Final_NOTEBOOK_Capstone_Project.ipynb

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    "colab": {
      "name": "Vanshika Raj - Final NOTEBOOK -Team 5 - Capstone Project.ipynb",
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      "display_name": "Python 3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/samchak18/Demand-Prediction-for-Public-Transport-in-Nairobi/blob/main/Final_NOTEBOOK_Capstone_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOGC-qoyhJeX"
      },
      "source": [
        "# <b><u> Project Title : Traffic Jam: Predicting People's Movement into Nairobi </u></b>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y06xIdG26kRF"
      },
      "source": [
        "## <b> Problem Description </b>\n",
        "\n",
        "### This challenge asks you to build a model that predicts the number of seats that Mobiticket can expect to sell for each ride, i.e. for a specific route on a specific date and time. There are 14 routes in this dataset. All of the routes end in Nairobi and originate in towns to the North-West of Nairobi towards Lake Victoria.\n",
        "\n",
        "\n",
        "### The towns from which these routes originate are:\n",
        "\n",
        "* Awendo\n",
        "* Homa Bay\n",
        "* Kehancha\n",
        "* Kendu Bay\n",
        "* Keroka\n",
        "* Keumbu\n",
        "* Kijauri\n",
        "* Kisii\n",
        "* Mbita\n",
        "* Migori\n",
        "* Ndhiwa\n",
        "* Nyachenge\n",
        "* Oyugis\n",
        "* Rodi\n",
        "* Rongo\n",
        "* Sirare\n",
        "* Sori\n",
        "\n",
        "### The routes from these 14 origins to the first stop in the outskirts of Nairobi takes approximately 8 to 9 hours from time of departure. From the first stop in the outskirts of Nairobi into the main bus terminal, where most passengers get off, in Central Business District, takes another 2 to 3 hours depending on traffic.\n",
        "\n",
        "### The three stops that all these routes make in Nairobi (in order) are:\n",
        "\n",
        "1. Kawangware: the first stop in the outskirts of Nairobi\n",
        "2. Westlands\n",
        "3. Afya Centre: the main bus terminal where most passengers disembark\n",
        "\n",
        "### All of these points are mapped [here](https://www.google.com/maps/d/viewer?mid=1Ef2pFdP8keVHHid8bwju2raoRvjOGagN&ll=-0.8281897101491997%2C35.51706279999996&z=8).\n",
        "\n",
        "### Passengers of these bus (or shuttle) rides are affected by Nairobi traffic not only during their ride into the city, but from there they must continue their journey to their final destination in Nairobi wherever that may be. Traffic can act as a deterrent for those who have the option to avoid buses that arrive in Nairobi during peak traffic hours. On the other hand, traffic may be an indication for people’s movement patterns, reflecting business hours, cultural events, political events, and holidays."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlLxAtlziMbP"
      },
      "source": [
        "## <b> Data Description </b>\n",
        "\n",
        "### <b>Nairobi Transport Data.csv (zipped)</b> is the dataset of tickets purchased from Mobiticket for the 14 routes from “up country” into Nairobi between 17 October 2017 and 20 April 2018. This dataset includes the variables: ride_id, seat_number, payment_method, payment_receipt, travel_date, travel_time, travel_from, travel_to, car_type, max_capacity.\n",
        "\n",
        "\n",
        "### Uber Movement traffic data can be accessed [here](https://movement.uber.com). Data is available for Nairobi through June 2018. Uber Movement provided historic hourly travel time between any two points in Nairobi. Any tables that are extracted from the Uber Movement platform can be used in your model.\n",
        "\n",
        "### Variables description:\n",
        "\n",
        "* #### ride_id: unique ID of a vehicle on a specific route on a specific day and time.\n",
        "* #### seat_number: seat assigned to ticket\n",
        "* #### payment_method: method used by customer to purchase ticket from Mobiticket (cash or Mpesa)\n",
        "* #### payment_receipt: unique id number for ticket purchased from Mobiticket\n",
        "* #### travel_date: date of ride departure. (MM/DD/YYYY)\n",
        "* #### travel_time: scheduled departure time of ride. Rides generally depart on time. (hh:mm)\n",
        "* #### travel_from: town from which ride originated\n",
        "* #### travel_to: destination of ride. All rides are to Nairobi.\n",
        "* #### car_type: vehicle type (shuttle or bus)\n",
        "* #### max_capacity: number of seats on the vehicle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MZrtk-daVAw",
        "outputId": "e5e43817-cf51-4d11-886a-c469d629463e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LybP2RqKaT3i"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yk_IfG2rUB4"
      },
      "source": [
        "import datetime"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dByMsuzT8Tnw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "b2242898-a77c-4602-ddaa-7b972a668dd0"
      },
      "source": [
        "# Load the dataset\n",
        "Transport_df = pd.read_csv('/content/drive/MyDrive/AlmaBetter/Cohort Aravali/Module 4/Week 1/Day 1/train_revised.csv')\n",
        "\n",
        "# Print the first 5 rows of the dataframe.\n",
        "Transport_df.head()"
      ],
      "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>ride_id</th>\n",
              "      <th>seat_number</th>\n",
              "      <th>payment_method</th>\n",
              "      <th>payment_receipt</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>travel_from</th>\n",
              "      <th>travel_to</th>\n",
              "      <th>car_type</th>\n",
              "      <th>max_capacity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1442</td>\n",
              "      <td>15A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>UZUEHCBUSO</td>\n",
              "      <td>17-10-17</td>\n",
              "      <td>7:15</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5437</td>\n",
              "      <td>14A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>TIHLBUSGTE</td>\n",
              "      <td>19-11-17</td>\n",
              "      <td>7:12</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5710</td>\n",
              "      <td>8B</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>EQX8Q5G19O</td>\n",
              "      <td>26-11-17</td>\n",
              "      <td>7:05</td>\n",
              "      <td>Keroka</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5777</td>\n",
              "      <td>19A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>SGP18CL0ME</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7:10</td>\n",
              "      <td>Homa Bay</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5778</td>\n",
              "      <td>11A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>BM97HFRGL9</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7:12</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ride_id seat_number payment_method  ... travel_to car_type max_capacity\n",
              "0     1442         15A          Mpesa  ...   Nairobi      Bus           49\n",
              "1     5437         14A          Mpesa  ...   Nairobi      Bus           49\n",
              "2     5710          8B          Mpesa  ...   Nairobi      Bus           49\n",
              "3     5777         19A          Mpesa  ...   Nairobi      Bus           49\n",
              "4     5778         11A          Mpesa  ...   Nairobi      Bus           49\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "mo1N_74wt1Am",
        "outputId": "df134681-1dff-48b2-8978-2f39cc793202"
      },
      "source": [
        "Transport_df.tail()"
      ],
      "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>ride_id</th>\n",
              "      <th>seat_number</th>\n",
              "      <th>payment_method</th>\n",
              "      <th>payment_receipt</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>travel_from</th>\n",
              "      <th>travel_to</th>\n",
              "      <th>car_type</th>\n",
              "      <th>max_capacity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>51640</th>\n",
              "      <td>13826</td>\n",
              "      <td>9B</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>8V2XDDZR6V</td>\n",
              "      <td>20-04-18</td>\n",
              "      <td>8:00</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51641</th>\n",
              "      <td>13809</td>\n",
              "      <td>18A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>4PEBSVJSNK</td>\n",
              "      <td>20-04-18</td>\n",
              "      <td>8:00</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51642</th>\n",
              "      <td>13809</td>\n",
              "      <td>17A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>LVN64LZDNN</td>\n",
              "      <td>20-04-18</td>\n",
              "      <td>8:00</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51643</th>\n",
              "      <td>13796</td>\n",
              "      <td>16B</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>REYBSKTYWN</td>\n",
              "      <td>20-04-18</td>\n",
              "      <td>7:08</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51644</th>\n",
              "      <td>14304</td>\n",
              "      <td>7</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>AQN7FBUSGP</td>\n",
              "      <td>14-11-17</td>\n",
              "      <td>5:10</td>\n",
              "      <td>Kisii</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ride_id seat_number payment_method  ... travel_to car_type max_capacity\n",
              "51640    13826          9B          Mpesa  ...   Nairobi      Bus           49\n",
              "51641    13809         18A          Mpesa  ...   Nairobi      Bus           49\n",
              "51642    13809         17A          Mpesa  ...   Nairobi      Bus           49\n",
              "51643    13796         16B          Mpesa  ...   Nairobi      Bus           49\n",
              "51644    14304           7          Mpesa  ...   Nairobi      Bus           49\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3pj9lfnhC4F"
      },
      "source": [
        "### **Understanding The data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jiWfwLEdg_CM",
        "outputId": "1807d57c-9498-44d3-f33d-b133f90a4876"
      },
      "source": [
        "Transport_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(51645, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MdB_lqRlhPLx"
      },
      "source": [
        "There are 51645 observations in the dataset. And has 10 columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2GFFZSskhNUo",
        "outputId": "774fc981-bb62-490a-9a7a-2a81bce87b11"
      },
      "source": [
        "Transport_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 51645 entries, 0 to 51644\n",
            "Data columns (total 10 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   ride_id          51645 non-null  int64 \n",
            " 1   seat_number      51645 non-null  object\n",
            " 2   payment_method   51645 non-null  object\n",
            " 3   payment_receipt  51645 non-null  object\n",
            " 4   travel_date      51645 non-null  object\n",
            " 5   travel_time      51645 non-null  object\n",
            " 6   travel_from      51645 non-null  object\n",
            " 7   travel_to        51645 non-null  object\n",
            " 8   car_type         51645 non-null  object\n",
            " 9   max_capacity     51645 non-null  int64 \n",
            "dtypes: int64(2), object(8)\n",
            "memory usage: 3.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "YzlVHU7YhjGQ",
        "outputId": "3263e9b3-1fb3-409a-d38c-bc6130afc759"
      },
      "source": [
        "Transport_df.describe(include='object')"
      ],
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>seat_number</th>\n",
              "      <th>payment_method</th>\n",
              "      <th>payment_receipt</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>travel_from</th>\n",
              "      <th>travel_to</th>\n",
              "      <th>car_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>51645</td>\n",
              "      <td>51645</td>\n",
              "      <td>51645</td>\n",
              "      <td>51645</td>\n",
              "      <td>51645</td>\n",
              "      <td>51645</td>\n",
              "      <td>51645</td>\n",
              "      <td>51645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "      <td>51645</td>\n",
              "      <td>149</td>\n",
              "      <td>78</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>1</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>EIK8ICDAQQ</td>\n",
              "      <td>10-12-17</td>\n",
              "      <td>7:09</td>\n",
              "      <td>Kisii</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>2065</td>\n",
              "      <td>51532</td>\n",
              "      <td>1</td>\n",
              "      <td>856</td>\n",
              "      <td>3926</td>\n",
              "      <td>22607</td>\n",
              "      <td>51645</td>\n",
              "      <td>31985</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       seat_number payment_method  ... travel_to car_type\n",
              "count        51645          51645  ...     51645    51645\n",
              "unique          61              2  ...         1        2\n",
              "top              1          Mpesa  ...   Nairobi      Bus\n",
              "freq          2065          51532  ...     51645    31985\n",
              "\n",
              "[4 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RI_4A-fVjx9b",
        "outputId": "e1fe8a2c-3a39-410c-ce76-61d5e7a47abb"
      },
      "source": [
        "Transport_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ride_id            0\n",
              "seat_number        0\n",
              "payment_method     0\n",
              "payment_receipt    0\n",
              "travel_date        0\n",
              "travel_time        0\n",
              "travel_from        0\n",
              "travel_to          0\n",
              "car_type           0\n",
              "max_capacity       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FoqaFmyyS2rt"
      },
      "source": [
        "We don't see any null values in the dataset now.\n",
        "\n",
        "so let us explore the dataset more."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D508Od3ExYVa",
        "outputId": "2764500f-86dc-461b-e40f-0740cd5952a0"
      },
      "source": [
        "len(Transport_df['ride_id'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6249"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4gUJT_bxx0b"
      },
      "source": [
        "column = Transport_df.columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vneyO9Dfyo9k",
        "outputId": "d16d728c-43dc-4f8e-e649-101ba66ebbbc"
      },
      "source": [
        "column"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ride_id', 'seat_number', 'payment_method', 'payment_receipt',\n",
              "       'travel_date', 'travel_time', 'travel_from', 'travel_to', 'car_type',\n",
              "       'max_capacity'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWRzbGUKvQqt"
      },
      "source": [
        "### Finding Our Target Variable\n",
        "\n",
        "Since we are not given the target variable so we need to find target variable first.\n",
        "\n",
        "There might be many ways of finding the target variable but here I am using one way that is I will find the count of each `ride_id` and that will be the `number_of_ticket` as our target variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xoLD9tzaysYB"
      },
      "source": [
        "ride_id_dic = {}\n",
        "for ride_id in Transport_df[\"ride_id\"]:\n",
        "    if not ride_id in ride_id_dic:\n",
        "        ride_id_dic[\"ride_id\"] = 1\n",
        "    else:\n",
        "        ride_id_dic[\"ride_id\"] += 1\n",
        "\n",
        "for i in range(len(Transport_df)):\n",
        "    ride_id = Transport_df.loc[i][\"ride_id\"]\n",
        "    Transport_df.at[i,\"number_of_tickets\"] = ride_id_dic[\"ride_id\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "fIx0I-isJEam",
        "outputId": "43a35457-02ef-4243-f603-db95b4fa6657"
      },
      "source": [
        "Transport_df"
      ],
      "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>ride_id</th>\n",
              "      <th>seat_number</th>\n",
              "      <th>payment_method</th>\n",
              "      <th>payment_receipt</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>travel_from</th>\n",
              "      <th>travel_to</th>\n",
              "      <th>car_type</th>\n",
              "      <th>max_capacity</th>\n",
              "      <th>number_of_tickets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1442</td>\n",
              "      <td>15A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>UZUEHCBUSO</td>\n",
              "      <td>17-10-17</td>\n",
              "      <td>7:15</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5437</td>\n",
              "      <td>14A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>TIHLBUSGTE</td>\n",
              "      <td>19-11-17</td>\n",
              "      <td>7:12</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5710</td>\n",
              "      <td>8B</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>EQX8Q5G19O</td>\n",
              "      <td>26-11-17</td>\n",
              "      <td>7:05</td>\n",
              "      <td>Keroka</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5777</td>\n",
              "      <td>19A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>SGP18CL0ME</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7:10</td>\n",
              "      <td>Homa Bay</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5778</td>\n",
              "      <td>11A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>BM97HFRGL9</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7:12</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51640</th>\n",
              "      <td>13826</td>\n",
              "      <td>9B</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>8V2XDDZR6V</td>\n",
              "      <td>20-04-18</td>\n",
              "      <td>8:00</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51641</th>\n",
              "      <td>13809</td>\n",
              "      <td>18A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>4PEBSVJSNK</td>\n",
              "      <td>20-04-18</td>\n",
              "      <td>8:00</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51642</th>\n",
              "      <td>13809</td>\n",
              "      <td>17A</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>LVN64LZDNN</td>\n",
              "      <td>20-04-18</td>\n",
              "      <td>8:00</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51643</th>\n",
              "      <td>13796</td>\n",
              "      <td>16B</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>REYBSKTYWN</td>\n",
              "      <td>20-04-18</td>\n",
              "      <td>7:08</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51644</th>\n",
              "      <td>14304</td>\n",
              "      <td>7</td>\n",
              "      <td>Mpesa</td>\n",
              "      <td>AQN7FBUSGP</td>\n",
              "      <td>14-11-17</td>\n",
              "      <td>5:10</td>\n",
              "      <td>Kisii</td>\n",
              "      <td>Nairobi</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>51645 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       ride_id seat_number  ... max_capacity number_of_tickets\n",
              "0         1442         15A  ...           49               1.0\n",
              "1         5437         14A  ...           49               1.0\n",
              "2         5710          8B  ...           49               1.0\n",
              "3         5777         19A  ...           49               1.0\n",
              "4         5778         11A  ...           49               1.0\n",
              "...        ...         ...  ...          ...               ...\n",
              "51640    13826          9B  ...           49               1.0\n",
              "51641    13809         18A  ...           49               1.0\n",
              "51642    13809         17A  ...           49               1.0\n",
              "51643    13796         16B  ...           49               1.0\n",
              "51644    14304           7  ...           49               1.0\n",
              "\n",
              "[51645 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nhFX9vySv7rz"
      },
      "source": [
        "#### Now we found our target variable so let us delete the duplicate values from `ride_id` "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNiPFBF2JOz-"
      },
      "source": [
        "Transport_df=Transport_df.drop_duplicates(\"ride_id\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGWNTPbgwjJj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6620e412-21c6-4383-da29-f38f231f4813"
      },
      "source": [
        "Transport_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6249, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QXJQQrA5RNnl"
      },
      "source": [
        "Now let us merge two datasets on `ride_id` key to make the final dataset where we have dependent as well as independent variables too."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3gT4khyT1ut"
      },
      "source": [
        "Now we have found our final dataset so let us remove some features that doesn't seem to be important for predicting the number of tickets in our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTdQoPB19bLF"
      },
      "source": [
        "Transport_df = Transport_df.drop(['seat_number','payment_method','payment_receipt'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gC8VAZ-u5NQq"
      },
      "source": [
        "### **Removing Constant Features**\n",
        "\n",
        "We need to first remove the constant features. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rN-Bmvf9-_h4"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUFkLaH646np"
      },
      "source": [
        "Transport_df.drop([\"travel_to\"],axis=1,inplace=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJe8Nk8hPzMB"
      },
      "source": [
        "### **Feature Engineering**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7uyw07tAY4D"
      },
      "source": [
        "#concatenate travel date and travel time column and make a new feature called date.\n",
        "Transport_df[\"date\"]=Transport_df[\"travel_date\"]+ \" \"+Transport_df[\"travel_time\"]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sZeM4lQ-ANsm"
      },
      "source": [
        "def  time_features(df): \n",
        "\n",
        "  '''This function takes dataframe as an argument and extracts the\n",
        "  different features from the date variable of the dataset and finaly returns the updated \n",
        "  dataset'''\n",
        "  \n",
        "  df[\"date\"]=pd.to_datetime(df[\"date\"])\n",
        "  df[\"day_of_week\"]=df[\"date\"].dt.dayofweek\n",
        "  df[\"day_of_year\"]=df[\"date\"].dt.dayofyear\n",
        "  df[\"day_of_month\"]=df[\"date\"].dt.day\n",
        "  df[\"hour\"]=df[\"date\"].dt.hour\n",
        "  df[\"minute\"]=df[\"date\"].dt.minute\n",
        "  df[\"is_weekend\"]=df[\"day_of_week\"].apply( lambda x : 1 if x  in [5,6] else 0 )\n",
        "  df[\"year\"]=df[\"date\"].dt.year\n",
        "  df[\"quarter\"]=df[\"date\"].dt.quarter\n",
        "  df[\"month\"]=df[\"date\"].dt.month\n",
        "  return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6zeNvx-kwqRE"
      },
      "source": [
        "Transport_df1 = time_features(Transport_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekKq0AlcwxNp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "76b4bc8c-5979-46a2-e7cf-9cefacdf5416"
      },
      "source": [
        "Transport_df1.head()"
      ],
      "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>ride_id</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>travel_from</th>\n",
              "      <th>car_type</th>\n",
              "      <th>max_capacity</th>\n",
              "      <th>number_of_tickets</th>\n",
              "      <th>date</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>day_of_month</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1442</td>\n",
              "      <td>17-10-17</td>\n",
              "      <td>7:15</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-10-17 07:15:00</td>\n",
              "      <td>1</td>\n",
              "      <td>290</td>\n",
              "      <td>17</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5437</td>\n",
              "      <td>19-11-17</td>\n",
              "      <td>7:12</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-19 07:12:00</td>\n",
              "      <td>6</td>\n",
              "      <td>323</td>\n",
              "      <td>19</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5710</td>\n",
              "      <td>26-11-17</td>\n",
              "      <td>7:05</td>\n",
              "      <td>Keroka</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-26 07:05:00</td>\n",
              "      <td>6</td>\n",
              "      <td>330</td>\n",
              "      <td>26</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5777</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7:10</td>\n",
              "      <td>Homa Bay</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-27 07:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>331</td>\n",
              "      <td>27</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5778</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7:12</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-27 07:12:00</td>\n",
              "      <td>0</td>\n",
              "      <td>331</td>\n",
              "      <td>27</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ride_id travel_date travel_time  ...  year quarter  month\n",
              "0     1442    17-10-17        7:15  ...  2017       4     10\n",
              "1     5437    19-11-17        7:12  ...  2017       4     11\n",
              "2     5710    26-11-17        7:05  ...  2017       4     11\n",
              "3     5777    27-11-17        7:10  ...  2017       4     11\n",
              "4     5778    27-11-17        7:12  ...  2017       4     11\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTHgL7MCxjkO"
      },
      "source": [
        "Transport_df1[\"travel_time\"] = Transport_df1[\"travel_time\"].str.split(':').apply(lambda x: int(x[0]) + int(x[1])/60)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzGQVPLtyxMW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "77ac3434-dae1-4152-fc77-8c7cdb1a8e6e"
      },
      "source": [
        "Transport_df1['period'] = np.nan\n",
        "Transport_df1.loc[Transport_df1.travel_time < 7, 'period'] = 'em'\n",
        "Transport_df1.loc[(Transport_df1.travel_time >= 7) & (Transport_df1.travel_time < 11), 'period'] = 'am'\n",
        "Transport_df1.loc[(Transport_df1.travel_time >= 11) & (Transport_df1.travel_time < 15), 'period'] = 'mid'\n",
        "Transport_df1.loc[(Transport_df1.travel_time >= 15) & (Transport_df1.travel_time < 19), 'period'] = 'eve'\n",
        "Transport_df1.loc[(Transport_df1.travel_time >= 19) & (Transport_df1.travel_time <= 24), 'period'] = 'pm'\n",
        "\n",
        "pcount = dict(Transport_df1['period'].value_counts())\n",
        "pcount\n",
        "\n",
        "Transport_df1['hourly_travelers'] = np.log1p(Transport_df1['period'].map(pcount))\n",
        "\n",
        "dcount = dict(Transport_df1[\"day_of_year\"].value_counts())\n",
        "Transport_df1[\"daily_travelers\"] = np.log1p(Transport_df1[\"day_of_year\"].map(dcount))\n",
        "Transport_df1.head()"
      ],
      "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>ride_id</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>travel_from</th>\n",
              "      <th>car_type</th>\n",
              "      <th>max_capacity</th>\n",
              "      <th>number_of_tickets</th>\n",
              "      <th>date</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>day_of_month</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>period</th>\n",
              "      <th>hourly_travelers</th>\n",
              "      <th>daily_travelers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1442</td>\n",
              "      <td>17-10-17</td>\n",
              "      <td>7.250000</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-10-17 07:15:00</td>\n",
              "      <td>1</td>\n",
              "      <td>290</td>\n",
              "      <td>17</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5437</td>\n",
              "      <td>19-11-17</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-19 07:12:00</td>\n",
              "      <td>6</td>\n",
              "      <td>323</td>\n",
              "      <td>19</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5710</td>\n",
              "      <td>26-11-17</td>\n",
              "      <td>7.083333</td>\n",
              "      <td>Keroka</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-26 07:05:00</td>\n",
              "      <td>6</td>\n",
              "      <td>330</td>\n",
              "      <td>26</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5777</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7.166667</td>\n",
              "      <td>Homa Bay</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-27 07:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>331</td>\n",
              "      <td>27</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.871201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5778</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-27 07:12:00</td>\n",
              "      <td>0</td>\n",
              "      <td>331</td>\n",
              "      <td>27</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.871201</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ride_id travel_date  travel_time  ... period hourly_travelers  daily_travelers\n",
              "0     1442    17-10-17     7.250000  ...     am         8.494743         0.693147\n",
              "1     5437    19-11-17     7.200000  ...     am         8.494743         0.693147\n",
              "2     5710    26-11-17     7.083333  ...     am         8.494743         0.693147\n",
              "3     5777    27-11-17     7.166667  ...     am         8.494743         3.871201\n",
              "4     5778    27-11-17     7.200000  ...     am         8.494743         3.871201\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSU_T86WxLLE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6f983b6-5a41-4d76-e2fc-cdafd5635703"
      },
      "source": [
        "Transport_df1.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ride_id', 'travel_date', 'travel_time', 'travel_from', 'car_type',\n",
              "       'max_capacity', 'number_of_tickets', 'date', 'day_of_week',\n",
              "       'day_of_year', 'day_of_month', 'hour', 'minute', 'is_weekend', 'year',\n",
              "       'quarter', 'month', 'period', 'hourly_travelers', 'daily_travelers'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uE02HAGmxQ2z"
      },
      "source": [
        "new_cols = ['day_of_year','daily_travelers','minute','day_of_month','hourly_travelers','is_weekend','day_of_week']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2bIcC3wqAmN4"
      },
      "source": [
        "transport_dataset=Transport_df1.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "2qf12DUDBR4X",
        "outputId": "362894cc-b1b1-4312-b3f2-b3ae0db2a6a7"
      },
      "source": [
        "transport_dataset.head()"
      ],
      "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>ride_id</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>travel_from</th>\n",
              "      <th>car_type</th>\n",
              "      <th>max_capacity</th>\n",
              "      <th>number_of_tickets</th>\n",
              "      <th>date</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>day_of_month</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>period</th>\n",
              "      <th>hourly_travelers</th>\n",
              "      <th>daily_travelers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1442</td>\n",
              "      <td>17-10-17</td>\n",
              "      <td>7.250000</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-10-17 07:15:00</td>\n",
              "      <td>1</td>\n",
              "      <td>290</td>\n",
              "      <td>17</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5437</td>\n",
              "      <td>19-11-17</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-19 07:12:00</td>\n",
              "      <td>6</td>\n",
              "      <td>323</td>\n",
              "      <td>19</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5710</td>\n",
              "      <td>26-11-17</td>\n",
              "      <td>7.083333</td>\n",
              "      <td>Keroka</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-26 07:05:00</td>\n",
              "      <td>6</td>\n",
              "      <td>330</td>\n",
              "      <td>26</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5777</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7.166667</td>\n",
              "      <td>Homa Bay</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-27 07:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>331</td>\n",
              "      <td>27</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.871201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5778</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-27 07:12:00</td>\n",
              "      <td>0</td>\n",
              "      <td>331</td>\n",
              "      <td>27</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.871201</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ride_id travel_date  travel_time  ... period hourly_travelers  daily_travelers\n",
              "0     1442    17-10-17     7.250000  ...     am         8.494743         0.693147\n",
              "1     5437    19-11-17     7.200000  ...     am         8.494743         0.693147\n",
              "2     5710    26-11-17     7.083333  ...     am         8.494743         0.693147\n",
              "3     5777    27-11-17     7.166667  ...     am         8.494743         3.871201\n",
              "4     5778    27-11-17     7.200000  ...     am         8.494743         3.871201\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XsKIdE5JTL8H",
        "outputId": "a75550f5-110c-44e0-af9a-7acaaf3aa66b"
      },
      "source": [
        "transport_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6249, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W-HqQxs_WQhh",
        "outputId": "dbe4257a-4975-4609-e007-f337e4c4ad61"
      },
      "source": [
        "transport_dataset.month.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12    1050\n",
              "2      985\n",
              "1      896\n",
              "3      853\n",
              "4      600\n",
              "11     414\n",
              "9      262\n",
              "7      254\n",
              "8      251\n",
              "10     247\n",
              "6      225\n",
              "5      212\n",
              "Name: month, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhgHJNyASpmR"
      },
      "source": [
        "dict_cov = {12:1,2:1,1:1,2:1,4:2,11:2,9:3,7:3,8:3,10:3,6:3,5:3}\n",
        "transport_dataset['month'].replace(dict_cov,inplace=True)\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGzp485bX2lA",
        "outputId": "a69ee498-6007-4924-fcb6-c19784c900fc"
      },
      "source": [
        "transport_dataset.month.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2931\n",
              "3    2304\n",
              "2    1014\n",
              "Name: month, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aDyy2ucX8Ye",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38ec6feb-50d5-460c-bb4d-18eb3451325d"
      },
      "source": [
        "transport_dataset.day_of_month.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([17, 19, 26, 27, 20, 18, 16, 15, 14, 13,  4, 28, 31, 30, 29, 25, 24,\n",
              "       23, 22, 21, 12,  3,  2,  1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6DKAv2rYCvq"
      },
      "source": [
        "conv_dict = {17:2, 19:2, 26:3, 27:3, 20:2, 18:2, 16:2, 15:2, 14:2, 13:2,  4:1, 28:2, 31:3, 30:3, 29:3, 25:3, 24:3,\n",
        "       23:3, 22:3, 21:3, 12:1,  3:1,  2:1,  1:2}\n",
        "transport_dataset['day_of_month'].replace(conv_dict,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bB4fGD76ZKpY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91ccda61-e845-4986-8815-4aea374bf5c5"
      },
      "source": [
        "transport_dataset['day_of_month'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2496\n",
              "2    2305\n",
              "3    1448\n",
              "Name: day_of_month, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3A4SSevaE03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "ca38f7b3-5cd2-48d2-aa9e-d76194fd2a0f"
      },
      "source": [
        "transport_dataset.head()"
      ],
      "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>ride_id</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>travel_from</th>\n",
              "      <th>car_type</th>\n",
              "      <th>max_capacity</th>\n",
              "      <th>number_of_tickets</th>\n",
              "      <th>date</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>day_of_month</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>period</th>\n",
              "      <th>hourly_travelers</th>\n",
              "      <th>daily_travelers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1442</td>\n",
              "      <td>17-10-17</td>\n",
              "      <td>7.250000</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-10-17 07:15:00</td>\n",
              "      <td>1</td>\n",
              "      <td>290</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5437</td>\n",
              "      <td>19-11-17</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-19 07:12:00</td>\n",
              "      <td>6</td>\n",
              "      <td>323</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5710</td>\n",
              "      <td>26-11-17</td>\n",
              "      <td>7.083333</td>\n",
              "      <td>Keroka</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-26 07:05:00</td>\n",
              "      <td>6</td>\n",
              "      <td>330</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5777</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7.166667</td>\n",
              "      <td>Homa Bay</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-27 07:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>331</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.871201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5778</td>\n",
              "      <td>27-11-17</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>Migori</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-11-27 07:12:00</td>\n",
              "      <td>0</td>\n",
              "      <td>331</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.871201</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ride_id travel_date  travel_time  ... period hourly_travelers  daily_travelers\n",
              "0     1442    17-10-17     7.250000  ...     am         8.494743         0.693147\n",
              "1     5437    19-11-17     7.200000  ...     am         8.494743         0.693147\n",
              "2     5710    26-11-17     7.083333  ...     am         8.494743         0.693147\n",
              "3     5777    27-11-17     7.166667  ...     am         8.494743         3.871201\n",
              "4     5778    27-11-17     7.200000  ...     am         8.494743         3.871201\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55575NKrcuzX"
      },
      "source": [
        "\n",
        "# Let us find the difference between next and previous buses\n",
        "\n",
        "def find_difference_between_bus(data):\n",
        "  \n",
        "  data.sort_values([\"travel_from\",\"date\"],inplace=True,ascending=True)\n",
        "  data[\"Time_gap_btw_0_1_next_bus\"]=(data[\"date\"]-data.groupby([\"travel_from\"]).date.shift(-1)).dt.total_seconds()/3600\n",
        "  data[\"Time_gap_btw_0_1_previous_bus\"]=(data[\"date\"]-data.groupby([\"travel_from\"]).date.shift(1)).dt.total_seconds()/3600\n",
        "  data[\"Time_gap_btw_0_2_next_bus\"]=(data[\"date\"]-data.groupby([\"travel_from\"]).date.shift(-2)).dt.total_seconds()/3600\n",
        "  data[\"Time_gap_btw_0_2_previous_bus\"]=(data[\"date\"]-data.groupby([\"travel_from\"]).date.shift(2)).dt.total_seconds()/3600\n",
        "  data[\"Time_gap_btw_0_3_next_bus\"]=(data[\"date\"]-data.groupby([\"travel_from\"]).date.shift(-3)).dt.total_seconds()/3600\n",
        "  data[\"Time_gap_btw_0_3_previous_bus\"]=(data[\"date\"]-data.groupby([\"travel_from\"]).date.shift(3)).dt.total_seconds()/3600\n",
        "  data[\"Time_gap_btw_next_previous_bus\"]=(data.groupby([\"travel_from\"]).date.shift(-1)-data.groupby([\"travel_from\"]).date.shift(1)).dt.total_seconds()/3600\n",
        "  cols=[\"Time_gap_btw_0_1_next_bus\", \"Time_gap_btw_0_1_previous_bus\", \"Time_gap_btw_0_2_next_bus\",\"Time_gap_btw_0_2_previous_bus\",\n",
        "      \"Time_gap_btw_0_3_next_bus\", \"Time_gap_btw_0_3_previous_bus\",\"Time_gap_btw_next_previous_bus\"]\n",
        "  data[cols]=data.groupby([\"travel_from\"])[cols].fillna(method=\"ffill\")\n",
        "  data[cols]=data.groupby([\"travel_from\"])[cols].fillna(method=\"backfill\")\n",
        "\n",
        "  return data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jB3K5FbXV6dN"
      },
      "source": [
        "data = find_difference_between_bus(transport_dataset)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGQGxNCXWXAs"
      },
      "source": [
        "We see that there are some null values present in the dataset so let us remove those null values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJo_UAik_n6m"
      },
      "source": [
        "data.dropna(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "rGichOnRdGS1",
        "outputId": "f3bbbbdc-b03b-4143-d004-a6231be5488e"
      },
      "source": [
        "data.head()"
      ],
      "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>ride_id</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>travel_from</th>\n",
              "      <th>car_type</th>\n",
              "      <th>max_capacity</th>\n",
              "      <th>number_of_tickets</th>\n",
              "      <th>date</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>day_of_month</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>period</th>\n",
              "      <th>hourly_travelers</th>\n",
              "      <th>daily_travelers</th>\n",
              "      <th>Time_gap_btw_0_1_next_bus</th>\n",
              "      <th>Time_gap_btw_0_1_previous_bus</th>\n",
              "      <th>Time_gap_btw_0_2_next_bus</th>\n",
              "      <th>Time_gap_btw_0_2_previous_bus</th>\n",
              "      <th>Time_gap_btw_0_3_next_bus</th>\n",
              "      <th>Time_gap_btw_0_3_previous_bus</th>\n",
              "      <th>Time_gap_btw_next_previous_bus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2152</th>\n",
              "      <td>6654</td>\n",
              "      <td>05-12-17</td>\n",
              "      <td>7.133333</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-05-12 07:08:00</td>\n",
              "      <td>4</td>\n",
              "      <td>132</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>4.025352</td>\n",
              "      <td>-5688.000000</td>\n",
              "      <td>5688.000000</td>\n",
              "      <td>-5688.066667</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-5688.083333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44611</th>\n",
              "      <td>12926</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>7.133333</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 07:08:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.066667</td>\n",
              "      <td>5688.000000</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-0.866667</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44617</th>\n",
              "      <td>12929</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 07:12:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.016667</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>-0.800000</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-215.933333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>0.083333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44650</th>\n",
              "      <td>12935</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>7.216667</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 07:13:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.783333</td>\n",
              "      <td>0.016667</td>\n",
              "      <td>-215.916667</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>-216.033333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44609</th>\n",
              "      <td>12924</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 08:00:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-215.133333</td>\n",
              "      <td>0.783333</td>\n",
              "      <td>-215.250000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>-239.133333</td>\n",
              "      <td>0.866667</td>\n",
              "      <td>215.916667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ride_id  ... Time_gap_btw_next_previous_bus\n",
              "2152      6654  ...                    5688.066667\n",
              "44611    12926  ...                    5688.066667\n",
              "44617    12929  ...                       0.083333\n",
              "44650    12935  ...                       0.800000\n",
              "44609    12924  ...                     215.916667\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIwXXWyqpVrh"
      },
      "source": [
        "from datetime import datetime\n",
        "from datetime import timedelta"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "5iukzFCCiWIU",
        "outputId": "982151e5-7298-4281-a9b3-6fc9ea1cc282"
      },
      "source": [
        "data.head()"
      ],
      "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>ride_id</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>travel_from</th>\n",
              "      <th>car_type</th>\n",
              "      <th>max_capacity</th>\n",
              "      <th>number_of_tickets</th>\n",
              "      <th>date</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>day_of_month</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>period</th>\n",
              "      <th>hourly_travelers</th>\n",
              "      <th>daily_travelers</th>\n",
              "      <th>Time_gap_btw_0_1_next_bus</th>\n",
              "      <th>Time_gap_btw_0_1_previous_bus</th>\n",
              "      <th>Time_gap_btw_0_2_next_bus</th>\n",
              "      <th>Time_gap_btw_0_2_previous_bus</th>\n",
              "      <th>Time_gap_btw_0_3_next_bus</th>\n",
              "      <th>Time_gap_btw_0_3_previous_bus</th>\n",
              "      <th>Time_gap_btw_next_previous_bus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2152</th>\n",
              "      <td>6654</td>\n",
              "      <td>05-12-17</td>\n",
              "      <td>7.133333</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-05-12 07:08:00</td>\n",
              "      <td>4</td>\n",
              "      <td>132</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>4.025352</td>\n",
              "      <td>-5688.000000</td>\n",
              "      <td>5688.000000</td>\n",
              "      <td>-5688.066667</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-5688.083333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44611</th>\n",
              "      <td>12926</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>7.133333</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 07:08:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.066667</td>\n",
              "      <td>5688.000000</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-0.866667</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44617</th>\n",
              "      <td>12929</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 07:12:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.016667</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>-0.800000</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-215.933333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>0.083333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44650</th>\n",
              "      <td>12935</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>7.216667</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 07:13:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.783333</td>\n",
              "      <td>0.016667</td>\n",
              "      <td>-215.916667</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>-216.033333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44609</th>\n",
              "      <td>12924</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>Awendo</td>\n",
              "      <td>Bus</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 08:00:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-215.133333</td>\n",
              "      <td>0.783333</td>\n",
              "      <td>-215.250000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>-239.133333</td>\n",
              "      <td>0.866667</td>\n",
              "      <td>215.916667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ride_id  ... Time_gap_btw_next_previous_bus\n",
              "2152      6654  ...                    5688.066667\n",
              "44611    12926  ...                    5688.066667\n",
              "44617    12929  ...                       0.083333\n",
              "44650    12935  ...                       0.800000\n",
              "44609    12924  ...                     215.916667\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xy9Vr_KuXZP7"
      },
      "source": [
        "Now let us find the distance of each origin point from the destination point.\n",
        "I used Nairobi Map to estimate the distance from origin point to destination map.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV24rean4IsN"
      },
      "source": [
        "distance = {'Migori': 370 , 'Keroka': 280, 'Homa Bay':360, 'Kisii':305.1, 'Keumbu':295, 'Rongo':332,\n",
        "'Kijauri':271, 'Oyugis':330.6, 'Awendo':351, 'Sirare':392, 'Nyachenge':326, 'Kehancha': 387.7,\n",
        "'Kendu Bay':347, 'Sori':399, 'Rodi':348, 'Mbita':401, 'Ndhiwa': 371}\n",
        "data[\"travel_from_distance\"]=data.travel_from.map(distance)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_uAqfXi4Pi2"
      },
      "source": [
        "time = {'Migori': 7*60+8 , 'Keroka': 5*60, 'Homa Bay':7*60, 'Kisii':5*60+34, 'Keumbu':5*60+20, 'Rongo':6*60+21,\n",
        "'Kijauri':60*4+50,'Oyugis':5*60+50, 'Awendo':6*60+38, 'Sirare':7*60+30, 'Nyachenge':6*60+10, 'Kehancha':7*60+10,\n",
        "'Kendu Bay':6*60+10, 'Sori':7*60+30, 'Rodi':6*60+40, 'Mbita':7*60+23, 'Ndhiwa': 7*60}\n",
        "data[\"travel_from_time\"]=data.travel_from.map(time)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3JQXICWH4WQ7"
      },
      "source": [
        "data[\"Speed\"]=data.travel_from_time/data.travel_from_distance"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YsShrSa84gXj"
      },
      "source": [
        "time = {'Migori': 7*60+8 , 'Keroka': 5*60, 'Homa Bay':7*60, 'Kisii':5*60+34, 'Keumbu':5*60+20, 'Rongo':6*60+21,\n",
        "'Kijauri':60*4+50,'Oyugis':5*60+50, 'Awendo':6*60+38, 'Sirare':7*60+30, 'Nyachenge':6*60+10, 'Kehancha':7*60+10,\n",
        "'Kendu Bay':6*60+10, 'Sori':7*60+30, 'Rodi':6*60+40, 'Mbita':7*60+23, 'Ndhiwa': 7*60}\n",
        "for key in time.keys(): \n",
        "    time[key]=timedelta( minutes=time[key])\n",
        "\n",
        "#find the date of traveling from origin \n",
        "data[\"Date_of_traveling_from_origin\"]=data.travel_from.map(time)\n",
        "\n",
        "# find the date of arrival at distination\n",
        "data[\"arrival_date\"]=data.date+ data.Date_of_traveling_from_origin\n",
        "data[\"hod_arrived_date\"]=data[\"arrival_date\"].dt.hour\n",
        "data[\"minute_arrived_date\"]=data[\"arrival_date\"].dt.minute\n",
        "del data[\"Date_of_traveling_from_origin\"],data[\"arrival_date\"]\n",
        "# data[\"is_rush_hour\"]=0\n",
        "# data.loc[data.hod_arrived_date.between(7,17),\"is_rush_hour\"]=1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxU8mGM4Lvs7"
      },
      "source": [
        "### **Encoding Categorical features**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M5vLGG5es8PG"
      },
      "source": [
        "from sklearn import preprocessing #Import LabelEncoder\n",
        "data = pd.get_dummies(data, columns=['travel_from','day_of_month','month'])\n",
        "label_enc = {'Bus':1,'shuttle':0}\n",
        "data.replace(label_enc,inplace=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "FVZRT5lL4p_L",
        "outputId": "60881477-b997-45f4-9c3e-b5b35d1b0ae7"
      },
      "source": [
        "data.head()"
      ],
      "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>ride_id</th>\n",
              "      <th>travel_date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>car_type</th>\n",
              "      <th>max_capacity</th>\n",
              "      <th>number_of_tickets</th>\n",
              "      <th>date</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>period</th>\n",
              "      <th>hourly_travelers</th>\n",
              "      <th>daily_travelers</th>\n",
              "      <th>Time_gap_btw_0_1_next_bus</th>\n",
              "      <th>Time_gap_btw_0_1_previous_bus</th>\n",
              "      <th>Time_gap_btw_0_2_next_bus</th>\n",
              "      <th>Time_gap_btw_0_2_previous_bus</th>\n",
              "      <th>Time_gap_btw_0_3_next_bus</th>\n",
              "      <th>Time_gap_btw_0_3_previous_bus</th>\n",
              "      <th>Time_gap_btw_next_previous_bus</th>\n",
              "      <th>travel_from_distance</th>\n",
              "      <th>travel_from_time</th>\n",
              "      <th>Speed</th>\n",
              "      <th>hod_arrived_date</th>\n",
              "      <th>minute_arrived_date</th>\n",
              "      <th>travel_from_Awendo</th>\n",
              "      <th>travel_from_Homa Bay</th>\n",
              "      <th>travel_from_Kehancha</th>\n",
              "      <th>travel_from_Keroka</th>\n",
              "      <th>travel_from_Keumbu</th>\n",
              "      <th>travel_from_Kijauri</th>\n",
              "      <th>travel_from_Kisii</th>\n",
              "      <th>travel_from_Mbita</th>\n",
              "      <th>travel_from_Migori</th>\n",
              "      <th>travel_from_Ndhiwa</th>\n",
              "      <th>travel_from_Nyachenge</th>\n",
              "      <th>travel_from_Rodi</th>\n",
              "      <th>travel_from_Rongo</th>\n",
              "      <th>travel_from_Sirare</th>\n",
              "      <th>travel_from_Sori</th>\n",
              "      <th>day_of_month_1</th>\n",
              "      <th>day_of_month_2</th>\n",
              "      <th>day_of_month_3</th>\n",
              "      <th>month_1</th>\n",
              "      <th>month_2</th>\n",
              "      <th>month_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2152</th>\n",
              "      <td>6654</td>\n",
              "      <td>05-12-17</td>\n",
              "      <td>7.133333</td>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017-05-12 07:08:00</td>\n",
              "      <td>4</td>\n",
              "      <td>132</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>2</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>4.025352</td>\n",
              "      <td>-5688.000000</td>\n",
              "      <td>5688.000000</td>\n",
              "      <td>-5688.066667</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-5688.083333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>351.0</td>\n",
              "      <td>398</td>\n",
              "      <td>1.133903</td>\n",
              "      <td>13</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44611</th>\n",
              "      <td>12926</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>7.133333</td>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 07:08:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.066667</td>\n",
              "      <td>5688.000000</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-0.866667</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>351.0</td>\n",
              "      <td>398</td>\n",
              "      <td>1.133903</td>\n",
              "      <td>13</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44617</th>\n",
              "      <td>12929</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 07:12:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.016667</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>-0.800000</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-215.933333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>351.0</td>\n",
              "      <td>398</td>\n",
              "      <td>1.133903</td>\n",
              "      <td>13</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44650</th>\n",
              "      <td>12935</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>7.216667</td>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 07:13:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.783333</td>\n",
              "      <td>0.016667</td>\n",
              "      <td>-215.916667</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>-216.033333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>351.0</td>\n",
              "      <td>398</td>\n",
              "      <td>1.133903</td>\n",
              "      <td>13</td>\n",
              "      <td>51</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44609</th>\n",
              "      <td>12924</td>\n",
              "      <td>01-04-18</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2018-01-04 08:00:00</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>am</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-215.133333</td>\n",
              "      <td>0.783333</td>\n",
              "      <td>-215.250000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>-239.133333</td>\n",
              "      <td>0.866667</td>\n",
              "      <td>215.916667</td>\n",
              "      <td>351.0</td>\n",
              "      <td>398</td>\n",
              "      <td>1.133903</td>\n",
              "      <td>14</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ride_id travel_date  travel_time  ...  month_1  month_2  month_3\n",
              "2152      6654    05-12-17     7.133333  ...        0        0        1\n",
              "44611    12926    01-04-18     7.133333  ...        1        0        0\n",
              "44617    12929    01-04-18     7.200000  ...        1        0        0\n",
              "44650    12935    01-04-18     7.216667  ...        1        0        0\n",
              "44609    12924    01-04-18     8.000000  ...        1        0        0\n",
              "\n",
              "[5 rows x 50 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsC8FbtDHPY7"
      },
      "source": [
        "### .."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1_oLMPdMuo8"
      },
      "source": [
        "### Training the models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SswJh_Wge16b"
      },
      "source": [
        "keys= [\"ride_id\"]\n",
        "Target_name=\"number_of_ticket\"\n",
        "not_used_cols=[\"ride_id\",\"travel_time\" ,\"travel_date\",\"date\",'max_capacity',\"number_of_tickets\",'period']\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4Za_QbYrxN7"
      },
      "source": [
        "features = data.drop(not_used_cols,axis=1).columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0tuic7Cs8zXb",
        "outputId": "f92378ba-0d02-4b4c-ca92-99736c073571"
      },
      "source": [
        "features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['car_type', 'day_of_week', 'day_of_year', 'hour', 'minute',\n",
              "       'is_weekend', 'year', 'quarter', 'hourly_travelers', 'daily_travelers',\n",
              "       'Time_gap_btw_0_1_next_bus', 'Time_gap_btw_0_1_previous_bus',\n",
              "       'Time_gap_btw_0_2_next_bus', 'Time_gap_btw_0_2_previous_bus',\n",
              "       'Time_gap_btw_0_3_next_bus', 'Time_gap_btw_0_3_previous_bus',\n",
              "       'Time_gap_btw_next_previous_bus', 'travel_from_distance',\n",
              "       'travel_from_time', 'Speed', 'hod_arrived_date', 'minute_arrived_date',\n",
              "       'travel_from_Awendo', 'travel_from_Homa Bay', 'travel_from_Kehancha',\n",
              "       'travel_from_Keroka', 'travel_from_Keumbu', 'travel_from_Kijauri',\n",
              "       'travel_from_Kisii', 'travel_from_Mbita', 'travel_from_Migori',\n",
              "       'travel_from_Ndhiwa', 'travel_from_Nyachenge', 'travel_from_Rodi',\n",
              "       'travel_from_Rongo', 'travel_from_Sirare', 'travel_from_Sori',\n",
              "       'day_of_month_1', 'day_of_month_2', 'day_of_month_3', 'month_1',\n",
              "       'month_2', 'month_3'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fdi3G8SsDhLs"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import RepeatedKFold\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error,r2_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KY0DPMBwBvoj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "55fe9c24-1c59-47d3-bd1c-f0ebb4a61278"
      },
      "source": [
        "data[features]"
      ],
      "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>car_type</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>year</th>\n",
              "      <th>quarter</th>\n",
              "      <th>hourly_travelers</th>\n",
              "      <th>daily_travelers</th>\n",
              "      <th>Time_gap_btw_0_1_next_bus</th>\n",
              "      <th>Time_gap_btw_0_1_previous_bus</th>\n",
              "      <th>Time_gap_btw_0_2_next_bus</th>\n",
              "      <th>Time_gap_btw_0_2_previous_bus</th>\n",
              "      <th>Time_gap_btw_0_3_next_bus</th>\n",
              "      <th>Time_gap_btw_0_3_previous_bus</th>\n",
              "      <th>Time_gap_btw_next_previous_bus</th>\n",
              "      <th>travel_from_distance</th>\n",
              "      <th>travel_from_time</th>\n",
              "      <th>Speed</th>\n",
              "      <th>hod_arrived_date</th>\n",
              "      <th>minute_arrived_date</th>\n",
              "      <th>travel_from_Awendo</th>\n",
              "      <th>travel_from_Homa Bay</th>\n",
              "      <th>travel_from_Kehancha</th>\n",
              "      <th>travel_from_Keroka</th>\n",
              "      <th>travel_from_Keumbu</th>\n",
              "      <th>travel_from_Kijauri</th>\n",
              "      <th>travel_from_Kisii</th>\n",
              "      <th>travel_from_Mbita</th>\n",
              "      <th>travel_from_Migori</th>\n",
              "      <th>travel_from_Ndhiwa</th>\n",
              "      <th>travel_from_Nyachenge</th>\n",
              "      <th>travel_from_Rodi</th>\n",
              "      <th>travel_from_Rongo</th>\n",
              "      <th>travel_from_Sirare</th>\n",
              "      <th>travel_from_Sori</th>\n",
              "      <th>day_of_month_1</th>\n",
              "      <th>day_of_month_2</th>\n",
              "      <th>day_of_month_3</th>\n",
              "      <th>month_1</th>\n",
              "      <th>month_2</th>\n",
              "      <th>month_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2152</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>132</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>2</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>4.025352</td>\n",
              "      <td>-5688.000000</td>\n",
              "      <td>5688.000000</td>\n",
              "      <td>-5688.066667</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-5688.083333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>351.0</td>\n",
              "      <td>398</td>\n",
              "      <td>1.133903</td>\n",
              "      <td>13</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44611</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.066667</td>\n",
              "      <td>5688.000000</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-0.866667</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>351.0</td>\n",
              "      <td>398</td>\n",
              "      <td>1.133903</td>\n",
              "      <td>13</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44617</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.016667</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>-0.800000</td>\n",
              "      <td>5688.066667</td>\n",
              "      <td>-215.933333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>351.0</td>\n",
              "      <td>398</td>\n",
              "      <td>1.133903</td>\n",
              "      <td>13</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44650</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-0.783333</td>\n",
              "      <td>0.016667</td>\n",
              "      <td>-215.916667</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>-216.033333</td>\n",
              "      <td>5688.083333</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>351.0</td>\n",
              "      <td>398</td>\n",
              "      <td>1.133903</td>\n",
              "      <td>13</td>\n",
              "      <td>51</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44609</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.526361</td>\n",
              "      <td>-215.133333</td>\n",
              "      <td>0.783333</td>\n",
              "      <td>-215.250000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>-239.133333</td>\n",
              "      <td>0.866667</td>\n",
              "      <td>215.916667</td>\n",
              "      <td>351.0</td>\n",
              "      <td>398</td>\n",
              "      <td>1.133903</td>\n",
              "      <td>14</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47649</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>277</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.891820</td>\n",
              "      <td>-744.000000</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>-760.083333</td>\n",
              "      <td>4015.916667</td>\n",
              "      <td>-1464.000000</td>\n",
              "      <td>4032.000000</td>\n",
              "      <td>1464.000000</td>\n",
              "      <td>399.0</td>\n",
              "      <td>450</td>\n",
              "      <td>1.127820</td>\n",
              "      <td>14</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48048</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>308</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.970292</td>\n",
              "      <td>-16.083333</td>\n",
              "      <td>744.000000</td>\n",
              "      <td>-720.000000</td>\n",
              "      <td>1464.000000</td>\n",
              "      <td>-736.083333</td>\n",
              "      <td>4759.916667</td>\n",
              "      <td>760.083333</td>\n",
              "      <td>399.0</td>\n",
              "      <td>450</td>\n",
              "      <td>1.127820</td>\n",
              "      <td>14</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47716</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>308</td>\n",
              "      <td>23</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>5.659482</td>\n",
              "      <td>3.970292</td>\n",
              "      <td>-703.916667</td>\n",
              "      <td>16.083333</td>\n",
              "      <td>-720.000000</td>\n",
              "      <td>760.083333</td>\n",
              "      <td>-736.083333</td>\n",
              "      <td>1480.083333</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>399.0</td>\n",
              "      <td>450</td>\n",
              "      <td>1.127820</td>\n",
              "      <td>6</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48412</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>338</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>8.494743</td>\n",
              "      <td>3.951244</td>\n",
              "      <td>-16.083333</td>\n",
              "      <td>703.916667</td>\n",
              "      <td>-720.000000</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>-736.083333</td>\n",
              "      <td>1464.000000</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>399.0</td>\n",
              "      <td>450</td>\n",
              "      <td>1.127820</td>\n",
              "      <td>14</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48114</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>338</td>\n",
              "      <td>23</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>5.659482</td>\n",
              "      <td>3.951244</td>\n",
              "      <td>-16.083333</td>\n",
              "      <td>16.083333</td>\n",
              "      <td>-720.000000</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>-736.083333</td>\n",
              "      <td>736.083333</td>\n",
              "      <td>720.000000</td>\n",
              "      <td>399.0</td>\n",
              "      <td>450</td>\n",
              "      <td>1.127820</td>\n",
              "      <td>6</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6246 rows × 43 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       car_type  day_of_week  day_of_year  ...  month_1  month_2  month_3\n",
              "2152          1            4          132  ...        0        0        1\n",
              "44611         1            3            4  ...        1        0        0\n",
              "44617         1            3            4  ...        1        0        0\n",
              "44650         1            3            4  ...        1        0        0\n",
              "44609         1            3            4  ...        1        0        0\n",
              "...         ...          ...          ...  ...      ...      ...      ...\n",
              "47649         1            3          277  ...        0        0        1\n",
              "48048         1            6          308  ...        0        1        0\n",
              "47716         1            6          308  ...        0        1        0\n",
              "48412         1            1          338  ...        1        0        0\n",
              "48114         1            1          338  ...        1        0        0\n",
              "\n",
              "[6246 rows x 43 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0rPBZgcfli9",
        "outputId": "710466e8-550d-474f-f48c-4c6e0846446a"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 6246 entries, 2152 to 48114\n",
            "Data columns (total 50 columns):\n",
            " #   Column                          Non-Null Count  Dtype         \n",
            "---  ------                          --------------  -----         \n",
            " 0   ride_id                         6246 non-null   int64         \n",
            " 1   travel_date                     6246 non-null   object        \n",
            " 2   travel_time                     6246 non-null   float64       \n",
            " 3   car_type                        6246 non-null   int64         \n",
            " 4   max_capacity                    6246 non-null   int64         \n",
            " 5   number_of_tickets               6246 non-null   float64       \n",
            " 6   date                            6246 non-null   datetime64[ns]\n",
            " 7   day_of_week                     6246 non-null   int64         \n",
            " 8   day_of_year                     6246 non-null   int64         \n",
            " 9   hour                            6246 non-null   int64         \n",
            " 10  minute                          6246 non-null   int64         \n",
            " 11  is_weekend                      6246 non-null   int64         \n",
            " 12  year                            6246 non-null   int64         \n",
            " 13  quarter                         6246 non-null   int64         \n",
            " 14  period                          6246 non-null   object        \n",
            " 15  hourly_travelers                6246 non-null   float64       \n",
            " 16  daily_travelers                 6246 non-null   float64       \n",
            " 17  Time_gap_btw_0_1_next_bus       6246 non-null   float64       \n",
            " 18  Time_gap_btw_0_1_previous_bus   6246 non-null   float64       \n",
            " 19  Time_gap_btw_0_2_next_bus       6246 non-null   float64       \n",
            " 20  Time_gap_btw_0_2_previous_bus   6246 non-null   float64       \n",
            " 21  Time_gap_btw_0_3_next_bus       6246 non-null   float64       \n",
            " 22  Time_gap_btw_0_3_previous_bus   6246 non-null   float64       \n",
            " 23  Time_gap_btw_next_previous_bus  6246 non-null   float64       \n",
            " 24  travel_from_distance            6246 non-null   float64       \n",
            " 25  travel_from_time                6246 non-null   int64         \n",
            " 26  Speed                           6246 non-null   float64       \n",
            " 27  hod_arrived_date                6246 non-null   int64         \n",
            " 28  minute_arrived_date             6246 non-null   int64         \n",
            " 29  travel_from_Awendo              6246 non-null   uint8         \n",
            " 30  travel_from_Homa Bay            6246 non-null   uint8         \n",
            " 31  travel_from_Kehancha            6246 non-null   uint8         \n",
            " 32  travel_from_Keroka              6246 non-null   uint8         \n",
            " 33  travel_from_Keumbu              6246 non-null   uint8         \n",
            " 34  travel_from_Kijauri             6246 non-null   uint8         \n",
            " 35  travel_from_Kisii               6246 non-null   uint8         \n",
            " 36  travel_from_Mbita               6246 non-null   uint8         \n",
            " 37  travel_from_Migori              6246 non-null   uint8         \n",
            " 38  travel_from_Ndhiwa              6246 non-null   uint8         \n",
            " 39  travel_from_Nyachenge           6246 non-null   uint8         \n",
            " 40  travel_from_Rodi                6246 non-null   uint8         \n",
            " 41  travel_from_Rongo               6246 non-null   uint8         \n",
            " 42  travel_from_Sirare              6246 non-null   uint8         \n",
            " 43  travel_from_Sori                6246 non-null   uint8         \n",
            " 44  day_of_month_1                  6246 non-null   uint8         \n",
            " 45  day_of_month_2                  6246 non-null   uint8         \n",
            " 46  day_of_month_3                  6246 non-null   uint8         \n",
            " 47  month_1                         6246 non-null   uint8         \n",
            " 48  month_2                         6246 non-null   uint8         \n",
            " 49  month_3                         6246 non-null   uint8         \n",
            "dtypes: datetime64[ns](1), float64(13), int64(13), object(2), uint8(21)\n",
            "memory usage: 1.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjX8FqOwQDHI"
      },
      "source": [
        "### **Training Gradient Boosting Regressor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVXf2DAepr8C",
        "outputId": "5818b918-fd1d-4af6-bcc1-7924d7c638f6"
      },
      "source": [
        "used_cols = ['day_of_year']\n",
        "# prev_adjR2=0\n",
        "\n",
        "for col in features:\n",
        "  used_cols.append(col)\n",
        "  dataset_pr = data[used_cols]\n",
        "  X= dataset_pr.copy()\n",
        "  y = data['number_of_tickets']\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33, random_state=42)\n",
        "  reg = GradientBoostingRegressor(random_state=0)\n",
        "  reg.fit(X_train, y_train)\n",
        "\n",
        "  # Predicting \n",
        "  y_pred = reg.predict(X_test)\n",
        "\n",
        "  #Find R-squared value\n",
        "  r2 = r2_score(y_test, y_pred)\n",
        "  \n",
        "  # Find Adjusted R-squared value\n",
        "  adj_r2=1-(1-r2_score(y_test, y_pred))*((X_test.shape[0]-1)/(X_test.shape[0]-X_test.shape[1]-1))\n",
        "  # if prev_adjR2<adj_r2:\n",
        "  #   prev_adjR2 = adj_r2\n",
        "  #   train_score = reg.score(X_train, y_train)\n",
        "  #   test_score = reg.score(X_test,y_test)\n",
        "  # else:\n",
        "  #   used_cols.pop()\n",
        "\n",
        "# print(f'Adjust_r2: {prev_adjR2}')\n",
        "# print(f'Train score: {train_score}')\n",
        "# print(f'Test score: {test_score}')\n",
        "# print('Important features are: \\n')\n",
        "used_cols"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['day_of_year',\n",
              " 'car_type',\n",
              " 'day_of_week',\n",
              " 'day_of_year',\n",
              " 'hour',\n",
              " 'minute',\n",
              " 'is_weekend',\n",
              " 'year',\n",
              " 'quarter',\n",
              " 'hourly_travelers',\n",
              " 'daily_travelers',\n",
              " 'Time_gap_btw_0_1_next_bus',\n",
              " 'Time_gap_btw_0_1_previous_bus',\n",
              " 'Time_gap_btw_0_2_next_bus',\n",
              " 'Time_gap_btw_0_2_previous_bus',\n",
              " 'Time_gap_btw_0_3_next_bus',\n",
              " 'Time_gap_btw_0_3_previous_bus',\n",
              " 'Time_gap_btw_next_previous_bus',\n",
              " 'travel_from_distance',\n",
              " 'travel_from_time',\n",
              " 'Speed',\n",
              " 'hod_arrived_date',\n",
              " 'minute_arrived_date',\n",
              " 'travel_from_Awendo',\n",
              " 'travel_from_Homa Bay',\n",
              " 'travel_from_Kehancha',\n",
              " 'travel_from_Keroka',\n",
              " 'travel_from_Keumbu',\n",
              " 'travel_from_Kijauri',\n",
              " 'travel_from_Kisii',\n",
              " 'travel_from_Mbita',\n",
              " 'travel_from_Migori',\n",
              " 'travel_from_Ndhiwa',\n",
              " 'travel_from_Nyachenge',\n",
              " 'travel_from_Rodi',\n",
              " 'travel_from_Rongo',\n",
              " 'travel_from_Sirare',\n",
              " 'travel_from_Sori',\n",
              " 'day_of_month_1',\n",
              " 'day_of_month_2',\n",
              " 'day_of_month_3',\n",
              " 'month_1',\n",
              " 'month_2',\n",
              " 'month_3']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQD-GgTIGFTT",
        "outputId": "8a8685d0-f39b-4eb2-ba87-aec4eba36962"
      },
      "source": [
        "r2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBzLCfpNGHTL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d849d666-cee9-49d3-9df0-3d297526025d"
      },
      "source": [
        "adj_r2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "12B2h_pzf-J8",
        "outputId": "8af2a6e2-6634-448f-d225-6249ceebce35"
      },
      "source": [
        "data.drop(['travel_date','period','date'],inplace=True,axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-119-0030a02498ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'travel_date'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'period'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4172\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4173\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4174\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4175\u001b[0m         )\n\u001b[1;32m   4176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3887\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3888\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3889\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3891\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3921\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3922\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3923\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3924\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   5285\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5286\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5287\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5288\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5289\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['travel_date' 'period'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "QcUL6VILhaBo",
        "outputId": "06b36732-0748-4599-b8b4-3e5c1a02e46c"
      },
      "source": [
        "data.drop(['date'],inplace=True,axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-142-5b2bbaf2e3b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4172\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4173\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4174\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4175\u001b[0m         )\n\u001b[1;32m   4176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3887\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3888\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3889\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3891\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3921\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3922\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3923\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3924\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   5285\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5286\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5287\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5288\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5289\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['date'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4mogcbKeObu",
        "outputId": "486409c2-ede9-41ee-db1f-adf2a181d76f"
      },
      "source": [
        "used_cols = ['day_of_year']\n",
        "\n",
        "# Data for all the independent variables\n",
        "X = data.drop(labels='number_of_tickets',axis=1)\n",
        "\n",
        "# Data for the dependent variable\n",
        "Y = data['number_of_tickets']\n",
        "\n",
        "## Splitting the data into train and test\n",
        "\n",
        "# Train-test split\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size = 0.25, random_state=42)\n",
        "\n",
        "\n",
        "from xgboost import XGBRegressor\n",
        "XG_model=XGBRegressor()\n",
        "\n",
        "XG_model.fit(X_train,Y_train)\n",
        "\n",
        "\n",
        "y_pred=XG_model.predict(X_test)\n",
        "y_pred_train=XG_model.predict(X_train)\n",
        "    \n",
        "#Find R-squared value\n",
        "r2 = r2_score(Y_test, y_pred)\n",
        "  \n",
        "  # Find Adjusted R-squared value\n",
        "adj_r2=1-(1-r2_score(Y_test, y_pred))*((X_test.shape[0]-1)/(X_test.shape[0]-X_test.shape[1]-1))\n",
        "\n",
        "# print(f'Adjust_r2: {prev_adjR2}')\n",
        "# print(f'Train score: {train_score}')\n",
        "# print(f'Test score: {test_score}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20:07:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5YUinPbhkxj",
        "outputId": "be4434ee-d156-4ac1-a936-ac50898448a9"
      },
      "source": [
        "r2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2225bxx0RHFX"
      },
      "source": [
        "### **Training XGboost**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "56iCH3mKItRf",
        "outputId": "752e8248-9cb3-4871-f03c-d408ec653584"
      },
      "source": [
        "import xgboost as xgb\n",
        "# dataset_pr = data[]\n",
        "X = data[used_cols].copy()\n",
        "y = data['number_of_ticket'].copy()\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33, random_state=42)\n",
        "dreg= xgb.XGBRegressor(\n",
        "                        booster= 'gbtree',\n",
        "                        colsample_bylevel= 1,\n",
        "                        colsample_bynode= 1,\n",
        "                        colsample_bytree= 0.7,\n",
        "                        eta= 0.004,\n",
        "                        gamma= 0,\n",
        "                        importance_type= 'gain',\n",
        "                        learning_rate= 0.1,\n",
        "                        max_delta_step= 0,\n",
        "                        max_depth= 9,\n",
        "                        min_child_weight= 10,\n",
        "                        n_estimators= 100,\n",
        "                        n_jobs= 1,\n",
        "                        objective= 'reg:linear',\n",
        "                        random_state= 0,\n",
        "                        reg_alpha= 0,\n",
        "                        reg_lambda= 1,\n",
        "                        scale_pos_weight= 1,\n",
        "                        subsample= 1,\n",
        "                        verbosity= 1)\n",
        "dreg.fit(X_train, y_train)\n",
        "y_pred = dreg.predict(X_test)\n",
        "#Find R-squared value\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "# Find Adjusted R-squared value\n",
        "adj_r2=1-(1-r2_score(y_test, y_pred))*((X_test.shape[0]-1)/(X_test.shape[0]-X_test.shape[1]-1))\n",
        "train_score = dreg.score(X_train, y_train)\n",
        "test_score = dreg.score(X_test,y_test)\n",
        "print(f'Train score: {train_score}')\n",
        "print(f'Test score: {test_score}')\n",
        "adj_r2\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2898\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'number_of_ticket'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-122-af2658881bae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# dataset_pr = data[]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mused_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'number_of_ticket'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.33\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2904\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2905\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2906\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2907\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2908\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2900\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2902\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'number_of_ticket'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRkHolFwRU_t"
      },
      "source": [
        "from sklearn import decomposition\n",
        "from sklearn import tree\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cgh3CGXTRTG8"
      },
      "source": [
        "### **Decision Tree Regressor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3I-U7t9h4To"
      },
      "source": [
        "X = data[features].copy()\n",
        "y = data['number_of_tickets'].copy()\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9-gpQAxaHq5"
      },
      "source": [
        "dec_reg =RandomForestRegressor(random_state=0,max_depth=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTb-RiKzhBBL",
        "outputId": "1293abe4-7336-486f-e17d-d810118b4e15"
      },
      "source": [
        "dec_reg.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=2, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=0, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mftvqCzJhdDA",
        "outputId": "3df7a0e9-0dc8-40fe-b72a-c1d61458cbad"
      },
      "source": [
        "dec_reg.score(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H39Oy8hnaP8H"
      },
      "source": [
        "parameters = {  'ccp_alpha':[0.01,0.1,1,10,100],\n",
        "              'max_depth': [4, 6, 8],\n",
        "            'min_samples_leaf': [40, 50],\n",
        "            'min_samples_split': [50, 100, 150]  \n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2sqoWzQlXFR"
      },
      "source": [
        "params = {\"min_child_weight\":[10,20], \n",
        "            'eta': [0.004,0.04,4,40], \n",
        "            'colsample_bytree':[0.7], \n",
        "            'max_depth': [7,9,11],\n",
        "          \n",
        "          }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JOCgkLbBqKyL"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lg9wEkL-N8qJ"
      },
      "source": [
        "### **Grid Search CV on XGboost algorithm**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idfBg83Vkn4O"
      },
      "source": [
        "import xgboost as xgb\n",
        "\n",
        "xgb = xgb.XGBRegressor(random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0oeDC0OEk2Ac",
        "outputId": "7e2b4ba3-4bd7-40a3-9fca-293f771d0166"
      },
      "source": [
        "reg_gs = GridSearchCV(xgb,param_grid=params, verbose=1,cv=3)\n",
        "reg_gs.fit(X, y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 24 candidates, totalling 72 fits\n",
            "[19:51:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[19:51:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:51:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[19:52:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  72 out of  72 | elapsed:   22.7s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[19:52:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, error_score=nan,\n",
              "             estimator=XGBRegressor(base_score=0.5, booster='gbtree',\n",
              "                                    colsample_bylevel=1, colsample_bynode=1,\n",
              "                                    colsample_bytree=1, gamma=0,\n",
              "                                    importance_type='gain', learning_rate=0.1,\n",
              "                                    max_delta_step=0, max_depth=3,\n",
              "                                    min_child_weight=1, missing=None,\n",
              "                                    n_estimators=100, n_jobs=1, nthread=None,\n",
              "                                    objective='reg:linear', random_state=0,\n",
              "                                    reg_alpha=0, reg_lambda=1,\n",
              "                                    scale_pos_weight=1, seed=None, silent=None,\n",
              "                                    subsample=1, verbosity=1),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'colsample_bytree': [0.7], 'eta': [0.004, 0.04, 4, 40],\n",
              "                         'max_depth': [7, 9, 11],\n",
              "                         'min_child_weight': [10, 20]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ymm6nUAUbsOO",
        "outputId": "ccbf2b5a-6487-474c-d53d-700a3fae2f9d"
      },
      "source": [
        "reg_gs.best_estimator_.get_params()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'base_score': 0.5,\n",
              " 'booster': 'gbtree',\n",
              " 'colsample_bylevel': 1,\n",
              " 'colsample_bynode': 1,\n",
              " 'colsample_bytree': 0.7,\n",
              " 'eta': 0.004,\n",
              " 'gamma': 0,\n",
              " 'importance_type': 'gain',\n",
              " 'learning_rate': 0.1,\n",
              " 'max_delta_step': 0,\n",
              " 'max_depth': 7,\n",
              " 'min_child_weight': 10,\n",
              " 'missing': None,\n",
              " 'n_estimators': 100,\n",
              " 'n_jobs': 1,\n",
              " 'nthread': None,\n",
              " 'objective': 'reg:linear',\n",
              " 'random_state': 0,\n",
              " 'reg_alpha': 0,\n",
              " 'reg_lambda': 1,\n",
              " 'scale_pos_weight': 1,\n",
              " 'seed': None,\n",
              " 'silent': None,\n",
              " 'subsample': 1,\n",
              " 'verbosity': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adOZUNOzdEsr"
      },
      "source": [
        "reg_optimal_model =reg_gs.best_estimator_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-LbVbYM4dQqG"
      },
      "source": [
        "train_preds = reg_optimal_model.predict(X_train)\n",
        "test_preds = reg_optimal_model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjAnTXDZn14G",
        "outputId": "ce03bdf9-fee0-4236-8b13-22ad52dc7ab8"
      },
      "source": [
        "reg_optimal_model.score(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cu83LIgR6wrs"
      },
      "source": [
        "\n",
        "#Find R-squared value\n",
        "r2 = r2_score(y_test, test_preds)\n",
        "# Find Adjusted R-squared value\n",
        "adj_r2=1-(1-r2_score(y_test, test_preds))*((X_test.shape[0]-1)/(X_test.shape[0]-X_test.shape[1]-1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYIaQ0gJ6_FG",
        "outputId": "6d4a0c70-d92b-428f-8ea5-9055a8ef3c6c"
      },
      "source": [
        "\n",
        "r2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qJjDlLVuSqu",
        "outputId": "a7ffeed6-aeb6-49b1-c16a-6badedf10af6"
      },
      "source": [
        "adj_r2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.04484605087014715"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tF6OvvPSdh0n"
      },
      "source": [
        "importances = reg_optimal_model.feature_importances_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zncnadi6dpEa"
      },
      "source": [
        "importance_dict = {'Feature' : list(X_train.columns),\n",
        "                   'Feature Importance' : importances}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMZKUz8Mdtyi"
      },
      "source": [
        "importance_df = pd.DataFrame(importance_dict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5LExJLhzdzb2"
      },
      "source": [
        "important_features=importance_df.sort_values(by=['Feature Importance'],ascending=False).head(20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l91CJZkHs-3M"
      },
      "source": [
        "imp_features = important_features['Feature'].tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qyi6bP4Xd4tU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "c1800aa1-a578-4a25-db46-73933de5faff"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.barplot(y = important_features['Feature'], x = important_features['Feature Importance'] )\n",
        "plt.title('10 Most Important features')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afeQTJppQZXS"
      },
      "source": [
        "### **Evaluating the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NO6jmSGFwf1C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bef524f9-44aa-4077-a0f1-0637578d3a8d"
      },
      "source": [
        "print('Mean Squared Error (MSE): ',mean_squared_error(y_test, test_preds))\n",
        "print('Mean Absolute Error (MAE): ',mean_absolute_error(y_test, test_preds))\n",
        "print('Root Mean Squared Error (MSE): ',np.sqrt(mean_squared_error(y_test, test_preds)))\n",
        "print('R2 Score: ',r2)\n",
        "print('Adjusted R2 Score: ',adj_r2)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error (MSE):  11.849300888127685\n",
            "Mean Absolute Error (MAE):  2.2667203277465098\n",
            "Root Mean Squared Error (MSE):  3.442281349356511\n",
            "R2 Score:  0.8421125457491907\n",
            "Adjusted R2 Score:  0.838668297862708\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqvPgnc-FdLd"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ijv_p9-qxkz8"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqmwvTdlFV3l"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}