UmojaHack_2021_Sendy_002.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "UmojaHack_2021_Sendy_002.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMattxgmwEMRG8gN5j80/Q6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mburia/2nd-Place-Solution-UmojaHack_2021-Sendy/blob/main/UmojaHack_2021_Sendy_002.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUocpC9JwZRy"
      },
      "source": [
        "# Zindi UmojaHack Africa 2021 #2: Sendy - Delivery Rider Response Challenge (INTERMEDIATE) by UmojaHack Africa"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3zp3rt_wsws"
      },
      "source": [
        "This notebook was created in Google Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaqyxSPsJmwc",
        "outputId": "658a3412-823e-4595-d901-0c69f7e8c69a"
      },
      "source": [
        "# Importing Libraries\n",
        "import requests\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",
        "# Set user authentification path\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "# logging to avoid discovery_cache error\n",
        "import logging\n",
        "logging.getLogger('googleapiclient.discovery_cache').setLevel(logging.ERROR)\n",
        "\n",
        "# Getting Id of data file and load data in Google Colab\n",
        "# https://drive.google.com/drive/folders/16RYdhdDcj_glWKjXR7OnT4bfcSXUymYw?usp=sharing\n",
        "file_list = drive.ListFile({'q': \"'16RYdhdDcj_glWKjXR7OnT4bfcSXUymYw' in parents and trashed=false\"}).GetList()\n",
        "\n",
        "# View files in the Drive Folder\n",
        "for file1 in file_list:\n",
        "  print('title: %s, id: %s' % (file1['title'], file1['id']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "title: Train.csv, id: 1V_4PpDw_HyxlJldsLVp_jD4MKJL3Z3TF\n",
            "title: Test.csv, id: 1IvFdJrFh5D7BCLdxIgkdc3TWDjmTMaOQ\n",
            "title: SampleSubmission.csv, id: 1dgWW9OIcgTh2jQgw50IBmqdsKLIAj0Yn\n",
            "title: Sendy_UmojaHack_StarterNotebook.ipynb, id: 1l-0j56vYE-n8dKQ_kBGTBOkI2NmUpbcq\n",
            "title: Riders.csv, id: 1i8rR25xDMe73IxqGdj4CKL64yWxj4ypP\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2Y00sMCMIwj"
      },
      "source": [
        "# Loading the Data from the source i.e. csv\n",
        "import os\n",
        "from io import StringIO\n",
        "\n",
        "# Now, we will be getting content of file by using id.\n",
        "# Train file\n",
        "train_file =  drive.CreateFile({'id': '1V_4PpDw_HyxlJldsLVp_jD4MKJL3Z3TF'})\n",
        "train_file.GetContentFile('Train.csv')\n",
        "# Test file\n",
        "test_file =  drive.CreateFile({'id': '1IvFdJrFh5D7BCLdxIgkdc3TWDjmTMaOQ'})\n",
        "test_file.GetContentFile('Test.csv')\n",
        "# Rider file\n",
        "rider_file =  drive.CreateFile({'id': '1i8rR25xDMe73IxqGdj4CKL64yWxj4ypP'})\n",
        "rider_file.GetContentFile('Riders.csv')\n",
        "# Data URL's\n",
        "train_url = 'https://drive.google.com/file/d/1V_4PpDw_HyxlJldsLVp_jD4MKJL3Z3TF/view?usp=sharing'\n",
        "test_url = 'https://drive.google.com/file/d/1IvFdJrFh5D7BCLdxIgkdc3TWDjmTMaOQ/view?usp=sharing'\n",
        "rider_url = 'https://drive.google.com/file/d/1i8rR25xDMe73IxqGdj4CKL64yWxj4ypP/view?usp=sharing'\n",
        "\n",
        "# Function to read CSV file\n",
        "def read_csv(url):\n",
        "  url = 'https://drive.google.com/uc?export=download&id=' + url.split('/')[-2]\n",
        "  csv_raw = requests.get(url).text\n",
        "  csv = StringIO(csv_raw)\n",
        "  return csv\n",
        "\n",
        "# Load the data\n",
        "import pandas as pd\n",
        "df_train = pd.read_csv(read_csv(train_url))\n",
        "df_test = pd.read_csv(read_csv(test_url))\n",
        "df_riders = pd.read_csv(read_csv(rider_url))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CEISdfQjO7kV"
      },
      "source": [
        "# Creating Copy to work with\n",
        "train = df_train.copy()\n",
        "test = df_test.copy()\n",
        "riders = df_riders.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qOljWkRzQQTX",
        "outputId": "e731924e-1191-4b73-e99a-840832ed60c6"
      },
      "source": [
        "# Determining the no. of records in our datasets\n",
        "print('The train dataset has ', train.shape[1], ' columns and ', train.shape[0], ' rows!')\n",
        "print('The test dataset has ', test.shape[1], ' columns and ', test.shape[0], ' rows!')\n",
        "print('The test dataset has ', riders.shape[1], ' columns and ', riders.shape[0], ' rows!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The train dataset has  21  columns and  179867  rows!\n",
            "The test dataset has  20  columns and  76791  rows!\n",
            "The test dataset has  4  columns and  2632  rows!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dvoeaoaQghx"
      },
      "source": [
        "# Separate target variable \n",
        "target = train['target']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGlxebDVSjnk"
      },
      "source": [
        "# Drop target column then join Train and test\n",
        "train.drop('target',axis = 1, inplace = True)\n",
        "\n",
        "# Merge the train and test dataframe\n",
        "frames = [train,test]\n",
        "df = pd.concat(frames)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdR9eN7_clKG",
        "outputId": "1afdfd8b-51eb-407a-c4f2-473d0dc86c59"
      },
      "source": [
        "# Checking unique values and number of rows asigned for each column in the dataset\n",
        "for i in df.columns:\n",
        "  \n",
        "  print('\\n')\n",
        "  \n",
        "  print(i,df[i].nunique())\n",
        "\n",
        "  print(i,df[i].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "ID 256658\n",
            "ID ['ID_SCUW21PVAU' 'ID_2HA7X30JMN' 'ID_IAJWDTBY6M' ... 'ID_VDI8L0SWNS'\n",
            " 'ID_7L22AF1T6K' 'ID_LMMA33Q7YL']\n",
            "\n",
            "\n",
            "order_id 64326\n",
            "order_id [ 4435 32711  8712 ... 11467 12867 12617]\n",
            "\n",
            "\n",
            "dispatch_day 31\n",
            "dispatch_day [27 30 14 22  3 18 28 20  6 12 17  7  5  1 15 31 16  4 25 19 11 23 21  9\n",
            " 13 10 26  2  8 29 24]\n",
            "\n",
            "\n",
            "dispatch_day_of_week 7\n",
            "dispatch_day_of_week [6 7 2 3 5 4 1]\n",
            "\n",
            "\n",
            "dispatch_time 5788\n",
            "dispatch_time ['09:02:54' '13:01:37' '10:01:00' ... '06:09:58' '20:08:49' '05:09:47']\n",
            "\n",
            "\n",
            "client_id 5457\n",
            "client_id [ 593630  837729  695129 ...  676936 2533910 2361571]\n",
            "\n",
            "\n",
            "client_type 2\n",
            "client_type ['Business' 'Personal']\n",
            "\n",
            "\n",
            "order_license_status 2\n",
            "order_license_status [0 1]\n",
            "\n",
            "\n",
            "order_carrier_type 3\n",
            "order_carrier_type [2 1 0]\n",
            "\n",
            "\n",
            "vendor_type 1\n",
            "vendor_type ['Bike']\n",
            "\n",
            "\n",
            "rider_id 775\n",
            "rider_id [ 30153  20884  33143  96531 103546  66125  66033  45701 117622  40066\n",
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            "  61594 117254  64630  36892  76199  52279   7912  95841  62514  37858\n",
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            "  86572  59662  86848  64676 111412 123142  24610  57845  32614  72565\n",
            " 115920 122153  20079  68425 105133 102258 100303 107111 100050 122245\n",
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            "   5566 109020  16951  99935 113367  14582  86871  34684  37191  41929\n",
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            "  77671  32499  24656  45540  55246 119853  63411 105938  19734  91103\n",
            "  82041 106329  71392 123188 111113  87377 101775  85767  92828 118174\n",
            "  11799  58535  91540  80592  68517  77579  54786 121509 101568  33212\n",
            " 117944 118335  66240 108100 122728 103592  22862 105547  65918 108997\n",
            "  40733  11891  57201  43033 118657 117162  37927   8832  73577  91655\n",
            "  87193 119186 117346  67551 101108  98762  67689  75992  92552  58167\n",
            "  72473 100004 101752  92598  50324  84939  64699 117070 116909  10373\n",
            " 113712   7406  50071  42550  45724  33994  85813 115713  76705 100395\n",
            "  11638  60812 122820 118381  56787  99268  71415 102005 116541  94001\n",
            "  86181  55131 123694 105639  25530  60053  10189   9246 117576  42619\n",
            "  68563  72197  30222  59547  57247  52210 113896  95473  49105 115874\n",
            " 113505 116863  63687  66286 117369  16399 110906  90942   1035 121233\n",
            "  68149 115828  70978  48622  51957]\n",
            "\n",
            "\n",
            "rider_license_status 2\n",
            "rider_license_status [0 1]\n",
            "\n",
            "\n",
            "rider_carrier_type 2\n",
            "rider_carrier_type [1 0]\n",
            "\n",
            "\n",
            "rider_amount 179\n",
            "rider_amount [1080  730  490  510  400  420  800  450  610  410  390  460  440  540\n",
            "  430  470  990  760  550  530  520  570  890  580  590 1380 1020  620\n",
            "  790  710  740  640  840  500  560  880 1950 1240  380  920  950  480\n",
            "  370  650  630  360  700  870  670  600 1390  930  810  720 1180  780\n",
            "  690  830  970  660 1010 1030  750  860  850  940 1480  770  200  680\n",
            " 1150  910  980 1040 1140  820 1170 1050 1060 1430 1900 1000 1230 1110\n",
            " 1190  900 1120 1260  310 1420 1200 1220 1710  960 1130 1320 1340 2060\n",
            " 1310 1580 1270 1290  340 1300 1790 1250 1090 1370 1100 1070 1280 1720\n",
            " 2030 1840 1330 1700 1540 2120 1770 1590 1160 1560 1530 1490 1970 1600\n",
            " 1440 1470 1610  210 1360 1640 2190 1520 1680 1740 1500 1450 1460 1670\n",
            " 1570 1410 1690 1510 1750 2140 1660 1650 1810 1880 1210 1630 1990 2050\n",
            " 2010 1350 1620 2070  320 1550  220 2150 1400 2090 1890 1870 1930 2020\n",
            " 1800 1780 2180 1730 2110 1850 2040 1760 2170 2130 1940]\n",
            "\n",
            "\n",
            "rider_lat 211604\n",
            "rider_lat [-42.698343  -42.7873167 -42.7491796 ... -42.8033492 -42.8410776\n",
            " -42.7923086]\n",
            "\n",
            "\n",
            "rider_long 208928\n",
            "rider_long [-17.2285394 -17.288252  -17.2878484 ... -17.3026148 -17.2908311\n",
            " -17.3022482]\n",
            "\n",
            "\n",
            "pickup_lat 8051\n",
            "pickup_lat [-42.6923711 -42.784046  -42.765204  ... -42.9058624 -42.808935\n",
            " -42.8378656]\n",
            "\n",
            "\n",
            "pickup_long 8074\n",
            "pickup_long [-17.2483048 -17.290121  -17.2937844 ... -17.2191941 -17.293128\n",
            " -17.2158709]\n",
            "\n",
            "\n",
            "drop_off_lat 12715\n",
            "drop_off_lat [-42.6874421 -42.6732673 -42.8139535 ... -41.293443  -42.7523\n",
            " -42.889742 ]\n",
            "\n",
            "\n",
            "drop_off_long 12759\n",
            "drop_off_long [-17.4246819 -17.2345951 -17.2948052 ... -15.4730707 -17.3318406\n",
            " -17.2171573]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D6Io2tDPoq79"
      },
      "source": [
        "## Feature Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nbo7yFgw-ndk"
      },
      "source": [
        "##### Rider Amount Class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "ybgR3jTJBq5a",
        "outputId": "5005c0c6-7e5b-4ed9-f005-dfad7098d3d2"
      },
      "source": [
        "# Let's view the distribution of the rider's payment for the dispatch\n",
        "hist_amount = df.rider_amount.hist(bins=25,figsize=[15,10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "id": "k0AROAE44w8a",
        "outputId": "45206b11-3da7-47f2-e7ae-814f28a815e4"
      },
      "source": [
        "import numpy as np\n",
        "# create a list of our conditions\n",
        "conditions = [\n",
        "    (df['rider_amount'] <= 390),\n",
        "    (df['rider_amount'] > 390) & (df['rider_amount'] <= 650),\n",
        "    (df['rider_amount'] > 650) & (df['rider_amount'] <= 820),\n",
        "    (df['rider_amount'] > 800) & (df['rider_amount'] <= 1200)\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['2', '4', '3', '1']\n",
        "\n",
        "# create a new column and use np.select to assign values to it using our lists as arguments\n",
        "df['rider_amount_range'] = np.select(conditions, values)\n",
        "\n",
        "# display updated DataFrame\n",
        "df.head(3)"
      ],
      "execution_count": null,
      "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>ID</th>\n",
              "      <th>order_id</th>\n",
              "      <th>dispatch_day</th>\n",
              "      <th>dispatch_day_of_week</th>\n",
              "      <th>dispatch_time</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_type</th>\n",
              "      <th>order_license_status</th>\n",
              "      <th>order_carrier_type</th>\n",
              "      <th>vendor_type</th>\n",
              "      <th>rider_id</th>\n",
              "      <th>rider_license_status</th>\n",
              "      <th>rider_carrier_type</th>\n",
              "      <th>rider_amount</th>\n",
              "      <th>rider_lat</th>\n",
              "      <th>rider_long</th>\n",
              "      <th>pickup_lat</th>\n",
              "      <th>pickup_long</th>\n",
              "      <th>drop_off_lat</th>\n",
              "      <th>drop_off_long</th>\n",
              "      <th>rider_amount_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_SCUW21PVAU</td>\n",
              "      <td>4435</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "      <td>09:02:54</td>\n",
              "      <td>593630</td>\n",
              "      <td>Business</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>30153</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1080</td>\n",
              "      <td>-42.698343</td>\n",
              "      <td>-17.228539</td>\n",
              "      <td>-42.692371</td>\n",
              "      <td>-17.248305</td>\n",
              "      <td>-42.687442</td>\n",
              "      <td>-17.424682</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_2HA7X30JMN</td>\n",
              "      <td>32711</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>13:01:37</td>\n",
              "      <td>837729</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bike</td>\n",
              "      <td>20884</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>-42.787317</td>\n",
              "      <td>-17.288252</td>\n",
              "      <td>-42.784046</td>\n",
              "      <td>-17.290121</td>\n",
              "      <td>-42.673267</td>\n",
              "      <td>-17.234595</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_IAJWDTBY6M</td>\n",
              "      <td>8712</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>10:01:00</td>\n",
              "      <td>695129</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>33143</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>490</td>\n",
              "      <td>-42.749180</td>\n",
              "      <td>-17.287848</td>\n",
              "      <td>-42.765204</td>\n",
              "      <td>-17.293784</td>\n",
              "      <td>-42.813953</td>\n",
              "      <td>-17.294805</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              ID  order_id  ...  drop_off_long  rider_amount_range\n",
              "0  ID_SCUW21PVAU      4435  ...     -17.424682                   1\n",
              "1  ID_2HA7X30JMN     32711  ...     -17.234595                   3\n",
              "2  ID_IAJWDTBY6M      8712  ...     -17.294805                   4\n",
              "\n",
              "[3 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghYjG_ZBDHLg"
      },
      "source": [
        "Day of month"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "LCpk_dUjDKG9",
        "outputId": "dac5c7f8-4e61-4abe-ed65-364349002e61"
      },
      "source": [
        "# Dispatches frequency per day of month\n",
        "df['dispatch_day'].value_counts().plot(kind='bar',figsize=[15,5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff2551be150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "id": "Y0JnaIY9DgIE",
        "outputId": "4f48d807-20cd-4202-c438-6814aa116b69"
      },
      "source": [
        "# create a list of our conditions\n",
        "conditions = [\n",
        "    (df['dispatch_day'] == 1),\n",
        "    (df['dispatch_day'] > 1) & (df['dispatch_day'] <= 6),\n",
        "    (df['dispatch_day'] > 6) & (df['dispatch_day'] <= 8),\n",
        "    (df['dispatch_day'] >8)\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['1', '2','3','4']\n",
        "\n",
        "# create a new column and use np.select to assign values to it using our lists as arguments\n",
        "df['dispatch_day_range'] = np.select(conditions, values)\n",
        "\n",
        "# display updated DataFrame\n",
        "df.head(3)"
      ],
      "execution_count": null,
      "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>ID</th>\n",
              "      <th>order_id</th>\n",
              "      <th>dispatch_day</th>\n",
              "      <th>dispatch_day_of_week</th>\n",
              "      <th>dispatch_time</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_type</th>\n",
              "      <th>order_license_status</th>\n",
              "      <th>order_carrier_type</th>\n",
              "      <th>vendor_type</th>\n",
              "      <th>rider_id</th>\n",
              "      <th>rider_license_status</th>\n",
              "      <th>rider_carrier_type</th>\n",
              "      <th>rider_amount</th>\n",
              "      <th>rider_lat</th>\n",
              "      <th>rider_long</th>\n",
              "      <th>pickup_lat</th>\n",
              "      <th>pickup_long</th>\n",
              "      <th>drop_off_lat</th>\n",
              "      <th>drop_off_long</th>\n",
              "      <th>rider_amount_range</th>\n",
              "      <th>dispatch_day_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_SCUW21PVAU</td>\n",
              "      <td>4435</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "      <td>09:02:54</td>\n",
              "      <td>593630</td>\n",
              "      <td>Business</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>30153</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1080</td>\n",
              "      <td>-42.698343</td>\n",
              "      <td>-17.228539</td>\n",
              "      <td>-42.692371</td>\n",
              "      <td>-17.248305</td>\n",
              "      <td>-42.687442</td>\n",
              "      <td>-17.424682</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_2HA7X30JMN</td>\n",
              "      <td>32711</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>13:01:37</td>\n",
              "      <td>837729</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bike</td>\n",
              "      <td>20884</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>-42.787317</td>\n",
              "      <td>-17.288252</td>\n",
              "      <td>-42.784046</td>\n",
              "      <td>-17.290121</td>\n",
              "      <td>-42.673267</td>\n",
              "      <td>-17.234595</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_IAJWDTBY6M</td>\n",
              "      <td>8712</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>10:01:00</td>\n",
              "      <td>695129</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>33143</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>490</td>\n",
              "      <td>-42.749180</td>\n",
              "      <td>-17.287848</td>\n",
              "      <td>-42.765204</td>\n",
              "      <td>-17.293784</td>\n",
              "      <td>-42.813953</td>\n",
              "      <td>-17.294805</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              ID  order_id  ...  rider_amount_range  dispatch_day_range\n",
              "0  ID_SCUW21PVAU      4435  ...                   1                   4\n",
              "1  ID_2HA7X30JMN     32711  ...                   3                   4\n",
              "2  ID_IAJWDTBY6M      8712  ...                   4                   4\n",
              "\n",
              "[3 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBmGlejzDEUT"
      },
      "source": [
        "Weekends"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "uDzCPnOtFw1e",
        "outputId": "460db8af-1e17-4b6a-d662-79cfafc61a6c"
      },
      "source": [
        "# Dispatches frequency per  weekday\n",
        "df['dispatch_day_of_week'].value_counts().plot(kind='bar',figsize=[15,5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdfe9ab0310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "id": "Pt5hrt9MDZs9",
        "outputId": "32a1b221-723e-4621-968a-4b3ac85aa8f4"
      },
      "source": [
        "# create a list of our conditions\n",
        "conditions = [\n",
        "    (df['dispatch_day_of_week'] == 1),\n",
        "    (df['dispatch_day_of_week'] == 7)\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['2', '1']\n",
        "\n",
        "# create a new column and use np.select to assign values to it using our lists as arguments\n",
        "df['weekend'] = np.select(conditions, values)\n",
        "\n",
        "# display updated DataFrame\n",
        "df.head(3)"
      ],
      "execution_count": null,
      "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>ID</th>\n",
              "      <th>order_id</th>\n",
              "      <th>dispatch_day</th>\n",
              "      <th>dispatch_day_of_week</th>\n",
              "      <th>dispatch_time</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_type</th>\n",
              "      <th>order_license_status</th>\n",
              "      <th>order_carrier_type</th>\n",
              "      <th>vendor_type</th>\n",
              "      <th>rider_id</th>\n",
              "      <th>rider_license_status</th>\n",
              "      <th>rider_carrier_type</th>\n",
              "      <th>rider_amount</th>\n",
              "      <th>rider_lat</th>\n",
              "      <th>rider_long</th>\n",
              "      <th>pickup_lat</th>\n",
              "      <th>pickup_long</th>\n",
              "      <th>drop_off_lat</th>\n",
              "      <th>drop_off_long</th>\n",
              "      <th>rider_amount_range</th>\n",
              "      <th>dispatch_day_range</th>\n",
              "      <th>weekend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_SCUW21PVAU</td>\n",
              "      <td>4435</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "      <td>09:02:54</td>\n",
              "      <td>593630</td>\n",
              "      <td>Business</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>30153</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1080</td>\n",
              "      <td>-42.698343</td>\n",
              "      <td>-17.228539</td>\n",
              "      <td>-42.692371</td>\n",
              "      <td>-17.248305</td>\n",
              "      <td>-42.687442</td>\n",
              "      <td>-17.424682</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_2HA7X30JMN</td>\n",
              "      <td>32711</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>13:01:37</td>\n",
              "      <td>837729</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bike</td>\n",
              "      <td>20884</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>-42.787317</td>\n",
              "      <td>-17.288252</td>\n",
              "      <td>-42.784046</td>\n",
              "      <td>-17.290121</td>\n",
              "      <td>-42.673267</td>\n",
              "      <td>-17.234595</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_IAJWDTBY6M</td>\n",
              "      <td>8712</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>10:01:00</td>\n",
              "      <td>695129</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>33143</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>490</td>\n",
              "      <td>-42.749180</td>\n",
              "      <td>-17.287848</td>\n",
              "      <td>-42.765204</td>\n",
              "      <td>-17.293784</td>\n",
              "      <td>-42.813953</td>\n",
              "      <td>-17.294805</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              ID  order_id  ...  dispatch_day_range  weekend\n",
              "0  ID_SCUW21PVAU      4435  ...                   4        0\n",
              "1  ID_2HA7X30JMN     32711  ...                   4        1\n",
              "2  ID_IAJWDTBY6M      8712  ...                   4        0\n",
              "\n",
              "[3 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWIkdougI8VJ"
      },
      "source": [
        "Busy Hours"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "0jGAN0_AI9wX",
        "outputId": "0c428593-39af-4d16-e037-a6ca57367310"
      },
      "source": [
        "# Convert time column to datetime\n",
        "df['dispatch_time'] = pd.to_datetime(df['dispatch_time'])\n",
        "\n",
        "# Extract Hour Column\n",
        "df['dispatch_hour'] = df['dispatch_time'].dt.hour\n",
        "\n",
        "# Let's view the hourly distribution of dispatches\n",
        "df['dispatch_hour'].value_counts().plot(kind='bar',figsize=[15,5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f18ab06c0d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7bhVXbdr_UBS"
      },
      "source": [
        "# Let's get the cos and cosine of our Time Variable\n",
        "time_column = [\"dispatch_time\"]\n",
        "\n",
        "for col in time_column:\n",
        "  df[col.split('_')[0] + '_hour_sine'] = np.sin(2 * np.pi * df[col].dt.hour/24)\n",
        "  df[col.split('_')[0] + '_hour_cos'] = np.cos(2* np.pi * df[col].dt.hour/24)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "Y0ak_At-L88L",
        "outputId": "074e19c6-fc22-4e5a-81a4-815b134cf6b9"
      },
      "source": [
        "# create a list of our conditions\n",
        "conditions = [\n",
        "    (df['dispatch_hour'] <= 6),\n",
        "    (df['dispatch_hour'] > 8) & (df['dispatch_hour'] <= 16),\n",
        "    (df['dispatch_hour'] > 16) & (df['dispatch_hour'] <= 20),\n",
        "    (df['dispatch_hour'] > 20)\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['1', '4', '3', '2']\n",
        "\n",
        "# create a new column and use np.select to assign values to it using our lists as arguments\n",
        "df['busy_hours'] = np.select(conditions, values)\n",
        "\n",
        "# display updated DataFrame\n",
        "df.head(2)"
      ],
      "execution_count": null,
      "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>ID</th>\n",
              "      <th>order_id</th>\n",
              "      <th>dispatch_day</th>\n",
              "      <th>dispatch_day_of_week</th>\n",
              "      <th>dispatch_time</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_type</th>\n",
              "      <th>order_license_status</th>\n",
              "      <th>order_carrier_type</th>\n",
              "      <th>vendor_type</th>\n",
              "      <th>rider_id</th>\n",
              "      <th>rider_license_status</th>\n",
              "      <th>rider_carrier_type</th>\n",
              "      <th>rider_amount</th>\n",
              "      <th>rider_lat</th>\n",
              "      <th>rider_long</th>\n",
              "      <th>pickup_lat</th>\n",
              "      <th>pickup_long</th>\n",
              "      <th>drop_off_lat</th>\n",
              "      <th>drop_off_long</th>\n",
              "      <th>rider_amount_range</th>\n",
              "      <th>dispatch_day_range</th>\n",
              "      <th>weekend</th>\n",
              "      <th>dispatch_hour</th>\n",
              "      <th>dispatch_hour_sine</th>\n",
              "      <th>dispatch_hour_cos</th>\n",
              "      <th>busy_hours</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_SCUW21PVAU</td>\n",
              "      <td>4435</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "      <td>2021-03-28 09:02:54</td>\n",
              "      <td>593630</td>\n",
              "      <td>Business</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>30153</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1080</td>\n",
              "      <td>-42.698343</td>\n",
              "      <td>-17.228539</td>\n",
              "      <td>-42.692371</td>\n",
              "      <td>-17.248305</td>\n",
              "      <td>-42.687442</td>\n",
              "      <td>-17.424682</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.707107</td>\n",
              "      <td>-0.707107</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_2HA7X30JMN</td>\n",
              "      <td>32711</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>2021-03-28 13:01:37</td>\n",
              "      <td>837729</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bike</td>\n",
              "      <td>20884</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>-42.787317</td>\n",
              "      <td>-17.288252</td>\n",
              "      <td>-42.784046</td>\n",
              "      <td>-17.290121</td>\n",
              "      <td>-42.673267</td>\n",
              "      <td>-17.234595</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>-0.258819</td>\n",
              "      <td>-0.965926</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              ID  order_id  ...  dispatch_hour_cos  busy_hours\n",
              "0  ID_SCUW21PVAU      4435  ...          -0.707107           4\n",
              "1  ID_2HA7X30JMN     32711  ...          -0.965926           4\n",
              "\n",
              "[2 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYqsgKsT-jN_"
      },
      "source": [
        "Geo Distance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgU-YABBOzRg"
      },
      "source": [
        "# Calculate distance between coordinates\n",
        "\n",
        "def distance_calc(lat1, lng1, lat2, lng2):\n",
        "    lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n",
        "    \n",
        "    AVG_EARTH_RADIUS = 6371  # in km\n",
        "    \n",
        "    lat = lat2 - lat1\n",
        "    \n",
        "    lng = lng2 - lng1\n",
        "    \n",
        "    d = np.sin(lat * 0.5) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(lng * 0.5) ** 2\n",
        "    \n",
        "    h = 2 * AVG_EARTH_RADIUS * np.arcsin(np.sqrt(d))\n",
        "    \n",
        "    return h"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxuINM4SO1da"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "# Let's create variables for the distances between the locations of the riders, pickup points and dropoff points\n",
        "df['rider_dropoff_dist'] = distance_calc(df.rider_lat, df.rider_long, df.drop_off_lat, df.drop_off_long)\n",
        "df['rider_pickup_dist'] = distance_calc(df.rider_lat, df.rider_long, df.pickup_lat, df.pickup_long)\n",
        "df['pickup_dropoff_dist'] = distance_calc(df.pickup_lat, df.pickup_long, df.drop_off_lat, df.drop_off_long)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3esDBwInRX4E"
      },
      "source": [
        "### Distance Ranges"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ME6ZOCMYUG9f"
      },
      "source": [
        "Pickup Drop_off ranges"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "Lkrg5bIUW4Jm",
        "outputId": "cf590c3a-3f6c-4908-9aa5-00af8a80fc11"
      },
      "source": [
        "# We can now view the distribution of the distance between pickup and drop off locations\n",
        "\n",
        "bin_values = np.arange(start=-0, stop=60, step=0.3)\n",
        "df['pickup_dropoff_dist'].hist(bins=bin_values, figsize=[14,6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f77cdfaa610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8VwJbL9SNoh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "outputId": "170b6908-68d7-4946-c87a-52261484e7b2"
      },
      "source": [
        "# create a list of our conditions\n",
        "conditions = [\n",
        "    (df['pickup_dropoff_dist'] <= 6),\n",
        "    (df['pickup_dropoff_dist'] > 6) & (df['pickup_dropoff_dist'] <= 16),\n",
        "    (df['pickup_dropoff_dist'] > 16) & (df['pickup_dropoff_dist'] <= 30),\n",
        "    (df['pickup_dropoff_dist'] > 30) & (df['pickup_dropoff_dist'] <= 50),\n",
        "    (df['pickup_dropoff_dist'] > 50)\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['5','4', '3', '2', '1']\n",
        "\n",
        "# create a new column and use np.select to assign values to it using our lists as arguments\n",
        "df['pickup_dropoff_dist_range'] = np.select(conditions, values)\n",
        "\n",
        "# display updated DataFrame\n",
        "df.head(2)"
      ],
      "execution_count": null,
      "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>ID</th>\n",
              "      <th>order_id</th>\n",
              "      <th>dispatch_day</th>\n",
              "      <th>dispatch_day_of_week</th>\n",
              "      <th>dispatch_time</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_type</th>\n",
              "      <th>order_license_status</th>\n",
              "      <th>order_carrier_type</th>\n",
              "      <th>vendor_type</th>\n",
              "      <th>rider_id</th>\n",
              "      <th>rider_license_status</th>\n",
              "      <th>rider_carrier_type</th>\n",
              "      <th>rider_amount</th>\n",
              "      <th>rider_lat</th>\n",
              "      <th>rider_long</th>\n",
              "      <th>pickup_lat</th>\n",
              "      <th>pickup_long</th>\n",
              "      <th>drop_off_lat</th>\n",
              "      <th>drop_off_long</th>\n",
              "      <th>rider_amount_range</th>\n",
              "      <th>dispatch_day_range</th>\n",
              "      <th>weekend</th>\n",
              "      <th>dispatch_hour</th>\n",
              "      <th>dispatch_hour_sine</th>\n",
              "      <th>dispatch_hour_cos</th>\n",
              "      <th>busy_hours</th>\n",
              "      <th>rider_dropoff_dist</th>\n",
              "      <th>rider_pickup_dist</th>\n",
              "      <th>pickup_dropoff_dist</th>\n",
              "      <th>pickup_dropoff_dist_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_SCUW21PVAU</td>\n",
              "      <td>4435</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "      <td>2021-03-28 09:02:54</td>\n",
              "      <td>593630</td>\n",
              "      <td>Business</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>30153</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1080</td>\n",
              "      <td>-42.698343</td>\n",
              "      <td>-17.228539</td>\n",
              "      <td>-42.692371</td>\n",
              "      <td>-17.248305</td>\n",
              "      <td>-42.687442</td>\n",
              "      <td>-17.424682</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.707107</td>\n",
              "      <td>-0.707107</td>\n",
              "      <td>4</td>\n",
              "      <td>16.076117</td>\n",
              "      <td>1.746491</td>\n",
              "      <td>14.426076</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_2HA7X30JMN</td>\n",
              "      <td>32711</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>2021-03-28 13:01:37</td>\n",
              "      <td>837729</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bike</td>\n",
              "      <td>20884</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>-42.787317</td>\n",
              "      <td>-17.288252</td>\n",
              "      <td>-42.784046</td>\n",
              "      <td>-17.290121</td>\n",
              "      <td>-42.673267</td>\n",
              "      <td>-17.234595</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>-0.258819</td>\n",
              "      <td>-0.965926</td>\n",
              "      <td>4</td>\n",
              "      <td>13.417651</td>\n",
              "      <td>0.394373</td>\n",
              "      <td>13.126454</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              ID  order_id  ...  pickup_dropoff_dist  pickup_dropoff_dist_range\n",
              "0  ID_SCUW21PVAU      4435  ...            14.426076                          4\n",
              "1  ID_2HA7X30JMN     32711  ...            13.126454                          4\n",
              "\n",
              "[2 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njkhyX66ULuF"
      },
      "source": [
        "Rider_Pickup_Ranges"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "ORwx5WhYZOa_",
        "outputId": "f9fc729b-f498-4cdf-d3a8-8c8cf724ceb8"
      },
      "source": [
        "# Let's also look at distribution of the distance between the rider and the pickup location\n",
        "\n",
        "bin_values = np.arange(start=-0, stop=4, step=0.1)\n",
        "df['rider_pickup_dist'].hist(bins=bin_values, figsize=[14,6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f407de5fb10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srO_8NMJSMmW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d65526b3-6536-4e98-9bad-141935d9ad3b"
      },
      "source": [
        "# create a list of our conditions\n",
        "conditions = [\n",
        "    (df['rider_pickup_dist'] <= 1),\n",
        "    (df['rider_pickup_dist'] > 1) & (df['rider_pickup_dist'] <= 1.5),\n",
        "    (df['rider_pickup_dist'] > 1.5) & (df['rider_pickup_dist'] <= 2.5),\n",
        "    (df['rider_pickup_dist'] > 2.5) & (df['rider_pickup_dist'] <= 3.7),\n",
        "    (df['rider_pickup_dist'] > 3.7)\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['1', '2', '3', '4','5']\n",
        "\n",
        "# create a new column and use np.select to assign values to it using our lists as arguments\n",
        "df['rider_pickup_dist_range'] = np.select(conditions, values)\n",
        "\n",
        "# display updated DataFrame\n",
        "df.head(3)"
      ],
      "execution_count": null,
      "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>ID</th>\n",
              "      <th>order_id</th>\n",
              "      <th>dispatch_day</th>\n",
              "      <th>dispatch_day_of_week</th>\n",
              "      <th>dispatch_time</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_type</th>\n",
              "      <th>order_license_status</th>\n",
              "      <th>order_carrier_type</th>\n",
              "      <th>vendor_type</th>\n",
              "      <th>rider_id</th>\n",
              "      <th>rider_license_status</th>\n",
              "      <th>rider_carrier_type</th>\n",
              "      <th>rider_amount</th>\n",
              "      <th>rider_lat</th>\n",
              "      <th>rider_long</th>\n",
              "      <th>pickup_lat</th>\n",
              "      <th>pickup_long</th>\n",
              "      <th>drop_off_lat</th>\n",
              "      <th>drop_off_long</th>\n",
              "      <th>rider_amount_range</th>\n",
              "      <th>dispatch_day_range</th>\n",
              "      <th>weekend</th>\n",
              "      <th>dispatch_hour</th>\n",
              "      <th>dispatch_hour_sine</th>\n",
              "      <th>dispatch_hour_cos</th>\n",
              "      <th>busy_hours</th>\n",
              "      <th>rider_dropoff_dist</th>\n",
              "      <th>rider_pickup_dist</th>\n",
              "      <th>pickup_dropoff_dist</th>\n",
              "      <th>pickup_dropoff_dist_range</th>\n",
              "      <th>rider_pickup_dist_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_SCUW21PVAU</td>\n",
              "      <td>4435</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "      <td>2021-03-28 09:02:54</td>\n",
              "      <td>593630</td>\n",
              "      <td>Business</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>30153</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1080</td>\n",
              "      <td>-42.698343</td>\n",
              "      <td>-17.228539</td>\n",
              "      <td>-42.692371</td>\n",
              "      <td>-17.248305</td>\n",
              "      <td>-42.687442</td>\n",
              "      <td>-17.424682</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.707107</td>\n",
              "      <td>-0.707107</td>\n",
              "      <td>4</td>\n",
              "      <td>16.076117</td>\n",
              "      <td>1.746491</td>\n",
              "      <td>14.426076</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_2HA7X30JMN</td>\n",
              "      <td>32711</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>2021-03-28 13:01:37</td>\n",
              "      <td>837729</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bike</td>\n",
              "      <td>20884</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>-42.787317</td>\n",
              "      <td>-17.288252</td>\n",
              "      <td>-42.784046</td>\n",
              "      <td>-17.290121</td>\n",
              "      <td>-42.673267</td>\n",
              "      <td>-17.234595</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>-0.258819</td>\n",
              "      <td>-0.965926</td>\n",
              "      <td>4</td>\n",
              "      <td>13.417651</td>\n",
              "      <td>0.394373</td>\n",
              "      <td>13.126454</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_IAJWDTBY6M</td>\n",
              "      <td>8712</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>2021-03-28 10:01:00</td>\n",
              "      <td>695129</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>33143</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>490</td>\n",
              "      <td>-42.749180</td>\n",
              "      <td>-17.287848</td>\n",
              "      <td>-42.765204</td>\n",
              "      <td>-17.293784</td>\n",
              "      <td>-42.813953</td>\n",
              "      <td>-17.294805</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>-0.866025</td>\n",
              "      <td>4</td>\n",
              "      <td>7.224872</td>\n",
              "      <td>1.846564</td>\n",
              "      <td>5.421337</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              ID  order_id  ...  pickup_dropoff_dist_range  rider_pickup_dist_range\n",
              "0  ID_SCUW21PVAU      4435  ...                          4                        3\n",
              "1  ID_2HA7X30JMN     32711  ...                          4                        1\n",
              "2  ID_IAJWDTBY6M      8712  ...                          5                        3\n",
              "\n",
              "[3 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7SbdQYYGUQzT"
      },
      "source": [
        "Rider Drop_off_Ranges"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "ix7qTLHCWF6C",
        "outputId": "059181ab-f7f6-48a2-afc7-c0bfe3158a57"
      },
      "source": [
        "# Let's also visualize the distance between the rider and drop off location\n",
        "\n",
        "bin_values = np.arange(start=-0, stop=30, step=0.3)\n",
        "df['rider_dropoff_dist'].hist(bins=bin_values, figsize=[14,6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f407596a610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4-N75D3RWOt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8f8e0ef7-ac9c-494d-c1ae-306557a73a42"
      },
      "source": [
        "# create a list of our conditions\n",
        "conditions = [\n",
        "    (df['rider_dropoff_dist'] <= 5),\n",
        "    (df['rider_dropoff_dist'] > 5) & (df['rider_dropoff_dist'] <= 10),\n",
        "    (df['rider_dropoff_dist'] > 10) & (df['rider_dropoff_dist'] <= 18),\n",
        "    (df['rider_dropoff_dist'] > 18) & (df['rider_dropoff_dist'] <= 30),\n",
        "    (df['rider_dropoff_dist'] > 30)\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['1', '2', '3', '4','5']\n",
        "\n",
        "# create a new column and use np.select to assign values to it using our lists as arguments\n",
        "df['rider_dropoff_dist_range'] = np.select(conditions, values)\n",
        "\n",
        "# display updated DataFrame\n",
        "df.head(3)"
      ],
      "execution_count": null,
      "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>ID</th>\n",
              "      <th>order_id</th>\n",
              "      <th>dispatch_day</th>\n",
              "      <th>dispatch_day_of_week</th>\n",
              "      <th>dispatch_time</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_type</th>\n",
              "      <th>order_license_status</th>\n",
              "      <th>order_carrier_type</th>\n",
              "      <th>vendor_type</th>\n",
              "      <th>rider_id</th>\n",
              "      <th>rider_license_status</th>\n",
              "      <th>rider_carrier_type</th>\n",
              "      <th>rider_amount</th>\n",
              "      <th>rider_lat</th>\n",
              "      <th>rider_long</th>\n",
              "      <th>pickup_lat</th>\n",
              "      <th>pickup_long</th>\n",
              "      <th>drop_off_lat</th>\n",
              "      <th>drop_off_long</th>\n",
              "      <th>rider_amount_range</th>\n",
              "      <th>dispatch_day_range</th>\n",
              "      <th>weekend</th>\n",
              "      <th>dispatch_hour</th>\n",
              "      <th>dispatch_hour_sine</th>\n",
              "      <th>dispatch_hour_cos</th>\n",
              "      <th>busy_hours</th>\n",
              "      <th>rider_dropoff_dist</th>\n",
              "      <th>rider_pickup_dist</th>\n",
              "      <th>pickup_dropoff_dist</th>\n",
              "      <th>pickup_dropoff_dist_range</th>\n",
              "      <th>rider_pickup_dist_range</th>\n",
              "      <th>rider_dropoff_dist_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_SCUW21PVAU</td>\n",
              "      <td>4435</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "      <td>2021-03-28 09:02:54</td>\n",
              "      <td>593630</td>\n",
              "      <td>Business</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>30153</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1080</td>\n",
              "      <td>-42.698343</td>\n",
              "      <td>-17.228539</td>\n",
              "      <td>-42.692371</td>\n",
              "      <td>-17.248305</td>\n",
              "      <td>-42.687442</td>\n",
              "      <td>-17.424682</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.707107</td>\n",
              "      <td>-0.707107</td>\n",
              "      <td>4</td>\n",
              "      <td>16.076117</td>\n",
              "      <td>1.746491</td>\n",
              "      <td>14.426076</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_2HA7X30JMN</td>\n",
              "      <td>32711</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>2021-03-28 13:01:37</td>\n",
              "      <td>837729</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bike</td>\n",
              "      <td>20884</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>-42.787317</td>\n",
              "      <td>-17.288252</td>\n",
              "      <td>-42.784046</td>\n",
              "      <td>-17.290121</td>\n",
              "      <td>-42.673267</td>\n",
              "      <td>-17.234595</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>-0.258819</td>\n",
              "      <td>-0.965926</td>\n",
              "      <td>4</td>\n",
              "      <td>13.417651</td>\n",
              "      <td>0.394373</td>\n",
              "      <td>13.126454</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_IAJWDTBY6M</td>\n",
              "      <td>8712</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>2021-03-28 10:01:00</td>\n",
              "      <td>695129</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>33143</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>490</td>\n",
              "      <td>-42.749180</td>\n",
              "      <td>-17.287848</td>\n",
              "      <td>-42.765204</td>\n",
              "      <td>-17.293784</td>\n",
              "      <td>-42.813953</td>\n",
              "      <td>-17.294805</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>-0.866025</td>\n",
              "      <td>4</td>\n",
              "      <td>7.224872</td>\n",
              "      <td>1.846564</td>\n",
              "      <td>5.421337</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              ID  order_id  ...  rider_pickup_dist_range  rider_dropoff_dist_range\n",
              "0  ID_SCUW21PVAU      4435  ...                        3                         3\n",
              "1  ID_2HA7X30JMN     32711  ...                        1                         3\n",
              "2  ID_IAJWDTBY6M      8712  ...                        3                         2\n",
              "\n",
              "[3 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j25FtQ-BNtbQ"
      },
      "source": [
        "Time and Distance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXROX-OGNxp0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "outputId": "7a7e21bf-ee68-415e-f1df-f2e5624be3c1"
      },
      "source": [
        "# create a list of our conditions\n",
        "conditions = [\n",
        "    (df['rider_dropoff_dist_range'] == '5') & (df['busy_hours'] == '1'),\n",
        "    (df['rider_dropoff_dist_range'] == '4') & (df['busy_hours'] == '2'),\n",
        "    (df['rider_dropoff_dist_range'] == '3') & (df['busy_hours'] == '3'),\n",
        "    (df['rider_dropoff_dist_range'] == '2') & (df['busy_hours'] == '4'),\n",
        "    (df['rider_dropoff_dist_range'] == '1') & (df['busy_hours'] == '4'),\n",
        "    (df['rider_dropoff_dist_range'] == '2') & (df['busy_hours'] == '0')\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['1', '2', '3', '4','6','5']\n",
        "\n",
        "# create a new column and use np.select to assign values to it using our lists as arguments\n",
        "df['time_distance_bias'] = np.select(conditions, values)\n",
        "\n",
        "# display updated DataFrame\n",
        "df.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\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>ID</th>\n",
              "      <th>order_id</th>\n",
              "      <th>dispatch_day</th>\n",
              "      <th>dispatch_day_of_week</th>\n",
              "      <th>dispatch_time</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_type</th>\n",
              "      <th>order_license_status</th>\n",
              "      <th>order_carrier_type</th>\n",
              "      <th>vendor_type</th>\n",
              "      <th>rider_id</th>\n",
              "      <th>rider_license_status</th>\n",
              "      <th>rider_carrier_type</th>\n",
              "      <th>rider_amount</th>\n",
              "      <th>rider_lat</th>\n",
              "      <th>rider_long</th>\n",
              "      <th>pickup_lat</th>\n",
              "      <th>pickup_long</th>\n",
              "      <th>drop_off_lat</th>\n",
              "      <th>drop_off_long</th>\n",
              "      <th>rider_amount_range</th>\n",
              "      <th>dispatch_day_range</th>\n",
              "      <th>weekend</th>\n",
              "      <th>dispatch_hour</th>\n",
              "      <th>dispatch_hour_sine</th>\n",
              "      <th>dispatch_hour_cos</th>\n",
              "      <th>busy_hours</th>\n",
              "      <th>rider_dropoff_dist</th>\n",
              "      <th>rider_pickup_dist</th>\n",
              "      <th>pickup_dropoff_dist</th>\n",
              "      <th>pickup_dropoff_dist_range</th>\n",
              "      <th>rider_pickup_dist_range</th>\n",
              "      <th>rider_dropoff_dist_range</th>\n",
              "      <th>time_distance_bias</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_SCUW21PVAU</td>\n",
              "      <td>4435</td>\n",
              "      <td>27</td>\n",
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              "      <td>2021-03-28 09:02:54</td>\n",
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              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>30153</td>\n",
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              "      <td>1</td>\n",
              "      <td>1080</td>\n",
              "      <td>-42.698343</td>\n",
              "      <td>-17.228539</td>\n",
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              "      <td>-17.248305</td>\n",
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              "      <td>0.707107</td>\n",
              "      <td>-0.707107</td>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>ID_2HA7X30JMN</td>\n",
              "      <td>32711</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>2021-03-28 13:01:37</td>\n",
              "      <td>837729</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bike</td>\n",
              "      <td>20884</td>\n",
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              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>-42.787317</td>\n",
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              "      <td>3</td>\n",
              "      <td>4</td>\n",
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              "      <td>13</td>\n",
              "      <td>-0.258819</td>\n",
              "      <td>-0.965926</td>\n",
              "      <td>4</td>\n",
              "      <td>13.417651</td>\n",
              "      <td>0.394373</td>\n",
              "      <td>13.126454</td>\n",
              "      <td>4</td>\n",
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              "      <td>3</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              ID  order_id  ...  rider_dropoff_dist_range  time_distance_bias\n",
              "0  ID_SCUW21PVAU      4435  ...                         3                   0\n",
              "1  ID_2HA7X30JMN     32711  ...                         3                   0\n",
              "\n",
              "[2 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rUM3CoTdaHcu"
      },
      "source": [
        "Weekday vs Time_distance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "PCT1_1t7aHDz",
        "outputId": "7f40a454-66c0-42d8-81c3-692bb9001b47"
      },
      "source": [
        "# create a list of our conditions\n",
        "conditions = [\n",
        "    (df['dispatch_day_of_week'] == '1') & (df['time_distance_bias'] == '5'), # Sunday  early morning & average distance\n",
        "    (df['dispatch_day_of_week'] == '7') & (df['time_distance_bias'] == '3'), # Saturday late night & long distance\n",
        "    (df['dispatch_day_of_week'] == '7') & (df['time_distance_bias'] == '5'), # Saturday early morning average distance\n",
        "    (df['dispatch_day_of_week'] == '0') & (df['time_distance_bias'] == '3'), # Weekday late night & long distance\n",
        "    (df['dispatch_day_of_week'] == '0') & (df['time_distance_bias'] == '6')  # Weekday daytime and average distance\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['1', '2', '3', '4','5']\n",
        "\n",
        "# create a new column and use np.select to assign values to it using our lists as arguments\n",
        "df['weekend_time_distance_bias'] = np.select(conditions, values)\n",
        "\n",
        "# display updated DataFrame\n",
        "df.head(2)"
      ],
      "execution_count": null,
      "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",
              "\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>ID</th>\n",
              "      <th>order_id</th>\n",
              "      <th>dispatch_day</th>\n",
              "      <th>dispatch_day_of_week</th>\n",
              "      <th>dispatch_time</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_type</th>\n",
              "      <th>order_license_status</th>\n",
              "      <th>order_carrier_type</th>\n",
              "      <th>vendor_type</th>\n",
              "      <th>rider_id</th>\n",
              "      <th>rider_license_status</th>\n",
              "      <th>rider_carrier_type</th>\n",
              "      <th>rider_amount</th>\n",
              "      <th>rider_lat</th>\n",
              "      <th>rider_long</th>\n",
              "      <th>pickup_lat</th>\n",
              "      <th>pickup_long</th>\n",
              "      <th>drop_off_lat</th>\n",
              "      <th>drop_off_long</th>\n",
              "      <th>rider_amount_range</th>\n",
              "      <th>dispatch_day_range</th>\n",
              "      <th>weekend</th>\n",
              "      <th>dispatch_hour</th>\n",
              "      <th>dispatch_hour_sine</th>\n",
              "      <th>dispatch_hour_cos</th>\n",
              "      <th>busy_hours</th>\n",
              "      <th>rider_dropoff_dist</th>\n",
              "      <th>rider_pickup_dist</th>\n",
              "      <th>pickup_dropoff_dist</th>\n",
              "      <th>pickup_dropoff_dist_range</th>\n",
              "      <th>rider_pickup_dist_range</th>\n",
              "      <th>rider_dropoff_dist_range</th>\n",
              "      <th>time_distance_bias</th>\n",
              "      <th>weekend_time_distance_bias</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>1.746491</td>\n",
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              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
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              "      <td>7</td>\n",
              "      <td>2021-03-28 13:01:37</td>\n",
              "      <td>837729</td>\n",
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              "      <td>0</td>\n",
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              "      <td>Bike</td>\n",
              "      <td>20884</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>-42.787317</td>\n",
              "      <td>-17.288252</td>\n",
              "      <td>-42.784046</td>\n",
              "      <td>-17.290121</td>\n",
              "      <td>-42.673267</td>\n",
              "      <td>-17.234595</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>-0.258819</td>\n",
              "      <td>-0.965926</td>\n",
              "      <td>4</td>\n",
              "      <td>13.417651</td>\n",
              "      <td>0.394373</td>\n",
              "      <td>13.126454</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              ID  order_id  ...  time_distance_bias  weekend_time_distance_bias\n",
              "0  ID_SCUW21PVAU      4435  ...                   0                           0\n",
              "1  ID_2HA7X30JMN     32711  ...                   0                           0\n",
              "\n",
              "[2 rows x 35 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jo6nn_a811UH"
      },
      "source": [
        "Money Earned VS time & Distance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eWfti7qK11oF",
        "outputId": "6e06e9b8-936f-40bf-bc63-538ef23a84b9"
      },
      "source": [
        "# create a list of our conditions\n",
        "conditions = [\n",
        "    (df['rider_amount_range'] == '4') & (df['busy_hours'] == '4'),\n",
        "    (df['rider_pickup_dist_range'] == '3') & (df['busy_hours'] == '2'),\n",
        "    (df['rider_pickup_dist_range'] == '1') & (df['rider_amount_range'] == '4'),\n",
        "    (df['rider_pickup_dist_range'] == '4') & (df['rider_amount_range'] == '4')\n",
        "    ]\n",
        "\n",
        "# create a list of the values we want to assign for each condition\n",
        "values = ['4','2','6','1']\n",
        "\n",
        "# create a new column and use np.select to assign values to it using our lists as arguments\n",
        "df['money_distance_bias'] = np.select(conditions, values)\n",
        "\n",
        "# display updated DataFrame\n",
        "df.head(3)"
      ],
      "execution_count": null,
      "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>ID</th>\n",
              "      <th>order_id</th>\n",
              "      <th>dispatch_day</th>\n",
              "      <th>dispatch_day_of_week</th>\n",
              "      <th>dispatch_time</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_type</th>\n",
              "      <th>order_license_status</th>\n",
              "      <th>order_carrier_type</th>\n",
              "      <th>vendor_type</th>\n",
              "      <th>rider_id</th>\n",
              "      <th>rider_license_status</th>\n",
              "      <th>rider_carrier_type</th>\n",
              "      <th>rider_amount</th>\n",
              "      <th>rider_lat</th>\n",
              "      <th>rider_long</th>\n",
              "      <th>pickup_lat</th>\n",
              "      <th>pickup_long</th>\n",
              "      <th>drop_off_lat</th>\n",
              "      <th>drop_off_long</th>\n",
              "      <th>rider_amount_range</th>\n",
              "      <th>dispatch_day_range</th>\n",
              "      <th>weekend</th>\n",
              "      <th>dispatch_hour</th>\n",
              "      <th>dispatch_hour_sine</th>\n",
              "      <th>dispatch_hour_cos</th>\n",
              "      <th>busy_hours</th>\n",
              "      <th>rider_dropoff_dist</th>\n",
              "      <th>rider_pickup_dist</th>\n",
              "      <th>pickup_dropoff_dist</th>\n",
              "      <th>pickup_dropoff_dist_range</th>\n",
              "      <th>rider_pickup_dist_range</th>\n",
              "      <th>rider_dropoff_dist_range</th>\n",
              "      <th>time_distance_bias</th>\n",
              "      <th>weekend_time_distance_bias</th>\n",
              "      <th>money_distance_bias</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_SCUW21PVAU</td>\n",
              "      <td>4435</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "      <td>2021-03-28 09:02:54</td>\n",
              "      <td>593630</td>\n",
              "      <td>Business</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>30153</td>\n",
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              "      <td>1</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_2HA7X30JMN</td>\n",
              "      <td>32711</td>\n",
              "      <td>30</td>\n",
              "      <td>7</td>\n",
              "      <td>2021-03-28 13:01:37</td>\n",
              "      <td>837729</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bike</td>\n",
              "      <td>20884</td>\n",
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              "      <td>-17.290121</td>\n",
              "      <td>-42.673267</td>\n",
              "      <td>-17.234595</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
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              "      <td>-0.258819</td>\n",
              "      <td>-0.965926</td>\n",
              "      <td>4</td>\n",
              "      <td>13.417651</td>\n",
              "      <td>0.394373</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_IAJWDTBY6M</td>\n",
              "      <td>8712</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>2021-03-28 10:01:00</td>\n",
              "      <td>695129</td>\n",
              "      <td>Personal</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Bike</td>\n",
              "      <td>33143</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>4</td>\n",
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              "      <td>4</td>\n",
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              "      <td>1.846564</td>\n",
              "      <td>5.421337</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              ID  order_id  ...  weekend_time_distance_bias  money_distance_bias\n",
              "0  ID_SCUW21PVAU      4435  ...                           0                    0\n",
              "1  ID_2HA7X30JMN     32711  ...                           0                    0\n",
              "2  ID_IAJWDTBY6M      8712  ...                           0                    4\n",
              "\n",
              "[3 rows x 36 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3j2Ys73j2gRS"
      },
      "source": [
        "Correcting Dtypes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDA26qKr0HHY"
      },
      "source": [
        "# let's convert some string columns to numeric\n",
        "df[['rider_dropoff_dist_range',\t'time_distance_bias',\t'weekend_time_distance_bias','busy_hours','weekend','money_distance_bias']] = df[['rider_dropoff_dist_range',\t'time_distance_bias',\t'weekend_time_distance_bias','busy_hours','weekend','money_distance_bias']].apply(pd.to_numeric)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFjpENv-Vxmc"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJ6GViaTYmes",
        "outputId": "a7893c6f-6c92-4dfa-c868-ebdd94603ba8"
      },
      "source": [
        "# Lets install catboost for creating a model\n",
        "!pip install catboost"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1e/21/d1718eb4c93d6bacdd540b3792187f32ccb1ad9c51b9c4f10875d63ec176/catboost-0.25-cp37-none-manylinux1_x86_64.whl (67.3MB)\n",
            "\u001b[K     |████████████████████████████████| 67.3MB 53kB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waRxBsBYgN3r"
      },
      "source": [
        "# Split train and test set\n",
        "train = df.iloc[:179867, :]\n",
        "test = df.iloc[179867:, :]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcppiidAVHCG"
      },
      "source": [
        "# Selecting the columns to be used as data for accuracy testing\n",
        "cols = [col for col in train.columns if col not in ['dispatch_time']]\n",
        "\n",
        "# Set the target and independent variables\n",
        "X = train[cols]\n",
        "y = target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCuxJEyEaOK9"
      },
      "source": [
        "test.drop(['dispatch_time'],axis = 1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuQJtltaYqQQ"
      },
      "source": [
        "# Fitting the data to model\n",
        "from catboost import CatBoostClassifier\n",
        "\n",
        "# You can increase the iterations parameter but it will take longer to learn\n",
        "model = CatBoostClassifier(iterations = 1000, early_stopping_rounds = 50)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XoP0CgdhZecY",
        "outputId": "0ce1077d-b606-45d7-e497-7824765bcfcf"
      },
      "source": [
        "# Here we fit our data then make predictions\n",
        "import numpy as np\n",
        "\n",
        "# Since Catboost accepts categorical features we'll make then identifiable to the model \n",
        "categorical_features_indices = np.where(X.dtypes != np.float)[0]\n",
        "\n",
        "# Fitting our data to the model\n",
        "model.fit(X,y,cat_features=categorical_features_indices)\n",
        "\n",
        "# Making Predictions\n",
        "y_pred = model.predict(test)\n",
        "model.score(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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            "343:\tlearn: 0.6607177\ttotal: 12m 47s\tremaining: 13.4s\n",
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            "345:\tlearn: 0.6606751\ttotal: 12m 51s\tremaining: 8.92s\n",
            "346:\tlearn: 0.6606312\ttotal: 12m 53s\tremaining: 6.69s\n",
            "347:\tlearn: 0.6605856\ttotal: 12m 56s\tremaining: 4.46s\n",
            "348:\tlearn: 0.6604617\ttotal: 12m 58s\tremaining: 2.23s\n",
            "349:\tlearn: 0.6604276\ttotal: 13m\tremaining: 0us\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03184575269504689"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYK5BtKjbGGd"
      },
      "source": [
        "# Let's create a submission file to input our predictions\n",
        "submissionFile= pd.DataFrame({\"ID\":df_test[\"ID\"]})\n",
        "submissionFile['target'] = y_pred\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rr_FOnjedsMn"
      },
      "source": [
        "# Saving our data in a CSV\n",
        "submissionFile.to_csv(\"submit.csv\",index=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}