx9+sdziJI/HczvPHzM++qLD6+4KcC0+EbkCQ+iE\nhG96MMQ//MBox3tZfcYZ8IfPO7z1/gCRJXRDwvXmqk+Ui/t7ZoXW6X8JaxwCS15QBIAIDUtzYxEk\nF9DyTh5Ivg+SRyoU/8155EIGgDkXfJd5a3nhT2TVYndzwjttpQkMgAwLL6kmIrRCQsgarTCZbC/W\ngYjQDAih0ZKyZAPbvi3WIdLCuDJRg91qr2lViT8l4twpMDMyAGmmLv2MBaDzPx8iQjuPcvDL1pG7\n86v2mrQAACAASURBVAUDpxM22yLJ6iX0igiGTnTyq8bXF+Wf9zgDOjNvo1Gx/OazVP8M8Xg8dcKL\nvR7PFeKxaxati+rba8JxIvjjL6c4Srx7x+PZNALgZz8yxrvf1MBrDgIc5ILvKGXcnAh+4aPjXe9i\nbfjXn5zggy84vP66xTc9GCK0hGvx8u7IdbW1TqRuP84FIQZyp+78Fz7JpMUlT5wnf3k4YWQMYMGl\n0UnGAJ0WPQiA22FpjgoarMV0Sxz3sIhWyGMdOlG50QoXEZzZdtmREkWsw7HToqN2gNKyk0UE66Qw\nFCJOP8XU3X5VSZmRZOqGl1O50Xq9xKGBpfnnbbFsPckY0Q6zaqtCKyD0U9naMv6MtVyxZesh9AJ6\nrrEImsGO8i5KpGE1ymF2iUsRb7ILh/dl1OMM8azMFVh166kfXuz1eK4QL9uzqPv9PItgnAl+6zPp\nrnfF47ljYAF+5s/G+Iknm6B9i9gIXhoz/vGfjPDSyE+6LEpAwI8/2Vz7dQx0LFz18S805jbx9HDC\npcfiBEbjIhaBWeMBGsFp8TsgrCX+lYGIIBNaVLOeYoimsQ7HW451KLY9LCIlLKNZYs6uMQZ7ETBx\njIFjJBmtlTVaOAaTEpaId0LCUSK4tUVX9TZwubg7mUlTMdCyRms0N3qZmC4iQpC7G5fNRr6KBGZ7\ny/iZGUeJFkzGNTFhFNdouyb7exnnRTlQHuUwzoAIPM2QDy12v1Lgahx2j8dTIa7G3ZHH48FBnJcr\n7XpH1qSfCH7/ee/q9Xi2jWPgn31whOf6GV4YMf7RB7zQuyxOgJ/+DwM819cIml60mjgWGC0yKxNL\ngCtZ7Q3yDODLYGYcpvqgfVbkC+1ir7FJiGhlIVxjHQy6IWHMwK0Jn8pR3iRE2jbfCQnjDDhMyt92\nHOh7S1ljHXjJvFwRwdgxbiWCJAM6IXCtYdfKkSWoa0+gYnddYWaMHOPWhHFznKGfal5q0xL2QsJ+\npPnfjUCdoav0MTQC0rgHDwCgFRKSTDZ6jTIzDhNBYKhWq+0mmQCy2XLGbUJ5seZ45ruUWWODEtYJ\nOic6udJPgZvj3X4R+YI2j8dTNt7Z6/FcEV7RU9tGlRt+L4NFMMkE7/2cd/V6PLsgYeAffmAEIvhy\nxBW5ORH8zudT/MDjBoZWe2iO7O1Lt9fFkApJZWIIAAluzDxNE+hUgZtj5LmiQPccZ6ghQHb8gBsa\nIFvDSQ1sPlrhIopIiaNEcCsB9kredjAT63CYSF6ot9ixGjpBkglaISFec+lRJoJBKuomF0FsCY0a\nOVaZGSmruORYr+0ikkGzksu/hwvyjOOUtxczUmWKYzxhYOVI9Qtg1omNMM+8r8s9uYg6nuskTi9C\nEeUwIsbYYVo02g5o6vwFgJHjC4sOPR6Pp454sdfjuSLc29YllnVm5NTV2/euXo9nZxRFXJ7F+JGv\njXFrInjfsyluTfTA/dWvibDfWF1JKPSrJeJwL39NApKSXmv2NQHCXkS3FbnpvwVRSAjM/JIvuwGB\na1ksgLSEc37T0QqXbbsXAQOn5W0ty6U69IpYh5FjHDtBz1weKcCisQ2diEoRGsdOhd5uCASmHirv\nNHc3k2lmY0hAJzSnxKZNMS2qckC4xWKyqqIlh1pc1yz5Kbhw9NZN6AWAcZ7Hc1VcvQXFNTbJgGae\nPX7euJXkJZ27pD5ni2clajQeeK4OXuz1eK4I97QMgpoUQJxH8VDoXb0ej6cuvP4ui9deD9CwhG98\nIMSfveDwzK0M3YiwjkHKGAORDBkTbEmaljWAlOxcKt6igcCc69q8/CAUPzGbq7htrAEmJcUBaLQC\nIWLBcSpImNENtpMrS6QiU5IJBk633S5525M843KR03uchzEvIvRmog7glM93gItILtLI7rM1c0QE\njoGE1WUcGAIzY1yUquU/Zwlo5eLuKlEM69IMCP1kx1kpFaIo6SqzuE6FXp3UqpvQq10ZQPsKqgJE\nhP3o4kkVES0x3XmuNfkYB4/HUy5XcFj3eO5M7mqaWs8Kj5xggsC7ej0eT234z14VoRcbxJbQEcE3\nPhDijfcG2I/M2g/7oVHRLlqjyGqWQtIoMxpCl6ELUgHiNV/DsWYV74KATpzIZR2b2WiFbcc6RJZg\nTREpAfQiLkXwHeY5IN0FsqhFBBO+WEAqBN5JdiKMQgQJEyIjYNHokUmmYoyIoLnDZeYiAidAmuk5\nz6J/ZomQsADCQB7NEFs9B6rgXC+iC3Y5oVIliPT8GrjyBL6+U+PeItdG1RjkyxrKEr6rxmWfh5qa\nZeerBep11ng8njrgxV6P5wpAAB7smp09KK+LY30oHMOvMfR4PPXhE1/NcHczF3sjQicyaJckGIbm\ndLHMuqjYlyGT9VzHZyEA2Zp5E4b0gZuZ4URzfrP83wI9ni27uSXGemwYLEU0RUmvO41W0FiHpmU0\ntvRFbfNtH6cqNrfD9VyMjhmT3HW7iDu1yJyO5ljTHWvcBAA0LBAb/RzGjjF0guHMz8ZGRbltizGF\nuFs4josJgYAI0XSfdZ9uTjK0QoNoC9EMy1JEOQwdsOdvswCo2zlJtKht3YmQ40THjlULOXeFiGCc\n6TneDXe9N7sj4fXy2j0ej6eqeLHX47kCPNQ1eRlOPW9Wbk4YiYkh/mbL4/HUhLfdH+DDLzq8//Mp\n3vlIiHc8HCIyhIM1snpniQwwykp2mxKhnwp6UXnfF4HRRvN1iAxhlGmUD6CP3YEBWrmwOHH6d6u6\nhxeBoAJz2XLiedEKnS3GOnQjwsQxBg5IMkZnxWDK41SF8EXEcBHByAmalpCxYMKCJNPfLZa4H6eC\nyADt8PT+FLtnzWLxD5siy8Xo4vqLz4i7szjWFsIqCr0FzYBUlCxB3LwKmFwAHzigu4YAPnaMVIC9\nBSdBqkIxHgHqvr+THd9JhkoUPdbn7PGsRI3GB8/VwYu9Hs8V4NFrFo0y7UhbJBMtXEn8cOTxeGrC\nQx2D//LxGCLASyPGe55J8IkbGX7siUZp21jXievy7FBC/g+pW9hlury/F5cl9hKSNdvNiiXvZs6S\nd7bAsIwGtQsg6LL8TXEqWiEFeuH2RLc4MAiM4CgV3Jow9sLlxeZOqJEUR4mKYxcJWy4v6BszMM4P\namyACQucEMJcNE4yQWhOO46NMShpvmRlCiEsIKAd0qXHqg4yWZAPAutErlw1mgHhOF1dAE8yxijT\nCQxbo86MYcqYsKBhaWsFklXFsUCg41MVqM9Z5PF46oBXVzyeK8Ab7wl2mmO3DmMn+NBXHB57oJ77\n7/F4dseb7rHYjw1AuoT2oEG4q2nw0ojxsZcytDAGb+Dx6bteFaETEJoB4SA2+KE3GGQCtEqcdOPc\nLbjqSw6dOlUNATIjYpa5YDXLX1jWLJXRZebz/97Q+tu4DGsAJ5tdzlt2tMJS284zhPt5hnAn4qWc\ns4Ex2I8YRyk0gzjEuaWwIoJB7ohtWJo65g5TgHAycdEJCZNMIy5SZsR29+5CES3VcyxohYR4wc9m\nUxEpZVLk1I6cTq548ixjECYMNJc89RwzBqlmSIc1EnonToXebkg7v96qwNAJLG1npcVlUF5AaanI\nEfZ4PJ718GKvx1NzAgIermleL+cPhe97NsFjD+x6bzweT5144m6Lv/G6BuJcJYxs7gwFwADeeA8j\ngIbefverIvz751LcGJfzBPWVAePY6aqEdki4u2lwKxHEJd5VjTN1DK67LJwA9DZgWxIRHCUCQEAb\n9iNto+TKEqYxEpukiFYYlxCtsCyGCHuRnlvHKRAbRitcfNvGGOzH6gzsp4KGVUF39rOZ5Nm2+9EZ\nR6xkaIYnxYVEhEZACIwKrAnLVt3OZzmJbdCJo2X3Q2SzzvAyaFgtkvNRDgoRoWFVAG/OjN2XHR9m\nxlGi5ZnbyuAuiwnnky7+84dj/Q7vVSyvODRAVmJev8fjuXPxYq/HU3NesW/BUs+83kEq+MiLDl8Z\nVvwJyePxVI633h+iF5up2HuWRtNOC5a+4xURvu6+AP/gD4dwAvzoEw0kGfBzHxkv7Rd94m6LrwwZ\n/+gDQ7zzkQhvuS9AaNU+O6+MahUmrGJvyrnzaMkxvhOoeDna0ENj4ULqhLTxbNXinW9SpCq7EO8y\nGoFBaFRgvDlh9FaIVlgFIsrdiLrtZIVYh1ZoEDLjONXism54IsaPnaBhb3+9yBLGThBbgohgkgnG\nmQrDkQES3l3EwOnYhtU+B0uUO92rey9mDYEgmGTLO1mvKpHVvHDNXdYiRQAI6fxJGGbGYaKu9nZV\nbdwX0A4JR4m+3ztZ8C1WIAQVcfXOElrC2Ft7rx4VO888dwZe7PV4as7j12wtbzg5L3D59WeSXe+K\nx+OpGYaAx65bXGZKJNJ80DAisAD/5Fs7079jEVxrEL66hNvXEvA3XtdAaAgZBB9/KcP/9edjfN9j\nMbqRwSBlNM8RulahHQAjB/QTBhEhIF1abhcUfY0xCMDABgVMAuAYl34Oa2+HVKRKeXPZisXLllmI\ndxmnohVSQSdcLlphHYKZbd9KBHvRcuJPaAx6IeOwiHWIAORZved9Rk0LHLKez04N4WgEek0Nnf7M\ntp2xq8Y2nEdgdHKlWeL+bYJGQBilgmbF3Iw7I4+i6acq1AekWb79VHCYMLpnyhSPUv3+6YZU2TK+\ni7B5nEc/AQ7Ki5ivHY71HqAbVu8z3PT3qcfjuXPwYq/HU2MiA7zlvgBxDcXe40TwJ192pS2r9ng8\ndw7f92ikjpwlHrb3IsKtRMeb//Bcgq+OZCmhFwBec2ARGGC/QSAQ3no/4fHr+mftgDB0goQBERV9\n4zWE38gaRLlROM0Yx04LsgISLY2qgNCwzWxBwma3ZYwBgZEJtpq7qrEOq0crrEMR6zB06mhsWF6q\nsMkYg4NYBdx+og50wvnnvDEGNj++mvd78jOR3axr+zzWjW04iyUg3UIMyLpEBhjR9o93lWBmTBgY\nOwD5OSsg7IWAzY9JL9LxdrZMsZ/nvHSjegq9Ba1A4zzGjmsXQ1EGIurkj+361/0muAM/Eo/HsyH8\ncOLx1JTrTcLf+YYWrjVNZQtB5sEiGGWC3/yMd/V6PJ7leMt9Ad56f4hrjeVuYYgI+5EOlm9/KDpV\nWrYozx5l+IPnU7wwZLw0Yhgi3NM0uBYbRJawHxv0IkI71NKfwxS4NWGMHeeFa6vB0KXv3VAFz8NE\nMEwZcsmbGLvNLioPDcFtSeAKzXbEtF3krmq0gkE3P29uTdY7X5bddjs06ISEcQYcJstvuxsZdXkL\n0LrARrIXG+zH5lwH8TZFlyQTHKUqTveicrYdGHU1X3ZN7hpDBIPNxbtUFWbGxDFuTtSNPsnUWd6L\ndNwm6J8V6ESIppEfJppT7USF3ipMtK0Ci0xXLrQDwsjJ1saZKpHkrt5GBYsKCUBUo8I/j8dTbbyz\n1+OpIQ90DP7Wm5u4FmvBSd0cBkeJ4PeeS/NyH4/Hs2kiA7z7ySbubRk0A+CDLzi855mkds76+9sG\n/9XjMa41zEoP3ESEVqAN3P/5a2L8zhfShTN733pfgHc8EuJ3v5jiN54Z4DUHFt/ycIhX7lsEROhE\nmLqNYwvEVjM8x04wyoBRJrDEaAVYOisxZUFgtFRnPwZSZhwnwIQFTasOpfO+B1LWJcmbwpA+NG8S\nEZk6erMNb8tMt7Gb79R1oxXWIcy3fZRIHsuw3LYFevyiNaIQtsHIMcaZoBWsF9twlmkMCKqc2qs0\nA8JxymjfAVEOjhkjBzjRIslibD4bh9MKCIOU0Zo5JoZOrokJa3TDojE6VSJjjSth6AqUyOrkGRFh\nlAHtal+ypSIiGDq9/qvo6gUBYQVFaI/HU0+82Ovx1IyX7Rn8+JNNXG+ok6xuZHkxy3uf9a5ej2db\nNEPCK3sGvciACPiG+0O88e4Af/SlFL/y6aQWy48bFnj3mxroRYRgDedLbDVuAcBS5Wzf9aoI97ct\nHuwYfP9jMT7yosNvfTbBi0PBW+8P8I6HQ3RCwrWGme6fJUJogEnG2IsMRk7QTwGRDA1L5xZZnUVE\ns2rbM3dsoTE4aAATxxg6LbpqBScP8CfvFRhngsaGBF9LKGWyUUQgUNcyi4rbjmc/H11uP6+Mryxs\nBXJXi2iF0YrRCutuuxcBg1QzTFuWES+w7ZFjCIBOBfMvZxnnQu8mSgWNMRDJkAmh6sa8YpxIeXsZ\n0duEmTHOgEkmABEsAd3QXDhehQYAESbu9DlP+fUoqF8RMotgmApS0bE6Jp3ojPLJwU6o13nzDor0\n0JglQSOorqJa9fHDsyI1Gz88VwMv9no8NeKhrsFPPNnEtRoJvSLqiHCZILSEYSr4nc8nGKS73jOP\n587haJKXTuXO025EaIeCtz8Y4fHrAf73Px6iGxkIBF8eVM/tSwB++GsbOGiYUjIGHQves0Q55N1N\nQi8mBAa4q2EgAL7xgRBvuDvAyAl6scHRhM8IlMoozwYMjB53FW9VcJ4wAGi+b2TOF35HmbZeneeY\njAODOFCxbZBy7lwWGFKnYcMCE9ZJtk040oqH0nXzPwe5ICG5+y4wejwCk7ulzXYezIOK5K4SEVoh\nIcwdeQnfXhS1yW13IkKSaa7lhBmdC7btcmENUHFtnYmYTTJxjFGGjQi9BQTaSQzIsmhxpWCYAr14\n13tzAjMjA1b+fJKMMXQn7upWaBCZxSakiAgNKxg6IA5u/7tqntXnIyIYZzK9LouM7CKr2E1XihAM\nBH2ncSZXnVlXb1Uh1NM97vF4qokXez2emnBXk/CTNRN6AeBWInj2MMMnvprhFT2Dh7oWv/MFr/R6\nPNvCEPDPvq0DADicCA5ifXg1RDhoqLD2v/2lNgwRHAt+4t8NdrzHt/NXXhniVQcWvaicsS9j4FM3\nFwutJKgb6jeeSfD2h0L0IkIz0H/uCQhZ7kTtdCzSTE5lqLPo0tnmjFZJpMtoI0vgfKXDvJgHEcEk\nw6XLrZuBQWxUzBqkcpuDJGPAbkAvVRFF4ARYSysgvSHtxrt1W1kDSIZpruWuOR2tAOxFPC2Q2jSR\nJVijkRKHCdCLbhf0mbWYLbLqYB84oFWRYzdLkgmGmbrjN+lkDYyOBZt2oJdBIyD0U6lEUdusSAtg\nYUc5oOfgKHfxUr6SomkJdoVJh9gSxpnA8fbiU8qkWAUydDIVvPfjk/dhjIElzgVP/TnGnSMG1MHV\nCwi21M/p8XjuAO6U8d3jqTUE4L9/somDmGol9LIIHAv+zw+NMHK73huP586EBfj0DQciwrUGoR1a\nzGpqnZBwKwF+94sJfvlT1YtXef1dFu96WYTrDVOaiEQETNxiFry/9poI73g4xKdvZHjqkxPcmmhs\nwzsfCfOyqZNYifCMwJAyAJnvijSkonHDCjIhHKca86Cirzamz3P13vZaxqBtgKZlDJyWZVnSLMpN\nfG2MHE8LjbI13Yyh0VUfu0YFHq5U7moRrXCcCo4SQSvkUrNmL8LObPswEbRDPnUu9l2Rg63Lwgdp\nhpR1MqMqFA7ldrD5POGAkLv1q4/VhR44TABQhm5IWxc4R44xcqdF2gnr5NdZd+0szKyiZj7+GACd\n0NwWY7MshgiW1N27VyOnq4hOuA1SLWBrWEJkgCN3+6qOdgAc5TEtloBeuN1yxF0hIvm5BhynjNYC\nEUq7gIBKjZ8ej6feeLHX46kBT94bYD+ijeUuboqRE3z4BeeFXo9nx/zTD44BAI90NfO7E2qkQPFg\nnLHgqQoKvXc3CT/4hsbKhWznUZR89RcoiDQEfMMDIXqRwZP3Grxy3yJl4M9fckj5ckFQIxwu3yci\ngoW6sdoWGLM+jAOXu3pv22dj0I1y12X+UB+Qvk6ZmZPFcvWGBeI1n5kNIX/3u0cL4aqVu0p59Eoh\nXCYZo73FWIdupFmmA6flgO3QYOQYLEBvZhyJLGGUZ4JWgTQ/Xq0tCL0AEOTXbh0oIl8yARImjBzQ\n3bLAOXbqpm3NFA3HpC7d8xzH55WtNSyVOq61KuR4voxTTl7R665pT4rHyDFGqaAdnmQOW2PQDRkG\n1RQ7N0Xh6u3k2fmHKQCo6BvOiVDaBbrypxrjp6dkKrbixXNn4MVej6cGvOtlIXpxea62bVBkY73n\nM9UTkDyeO5Uv9Bl/9w8G+IHHG3jtdYtebJBmgi8NqqdQxBb48Seb6IXrFbKd5dZE8L5nk2me4UV0\nI0Jo1dnYCQn3NA0yAbr3h2iFdOGYnOWlY40FXTpp/jAaBRYRVKxlYGW3nTEGvVgFkn6q4nYvLu84\nBkbFxzLKw8oqeiuDKueuLhKtsCniwMAazRC+Oc4AInTC00Jb0wKHrKt6dl1mlbLg2AlaFttzQuf/\nrkoMyGUUAnjAguNk+98BRab47LGyRCDkpZPmpGxtnMc0WFIXb7ChMSMwp7e/CVyexU0E9KLlNqKT\nUXo8iu8MLfuk28YCLWDTODUA2I/0mNUxomIdCldvIyCEhhBGJxFKwwyAE4S2Gm5fgqBZM2OPx+Op\nLl7s9XgqTmiABzqmdhlOCQPP3Mrw0qiiT80ezx3KIAV+9iNjvP4ui297JMRRIvj1p6s3KfODr2/g\netOgWeLgl7K2k//25y/PDe9GhPvaBn//D4b4+vsDvP3BEI2AsBcRegtYWSd5tsGiD49FkVuBMQZl\nvPPAGPRCxmF6UsxTBrZEN26xRxlvL5N2HoHR41TV3NXLohU2SZBnCCesRYNnY0tMHoMxdoJWuLvj\nV4hpTYuFs1/LwBgDkQyZEOqk1wQEgAhJtr1zCdAJvTHrypLZjN1mQBimjIRPIlXaS5StrUsrIF3q\nv+Sqistg0fMyEyAkIBVByrdfR2c5K/ACAgPKo0nmzyYGRrPcLQFDp99JdVshWAZpPgEVzxzn2Qgl\nJxojdJgXpu7S7WvzlRQej8dTBl7s9XgqTidUl1EdXCKzHE0YH3je5zd4PFXlYy9l+NhLi5WUbZvv\neEWIx66XV8hWtJMfJYL/5+NjuAVMbP/Na2M8es0iE+AjLzr83x+f4BU9i3e9LETzkrunolittaCr\ntyhyW9QFvCzGGESGcZwKelE53ycmd9aVsdyZcjefYy1J2yWW8qzlClNEK4zzaAXHjNaWZoSJKI8m\nuf0cOswDa3f5GWYi6KeChgUaWxR6C4gIjnXSoC5oZq5g5LabF2qMAYExzgTtGSEuMsCItDx01bK1\ndSiyfydZOfnYhbN0kkf/FDm5g5QxuGBMLr63xjPZxO0ACOh2F+88inFBoA7p2NbDdV4WxSrDZnD+\nMSMihAT0YnX7jnfs9jUEHDTunM/H4/FsFi/2ejwV53qznl/6AnWqeTwezzK8at/gP3n5+oVsLAKC\njkU3J4Kvjhg/82cjvDC8fFxqBcCj1yyuN/Qh7+0Phnji7gCh0SXEl1EUli2qv+lyXGzU1dq0Wlbl\nWKMp1qX4ZBgoxYFM0FK5uITXWgdrdGVKHWgEBoFRcXMy4Z2XLRnS8zjaYeDxIC+eKiNeZBUiAyQs\naFSm4m8xGpbQ5+1n1TYDdZ22ZqIviAj7JUbOLItOaGhR2yKZ6/MQESSs98ICjVUIZ47tvDH5bBZv\nK6C1naaR0dcra/yvC+NMj2G8wJhk8rLJ5g7dvgTs9Nz3bJA7aJLFUx282OvxVJxrTXOqSbcuENXL\n2eLxeHbPfkz4sSeaOFijkC0Twa2JwLEgtAQC8HvPJfiVTydYdP6JCDhOBCyMTqjlmPe2zMJZnONM\nlirAGTnZmKu3wBiD2DIGTtArYTn0rBu3jLE+NKcdtcwMJ4BjFc8da2wEQSM2NpU7GRIwRH1yVwND\n2I+0iO8wFXRCPiUobZN2ABwm6mrfxXJxzpe7d0tefr8MoQGSGi5qsqRCU8JAYwunz9mytarRsIRJ\nJitHyxRRIvpa57vMzxuTHQsGqYBFIxdis7iL9yKKbQ2doFfRiJqyUacu0A6XO4aLuH0BIIPmdJcp\nABMBez7GwePxlIQXez2eitMNCUv2N+wcEYGIlkF5PB7PIgQEvPtNDfRiujTD8Dxml8r+wV+k+OVP\nTfD3vu0eAMAvfep4qdcapMDf+b0hXr5n8M0Phvi6+wNYEK43Lx+MC1dWe8E7rGWL3NahYTW3MWFa\ny7FWYOjExbwu1uiy6RvjLDfA5C4/qJjcysvABqke301NJha5s4yTwq2qQ6T5nUcpcJwCsdlerMMs\nxhi0Q42WiOx2S9pYVCTT82V3N02Fxl2FkrplICJEVjDKgMaGng6LsrVJJsAWytbWwRDBkrp7u9Hy\nv1+sbNu7xG3fsJhGNTgWONFzqLukQLkIRYHi2Yzkq8ogF9vXyaGe6/YtwqRFEFlGs6S4BwPsNO/c\n4/FcLbzY6/FUnHZICGs2C58w8MU+46u+nM3j8SzI9z8e4762RWsFR6BjwY0x4wt9xtfsW3zzQyG+\n+aEQwBBjLGfze911i8euWbRCwtseCJGx4He+kOJbH17sdVzekH5Rcc4syZJFbutgjEEzUHdXGWVH\noQGSkmKfIwNIYGBIXYZmjgBkSZBteB5RRMCiYlRdKLSbdgAMHJBM+FKhaRNE1mDoNB96Gw612Uke\nYLeuXmCmpI0Jpi6zBTmFm7XsKIckYwzdiT7W2mLZ2jq0AkI/5ZWOR8Pq75pLToJigmTo1OG8F24u\nzscYA0O6raIEbDYyIjRahHcVSHPhvFfSeHDW7TvJBJEhMM7EPQRYK+eZSM8dj8fjKQMv9no8Feeg\nQajTBLyI4HDC+NWnJ7veFY/HUxMe7hq8+d4ABzEtJQCwaOnaIBX8609M8IV+hr/3TW0AwK89PcG3\nv7oHt2CiLAH4K68M8R2vjE/9WWAIf/nlkWbKMl/qGhxlgmiJh7VJpo6rbdEIDEYuwzjT1vt1sETI\npBzllYgWchSGhjAuy058wb5kgiWnCXaLXjd6XHoRoZ8IDhNBN7r8nC2bvRA4TAVpJhubrD5VXiVA\nO1zPwVcmlgipCMIKxhNchMmjWcYZ0FrzUDIzRlk+mUW6Qq2xg7K1ddBoC1op2iLIxewk40vP1BLg\nZAAAIABJREFUy9iaUlZaLEI7APqpfncmmTq5dXJS32ezZo70eQxSQbyhjF1DNP3utJiJe3DqBB+k\nGWJLK7l9i5UsnivIDleceO5cvNjr8VSca40qppnNJ2HguWPG5w59hIPH41mM1xxYNOziQm9RfHM4\nYXzgSyl+7ekE4wz45+/qAADe/f5jCIBvffViT9CxBX74axt4/HqgmbDhiSghog6hkRP0U6BheW75\nk+SZoa0F764ca4TDtqN6WiFh6IDGms3shrD17ydLKlRskpCANJPaOawM1FkeWcJepMVXl52zG9kP\nY9CwjGMn2C/ZwVlc+0V5VTuktZx0myA0QJqhlk9ZzYAwSBmtFWY6mFldornb30CdomENXLznURS1\njdzy0RZEhNAIxg6IKuTwDowBJMNhomWK7QCIA93Bm2PG2EntYwQy3l40UoEhQiskNEWQMmHodJyi\n3O27zEQU5atbNjyn6fF47gBqeBvi8dxZ7MWmNs7ewtX7bz7lXb0ej2dxWApP4uVkLDhK1c37gS+l\n+O1nU4yzPD+WBf/gj4YLvxYAXG8Q/sevb6ETEQy0HGVWmCiWbwYhcCuRC33CST7HJaCFCr4mmWaM\nbnupfZwvtR84QWeNB3tLAIhKX/Z9EWYLwpElTGMB6oQ1JwIBEaEdEqK8KGqSbTfWoRkYjB1jkAo6\nF8Q5qLtQsBeeP9kjubBPpNfUUaLlVc2A0AgqpKLNEJh6nj+ATjwNiRZaxVDgWGMasrxsrWGB2NKV\ncIjGVlcSrBzlwOXHYqxLNyIIcFuRYzvUCJiQZaXc/KowdMsVpJaJZl/rhBvnETOD3O0bLeD2JSKw\n6OTCIN3ijns8nitJdb55PB7PubQCQl3ul8eZ4HOHGZ49qulTjsfj2QmfvJFhkslU2DkPEcHRhPHi\nWMeXdkh45yPRVEhiAX783w3wwnBxqffVBxZ//5vb6ESE0Nwu9M6iIprmGs5j5FS8PU4YtxJBckG4\nbOFQXNQFXDadAEhZhZpVKY5VSbG9C1Ecfl5jvy/DmsUnH6pEaAiOb/+zXn6NHCay1ue9LN0ISEUd\n7OeRiWDgZCqKnMWx4FYi6Kf6M4eJOvZ6EaFR4bXOa6aj7BTKi9PGl1zUzIxRyrgxZvRzUaobGvQi\nQjMwV0LoBdSxaQCMlhjkJM90PU6rOYoExtwm9ALqPm1Z4DiVaZZ83ShW4rQrkMFjiNAODfYj/bdj\n4DAFbk34wnsDFp/b6/F4yqG6d0oejwcA0AzqcaGKCPqJ4D2fSXa9Kx6Pp2Z8acD40AsONye3C76S\nZwu+OGZ84Msp3v/5kzHmf/n3Azx/vJp49S0Ph/jJNzcBAC2rrfAXOUZHTmBpfkM6iwpReyGm4ppc\nEHLgcvF4V8JQ4ULur+ke0izjtXdn8e3ln9E52mBpFJ/JRZMPVcQQIOfI1IZU8A0toZ8Cky19YIHR\nIq7j9Pzrup8ILAGdkDBhdeYXpJmKvFHuVj5MBIawk9K5Vanb+VPQDGiu2MfMuDVhHKbqXm6HhP2I\nsBcZBGa5zPW60Ay0uO4itOhMcJRP9A3yc7dXo/MVAOLAoBUAAydbGyfKJMkLUredU34R6vYl9GKD\nvXxieeCAG+MMw7wAcBaBz+31eDzl4GMcPJ4K08yv0DrcPI8zwbNHPqvX4/Gsxv/7iQn2Y4OX9wya\nlhAHBBHgKGGEVrPLf+FjE9zfNvjsrRE+eWN1P+nbHwzw1x89KWJLBYgv+PnCLdS5wC2UMADRB/vi\n4e0iF/DYaTf9YQpYYnSC7YkCzIwkE7RDg4ETjNzqma6WgAtMShvhJJt2Q69vDAAGSx5VURMszb9f\nICJ0QhXxBk4w4e2cc02rUQ1nCwHHuZO/E+oESkCMfiroRRpvMsp0EiYODJDqZ9Hddrj1ihhjAKnf\n+VMQ0MXlYgLNNQ9qvNR/GYrM4fOiLTgvCkwynWgJiNANUSmxcVlia0BgDBwQGKlVqd7IVTtr3ebx\nOq2ZbF+NfNFsXz32uurGc8WowbO85+rhxV6Pp8Lsx2arjqlVKRw6v/Jpn9Xr8XhWI2Xgn35whIe7\nBq+7bvFQ12DoBB97KcNb7w/xxnsCNAN1AX9psNo2DAFvvjfAl4c6sH70RYdf+uQEP/0NLcR2fk6h\ny91CoZmvLo6dTAthRpkKkvOWMhficTfUtvfjVHCYAgEx2lsQ4CZcFAipq/I4FYwzRtNqZucy2w+N\nZlpuk9ls2k2hZXuETejJi+Q5n/154PKJ3+JvM2bYOZ9hZAnWAP1Ez7m9cP7PloExBu1QhaPIqjte\nBTJ1hRbnWjvQTOyjBGAI2gFNhcb2RbMmFUUgYGzm/Nk0RbnY6JxyMWMMRDJkQnfMQ6RGWwiGDtiL\nChevCovFLXrL6lhYJxfvRUTWYJxprjsgCAzQqrjdtFhds81itlWZzfYtegiGDhg51szvmsZoeDye\nanGnfE97PLWkE+2g6nwFBk7w8a9m+GK/Bsq0x+OpNF/s86mx5JGuwRvvCfDZWxlGbvXXbYfAf/fG\nJh7ZM3j6ZoZ3v/94uuD95z86xo880cDdjfOjHMbZxYU1Rft3nD9kTjJB5wKBKp0uNdVf6MUEx7r0\n9zAVRIYvLXJZFeaicd1My+f2I32P4yzPphTOH0RvL/E5iyF9yN4moSEMN5yHafKinFURUSGIRZ3P\nKQuymSLCTojbzikVmPV3HKugpFKLivDtS8r0iAgEgWMVxOdhidCLNF7hKBW0LKuDdkNE1mCUMY4T\nwV4EDFINOJl1jRpj0I0YxwnyDO1qC0uXQVjv/NkVnOfNFs7C8yYmGpYwcoK4wg7KsmkGhOOUcZzy\ndPyOFyjcqjOtADhKdIJmkgENK5XOYk4ZgAjMBZOyVYTyb4VepJnrmpm8453yeDxXAi/2ejwVhlB9\nrVdEMEwFv/q0d/V6PJ7y+Ztf2wAA/B9/Olr5NR7oGPz4kw3sxwbdkPCqA+BN9wb44FdUPX71gb1w\nrI0tYZBn6533YD/JVLwyxsAxg4guzNw7TygJDKEXE9Jc9E0yQWwZjZLFhMLVO7sinojQDAjNQEuz\nHKv4m6QAoIpjZFXMPrs8+aLogE0xL5u2TAIDJCxoLPgtnLJmdqbZiUALnIhloQFio0vCB6meM5kA\nLlOX9/Td5O40m/+ONSoWJwvOpRJpnvFFsST6c4RudBLrkLIuI96UcNUNNHd3ktHU1X6W0BgcNDay\n+a2j6RP1EEQlv+aHhVNVNEZs5AROdEJolobVcYSl2uJfmQSkAn7K6kKPbL0ExVUIjMG1/Hq8OWZM\nMjkVxVI1xk4Q1eB6O8vsSqDQ6DeBj3HweDxl4MVej6fC1GHl4jB39b40qqGFxePxVJ5//CcjjJ2s\nvGw/RIr/4S1NHMQGoQFuTQQvDgXP3FTrzDc+EOBbHw5xfY6rFzjJbJww0DwzLosIEtZsUiAv1BTB\nYQIEpA/Hs4Io547P5hytIDTqunRMmq2aCRoBI14yXuE8mBkjp1m9896rJYK1KnDfGGe6dJeAiZM8\nlziDIUJcRD4U72uOEL4JtiEwW1queO44FRioyBeQnjPzjkfDci6wAiGpCBxYwALnutIScJ7reDkh\nLScURFbPz36q52wv2sznaIxBK2AM84mROmeaLoKlE4G+iG3R972Z8/YwP0GaAU3Hq4soXOTj3MV7\nnlM1zceLMDr9WkWm9TgTtCos/pVJEW2RMs7NMb7qtAJgmLt7q9gjUqykiGuowZ9dCVQUtNUhxs+z\nBBW8bjxXHy/2ejwVpupLAFnUgfbrz3hXr8fj2QxHyeoDYYQULSToNgwMAS+NGX/+UoZf+NgYKQOP\nXbP43kdjXG+YCx1q6nwFhilPizMLdGn+ScyDyd2JKpQA/ZRBIMRWxZR0psjtou2FFugZTEtcxk7Q\nDBiNNZbbT1jF2UV6rjh3KMdW96dhaZpjO5mJfBDhuUL4pig+qU0KzKEBxksspdVYAixUdBcHBvES\nd+CFnrZI1q81hPGSjXk2n2BQwVfQDs8v5lqXODAYuOyOEAgDo9fbUcL5RJWAQNiPN/PeiXQsOk71\nejSQc4XfjAWjaUyDwIDmOlWbAdBPzz/vIgNMMhUB7xRiS0hYtjqxVRXiwGDoMqRMGyvGXIez0Uh1\nIc2/Zwutl4iQsU7a9Ne49/F4PB7Ai70eT6UZuk0vVF2P41TwoRccXhhWeS89Hs+dyCNdgyYStANS\noXfEeO/nErz32RSARjv8yBMNHDTMQm3jkQGGRJi40/mmY6eCydmH/9AYhJEKkgkDI4dpkdmi7qOi\nxCU0QJKLviOXoRUS4hXEuMtcvbNMOI8SmvlZIkJAJ+5EzhvFC/F3nGUACA2j73FTgkiRTZuyumI3\nQfGyi5apGdpcaVzhpBRcHu1kV4z6JyJ0QxW4Bw5wzGiVvLyIpxEnd4DYm38OBkAjAAIiHM4RTsug\nFRD6KWM/IjBUiC2EX0s60TTK7ykNFosiCPIytkLgY1aH+dgBIKBxZ+mdCIyOOwnfee+dmWFIz6Eq\nRiUMa5ohfZzqvcXsmMDQSZR+srv98ng8VwMv9no8FeZLxwxLm3s4WAcWdZr9+tP+bsTj8VSLr78v\nwPc/HqMTECJL0wKxT95Qq2YvJvzkk01ci82FxWuzqLtVG7MLV6aIIBUVTuZhjEHDqOBTiCXLCpSF\nwzbK3aZDp47aZVy+Y8e3ZfVexCTDpQ6uIs4hnnH9JiyYZMCYAZEModGfCahc8XfRbNpVMcYAkiET\nwiJG1MjQVMzfBCICFp24uAijCv1K7sMiuzk0gn4qSCaMvbC8z62Il9iG1pvlkyyOdbtEwMGmZgbO\nwRiD/dtOTgaLCvJlo6+pKwfiwCAwWkrp8hUGw5QRrVAoFluNkymEYwLQDAnxAlERV5HY6oqGxh30\nBD1xjKHT55DOJSWRu8DlBanzopGqysSdRK+cpRMRvuKNNB6PZ03uoK8qj6d+JAzcHAsOYiz0sLlN\n+ongP37F4dAvM/J4PBXBEPDXXhPhmx7QDN7CsWtIRbIn7gnw5UGCv/XmJvYbtLRDqWEpz7hkhMbA\nCQCRhct6jDFrRR0UuZHjTJeJjzPG2AGd8PIM1JETtBZ09QIqLkZLiEKzrt9WkAvhuet34Iqf0uzh\nMkrnls2mXQXB4nFKhjCdVNgEBHUOX3bjrnEkml+56hEOiliHRGMduhGXkrGbyubylpkZA6elaMXr\nE/TeqR2qK37Xy++LCZFNaFJEhNgIhhlORYQExqAbrf66DaP3oq3QnMrovlOJ8++AXZ9L28Axq7uU\nTj7/Kgr8g1RKn0zcNMwqoLfD2yOkDFBJUd2zJhW8djxXHy/2ejwV5/NHGV62Zyt1sbJoadBvfsa7\nej0eTzX49peF+I5XRggIuHZOBm8nJLz9oRCPXrO4p2XQXmEGrRBUjlPgIFYBddtL0o81hQL9FIAI\nDBH6KUDguaLvyKm1cVFXr2P9+XUciEUERZS7fllUnB1ngknu+g2MOgQvKjOb//oabHBjrIVxllRw\nJei/p/9gdRHAEsHxYsuWN10aZ4iQLWgLJaijdY14Zxgi7EXIc6eBpj3fSX5zwoAA3ejyCYckU3Fj\nE0wyFcPboVHh58znMXSCDKsL4GUQGEI653wqStxGTq+VXkRLnU9ZXlCFkiccrDVAyhARGKqPmLYp\nTB4hM86A1hU9HClz/j0j+Tiw3Lm4TVIWMATdqjliLuEo1az088aCwBDaXuz1eDwlUCX9yOPxnMOn\nbmR48t5qZVEdJ4I/+bLD4cS7ej0ez24JDfCDb2jgtdctepGZ20TfCAzuIm2PX1ZImaUZECaJYOy0\neKkbrvsOFsex5rbuhZqdmBFhLyQI1N1UiL6zS++ZGeMlXb3jrFzxknIx1hqgEdDU9Ttw6kREBkBY\noyrsfNHQMcOxinopAwR9KGZRsSsT1bqKb6aTb6jcAiwnOfgEOiUKnycSB0bdqItQaP6bcvwtuy9l\nJEoQEVohIcwEx04d7e3g5NwapqzuWauCcMPy3IK6QbpcjMiyBFajQ+bdKxkAaQac1ZqZGaMMmMwc\nMIK60+N11PLz9pHUJTtLlkdiJXnBVGR1gmGRIiy9jk76HQjYiEjUDHRMaNjqRYrtgmZAGKaC1hbH\n/k0zzZbPSykjo7nzGuEh6EbV+9wlL4mOze2Z+VVmlOr3+Dz3bmiAlhd7PR5PCXix1+OpOJ+8kSET\nqUxur4hgzIL3fs67ej0ez24JCPiptzTxUMdiP75cwI3s8tENZyHS9vqBk623fx+nGl9gDaEz8/BN\nALoRIcsffg9TwBKjE+RFa0uIbMyMlAXdDTkwAd0fQyqu7Ucq1qYCTJy6fiHq1m3YE9fvJGMM3Yko\n27CEwGDGWX3+5yq505EBiKgwXgjCsyIx8j8/L7bh1oTz/VbRUF3ENP1/milFSzeUIxwsEVsRGjol\nXq5LaAl7poh1AHoRI4OeW92Qpo7V41QwznSygaDHPM1UKATpz5Z9H1PkYI+cuhDn3SsF5uT46TkO\nnWhAXlgWmqlgn7FOQoyycjOLI4vc1S55aeOJSNsKgDiPgxmAp0VYkk9Q8Mw5K/l5WwjEDUulRKPM\n3W8DjDJZOL/6qhMaALS5iZ1tM3I6IUikmc6xPblObX7d3yo5v7sMEtaVho0KmWEuY5Qyxvm4OS8S\nJTCo1XvyeDzVxYu9Hk/FuTEWfOpGhvY91VjWM3KCZw8ZXx17V6/H49ktb38oxAMLCr1lElkVt5Js\ne9ssXL0XOX5svuQ2y4W3w3wpbitY3NU7ygAD2ng8RcoC7RLLXb/QB1xdzk5IMsEoU0GuiHwAgP0l\nS7amogUwRw+eLxIL8uXxrMJlIbQ51ggJFYbl1HY2ddRCUxyLyyd+7Qbygy1pju9xKtOs/tienCeh\nOfn7o/Tk9whaZtWw5V6jzIxhBqSZHo/2JZmioSEkGaOfMFye6xtb3a+zoktoCJEVHCX6Xm4vWluN\n4sy9leiJFM8RaZsWOGTRiIwCKYRhnWSwdFog3iTGGFhSAbqKDs9tY4hgoONTuzra59IwM45SHcHa\n4fmrYorr/qjk/O51EREMna7UqZIAfRHMJ0LvRd+vgxR4cbThQHrP1qGanKeeq4UXez2eGvCbzyR4\n9MCiFezW3SuiAsK/+fRkZ/vg8Xg8Bd/wQDB1y8Vmu+NjMyAkLEgyRmQ3fxPfz129ixQkWUPoxYTj\nlOF4OVdvsmFXb8Ekw7n7RUQISQU3QEXLlAljJ1vNWyWi3EVMuw16zVFBI0PCBEsyjZ4475zfVH4w\nEaEbESZOhdYJA40Zd2OR77lJUmYMcoHKAOhEmtF72fsNcgu2LPg7RWbxYVLeNW6MASTLreCERnC+\nu88Yg17Ip39vxzQDXVlQlVVmu6YZ6PjarmmUAzPjMNUxpBfNd5kCep3sRSpC9hNBO9zOd95FjDNd\nWdMItreyZl10IhWXTqQ6EfxF34u9Ho9nfbzY6/HUgOeOGc8dM3qxQbzD+5qEgS/2Gc/5mxCPx1MB\nPvyiwxf7jLc9ECIzm8mrnIfGDEjeBL7Z5byOdcxdJsePmaeFWFVz9Rau2UW+z0zuwKxSbv2uCA1h\n6ASYLv4HZpOJqfhnw/nBcWAQWpm6fBd1+2WsURyzub+LUEQ1jJ0ARAiNim12CdHREOEgXu4c0nNP\nMEhxaX7uovRyMfxWIjoRM+d1qyDwzhIaA5FsoSzhO4HCBZsyI6zYZ3UZjhn9VIXHRcvXiDQ6aOwY\nAwdkMj+fe9OwaEHevMzbKjL7fXwRIoKAgC8P/HOWx+NZHy/2ejw14TefSfBgx+Ce5uIP7mUiIjic\nMH7tae/q9Xg8u4cAfOcrT9ZXl7xqfSEaljDJgKEDOtHmtnOc5tm1S4z9E9afX87Vq0tMN43Lg3Pt\nFvOOrwKWAAdgP1fJOZ8EYGghWxEzwfnPblIwNETohnru91OgFTDiOW6/ogStKCcbZ0Drgl0rxF3H\nedxH7rJuXRLVsAmaVvOPhymjVYLjvfhMLDGcCKKNBX+UT2xpmiV8p0NECEgwckC4wbG/bBwz+okg\ntIR2sHy0SiMwsEYnehwzuptqXLyAkdMv+127i5fhKM1XrVyyy5kAhxOB80l5njuA7/3e7/0eAO8A\n8EYATwDoAvhXTz311H+94O//HIAfyv/31U899dQzc37uPgB/G8B3AngEwBjA5wC876mnnvqf5vzO\nkwB+CsBfAnA3gFsAPgng55966qlfPPOzPwTg6/P38QYATQD/61NPPfXTC74PAvA+AO/K/yh86qmn\n3CK/exle7PV4asKnbmY4SgQHcXkOk2UYOMGnb2b47KGfbfZ4PLtHAPzd3x/ge14T4w13BzsRIIgI\nnRDop7LRsh4RWco9xqz5mp0lXL3FyD7JZGMxAAVJJksJ1x5F5HQm8DQ+Abu5odesXEKYCQZOkPHt\ngujscvFuqFnMs0VzxSRDmv9TnBYELaVrhxq5YDfsNp8H5aJ2PxWkE122X0ZmaWi0vK5OT2KaJazO\nSn/9qrt802N/mSQZY+A0K3qdiYvQaKzDLorbinLDbo3iM4ap5u33FnBRZwI8e5RtZ8c8nt3z01CR\n9xjAcwAeW/QX/3/23jxOlqys8/6eE0sutd17u5tuoBuahm4E2VtARATEZVx4BxVjlHFchhkdX3HB\nbRxwcBlx0EHHcdxeN5wZt4lRgXEBZxpEQBAEBZpF6IWmoen19r21ZmYs53n/eCLqZtXNqsqqyvXW\n+X4+9cmqrIjIyMiIE3l+53d+T5IkL0SF3g1gcZ/lng38GdAG/gJ4PSrGPgb4BuAisTdJkpcB/wU4\nB/w5cBdwBngCKhj/912r/BywUi3/WeDRw76PipcBz0dF6OYh192XOfqK4fF4fvejPb7rKU2umLC7\n11WFENKPe1evx+OZHbISnnhFyEJ4sGNmXIRWi/VsFLA8JoeXAIfRumpX72GOSWgti5FjI4PMCc1A\nRl5UqyZ3w0U4eHbiONx5MCniwGCNCqJF5lisYhq6hQ461Fm+xhicQK90PNh1O4TdwGgMS2j0Pc5S\nLmzYV3xuPQeDI7YMLK42/Db1Op0nNHPY0SuFVjg7n8+0qMcXcweHrBs5cVToVad6cwTxC9Mq3NYr\nBWQ0Ay6TICt1lsJStH8u8vbyTo01nkuQGbqnzRAvR0XeW1GH718Ns1KSJFcAvwH8T+Cqat1By10F\nvBFYBZ6Zpukndv3/omGjJEm+DPhF4P8CL07TdP2gdVDR+GNpmn4qSZJvBV43zPuotvdY4GeA11bb\neeSw6w6DF3s9njnilnMlt5wrWYrNRL9ob2TC++4ueKDj5xV5PJ7Z4PrTli97ZIwTIbLjESWHZSEa\nn8MrKx3GGIY1Lh/F1VsTWcvppopxW7nmIrZDRjp13tV5vfPRV58pRGZT7IULgqiKP4BRJXMhstv5\npqCO1jjQXOhZFHb3ohastWCgFojqVcLvQsShc1vrr3DzVvCsGWpxxWYwX/s9DkyV6dwpoTHDPepa\n6G0Hmrc9KqZRuK1bzs9AYeEcm7nQDofLwRcRCgcfO+vFXs/JIE3TbXE3SZLDrPrr1eN3AX+8z3Kv\nAC4Dvmm30Fu9fj5gnf8EdICX7BZ691onTdM3D7PTu0mSJAT+B3A78GOo2DtSZvjW5PF4BvGnt2Vc\nfzqY2BdtJ0KnFP73bdnYX8vj8XiG5TGnAh5/eUgz0KmPiIy9sNhe1O7eTgkLI+7rbhYqjA47bfoo\nrt7dNAItBtotHFu5o2MMrRGJvoXTTq31eb2HphSdQj2rGDRmqldpFacGTFuuox/mlf6CgaUInULY\nyAEci4cQfW1V8KwQwzwdjsiq4OZRGlWm86xGOfTKuiiiGUvU0bgLt5VOZxa2Q509IKijfh5Yz7W9\nHlZgV9Oy8cYaj2cfKufsi4AXpWl69gCR+BvRaIW/TJLk8cAL0DiH24A3p2m6sWvbTwCeBLwBeDBJ\nkucDN6JNzweAv0rTdJRzcn4UeCrwrDRNe4cUvIdi9u5KHo9nX+5cd3x63W0XOhk353rCWz+VsZb5\nLx8ej2d2ePdnCz5wX87dm47bV0vWcyErp9dOtUPt9B8GrdDt6BX6U7idDXunCjc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LoUHw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X7XJacoF2goIt2Fa0eMkOh+NziqIjKNimr8bSSKtkgaud5qvbi5J3vSDHxvy58R2NUFpzpqo4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TxA67npZHFmFvW71lqD5BhRdOvZmlaKzJimDweN2Eo3TysyI2wraaqp7eBpVvAN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\n0RYSJ/ztPj/Jm+pzklJKKaU2GQ171ZZUjfzETLhBx0fNTMicnBX09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CeK938j8BX8lO1F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\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znYaqeEVERERERERWTRmEdIPCXuHCjQGV0A3cIm3ziv5ejzcG9jGuFW9FP+8t+zI+cHsT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\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 701,
              "height": 591
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qszr8S5vCG4E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# years=[2017,2018]\n",
        "# for year in years:\n",
        "#   figure, ax1 = plt.subplots(figsize=(12,10))\n",
        "#   ax2 = ax1.twinx()\n",
        "#   sns.set(style=\"white\")\n",
        "#   sns.set(style=\"ticks\")\n",
        "#   sns.lineplot(nyc_df['tpep_pickup_datetime'].loc[nyc_df['tpep_pickup_datetime'].dt.year == year].dt.dayofyear, nyc_df['rain'].loc[nyc_df['tpep_pickup_datetime'].dt.year == year],ax=ax1,color=\"coral\", label=\"rain\")\n",
        "#   sns.lineplot(np.arange(1,32), nyc_df['tpep_pickup_datetime'].loc[nyc_df['tpep_pickup_datetime'].dt.year == year].dt.dayofyear.value_counts(sort=False), ax=ax2, label=\"pickup counts\")\n",
        "#   ax1.set_ylim(bottom= -1, top=50)\n",
        "#   ax2.set_ylim(bottom=800, top=3500)\n",
        "#   ax1.set(xlabel=\"day of year\", ylabel=\"rain (mm)\", title=\"Precipitation vs. Pickup Counts in {:4d}\".format(year))\n",
        "#   ax2.set(ylabel=\"count\")\n",
        "#   ax1.legend(loc = (0,.95), frameon = False)\n",
        "#   ax2.legend( loc = (0, .90), frameon = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoKEi22PGX9y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# years=[2017,2018]\n",
        "# for year in years:\n",
        "#   figure, ax1 = plt.subplots(figsize=(12,10))\n",
        "#   ax2 = ax1.twinx()\n",
        "#   sns.set(style=\"white\")\n",
        "#   sns.set(style=\"ticks\")\n",
        "#   sns.lineplot(nyc_df['tpep_pickup_datetime'].loc[nyc_df['tpep_pickup_datetime'].dt.year == year].dt.dayofyear, nyc_df['temperature_avg'].loc[nyc_df['tpep_pickup_datetime'].dt.year == year],ax=ax1,color=\"coral\", label=\"avg temperature\")\n",
        "#   sns.lineplot(np.arange(1,32), nyc_df['tpep_pickup_datetime'].loc[nyc_df['tpep_pickup_datetime'].dt.year == year].dt.dayofyear.value_counts(sort=False), ax=ax2, label=\"pickup counts\")\n",
        "#   ax1.set_ylim(bottom= -15, top=50)\n",
        "#   ax2.set_ylim(bottom=800, top=3500)\n",
        "#   ax1.set(xlabel=\"day of year\", ylabel=\"avg temperature (C)\", title=\"Average Temperature vs. Pickup Counts in {:4d}\".format(year))\n",
        "#   ax2.set(ylabel=\"count\")\n",
        "#   ax1.legend(loc = (0,.95), frameon = False)\n",
        "#   ax2.legend( loc = (0, .90), frameon = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppnXWp92TpQ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# years=[2017,2018]\n",
        "# for year in years:\n",
        "#   figure, ax1 = plt.subplots(figsize=(12,10))\n",
        "#   ax2 = ax1.twinx()\n",
        "#   sns.set(style=\"white\")\n",
        "#   sns.set(style=\"ticks\")\n",
        "#   sns.lineplot(nyc_df['tpep_pickup_datetime'].loc[nyc_df['tpep_pickup_datetime'].dt.year == year].dt.dayofyear, nyc_df['humidity_avg'].loc[nyc_df['tpep_pickup_datetime'].dt.year == year],ax=ax1,color=\"coral\", label=\"avg humidity\")\n",
        "#   sns.lineplot(np.arange(1,32), nyc_df['tpep_pickup_datetime'].loc[nyc_df['tpep_pickup_datetime'].dt.year == year].dt.dayofyear.value_counts(sort=False), ax=ax2, label=\"pickup counts\")\n",
        "#   ax1.set_ylim(bottom= 40, top=150)\n",
        "#   ax2.set_ylim(bottom=800, top=3500)\n",
        "#   ax1.set(xlabel=\"day of year\", ylabel=\"avg humidity (%)\", title=\"Average Humidity vs. Pickup Counts in {:4d}\".format(year))\n",
        "#   ax2.set(ylabel=\"count\")\n",
        "#   ax1.legend(loc = (0,.95), frameon = False)\n",
        "#   ax2.legend( loc = (0, .90), frameon = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZatRrMEvRiN",
        "colab_type": "text"
      },
      "source": [
        "## Produce the target data for training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UngX8rB20oB",
        "colab_type": "text"
      },
      "source": [
        "####Check that all is good with nyc_df"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzJtIPhA28uW",
        "colab_type": "code",
        "outputId": "bb898dc2-6d6f-45ef-85ca-7f5b087aff50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        }
      },
      "source": [
        "nyc_df"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>tpep_pickup_datetime</th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>temp_avg</th>\n",
              "      <th>humidity_avg</th>\n",
              "      <th>dayOfMonth</th>\n",
              "      <th>dayOfWeek</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>2017-01-01 00:00:02</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-01-01 00:39:22</td>\n",
              "      <td>4</td>\n",
              "      <td>7.75</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>27.96</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>2017-01-01 00:00:06</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:23:37</td>\n",
              "      <td>2</td>\n",
              "      <td>12.60</td>\n",
              "      <td>5</td>\n",
              "      <td>N</td>\n",
              "      <td>161</td>\n",
              "      <td>265</td>\n",
              "      <td>1</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>70.30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>2017-01-01 00:00:10</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:24:52</td>\n",
              "      <td>1</td>\n",
              "      <td>6.60</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>232</td>\n",
              "      <td>2</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>24.30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>2017-01-01 00:00:12</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-01-01 00:12:53</td>\n",
              "      <td>1</td>\n",
              "      <td>1.70</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>125</td>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>9.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>10.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>2017-01-01 00:00:13</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-01-01 00:15:39</td>\n",
              "      <td>1</td>\n",
              "      <td>4.68</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>140</td>\n",
              "      <td>223</td>\n",
              "      <td>1</td>\n",
              "      <td>16.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.45</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>22.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</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",
              "      <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>2019-01-31</th>\n",
              "      <td>2019-01-31 23:59:51</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-02-01 00:28:56</td>\n",
              "      <td>6</td>\n",
              "      <td>11.23</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>125</td>\n",
              "      <td>247</td>\n",
              "      <td>1</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>7.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>42.36</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-13</td>\n",
              "      <td>42</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-31</th>\n",
              "      <td>2019-01-31 23:59:51</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-01 00:23:33</td>\n",
              "      <td>2</td>\n",
              "      <td>6.50</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>4</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>21.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>3.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>26.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-13</td>\n",
              "      <td>42</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-31</th>\n",
              "      <td>2019-01-31 23:59:51</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-01 00:30:54</td>\n",
              "      <td>1</td>\n",
              "      <td>6.90</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>158</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>25.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>5.35</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>32.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-13</td>\n",
              "      <td>42</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-31</th>\n",
              "      <td>2019-01-31 23:59:52</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-01 00:34:16</td>\n",
              "      <td>1</td>\n",
              "      <td>9.80</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>164</td>\n",
              "      <td>160</td>\n",
              "      <td>2</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>34.30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-13</td>\n",
              "      <td>42</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-31</th>\n",
              "      <td>2019-01-31 23:59:54</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-02-01 00:12:36</td>\n",
              "      <td>1</td>\n",
              "      <td>4.03</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>79</td>\n",
              "      <td>146</td>\n",
              "      <td>2</td>\n",
              "      <td>13.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>14.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-13</td>\n",
              "      <td>42</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7354109 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           tpep_pickup_datetime  VendorID  ... dayOfMonth  dayOfWeek\n",
              "2017-01-01  2017-01-01 00:00:02         2  ...          1          6\n",
              "2017-01-01  2017-01-01 00:00:06         1  ...          1          6\n",
              "2017-01-01  2017-01-01 00:00:10         1  ...          1          6\n",
              "2017-01-01  2017-01-01 00:00:12         1  ...          1          6\n",
              "2017-01-01  2017-01-01 00:00:13         2  ...          1          6\n",
              "...                         ...       ...  ...        ...        ...\n",
              "2019-01-31  2019-01-31 23:59:51         2  ...         31          3\n",
              "2019-01-31  2019-01-31 23:59:51         1  ...         31          3\n",
              "2019-01-31  2019-01-31 23:59:51         1  ...         31          3\n",
              "2019-01-31  2019-01-31 23:59:52         1  ...         31          3\n",
              "2019-01-31  2019-01-31 23:59:54         2  ...         31          3\n",
              "\n",
              "[7354109 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dPoUMdX3Cmm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cu9LLs6OvRiO",
        "colab_type": "text"
      },
      "source": [
        "#### Produce a 2017 and 2018 dataframe\n",
        "\n",
        "2017 and 2018 will act as training and 2019 as testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-EDh_2evRiO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_17 = nyc_df.loc[(nyc_df[\"tpep_pickup_datetime\"].dt.year == 2017)]\n",
        "df_18 = nyc_df.loc[(nyc_df[\"tpep_pickup_datetime\"].dt.year == 2018)]\n",
        "df_19 = nyc_df.loc[(nyc_df[\"tpep_pickup_datetime\"].dt.year == 2019)]\n",
        "\n",
        "df_17 = df_17.drop(columns=[  'tpep_pickup_datetime',\n",
        "                              'tpep_dropoff_datetime',\n",
        "                              'DOLocationID',\n",
        "                              'VendorID',\n",
        "                              'RatecodeID',\n",
        "                              'store_and_fwd_flag',\n",
        "                              'payment_type',\n",
        "                              'passenger_count',\n",
        "                              'fare_amount',\n",
        "                              'total_amount',\n",
        "                              'trip_distance',\n",
        "                              'extra',\n",
        "                              'mta_tax',\n",
        "                              'tip_amount',\n",
        "                              'tolls_amount',\n",
        "                              'improvement_surcharge'])\n",
        "\n",
        "df_18 = df_18.drop(columns=[  'tpep_pickup_datetime',\n",
        "                              'tpep_dropoff_datetime',\n",
        "                              'DOLocationID',\n",
        "                              'VendorID',\n",
        "                              'RatecodeID',\n",
        "                              'store_and_fwd_flag',\n",
        "                              'payment_type',\n",
        "                              'passenger_count',\n",
        "                              'fare_amount',\n",
        "                              'total_amount',\n",
        "                              'trip_distance',\n",
        "                              'extra',\n",
        "                              'mta_tax',\n",
        "                              'tip_amount',\n",
        "                              'tolls_amount',\n",
        "                              'improvement_surcharge'])\n",
        "\n",
        "df_19 = df_19.drop(columns=[  'tpep_pickup_datetime',\n",
        "                              'tpep_dropoff_datetime',\n",
        "                              'DOLocationID',\n",
        "                              'VendorID',\n",
        "                              'RatecodeID',\n",
        "                              'store_and_fwd_flag',\n",
        "                              'payment_type',\n",
        "                              'passenger_count',\n",
        "                              'fare_amount',\n",
        "                              'total_amount',\n",
        "                              'trip_distance',\n",
        "                              'extra',\n",
        "                              'mta_tax',\n",
        "                              'tip_amount',\n",
        "                              'tolls_amount',\n",
        "                              'improvement_surcharge'])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBlkbg2v4Nrj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_17_counts = df_17.groupby(['dayOfMonth','PULocationID']).size().reset_index(name='count')['count']\n",
        "df_17 = df_17.drop_duplicates(['dayOfMonth','PULocationID'])\n",
        "df_17 = df_17.reset_index(drop=True)\n",
        "df_17['counts'] = df_17_counts\n",
        "\n",
        "df_18_counts = df_18.groupby(['dayOfMonth','PULocationID']).size().reset_index(name='count')['count']\n",
        "df_18 = df_18.drop_duplicates(['dayOfMonth','PULocationID'])\n",
        "df_18 = df_18.reset_index(drop=True)\n",
        "df_18['counts'] = df_18_counts\n",
        "\n",
        "df_19_counts = df_19.groupby(['dayOfMonth','PULocationID']).size().reset_index(name='count')['count']\n",
        "df_19 = df_19.drop_duplicates(['dayOfMonth','PULocationID'])\n",
        "df_19 = df_19.reset_index(drop=True)\n",
        "df_19['counts'] = df_19_counts\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVGV6wirBasQ",
        "colab_type": "code",
        "outputId": "45349514-7081-4009-d51b-9049a6f1b79b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "df_17"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>PULocationID</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>temp_avg</th>\n",
              "      <th>humidity_avg</th>\n",
              "      <th>dayOfMonth</th>\n",
              "      <th>dayOfWeek</th>\n",
              "      <th>counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>186</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>161</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>125</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>140</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>411</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>224</td>\n",
              "      <td>1.52</td>\n",
              "      <td>-1</td>\n",
              "      <td>64</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>1423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>120</td>\n",
              "      <td>1.52</td>\n",
              "      <td>-1</td>\n",
              "      <td>64</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>1783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992</th>\n",
              "      <td>194</td>\n",
              "      <td>1.52</td>\n",
              "      <td>-1</td>\n",
              "      <td>64</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993</th>\n",
              "      <td>128</td>\n",
              "      <td>1.52</td>\n",
              "      <td>-1</td>\n",
              "      <td>64</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>1291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994</th>\n",
              "      <td>12</td>\n",
              "      <td>1.52</td>\n",
              "      <td>-1</td>\n",
              "      <td>64</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>1361</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1995 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      PULocationID  precipitation  temp_avg  ...  dayOfMonth  dayOfWeek  counts\n",
              "0              186           0.00         7  ...           1          6     545\n",
              "1              161           0.00         7  ...           1          6     102\n",
              "2              125           0.00         7  ...           1          6    1020\n",
              "3              140           0.00         7  ...           1          6     276\n",
              "4               68           0.00         7  ...           1          6     411\n",
              "...            ...            ...       ...  ...         ...        ...     ...\n",
              "1990           224           1.52        -1  ...          31          1    1423\n",
              "1991           120           1.52        -1  ...          31          1    1783\n",
              "1992           194           1.52        -1  ...          31          1     774\n",
              "1993           128           1.52        -1  ...          31          1    1291\n",
              "1994            12           1.52        -1  ...          31          1    1361\n",
              "\n",
              "[1995 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkOGgq0LIUOV",
        "colab_type": "code",
        "outputId": "0e454f86-f282-4f3e-cb53-972f90519427",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "df_18"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>PULocationID</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>temp_avg</th>\n",
              "      <th>humidity_avg</th>\n",
              "      <th>dayOfMonth</th>\n",
              "      <th>dayOfWeek</th>\n",
              "      <th>counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-11</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>161</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-11</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>158</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-11</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-11</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>170</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-11</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>250</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>243</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3</td>\n",
              "      <td>44</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>1403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3</td>\n",
              "      <td>44</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>1713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>127</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3</td>\n",
              "      <td>44</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>194</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3</td>\n",
              "      <td>44</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>1209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>128</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3</td>\n",
              "      <td>44</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>1504</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2006 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      PULocationID  precipitation  temp_avg  ...  dayOfMonth  dayOfWeek  counts\n",
              "0               90            0.0       -11  ...           1          0     343\n",
              "1              161            0.0       -11  ...           1          0     109\n",
              "2              158            0.0       -11  ...           1          0     710\n",
              "3              237            0.0       -11  ...           1          0     187\n",
              "4              170            0.0       -11  ...           1          0     250\n",
              "...            ...            ...       ...  ...         ...        ...     ...\n",
              "2001           243            0.0        -3  ...          31          2    1403\n",
              "2002            12            0.0        -3  ...          31          2    1713\n",
              "2003           127            0.0        -3  ...          31          2     821\n",
              "2004           194            0.0        -3  ...          31          2    1209\n",
              "2005           128            0.0        -3  ...          31          2    1504\n",
              "\n",
              "[2006 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWXxikue8M5R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "ff1f7d6e-e3dd-4fec-9b00-7f21015c72e2"
      },
      "source": [
        "df_19"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>PULocationID</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>temp_avg</th>\n",
              "      <th>humidity_avg</th>\n",
              "      <th>dayOfMonth</th>\n",
              "      <th>dayOfWeek</th>\n",
              "      <th>counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>237</td>\n",
              "      <td>1.52</td>\n",
              "      <td>9</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>114</td>\n",
              "      <td>1.52</td>\n",
              "      <td>9</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>161</td>\n",
              "      <td>1.52</td>\n",
              "      <td>9</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>233</td>\n",
              "      <td>1.52</td>\n",
              "      <td>9</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50</td>\n",
              "      <td>1.52</td>\n",
              "      <td>9</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>221</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>152</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-13</td>\n",
              "      <td>42</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>1291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>127</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-13</td>\n",
              "      <td>42</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>1357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>12</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-13</td>\n",
              "      <td>42</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>153</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-13</td>\n",
              "      <td>42</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>194</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-13</td>\n",
              "      <td>42</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>1334</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2019 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      PULocationID  precipitation  temp_avg  ...  dayOfMonth  dayOfWeek  counts\n",
              "0              237           1.52         9  ...           1          1     209\n",
              "1              114           1.52         9  ...           1          1      89\n",
              "2              161           1.52         9  ...           1          1     584\n",
              "3              233           1.52         9  ...           1          1     149\n",
              "4               50           1.52         9  ...           1          1     221\n",
              "...            ...            ...       ...  ...         ...        ...     ...\n",
              "2014           152           0.00       -13  ...          31          3    1291\n",
              "2015           127           0.00       -13  ...          31          3    1357\n",
              "2016            12           0.00       -13  ...          31          3     707\n",
              "2017           153           0.00       -13  ...          31          3     978\n",
              "2018           194           0.00       -13  ...          31          3    1334\n",
              "\n",
              "[2019 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3EyW0yCHLVT",
        "colab_type": "code",
        "outputId": "5727ea2a-e2e5-4ee2-e11e-bf5f7c6b5936",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "\n",
        "print(df_17.shape)\n",
        "print(df_18.shape)\n",
        "print(df_19.shape)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1995, 7)\n",
            "(2006, 7)\n",
            "(2019, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C90yr_4A8ZHA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_17_18 = df_17.append(df_18, ignore_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4EDKi4IL8uHP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "a633ae6e-fba2-461c-f4d0-98209d783784"
      },
      "source": [
        "print(df_17_18.shape)\n",
        "print(df_19.shape)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4001, 7)\n",
            "(2019, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxiMYy3cvRic",
        "colab_type": "text"
      },
      "source": [
        "## Remove unused features before the training "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nF6cne-_vRit",
        "colab_type": "code",
        "outputId": "77a92def-0560-41e8-a913-63ae12f1629f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "#target=nycmodel[['count']]\n",
        "#data=nycmodel[[col for col in nycmodel.columns if col not in ['count']]]\n",
        "\n",
        "x_train = df_17_18.drop(columns=['counts'])\n",
        "y_train = df_17_18['counts']\n",
        "\n",
        "x_test  = df_19.drop(columns=['counts'])\n",
        "y_test  = df_19['counts']\n",
        "# x_train = nyc_df['tpep_pickup_datetime'].dt.year == 2017\n",
        "\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4001, 6)\n",
            "(2019, 6)\n",
            "(4001,)\n",
            "(2019,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_5r8UM8SC19",
        "colab_type": "code",
        "outputId": "69b287c4-16d5-46b6-a75e-44601569c128",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "x_train"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>PULocationID</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>temp_avg</th>\n",
              "      <th>humidity_avg</th>\n",
              "      <th>dayOfMonth</th>\n",
              "      <th>dayOfWeek</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>186</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>161</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>125</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>140</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>6</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3996</th>\n",
              "      <td>243</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3</td>\n",
              "      <td>44</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3997</th>\n",
              "      <td>12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3</td>\n",
              "      <td>44</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3998</th>\n",
              "      <td>127</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3</td>\n",
              "      <td>44</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3999</th>\n",
              "      <td>194</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3</td>\n",
              "      <td>44</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4000</th>\n",
              "      <td>128</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3</td>\n",
              "      <td>44</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4001 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      PULocationID  precipitation  ...  dayOfMonth  dayOfWeek\n",
              "0              186            0.0  ...           1          6\n",
              "1              161            0.0  ...           1          6\n",
              "2              125            0.0  ...           1          6\n",
              "3              140            0.0  ...           1          6\n",
              "4               68            0.0  ...           1          6\n",
              "...            ...            ...  ...         ...        ...\n",
              "3996           243            0.0  ...          31          2\n",
              "3997            12            0.0  ...          31          2\n",
              "3998           127            0.0  ...          31          2\n",
              "3999           194            0.0  ...          31          2\n",
              "4000           128            0.0  ...          31          2\n",
              "\n",
              "[4001 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMlRwUm3SL3P",
        "colab_type": "code",
        "outputId": "195882ce-8ecb-4271-f5b8-298272500c69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "y_train"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        545\n",
              "1        102\n",
              "2       1020\n",
              "3        276\n",
              "4        411\n",
              "        ... \n",
              "3996    1403\n",
              "3997    1713\n",
              "3998     821\n",
              "3999    1209\n",
              "4000    1504\n",
              "Name: counts, Length: 4001, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MowLglYXvRiw",
        "colab_type": "text"
      },
      "source": [
        "## Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cYPpobpvRiw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def linearRegression(x_train, y_train, x_test, y_test):\n",
        "    \n",
        "    #standardizing the data\n",
        "    x_train_std = StandardScaler().fit_transform(x_train)\n",
        "    x_test_std = StandardScaler().fit_transform(x_test)\n",
        "    \n",
        "    #hyper-paramater tuning\n",
        "    clf = SGDRegressor(loss = \"squared_loss\", penalty = \"l2\")\n",
        "    values = [10**-14, 10**-12, 10**-10, 10**-8, 10**-6, 10**-4, 10**-2, 10**0, 10**2, 110, 120, 130, 131, 135, 140, 145, 150, 160, 170, 10**4, 10**6]\n",
        "    hyper_parameter = {\"alpha\": values}\n",
        "    best_parameter = GridSearchCV(clf, hyper_parameter, scoring = \"neg_mean_absolute_error\", cv=KFold(n_splits=3, random_state=1, shuffle=True))\n",
        "    best_parameter.fit(x_train_std, y_train)\n",
        "    alpha = best_parameter.best_params_[\"alpha\"]\n",
        "    print(\"alpha: \" + str(alpha))\n",
        "    \n",
        "    #applying linear regression with best hyper-parameter\n",
        "    clf = SGDRegressor(loss = \"squared_loss\", penalty = \"l2\", alpha = alpha)\n",
        "    clf.fit(x_train_std, y_train)\n",
        "    y_pred = clf.predict(x_train_std)\n",
        "    train_MAPE = mean_absolute_error(y_train, y_pred)/ (sum(y_train)/len(y_train))\n",
        "    train_MSE = mean_squared_error(y_train, y_pred)\n",
        "    y_pred = clf.predict(x_test_std)\n",
        "    test_MAPE = mean_absolute_error(y_test, y_pred)/ (sum(y_test)/len(y_test))\n",
        "    test_MSE = mean_squared_error(y_test, y_pred)\n",
        "    r2 = clf.score(x_test_std,y_test)\n",
        "    \n",
        "    return train_MAPE, train_MSE, test_MAPE, test_MSE, r2\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mxJxHuErvRiz",
        "colab_type": "text"
      },
      "source": [
        "# Random Forests\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xb30wc62vRiz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def randomForest(x_train, y_train, x_test, y_test):\n",
        "  values = [320, 350, 400, 500, 600, 700, 800, 900]\n",
        "  clf = RandomForestRegressor(n_jobs = -1)\n",
        "  hyper_parameter = {\"n_estimators\": values}\n",
        "  best_parameter = GridSearchCV(clf, hyper_parameter, scoring = \"neg_mean_absolute_error\", cv=KFold(n_splits=3, random_state=1, shuffle=True), verbose=2)\n",
        "  best_parameter.fit(x_train, y_train)\n",
        "  estimators = best_parameter.best_params_[\"n_estimators\"]\n",
        "  print(\"estimators: \" + str(estimators))\n",
        "\n",
        "  #applying random forest with best hyper-parameter\n",
        "  clf = RandomForestRegressor(n_estimators = estimators, n_jobs = -1, oob_score=True)\n",
        "  clf.fit(x_train, y_train)\n",
        "  y_pred = clf.predict(x_train)\n",
        "  train_MAPE = mean_absolute_error(y_train, y_pred)/ (sum(y_train)/len(y_train))\n",
        "  train_MSE = mean_squared_error(y_train, y_pred)\n",
        "  y_pred = clf.predict(x_test)\n",
        "  test_MAPE = mean_absolute_error(y_test, y_pred)/ (sum(y_test)/len(y_test))\n",
        "  test_MSE = mean_squared_error(y_test, y_pred)\n",
        "  train_accuracy = clf.score(x_train, y_train)\n",
        "  test_accuracy = clf.score(x_test, y_test)\n",
        "\n",
        "\n",
        "  feature_importances = clf.feature_importances_\n",
        "  oob_score = clf.oob_score_\n",
        "\n",
        "  \n",
        "  return train_MAPE, train_MSE, test_MAPE, test_MSE, train_accuracy, test_accuracy, feature_importances, oob_score\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXU1NGsQY3rt",
        "colab_type": "text"
      },
      "source": [
        "# K Neighbors Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eL7ERNPmY8h0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def kNeighbors(x_train, y_train, x_test, y_test):\n",
        "  n_neighbors = [5, 10, 15, 20, 30]\n",
        "  clf = KNeighborsRegressor(n_jobs = -1)\n",
        "  hyper_parameter = {\"n_neighbors\": n_neighbors}\n",
        "  best_parameter = GridSearchCV(clf, hyper_parameter, scoring = \"neg_mean_absolute_error\", cv = KFold(n_splits=3, random_state=1, shuffle=True), verbose=2)\n",
        "  best_parameter.fit(x_train, y_train)\n",
        "  neighbors = best_parameter.best_params_[\"n_neighbors\"]\n",
        "  print(\"neighbors: \" + str(neighbors))\n",
        "\n",
        "  clf = KNeighborsRegressor(n_neighbors = neighbors, n_jobs = -1)\n",
        "  clf.fit(x_train, y_train)\n",
        "  y_pred = clf.predict(x_train)\n",
        "  train_MAPE = mean_absolute_error(y_train, y_pred)/ (sum(y_train)/len(y_train))\n",
        "  train_MSE = mean_squared_error(y_train, y_pred)\n",
        "  y_pred = clf.predict(x_test)\n",
        "  test_MAPE = mean_absolute_error(y_test, y_pred)/ (sum(y_test)/len(y_test))\n",
        "  test_MSE = mean_squared_error(y_test, y_pred)\n",
        "  train_accuracy = clf.score(x_train, y_train)\n",
        "  test_accuracy = clf.score(x_test, y_test)\n",
        "  \n",
        "  return train_MAPE, train_MSE, test_MAPE, test_MSE, train_accuracy, test_accuracy\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9-YVylXvRi5",
        "colab_type": "text"
      },
      "source": [
        "# XgBoost Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7PJ3VZTvRi7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def xgboost_reg(x_train, y_train, x_test, y_test):\n",
        "    #hyper-parameter tuning\n",
        "    hyper_parameter = {\"max_depth\":[1,2,3,4,5,6], \"n_estimators\":[40, 80, 150, 300, 500, 600], \"objective\":['reg:squarederror']}\n",
        "    clf = xgb.XGBRegressor()\n",
        "    best_parameter = GridSearchCV(clf, hyper_parameter, scoring = \"neg_mean_absolute_error\", cv= KFold(n_splits=3, random_state=1, shuffle=True))\n",
        "    best_parameter.fit(x_train, y_train)\n",
        "    estimators = best_parameter.best_params_[\"n_estimators\"]\n",
        "    depth = best_parameter.best_params_[\"max_depth\"]\n",
        "    print(\"estimators: \" + str(estimators))\n",
        "    print(\"depth: \" + str(depth))\n",
        "    \n",
        "    #applying xgboost regressor with best hyper-parameter\n",
        "    clf = xgb.XGBRegressor(max_depth = depth, n_estimators = estimators)\n",
        "    clf.fit(x_train, y_train)\n",
        "    y_pred = clf.predict(x_train)\n",
        "    train_MAPE = mean_absolute_error(y_train, y_pred)/ (sum(y_train)/len(y_train))\n",
        "    train_MSE = mean_squared_error(y_train, y_pred)\n",
        "    y_pred = clf.predict(x_test)\n",
        "    test_MAPE = mean_absolute_error(y_test, y_pred)/ (sum(y_test)/len(y_test))\n",
        "    test_MSE = mean_squared_error(y_test, y_pred)\n",
        "    train_accuracy = clf.score(x_train, y_train)\n",
        "    test_accuracy = clf.score(x_test, y_test)\n",
        "    \n",
        "    return train_MAPE, train_MSE, test_MAPE, test_MSE, train_accuracy, test_accuracy\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVkqYWGwvRjB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Y8-kEzXvRjJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFSi7TPfOIE4",
        "colab_type": "text"
      },
      "source": [
        "# Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYmNr3pEvRjM",
        "colab_type": "code",
        "outputId": "b49cec27-d166-4229-81d9-e6517a44f14f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "#Linear Regression Model\n",
        "trainMAPE_lr, trainMSE_lr, testMAPE_lr, testMSE_lr, r2 = linearRegression(x_train, y_train, x_test, y_test)\n",
        "print(trainMAPE_lr*100, trainMSE_lr, testMAPE_lr*100, testMSE_lr, r2)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "alpha: 1e-14\n",
            "71.79861822939507 1410132.09243718 81.74325932881624 1046655.7709226372 -0.07540069576668373\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FelWI-QeEjdk",
        "colab_type": "code",
        "outputId": "3eefb231-041c-4575-b738-aa92b0056538",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 999
        }
      },
      "source": [
        "#Random Forest Model\n",
        "trainMAPE_rf, trainMSE_rf, testMAPE_rf, testMSE_rf, train_accuracy, test_accuracy, feature_importances_rf, oob_score_rf = randomForest(x_train, y_train, x_test, y_test)\n",
        "print(trainMAPE_rf*100, trainMSE_rf, testMAPE_rf*100, testMSE_rf, oob_score_rf, train_accuracy, test_accuracy)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n",
            "[CV] n_estimators=320 ................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................. n_estimators=320, total=   3.3s\n",
            "[CV] n_estimators=320 ................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.3s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ................................. n_estimators=320, total=   2.9s\n",
            "[CV] n_estimators=320 ................................................\n",
            "[CV] ................................. n_estimators=320, total=   2.9s\n",
            "[CV] n_estimators=350 ................................................\n",
            "[CV] ................................. n_estimators=350, total=   3.1s\n",
            "[CV] n_estimators=350 ................................................\n",
            "[CV] ................................. n_estimators=350, total=   3.1s\n",
            "[CV] n_estimators=350 ................................................\n",
            "[CV] ................................. n_estimators=350, total=   3.1s\n",
            "[CV] n_estimators=400 ................................................\n",
            "[CV] ................................. n_estimators=400, total=   3.2s\n",
            "[CV] n_estimators=400 ................................................\n",
            "[CV] ................................. n_estimators=400, total=   3.3s\n",
            "[CV] n_estimators=400 ................................................\n",
            "[CV] ................................. n_estimators=400, total=   3.2s\n",
            "[CV] n_estimators=500 ................................................\n",
            "[CV] ................................. n_estimators=500, total=   3.5s\n",
            "[CV] n_estimators=500 ................................................\n",
            "[CV] ................................. n_estimators=500, total=   3.6s\n",
            "[CV] n_estimators=500 ................................................\n",
            "[CV] ................................. n_estimators=500, total=   3.5s\n",
            "[CV] n_estimators=600 ................................................\n",
            "[CV] ................................. n_estimators=600, total=   3.8s\n",
            "[CV] n_estimators=600 ................................................\n",
            "[CV] ................................. n_estimators=600, total=   3.9s\n",
            "[CV] n_estimators=600 ................................................\n",
            "[CV] ................................. n_estimators=600, total=   3.9s\n",
            "[CV] n_estimators=700 ................................................\n",
            "[CV] ................................. n_estimators=700, total=   4.3s\n",
            "[CV] n_estimators=700 ................................................\n",
            "[CV] ................................. n_estimators=700, total=   4.3s\n",
            "[CV] n_estimators=700 ................................................\n",
            "[CV] ................................. n_estimators=700, total=   4.2s\n",
            "[CV] n_estimators=800 ................................................\n",
            "[CV] ................................. n_estimators=800, total=   4.6s\n",
            "[CV] n_estimators=800 ................................................\n",
            "[CV] ................................. n_estimators=800, total=   4.6s\n",
            "[CV] n_estimators=800 ................................................\n",
            "[CV] ................................. n_estimators=800, total=   4.6s\n",
            "[CV] n_estimators=900 ................................................\n",
            "[CV] ................................. n_estimators=900, total=   4.9s\n",
            "[CV] n_estimators=900 ................................................\n",
            "[CV] ................................. n_estimators=900, total=   4.9s\n",
            "[CV] n_estimators=900 ................................................\n",
            "[CV] ................................. n_estimators=900, total=   4.9s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:  1.5min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "estimators: 400\n",
            "27.9195233715612 217035.11021087694 82.57515680330955 1112606.0254540213 -0.1170642912592803 0.8483421190515441 -0.14316218104137057\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7DcE58kEltD",
        "colab_type": "code",
        "outputId": "3e0506f6-eb38-4c20-b41c-fc8cf9610161",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#XGB Model\n",
        "trainMAPE_xgb, trainMSE_xgb, testMAPE_xgb, testMSE_xgb, train_accuracy, test_accuracy= xgboost_reg(x_train, y_train, x_test, y_test)\n",
        "print(trainMAPE_xgb*100, trainMSE_xgb, testMAPE_xgb*100, testMSE_xgb, train_accuracy, test_accuracy)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "estimators: 40\n",
            "depth: 3\n",
            "[08:50:19] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "68.97922267641674 1319462.3031985469 79.92417359904704 1008289.9205805195 0.07799776404826031 -0.03598118144515672\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  if getattr(data, 'base', None) is not None and \\\n",
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
            "  data.base is not None and isinstance(data, np.ndarray) \\\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5u7VgRVZ7b5",
        "colab_type": "code",
        "outputId": "ac1321e2-04b5-4f26-9d54-5f6cdd36a69d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        }
      },
      "source": [
        "#KNR Model\n",
        "trainMAPE_knr, trainMSE_knr, testMAPE_knr, testMSE_knr, train_accuracy, test_accuracy= kNeighbors(x_train, y_train, x_test, y_test)\n",
        "print(trainMAPE_knr*100, trainMSE_knr, testMAPE_knr*100, testMSE_knr, train_accuracy, test_accuracy)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n",
            "[CV] n_neighbors=5 ...................................................\n",
            "[CV] .................................... n_neighbors=5, total=   1.8s\n",
            "[CV] n_neighbors=5 ...................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.8s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .................................... n_neighbors=5, total=   1.8s\n",
            "[CV] n_neighbors=5 ...................................................\n",
            "[CV] .................................... n_neighbors=5, total=   1.8s\n",
            "[CV] n_neighbors=10 ..................................................\n",
            "[CV] ................................... n_neighbors=10, total=   1.8s\n",
            "[CV] n_neighbors=10 ..................................................\n",
            "[CV] ................................... n_neighbors=10, total=   1.8s\n",
            "[CV] n_neighbors=10 ..................................................\n",
            "[CV] ................................... n_neighbors=10, total=   1.8s\n",
            "[CV] n_neighbors=15 ..................................................\n",
            "[CV] ................................... n_neighbors=15, total=   1.8s\n",
            "[CV] n_neighbors=15 ..................................................\n",
            "[CV] ................................... n_neighbors=15, total=   1.8s\n",
            "[CV] n_neighbors=15 ..................................................\n",
            "[CV] ................................... n_neighbors=15, total=   1.8s\n",
            "[CV] n_neighbors=20 ..................................................\n",
            "[CV] ................................... n_neighbors=20, total=   1.7s\n",
            "[CV] n_neighbors=20 ..................................................\n",
            "[CV] ................................... n_neighbors=20, total=   1.8s\n",
            "[CV] n_neighbors=20 ..................................................\n",
            "[CV] ................................... n_neighbors=20, total=   1.8s\n",
            "[CV] n_neighbors=30 ..................................................\n",
            "[CV] ................................... n_neighbors=30, total=   1.8s\n",
            "[CV] n_neighbors=30 ..................................................\n",
            "[CV] ................................... n_neighbors=30, total=   1.8s\n",
            "[CV] n_neighbors=30 ..................................................\n",
            "[CV] ................................... n_neighbors=30, total=   1.7s\n",
            "neighbors: 30\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:   26.7s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "70.05933795401101 1347247.9653058958 83.80345094375895 1103577.1843982171 0.05858194403712946 -0.13388537559766722\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6xioJ6gR3nn",
        "colab_type": "code",
        "outputId": "2e8b27c2-11c4-4994-ab28-02bcc7b294f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "feature_importances_rf"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.5554627 , 0.04837769, 0.10020335, 0.1021409 , 0.11699362,\n",
              "       0.07682174])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgRuNF9Fjzwm",
        "colab_type": "code",
        "outputId": "6b42c0a1-5e43-4ab4-f761-b4a26d8bb7e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "source": [
        "import seaborn as sns\n",
        "feature_importance_tuple = list(zip(x_train.columns,feature_importances_rf))\n",
        "df_feature_importances = pd.DataFrame(feature_importance_tuple, columns=['feature','importance']).sort_values('importance',ascending=False)\n",
        "df_feature_importances\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.set(style=\"white\")\n",
        "sns.set(style=\"ticks\")\n",
        "ax = sns.barplot(x=\"feature\", y=\"importance\", data=df_feature_importances)\n",
        "ax.set_title(\"\")\n"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAABghPAMAAAAAEYIzwAAAABghPAMAAAAAEYIzwAAAABghPAMAAAA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\nE+IZAAAAAEyIZwAAAAAw8Qc05lsLvRMxHAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 615,
              "height": 485
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sma6qmXiM0py",
        "colab_type": "text"
      },
      "source": [
        "# Plotting Mean Absolute Percentage Error\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9IGkRGtM0AZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "outputId": "f6470698-d6da-4025-dc89-3f2e8f7751d1"
      },
      "source": [
        "# Original Results\n",
        "mape = {'Models': ['Linear', 'Random Forest', 'XGBoost'], 'Test MAPE (%)': [75.31, 77.89, 75.23]}\n",
        "df_mape = pd.DataFrame(data=mape)\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.set(style=\"white\")\n",
        "sns.set(style=\"ticks\")\n",
        "ax = sns.barplot(x=\"Models\", y=\"Test MAPE (%)\", data=df_mape)\n",
        "ax.set_title(\"Test MAPE of Used Models\")\n",
        "ax.set_ylim(70, None)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70, 81.7845)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t2/n4449t9pubm8uAAQPY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\nYyIiIiIiIiIiUmL9H0NIoIrRC+ztAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 611,
              "height": 500
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60fFJDrLyfHV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "outputId": "bc2a1987-085f-4994-9d41-c020e78900a9"
      },
      "source": [
        "# New Results\n",
        "mape = {'Models': ['Linear', 'Random Forest', 'XGBoost', 'KNeighbors'], 'Test MAPE (%)': [81.74, 82.58, 79.92, 83.8]}\n",
        "df_mape = pd.DataFrame(data=mape)\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.set(style=\"white\")\n",
        "sns.set(style=\"ticks\")\n",
        "ax = sns.barplot(x=\"Models\", y=\"Test MAPE (%)\", data=df_mape)\n",
        "ax.set_title(\"Test MAPE of Used Models\")\n",
        "ax.set_ylim(70, None)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70, 87.99)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 611,
              "height": 500
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JAsNUrqVi7R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}