Uber_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xypdF9SidM1M"
   },
   "source": [
    "# Uber Data Analysis\n",
    "-----------------------------------------------------------------\n",
    "\n",
    "- Defining the problem statement\n",
    "\n",
    "\n",
    "- Collecting the data\n",
    "    * Kaggle\n",
    "    \n",
    "    \n",
    "- Exploratory data analysis\n",
    "\n",
    "\n",
    "- Feature engineering\n",
    "\n",
    "\n",
    "- Modelling\n",
    "\n",
    "\n",
    "- Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ffa4DvAodM1N"
   },
   "source": [
    "## 1. Defining the problem statement\n",
    "____________________________________________________\n",
    "\n",
    "In this project, we study the data of Uber which is present in tabular format in which we use different libraries like numpy, pandas and matplotlib and different machine learning algorithms. \n",
    "\n",
    "We study different columns of the table and try to co-relate them with others and find a relation between those two. \n",
    "\n",
    "We try to find and analyze those key factors like date, month etc which helps Uber Company to enhance their business by focusing on those services and make required changes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"Uber_image.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url= \"Uber_image.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kf3pyArFdM1N"
   },
   "source": [
    "## 2. Collecting the data\n",
    "___________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "spWe8wX-dM1O"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "uber_dataset = pd.read_csv(r\"C:\\Users\\Manish Arora\\Downloads\\uber_project\\rideshare.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N-viUF78dM1P"
   },
   "source": [
    "## 3. Exploratory data analysis\n",
    "__________________________________________________\n",
    "\n",
    "Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.\n",
    "\n",
    "It is a good practice to understand the data first and try to gather as many insights from it.\n",
    "\n",
    "EDA is all about making sense of data in hand.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "id": "CiH0o0t3dM1P",
    "outputId": "ad22cc1e-e136-4ec1-8a7f-ba83c7e801a8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>datetime</th>\n",
       "      <th>timezone</th>\n",
       "      <th>source</th>\n",
       "      <th>destination</th>\n",
       "      <th>product_id</th>\n",
       "      <th>...</th>\n",
       "      <th>uvIndexTime</th>\n",
       "      <th>temperatureMin</th>\n",
       "      <th>temperatureMinTime</th>\n",
       "      <th>temperatureMax</th>\n",
       "      <th>temperatureMaxTime</th>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <th>apparentTemperatureMinTime</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "      <th>apparentTemperatureMaxTime</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>424553bb-7174-41ea-aeb4-fe06d4f4b9d7</td>\n",
       "      <td>1544952608</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>12/16/2018 9:30</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>North Station</td>\n",
       "      <td>lyft_line</td>\n",
       "      <td>...</td>\n",
       "      <td>1544979600</td>\n",
       "      <td>39.89</td>\n",
       "      <td>1545012000</td>\n",
       "      <td>43.68</td>\n",
       "      <td>1544968800</td>\n",
       "      <td>33.73</td>\n",
       "      <td>1545012000</td>\n",
       "      <td>38.07</td>\n",
       "      <td>1544958000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4bd23055-6827-41c6-b23b-3c491f24e74d</td>\n",
       "      <td>1543284024</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>11/27/2018 2:00</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>North Station</td>\n",
       "      <td>lyft_premier</td>\n",
       "      <td>...</td>\n",
       "      <td>1543251600</td>\n",
       "      <td>40.49</td>\n",
       "      <td>1543233600</td>\n",
       "      <td>47.30</td>\n",
       "      <td>1543251600</td>\n",
       "      <td>36.20</td>\n",
       "      <td>1543291200</td>\n",
       "      <td>43.92</td>\n",
       "      <td>1543251600</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4f9fee41-fde3-4767-bbf1-a00e108701fb</td>\n",
       "      <td>1543818483</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>12/3/2018 6:28</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>lyft_line</td>\n",
       "      <td>...</td>\n",
       "      <td>1543852800</td>\n",
       "      <td>43.09</td>\n",
       "      <td>1543896000</td>\n",
       "      <td>57.02</td>\n",
       "      <td>1543852800</td>\n",
       "      <td>39.90</td>\n",
       "      <td>1543896000</td>\n",
       "      <td>56.35</td>\n",
       "      <td>1543852800</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9043bf77-1d45-4a93-9520-a083e0277f16</td>\n",
       "      <td>1543594384</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>11/30/2018 16:13</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>Back Bay</td>\n",
       "      <td>Northeastern University</td>\n",
       "      <td>lyft_premier</td>\n",
       "      <td>...</td>\n",
       "      <td>1543593600</td>\n",
       "      <td>28.64</td>\n",
       "      <td>1543579200</td>\n",
       "      <td>42.32</td>\n",
       "      <td>1543600800</td>\n",
       "      <td>29.29</td>\n",
       "      <td>1543579200</td>\n",
       "      <td>40.48</td>\n",
       "      <td>1543611600</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>357559cb-8c58-4278-a41a-e33b2e0997a3</td>\n",
       "      <td>1544728504</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>12/13/2018 19:15</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>North End</td>\n",
       "      <td>West End</td>\n",
       "      <td>55c66225-fbe7-4fd5-9072-eab1ece5e23e</td>\n",
       "      <td>...</td>\n",
       "      <td>1544716800</td>\n",
       "      <td>18.29</td>\n",
       "      <td>1544688000</td>\n",
       "      <td>33.83</td>\n",
       "      <td>1544731200</td>\n",
       "      <td>13.79</td>\n",
       "      <td>1544688000</td>\n",
       "      <td>32.85</td>\n",
       "      <td>1544734800</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     id   timestamp  hour  day  month  \\\n",
       "0  424553bb-7174-41ea-aeb4-fe06d4f4b9d7  1544952608     9   16     12   \n",
       "1  4bd23055-6827-41c6-b23b-3c491f24e74d  1543284024     2   27     11   \n",
       "2  4f9fee41-fde3-4767-bbf1-a00e108701fb  1543818483     6    3     12   \n",
       "3  9043bf77-1d45-4a93-9520-a083e0277f16  1543594384    16   30     11   \n",
       "4  357559cb-8c58-4278-a41a-e33b2e0997a3  1544728504    19   13     12   \n",
       "\n",
       "           datetime          timezone            source  \\\n",
       "0   12/16/2018 9:30  America/New_York  Haymarket Square   \n",
       "1   11/27/2018 2:00  America/New_York  Haymarket Square   \n",
       "2    12/3/2018 6:28  America/New_York          Back Bay   \n",
       "3  11/30/2018 16:13  America/New_York          Back Bay   \n",
       "4  12/13/2018 19:15  America/New_York         North End   \n",
       "\n",
       "               destination                            product_id  ...  \\\n",
       "0            North Station                             lyft_line  ...   \n",
       "1            North Station                          lyft_premier  ...   \n",
       "2  Northeastern University                             lyft_line  ...   \n",
       "3  Northeastern University                          lyft_premier  ...   \n",
       "4                 West End  55c66225-fbe7-4fd5-9072-eab1ece5e23e  ...   \n",
       "\n",
       "  uvIndexTime  temperatureMin  temperatureMinTime  temperatureMax  \\\n",
       "0  1544979600           39.89          1545012000           43.68   \n",
       "1  1543251600           40.49          1543233600           47.30   \n",
       "2  1543852800           43.09          1543896000           57.02   \n",
       "3  1543593600           28.64          1543579200           42.32   \n",
       "4  1544716800           18.29          1544688000           33.83   \n",
       "\n",
       "   temperatureMaxTime  apparentTemperatureMin  apparentTemperatureMinTime  \\\n",
       "0          1544968800                   33.73                  1545012000   \n",
       "1          1543251600                   36.20                  1543291200   \n",
       "2          1543852800                   39.90                  1543896000   \n",
       "3          1543600800                   29.29                  1543579200   \n",
       "4          1544731200                   13.79                  1544688000   \n",
       "\n",
       "  apparentTemperatureMax apparentTemperatureMaxTime  price  \n",
       "0                  38.07                 1544958000    5.0  \n",
       "1                  43.92                 1543251600   11.0  \n",
       "2                  56.35                 1543852800    3.0  \n",
       "3                  40.48                 1543611600   13.5  \n",
       "4                  32.85                 1544734800    7.5  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XdZMx0oAdM1U",
    "outputId": "d17c8220-dbf1-4f43-f3c4-9b85353081e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(322844, 56)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X5IjbtnkdM1V",
    "outputId": "5052b360-dc4e-4d4d-84df-9c4a8fc2a7e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 322844 entries, 0 to 322843\n",
      "Data columns (total 56 columns):\n",
      "id                             322844 non-null object\n",
      "timestamp                      322844 non-null int64\n",
      "hour                           322844 non-null int64\n",
      "day                            322844 non-null int64\n",
      "month                          322844 non-null int64\n",
      "datetime                       322844 non-null object\n",
      "timezone                       322844 non-null object\n",
      "source                         322844 non-null object\n",
      "destination                    322844 non-null object\n",
      "product_id                     322844 non-null object\n",
      "name                           322844 non-null object\n",
      "distance                       322844 non-null float64\n",
      "surge_multiplier               322844 non-null float64\n",
      "latitude                       322844 non-null float64\n",
      "longitude                      322844 non-null float64\n",
      "temperature                    322844 non-null float64\n",
      "apparentTemperature            322844 non-null float64\n",
      "short_summary                  322844 non-null object\n",
      "long_summary                   322844 non-null object\n",
      "precipIntensity                322844 non-null float64\n",
      "precipProbability              322844 non-null float64\n",
      "humidity                       322844 non-null float64\n",
      "windSpeed                      322844 non-null float64\n",
      "windGust                       322844 non-null float64\n",
      "windGustTime                   322844 non-null int64\n",
      "visibility                     322844 non-null float64\n",
      "temperatureHigh                322844 non-null float64\n",
      "temperatureHighTime            322844 non-null int64\n",
      "temperatureLow                 322844 non-null float64\n",
      "temperatureLowTime             322844 non-null int64\n",
      "apparentTemperatureHigh        322844 non-null float64\n",
      "apparentTemperatureHighTime    322844 non-null int64\n",
      "apparentTemperatureLow         322844 non-null float64\n",
      "apparentTemperatureLowTime     322844 non-null int64\n",
      "icon                           322844 non-null object\n",
      "dewPoint                       322844 non-null float64\n",
      "pressure                       322844 non-null float64\n",
      "windBearing                    322844 non-null int64\n",
      "cloudCover                     322844 non-null float64\n",
      "uvIndex                        322844 non-null int64\n",
      "visibility.1                   322844 non-null float64\n",
      "ozone                          322844 non-null float64\n",
      "sunriseTime                    322844 non-null int64\n",
      "sunsetTime                     322844 non-null int64\n",
      "moonPhase                      322844 non-null float64\n",
      "precipIntensityMax             322844 non-null float64\n",
      "uvIndexTime                    322844 non-null int64\n",
      "temperatureMin                 322844 non-null float64\n",
      "temperatureMinTime             322844 non-null int64\n",
      "temperatureMax                 322844 non-null float64\n",
      "temperatureMaxTime             322844 non-null int64\n",
      "apparentTemperatureMin         322844 non-null float64\n",
      "apparentTemperatureMinTime     322844 non-null int64\n",
      "apparentTemperatureMax         322844 non-null float64\n",
      "apparentTemperatureMaxTime     322844 non-null int64\n",
      "price                          267749 non-null float64\n",
      "dtypes: float64(28), int64(18), object(10)\n",
      "memory usage: 137.9+ MB\n"
     ]
    }
   ],
   "source": [
    "uber_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "id": "-C2M6UZkdM1W",
    "outputId": "8761f1bc-4371-4ae6-9cd5-43188090cd78"
   },
   "outputs": [
    {
     "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>timestamp</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>distance</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>temperature</th>\n",
       "      <th>apparentTemperature</th>\n",
       "      <th>...</th>\n",
       "      <th>uvIndexTime</th>\n",
       "      <th>temperatureMin</th>\n",
       "      <th>temperatureMinTime</th>\n",
       "      <th>temperatureMax</th>\n",
       "      <th>temperatureMaxTime</th>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <th>apparentTemperatureMinTime</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "      <th>apparentTemperatureMaxTime</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>3.228440e+05</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.228440e+05</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>3.228440e+05</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>3.228440e+05</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>3.228440e+05</td>\n",
       "      <td>322844.000000</td>\n",
       "      <td>3.228440e+05</td>\n",
       "      <td>267749.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.544047e+09</td>\n",
       "      <td>11.611986</td>\n",
       "      <td>17.793380</td>\n",
       "      <td>11.587045</td>\n",
       "      <td>2.189991</td>\n",
       "      <td>1.005900</td>\n",
       "      <td>42.338073</td>\n",
       "      <td>-71.066162</td>\n",
       "      <td>39.586898</td>\n",
       "      <td>35.882048</td>\n",
       "      <td>...</td>\n",
       "      <td>1.544045e+09</td>\n",
       "      <td>33.458969</td>\n",
       "      <td>1.544042e+09</td>\n",
       "      <td>45.263932</td>\n",
       "      <td>1.544048e+09</td>\n",
       "      <td>29.730870</td>\n",
       "      <td>1.544049e+09</td>\n",
       "      <td>41.997339</td>\n",
       "      <td>1.544049e+09</td>\n",
       "      <td>14.596878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>6.893543e+05</td>\n",
       "      <td>6.945794</td>\n",
       "      <td>9.977869</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>1.150471</td>\n",
       "      <td>0.059262</td>\n",
       "      <td>0.047918</td>\n",
       "      <td>0.020321</td>\n",
       "      <td>6.719967</td>\n",
       "      <td>7.913956</td>\n",
       "      <td>...</td>\n",
       "      <td>6.913761e+05</td>\n",
       "      <td>6.468837</td>\n",
       "      <td>6.903767e+05</td>\n",
       "      <td>5.647543</td>\n",
       "      <td>6.903028e+05</td>\n",
       "      <td>7.113967</td>\n",
       "      <td>6.873598e+05</td>\n",
       "      <td>6.941223</td>\n",
       "      <td>6.912309e+05</td>\n",
       "      <td>9.573120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.543204e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.214800</td>\n",
       "      <td>-71.105400</td>\n",
       "      <td>18.910000</td>\n",
       "      <td>12.130000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.543162e+09</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>1.543122e+09</td>\n",
       "      <td>33.510000</td>\n",
       "      <td>1.543154e+09</td>\n",
       "      <td>11.810000</td>\n",
       "      <td>1.543136e+09</td>\n",
       "      <td>28.950000</td>\n",
       "      <td>1.543187e+09</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.543444e+09</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.350300</td>\n",
       "      <td>-71.081000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>31.910000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.543421e+09</td>\n",
       "      <td>30.170000</td>\n",
       "      <td>1.543399e+09</td>\n",
       "      <td>42.570000</td>\n",
       "      <td>1.543439e+09</td>\n",
       "      <td>27.760000</td>\n",
       "      <td>1.543399e+09</td>\n",
       "      <td>36.570000</td>\n",
       "      <td>1.543439e+09</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1.543738e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.170000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.351900</td>\n",
       "      <td>-71.063100</td>\n",
       "      <td>40.490000</td>\n",
       "      <td>35.920000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.543770e+09</td>\n",
       "      <td>34.240000</td>\n",
       "      <td>1.543727e+09</td>\n",
       "      <td>44.660000</td>\n",
       "      <td>1.543788e+09</td>\n",
       "      <td>30.130000</td>\n",
       "      <td>1.543745e+09</td>\n",
       "      <td>40.950000</td>\n",
       "      <td>1.543788e+09</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.544828e+09</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.364700</td>\n",
       "      <td>-71.054200</td>\n",
       "      <td>43.580000</td>\n",
       "      <td>40.080000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.544807e+09</td>\n",
       "      <td>38.880000</td>\n",
       "      <td>1.544789e+09</td>\n",
       "      <td>46.910000</td>\n",
       "      <td>1.544814e+09</td>\n",
       "      <td>35.710000</td>\n",
       "      <td>1.544789e+09</td>\n",
       "      <td>44.120000</td>\n",
       "      <td>1.544818e+09</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.545161e+09</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.860000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>42.366100</td>\n",
       "      <td>-71.033000</td>\n",
       "      <td>57.220000</td>\n",
       "      <td>57.220000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.545152e+09</td>\n",
       "      <td>43.100000</td>\n",
       "      <td>1.545192e+09</td>\n",
       "      <td>57.870000</td>\n",
       "      <td>1.545109e+09</td>\n",
       "      <td>40.050000</td>\n",
       "      <td>1.545134e+09</td>\n",
       "      <td>57.200000</td>\n",
       "      <td>1.545109e+09</td>\n",
       "      <td>89.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          timestamp           hour            day          month  \\\n",
       "count  3.228440e+05  322844.000000  322844.000000  322844.000000   \n",
       "mean   1.544047e+09      11.611986      17.793380      11.587045   \n",
       "std    6.893543e+05       6.945794       9.977869       0.492366   \n",
       "min    1.543204e+09       0.000000       1.000000      11.000000   \n",
       "25%    1.543444e+09       6.000000      13.000000      11.000000   \n",
       "50%    1.543738e+09      12.000000      17.000000      12.000000   \n",
       "75%    1.544828e+09      18.000000      28.000000      12.000000   \n",
       "max    1.545161e+09      23.000000      30.000000      12.000000   \n",
       "\n",
       "            distance  surge_multiplier       latitude      longitude  \\\n",
       "count  322844.000000     322844.000000  322844.000000  322844.000000   \n",
       "mean        2.189991          1.005900      42.338073     -71.066162   \n",
       "std         1.150471          0.059262       0.047918       0.020321   \n",
       "min         0.020000          1.000000      42.214800     -71.105400   \n",
       "25%         1.280000          1.000000      42.350300     -71.081000   \n",
       "50%         2.170000          1.000000      42.351900     -71.063100   \n",
       "75%         2.880000          1.000000      42.364700     -71.054200   \n",
       "max         7.860000          2.000000      42.366100     -71.033000   \n",
       "\n",
       "         temperature  apparentTemperature  ...   uvIndexTime  temperatureMin  \\\n",
       "count  322844.000000        322844.000000  ...  3.228440e+05   322844.000000   \n",
       "mean       39.586898            35.882048  ...  1.544045e+09       33.458969   \n",
       "std         6.719967             7.913956  ...  6.913761e+05        6.468837   \n",
       "min        18.910000            12.130000  ...  1.543162e+09       15.630000   \n",
       "25%        36.500000            31.910000  ...  1.543421e+09       30.170000   \n",
       "50%        40.490000            35.920000  ...  1.543770e+09       34.240000   \n",
       "75%        43.580000            40.080000  ...  1.544807e+09       38.880000   \n",
       "max        57.220000            57.220000  ...  1.545152e+09       43.100000   \n",
       "\n",
       "       temperatureMinTime  temperatureMax  temperatureMaxTime  \\\n",
       "count        3.228440e+05   322844.000000        3.228440e+05   \n",
       "mean         1.544042e+09       45.263932        1.544048e+09   \n",
       "std          6.903767e+05        5.647543        6.903028e+05   \n",
       "min          1.543122e+09       33.510000        1.543154e+09   \n",
       "25%          1.543399e+09       42.570000        1.543439e+09   \n",
       "50%          1.543727e+09       44.660000        1.543788e+09   \n",
       "75%          1.544789e+09       46.910000        1.544814e+09   \n",
       "max          1.545192e+09       57.870000        1.545109e+09   \n",
       "\n",
       "       apparentTemperatureMin  apparentTemperatureMinTime  \\\n",
       "count           322844.000000                3.228440e+05   \n",
       "mean                29.730870                1.544049e+09   \n",
       "std                  7.113967                6.873598e+05   \n",
       "min                 11.810000                1.543136e+09   \n",
       "25%                 27.760000                1.543399e+09   \n",
       "50%                 30.130000                1.543745e+09   \n",
       "75%                 35.710000                1.544789e+09   \n",
       "max                 40.050000                1.545134e+09   \n",
       "\n",
       "       apparentTemperatureMax  apparentTemperatureMaxTime          price  \n",
       "count           322844.000000                3.228440e+05  267749.000000  \n",
       "mean                41.997339                1.544049e+09      14.596878  \n",
       "std                  6.941223                6.912309e+05       9.573120  \n",
       "min                 28.950000                1.543187e+09       2.500000  \n",
       "25%                 36.570000                1.543439e+09       7.500000  \n",
       "50%                 40.950000                1.543788e+09      10.500000  \n",
       "75%                 44.120000                1.544818e+09      22.500000  \n",
       "max                 57.200000                1.545109e+09      89.500000  \n",
       "\n",
       "[8 rows x 46 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G79HQLtXdM1W",
    "outputId": "db1e3ce5-826c-4451-fe6d-a2f1a9e69a34"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                 0\n",
       "timestamp                          0\n",
       "hour                               0\n",
       "day                                0\n",
       "month                              0\n",
       "datetime                           0\n",
       "timezone                           0\n",
       "source                             0\n",
       "destination                        0\n",
       "product_id                         0\n",
       "name                               0\n",
       "distance                           0\n",
       "surge_multiplier                   0\n",
       "latitude                           0\n",
       "longitude                          0\n",
       "temperature                        0\n",
       "apparentTemperature                0\n",
       "short_summary                      0\n",
       "long_summary                       0\n",
       "precipIntensity                    0\n",
       "precipProbability                  0\n",
       "humidity                           0\n",
       "windSpeed                          0\n",
       "windGust                           0\n",
       "windGustTime                       0\n",
       "visibility                         0\n",
       "temperatureHigh                    0\n",
       "temperatureHighTime                0\n",
       "temperatureLow                     0\n",
       "temperatureLowTime                 0\n",
       "apparentTemperatureHigh            0\n",
       "apparentTemperatureHighTime        0\n",
       "apparentTemperatureLow             0\n",
       "apparentTemperatureLowTime         0\n",
       "icon                               0\n",
       "dewPoint                           0\n",
       "pressure                           0\n",
       "windBearing                        0\n",
       "cloudCover                         0\n",
       "uvIndex                            0\n",
       "visibility.1                       0\n",
       "ozone                              0\n",
       "sunriseTime                        0\n",
       "sunsetTime                         0\n",
       "moonPhase                          0\n",
       "precipIntensityMax                 0\n",
       "uvIndexTime                        0\n",
       "temperatureMin                     0\n",
       "temperatureMinTime                 0\n",
       "temperatureMax                     0\n",
       "temperatureMaxTime                 0\n",
       "apparentTemperatureMin             0\n",
       "apparentTemperatureMinTime         0\n",
       "apparentTemperatureMax             0\n",
       "apparentTemperatureMaxTime         0\n",
       "price                          55095\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "byuNn6SDdM1X"
   },
   "source": [
    "## 4. Feature Engineering\n",
    "-----------------------------------------------------\n",
    "\n",
    "What is a feature and why we need the engineering of it? Basically, all machine learning algorithms use some input data to create outputs. This input data comprise features, which are usually in the form of structured columns. Algorithms require features with some specific characteristic to work properly. Here, the need for feature engineering arises. \n",
    "\n",
    "I think feature engineering efforts mainly have two goals:\n",
    "\n",
    "1) Preparing the proper input dataset, compatible with the machine learning algorithm requirements.\n",
    "\n",
    "2) Improving the performance of machine learning models.\n",
    "\n",
    "**According to a survey in Forbes, data scientists spend 80% of their time on data preparation:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dMEqz3Nn671w"
   },
   "source": [
    "### Ploting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "JWSl-RxHdM1X"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i58T7AyjdM1X"
   },
   "source": [
    "#### Strip Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "kcMM9ZzAdM1X",
    "outputId": "0783293e-b75e-4fab-af40-ce3b7026600d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x159c0d46508>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(data=uber_dataset, x='price', y='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "Wd3NSU2Kpbif",
    "outputId": "4b90cbd0-97a8-4924-e9c9-4201f7fc1fec"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16b2d613288>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(data=uber_dataset, x='price', y='icon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "g-7nA4HMdM1Y",
    "outputId": "00a0b769-b289-4fe4-c335-1f708a3ade59"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16b387530c8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(data=uber_dataset, x='surge_multiplier', y='source')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "Lac88Z51dM1Y",
    "outputId": "b3acbdc3-ff12-406c-8a15-2d5ac86fb34c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16b387ecbc8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(data=uber_dataset, x='surge_multiplier', y='hour')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qi1G0xr8dM1Y"
   },
   "source": [
    "#### Converting Timestamp to Datetime value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wCQGBqcFdM1Y",
    "outputId": "c04bb4db-11c9-4a42-98fb-220d362f324e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1544952608\n",
       "1    1543284024\n",
       "2    1543818483\n",
       "3    1543594384\n",
       "4    1544728504\n",
       "Name: timestamp, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset['timestamp'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt_object = 2018-12-16 15:00:08\n",
      "dt_object = 2018-11-27 07:30:24\n",
      "dt_object = 2018-12-03 11:58:03\n",
      "dt_object = 2018-11-30 21:43:04\n",
      "dt_object = 2018-12-14 00:45:04\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "timestamp1 = 1544952608\n",
    "timestamp2 = 1543284024\n",
    "timestamp3 = 1543818483\n",
    "timestamp4 = 1543594384\n",
    "timestamp5 = 1544728504\n",
    "dt_object1 = datetime.fromtimestamp(timestamp1)\n",
    "dt_object2 = datetime.fromtimestamp(timestamp2)\n",
    "dt_object3 = datetime.fromtimestamp(timestamp3)\n",
    "dt_object4 = datetime.fromtimestamp(timestamp4)\n",
    "dt_object5 = datetime.fromtimestamp(timestamp5)\n",
    "\n",
    "print(\"dt_object =\", dt_object1)\n",
    "print(\"dt_object =\", dt_object2)\n",
    "print(\"dt_object =\", dt_object3)\n",
    "print(\"dt_object =\", dt_object4)\n",
    "print(\"dt_object =\", dt_object5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- So by this timestamp to datetime conversion we get to know that, our data is of the year 2018 and in the month of november and december only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dRoH3R3SdM1Z"
   },
   "source": [
    "#### Bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 339
    },
    "id": "p-9XJDcldM1Z",
    "outputId": "29890236-b7b1-4204-d09e-24d761afcb94"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1d2295048>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uber_dataset['month'].value_counts().plot(kind='bar', figsize=(10,5), color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "GlQWkPWydM1a",
    "outputId": "a6a6f2c3-b128-454b-f1e3-86e887bc6f37"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1d271a408>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAGcCAYAAAB6JswSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deZhlZXXv8e8PcGQQkHYCFTWowQFEFBSSOEQFcxVwiHpFiBMmcYDovV41GhwTTeKsMWIEcQbFgRgUEFFiAKVBBBSMHZwQZBAUFBWBdf/Yu+jTRXUNDdXv3lXfz/Ocp855zzlVq7p37bP2O6w3VYUkSZLWvw1aByBJkrRcmYhJkiQ1YiImSZLUiImYJElSIyZikiRJjWzUOoB1tdVWW9W2227bOgxJkqQ5nX766ZdV1Yrp7aNNxLbddltWrlzZOgxJkqQ5JfnRTO0OTUqSJDViIiZJktSIiZgkSVIjJmKSJEmNmIhJkiQ1YiImSZLUiImYJElSIyZikiRJjZiISZIkNWIiJkmS1IiJmCRJUiOj3WtyIfK6LOr3r4NrUb+/JElamuwRkyRJasRETJIkqRETMUmSpEaWxRyxMVvM+W3ObZMkqS0TMS2KMS+QGGvy67/5zMYaN4w39rHGDV6gav0zEZMkaT0w+Z3Zck9+nSMmSZLUiD1ikiRpSRpDL6Q9YpIkSY2YiEmSJDViIiZJktSIiZgkSVIjJmKSJEmNmIhJkiQ1YiImSZLUiImYJElSIyZikiRJjZiISZIkNWIiJkmS1MiciViSuyY5Mcm5Sb6T5MC+/bVJfprkzP72+In3vDLJqiTfS/K4ifY9+rZVSV4x0X6PJN9I8v0kRyS55c39i0qSJA3NfHrErgVeVlV/COwKvDDJ9v1zb6+qHfvbMQD9c08H7gfsAfxLkg2TbAi8F9gT2B54xsT3eUv/vbYDrgCeezP9fpIkSYM1ZyJWVRdV1Rn9/auAc4GtZ3nLXsAnq+p3VfUDYBXw0P62qqrOr6prgE8CeyUJ8Cjg0/37Dwf2XtdfSJIkaSwWNEcsybbAg4Bv9E0vSnJWkkOTbNG3bQ38ZOJtF/Rta2u/PfCLqrp2WvtMP/+AJCuTrLz00ksXErokSdLgzDsRS7IJcBRwUFVdCbwPuBewI3AR8Napl87w9lqH9hs3Vh1SVTtX1c4rVqyYb+iSJEmDtNF8XpTkFnRJ2Meq6jMAVXXxxPMfAL7QP7wAuOvE27cBLuzvz9R+GbB5ko36XrHJ10uSJC1Z81k1GeCDwLlV9baJ9jtPvGwf4Jz+/tHA05PcKsk9gO2AbwKnAdv1KyRvSTeh/+iqKuBE4Cn9+/cHPn/Tfi1JkqThm0+P2G7As4Czk5zZt72KbtXjjnTDiD8EXgBQVd9JciTwXboVly+squsAkrwIOBbYEDi0qr7Tf7//B3wyyRuBb9ElfpIkSUvanIlYVX2dmedxHTPLe94EvGmG9mNmel9VnU+3qlKSJGnZsLK+JElSIyZikiRJjZiISZIkNWIiJkmS1IiJmCRJUiMmYpIkSY2YiEmSJDViIiZJktSIiZgkSVIjJmKSJEmNmIhJkiQ1YiImSZLUiImYJElSIyZikiRJjZiISZIkNWIiJkmS1IiJmCRJUiMmYpIkSY2YiEmSJDViIiZJktSIiZgkSVIjJmKSJEmNmIhJkiQ1YiImSZLUiImYJElSIyZikiRJjZiISZIkNWIiJkmS1IiJmCRJUiMmYpIkSY2YiEmSJDViIiZJktSIiZgkSVIjJmKSJEmNmIhJkiQ1YiImSZLUiImYJElSIyZikiRJjcyZiCW5a5ITk5yb5DtJDuzbt0xyfJLv91+36NuT5F1JViU5K8lOE99r//7130+y/0T7g5Oc3b/nXUmyGL+sJEnSkMynR+xa4GVV9YfArsALk2wPvAI4oaq2A07oHwPsCWzX3w4A3gdd4gYcDOwCPBQ4eCp5619zwMT79rjpv5okSdKwzZmIVdVFVXVGf/8q4Fxga2Av4PD+ZYcDe/f39wI+XJ1Tgc2T3Bl4HHB8VV1eVVcAxwN79M9tVlWnVFUBH574XpIkSUvWguaIJdkWeBDwDeCOVXURdMkacIf+ZVsDP5l42wV922ztF8zQPtPPPyDJyiQrL7300oWELkmSNDjzTsSSbAIcBRxUVVfO9tIZ2mod2m/cWHVIVe1cVTuvWLFirpAlSZIGbV6JWJJb0CVhH6uqz/TNF/fDivRfL+nbLwDuOvH2bYAL52jfZoZ2SZKkJW0+qyYDfBA4t6reNvHU0cDUysf9gc9PtO/Xr57cFfhlP3R5LPDYJFv0k/QfCxzbP3dVkl37n7XfxPeSJElasjaax2t2A54FnJ3kzL7tVcCbgSOTPBf4MfDU/rljgMcDq4CrgWcDVNXlSd4AnNa/7vVVdXl//6+ADwG3Ab7Y3yRJkpa0OROxqvo6M8/jAnj0DK8v4IVr+V6HAofO0L4SuP9csUiSJC0lVtaXJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElqxERMkiSpkTkTsSSHJrkkyTkTba9N8tMkZ/a3x08898okq5J8L8njJtr36NtWJXnFRPs9knwjyfeTHJHkljfnLyhJkjRU8+kR+xCwxwztb6+qHfvbMQBJtgeeDtyvf8+/JNkwyYbAe4E9ge2BZ/SvBXhL/722A64AnntTfiFJkqSxmDMRq6qTgMvn+f32Aj5ZVb+rqh8Aq4CH9rdVVXV+VV0DfBLYK0mARwGf7t9/OLD3An8HSZKkUbopc8RelOSsfuhyi75ta+AnE6+5oG9bW/vtgV9U1bXT2meU5IAkK5OsvPTSS29C6JIkSe2tayL2PuBewI7ARcBb+/bM8Npah/YZVdUhVbVzVe28YsWKhUUsSZI0MButy5uq6uKp+0k+AHyhf3gBcNeJl24DXNjfn6n9MmDzJBv1vWKTr5ckSVrS1qlHLMmdJx7uA0ytqDwaeHqSWyW5B7Ad8E3gNGC7foXkLekm9B9dVQWcCDylf//+wOfXJSZJkqSxmbNHLMkngEcAWyW5ADgYeESSHemGEX8IvACgqr6T5Ejgu8C1wAur6rr++7wIOBbYEDi0qr7T/4j/B3wyyRuBbwEfvNl+O0mSpAGbMxGrqmfM0LzWZKmq3gS8aYb2Y4BjZmg/n25VpSRJ0rJiZX1JkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIamTMRS3JokkuSnDPRtmWS45N8v/+6Rd+eJO9KsirJWUl2mnjP/v3rv59k/4n2Byc5u3/Pu5Lk5v4lJUmShmg+PWIfAvaY1vYK4ISq2g44oX8MsCewXX87AHgfdIkbcDCwC/BQ4OCp5K1/zQET75v+syRJkpakOROxqjoJuHxa817A4f39w4G9J9o/XJ1Tgc2T3Bl4HHB8VV1eVVcAxwN79M9tVlWnVFUBH574XpIkSUvaus4Ru2NVXQTQf71D37418JOJ113Qt83WfsEM7ZIkSUvezT1Zf6b5XbUO7TN/8+SAJCuTrLz00kvXMURJkqRhWNdE7OJ+WJH+6yV9+wXAXSdetw1w4Rzt28zQPqOqOqSqdq6qnVesWLGOoUuSJA3DuiZiRwNTKx/3Bz4/0b5fv3pyV+CX/dDlscBjk2zRT9J/LHBs/9xVSXbtV0vuN/G9JEmSlrSN5npBkk8AjwC2SnIB3erHNwNHJnku8GPgqf3LjwEeD6wCrgaeDVBVlyd5A3Ba/7rXV9XUAoC/oluZeRvgi/1NkiRpyZszEauqZ6zlqUfP8NoCXriW73MocOgM7SuB+88VhyRJ0lJjZX1JkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElq5CYlYkl+mOTsJGcmWdm3bZnk+CTf779u0bcnybuSrEpyVpKdJr7P/v3rv59k/5v2K0mSJI3DzdEj9siq2rGqdu4fvwI4oaq2A07oHwPsCWzX3w4A3gdd4gYcDOwCPBQ4eCp5kyRJWsoWY2hyL+Dw/v7hwN4T7R+uzqnA5knuDDwOOL6qLq+qK4DjgT0WIS5JkqRBuamJWAHHJTk9yQF92x2r6iKA/usd+vatgZ9MvPeCvm1t7TeS5IAkK5OsvPTSS29i6JIkSW1tdBPfv1tVXZjkDsDxSc6b5bWZoa1mab9xY9UhwCEAO++884yvkSRJGoub1CNWVRf2Xy8BPks3x+vifsiR/usl/csvAO468fZtgAtnaZckSVrS1jkRS7Jxkk2n7gOPBc4BjgamVj7uD3y+v380sF+/enJX4Jf90OWxwGOTbNFP0n9s3yZJkrSk3ZShyTsCn00y9X0+XlVfSnIacGSS5wI/Bp7av/4Y4PHAKuBq4NkAVXV5kjcAp/Wve31VXX4T4pIkSRqFdU7Equp8YIcZ2n8OPHqG9gJeuJbvdShw6LrGIkmSNEZW1pckSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKkREzFJkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRkzEJEmSGjERkyRJasRETJIkqRETMUmSpEZMxCRJkhoxEZMkSWrEREySJKmRwSRiSfZI8r0kq5K8onU8kiRJi20QiViSDYH3AnsC2wPPSLJ926gkSZIW1yASMeChwKqqOr+qrgE+CezVOCZJkqRFlapqHQNJngLsUVXP6x8/C9ilql407XUHAAf0D+8DfG+RQtoKuGyRvvdiGmvcMN7Yxxo3jDf2scYN4419rHHDeGMfa9ww3tgXO+67V9WK6Y0bLeIPXIjM0HajDLGqDgEOWfRgkpVVtfNi/5yb21jjhvHGPta4YbyxjzVuGG/sY40bxhv7WOOG8cbeKu6hDE1eANx14vE2wIWNYpEkSVovhpKInQZsl+QeSW4JPB04unFMkiRJi2oQQ5NVdW2SFwHHAhsCh1bVdxqGtOjDn4tkrHHDeGMfa9ww3tjHGjeMN/axxg3jjX2sccN4Y28S9yAm60uSJC1HQxmalCRJWnZMxCRJkhoxEZMkSWpkEJP1W0tyQlU9eq62oUnykap61lxtQ5RkJXAY8PGquqJ1PPOV5CjgUOCLVXV963jmI8mWsz1fVZevr1jWVZINq+q61nEsVJL7V9U5reNYqCS3Ap4MbMvE50RVvb5VTAuVZOOq+nXrOOaS5EmzPV9Vn1lfsSzUmGOH4RznyzoRS3Jr4LbAVkm2YHVh2c2AuzQLbP7uN/mg37PzwY1iWainA88GTptIyo6r4a8eeR9d3O9K8ingQ1V1XuOY5nI6XYHkAHcDrujvbw78GLhHu9DmbVWSTwOHVdV3WwezAP/al+T5EN1Fxy8axzNfnwd+SXfs/K5xLAuS5OHAvwGbAHdLsgPwgqr667aRrdUT+q93AB4OfKV//Ejgq8CQk5knzPJcMezYYSDH+bJeNZnkQOAguqTrp6xOxK4EPlBV72kV22ySvBJ4FXAb4OqpZuAa4JCqemWr2BYqyQbA/6JLcK6n621659B7aZLcDngG8LfAT4APAB+tqt83DWwWSf4VOLqqjukf7wn8aVW9rG1kc0uyKauT9w3ojpNPVtWVTQObhyTbAc8Bngp8ky6ZPL5tVLNLck5V3b91HOsiyTeAp9Ad6w/q2wb/+yT5AvD8qrqof3xn4L1VNWuvk9bdUI6LZZ2ITUny4qp6d+s4FirJP4wp6ZouyQPpPlgfT1dD7mPA7sCzqmrHlrHNJsntgX2BZ9HtADEV9wOq6hENQ5tVktOr6sHT2ka3FUmSPwY+Qdej92ngDVW1qm1Us+t7q/cG3kV3oRfgVUMduklyCPDuqjq7dSwLleQbVbVLkm9NJGLfrqodWsc2m+lJQX+RetYQEoW1SfLS2Z6vqretr1jWxVCO82U9NDnh+iSbTw0b9MOUz6iqf2kc11y+meR2VfVLgCSbA4+oqs81jmtOSU4HfgF8EHhFVU11C38jyW7tIptdks8A9wU+Ajxh6uoVOKIfYh2yy5K8Gvgo3bDBvsDP24Y0P30i82d0ifu2wFvpEuA/Ao4B7t0suFlMXGz8GXA83TFzRpK7AKcw3KGb3YG/SPIDuiGbAFVVD2wb1rz8pB+erH5Y+CXAuY1jmo+vJjmW7iKj6HqAT2wb0pw2bR3ATTSI49weMSDJmdN7YCavpoZqrHEDJLlnVZ3fOo6FSvL4qaG9ibZbTSSSg9VP2j8Y+OO+6STgdUMfBgZIcj7dh9IHq+rkac+9q6pe0iay2SU5iW7Y+tNV9Ztpzz2rqj7SJrLZJbn7TO1V9aP1HctCJdkKeCfwp3QfrMcBB1bV4C86+snvf9Q/PKmqPtsynqVuKMe5iRiQ5Cxgh6mJ4v3V91lVdb/Z39lWkrOmZ+5Jzq6qB7SKaSGS/BndgoNbT7UNfVVWkjOqaqe52nTzSrJJVf2qdRwLleSgqnrHtLYDq+qdrWKar36S+1RS8J9V9e2W8cxHf+5+SVW9vXUsy0GSl1fVPyZ5N10v3hqGeoE0aQjHuUOTnWOBI/vJzAX8JfCltiHNy8okbwPeSxf3i+lWfwxe/299W7qVQf9GN7n2m02DmkWSOwFbA7dJ8iDWXGF722aBLUCSewP/hxsv1X5Uq5gW4NokL+TGiftz2oU0L/sB75jW9hd0PTaD1S9kej6rh04/muSQoc+lrarrkuwFjC4R63vD3kK3ejKsHibbrGlgs5sa8h36tIwZDeU4t0eMGyZFvgB4NKu7sv9t6HWLkmwMvIY1u+DfOJLaOWdV1QMnvm4CfKaqHts6tpkk2Z/uA3Rn1jzpXEVXwmKoc31ukOTbwL/SJes3HNtVNfjkvS8Vch7wv4HXA88Ezq2qA5sGthZJnkEX6+7Af048tSlwXVX9aZPA5qkfJXjY1LmkP9ecMoY5YkneBNwOOAK44VxYVWc0C2oekqyim0M4hvlsS8JQjnN7xIC+MOf7+tto9AfPK1rHsY6m5stc3U9c/jkDrmdVVYcDhyd5clUd1TqedXRtVY3qGJ/wB1X11CR7VdXhST5O15M9VCcDFwFb0S0smHIVcFaTiBYmTCTr/f2s5bVD8/D+6+Q0hwKG3vN78diSsCT/zgxDklOq6onrMZx1MYjjfFknYkmOrKo/T3I2M49vD/LqL8k7quqgtf0RjODgB/hCv8rzn4Az6H6PD7QNae2S7FtVHwW2nWnJ9tCXaff+PclfA59lonjhGCbrA1P12X6R5P7Az+iGWAepn+z7I+BhrWNZR4fRrWCemiy+N90K58Grqke2jmEdrUxyBPA51vz7HHJv+z/3X0N3/n5ew1jWxSCO82U9NJnkzlV10VBWTsxXkgdX1elJ/mSm56vqa+s7ppsi3TYTt54qwzFESV5QVe9PcvBMz1fV69Z3TAvVL9Gerqrqnus9mAVK8jzgKOCBdCfPTYDXVNX7mwa2Fkm+XlW7J7mKNS+WxjDvB4AkO9ENrYZuBd+3Goc0byNdCHTYDM01gnmQwHhW7E83hON8WSdicMMqm2OHPmdjuj7uw6tq39axLFSf+P66qi5LsivdH8GqMdQ/k5ayJJtV1ZVZy/6kY+g9XdtCoKp6btPAlrgxrR4f2nG+wfr8YUPUT8i/Ot2WNaPRx72iL1g4GkleQ7eX2qlJ3ki3omwr4MAk01eXDU6Sf0yyWZJbJDkhyWVJBp0MJ3n5xP2nTnvu79d/RAuX5H+SfCzJXybZvnU885XkXn2PL0kekeQl/ZD8UH28/3o63aKUqdvU4zF4eFXtB1zR91Q/DLhr45jWKsmRE/ffMu2549Z/RPOXZMupG7Bhki2mtQ3VoI7zZd8jBjf8IexKV/l6cpXNoGugJHk/sBNwNGvGPdj5Skm+C+xId8X6Y+BOVXV1ko2AM2vA23nA6iK6Sfahm0/wN8CJNeDtUyavVKdftY7lKrZPZnahq/ezG93uBt+uqn2aBjaHJGfSrbTdlm5xwdHAfarq8S3jWsqyeoujU4En0S0EOqeqtmsc2oyy5lZM0/8+Bz3c1093KGae4D6KaQ9DsKwn60/4j/42aQwZ6oX9bQNWbzUx9Lh/W1XXANck+Z+quhqgqq5Nck3j2ObjFv3XxwOfqKrLk8EvJsta7s/0eKiuo5uwfx3d5vAXA5c0jWh+ru+P7X2Ad1TVu5MMfq5VkhOq6tFztQ3UTAuB/q1tSLOa7Zw96PN5VQ12pft8DOU4NxHrbD690nVf6G3ovltVn5psmD70NECb94ULA2zW36d/PIbh4aOTnEdXfuOvk6wAfts4prnUWu7P9HiorgTOBt4GfKBGsF1N7/d9TbH9gSf0bbeY5fVNJbk1XW/1Vun23J0sXHyXZoEtQFW9ob97VJIvMPCFQMBt0xWJ3oA1C0YHuE3TyJaooR3nDk2y1m1rBt0lDOPcbmctK4NuUFXPXl+xLFS6wr+70lWTvrKv4r0xsGlV/axtdGuX5Dq6oeupE/vVU0/RfUgNNjGYkq5a+u7AQ4Fr6Op0nVRVJzQNbA79fLa/pCsS+Ykk9wCeVlVvbhzajPoL0IPoPox+yuoPqCvpEuD3tIptvpLsN1N7VX14fccyH0lm3dh7xOU4Bmtox/myTsSy9urXm9EVvxzkSsoke9INjf05XfXoKZsB21fVQ5sEtgwkOaWqxlobavSS3BfYk+4keoeqGmyPwchXNr+4Br6d0dqk2/dwyq3pdkw5o6qe0igkDdRQjvPlPjQ51urXF9Kt7Hgia+4teRXd5HEtnuOSPJluO6blexWzniU5im6Rxyq6i6b9gG80DWoOfY/piiS37OdFjkY/l+3+wPasWYtrkL1Kk6rqxZOP+xXxH2kUzrLRX3jckTX3sf1xu4jmNpTjfFn3iE3ph5d+U1XXp9sY+b7AF6vq93O8takkt5iKsR/nvmtVDTmBHL2+QOfGdJPGf8OICnSOWZKH0PVqDHr/1+nGuLIZIF3h4kfQfUAdQ9cL+fUx9ioluQVwVlX9YetYlqokLwYOpltEc33fXEPdnWbKUI7z5d4jNuUk4I/6ZOYEut6mp9FtLDxkxyd5It3/45nApUm+VlU32oJnDJLcqqp+N/cr26mqTed+lW5uVXVakocn2ZY1r7iH3kMz08rmMXgKsAPwrap6dpI7MuyVhzfImlu/bUD3IXvk2t+hm8GBdGVZxrKIZsogjnMTsU76WlbPBd5dVf84hiXmwO366sDPAw6rqoPT7SY/eEkOndy6I8kmwOfp5nMMVrpaFc8E7lFVb0hyV+DOVfXNxqEtaUk+AtyL7oJjqlesgEEnYn1BUZJsXFW/nuv1AzI1QnBtks3oSoWMpSbUP0/cvxb4UVVd0CqY+RpKKYV19BNgyCtT12YQx7mJWCdJHkb3ATu1DcYY/m02SnJnukn7f9s6mAX6aZL3VdVf9T2R/8GAN/2e8C90Xe+PAt4A/Ap4L/CQlkHNR18q5C3AHVi9PH4sw6o70y1EGdVciv688kG6vTHvlmQH4AVV9ddtI5vTyr4W1wfo5qH+ChjFxUaNb6/dQZVSWEfnA19N8h+suWH5oIfgGchxPoZkY304CHgl8Nmq+k6SewKzLikeiNfTVev+ej90c0/g+41jmpeqek2St6TbF+7BwJur6qjWcc3DLlW101SPaVVdkfFsM/WPwBOq6tzWgayDc4A70S2uGZN3AI+jmyNGVX07yR+3DWluE4nivyb5ErDZWOaf5sYbrd/wFMO88HgBq0spnM6apRTe2yqoBfpxf7tlfxuFoRznTtbXejVRwBW6E85r6K5AvgRQVZ9pEdd8JfkG8HDgtD4hWwEcN/SacwBJ/quqdmsdx7roay3tSHesTF5xP7FZUPMwsd3O5DY2364Bb4kF4x4mS/J64Gd0KyWnphJsWlX/2DSwOQyllMJNkWRTumT3V61jmY+hHOfLukcsyTuq6qBpkztvMNSTfJKX9/PY3s3McQ95j8wnTHv8LbpK40+g+10GnYgB7wI+C9whyZvoJnu+um1Is5tIflcmOQL4HGsmM0P/Nwd4besA1tFPkjwcqL7n9CV0BYEHaYkMkz2uqnaZePy+/gJq0IkY8LMkm1bVVUleTbfa9o1VdUbrwObSl4D4CLBl//gyYL+q+k7TwNZiaMf5sk7EWF1b5p9nfdXwTJ3I1/su8TfVkCvnz0dVfbwK7REAABmtSURBVCzJ6XSLCgLsPYKhvsnk92rgsROPx5D8UlVfS3J3YLuq+nKS2wIbto5rHv4SeCewNXABcBzwwqYRzW4pDJNdl+SZwCfpju9nsHqBx5C9pqo+lWR3uuHsfwbeR7fZ/dAdAry0qk4ESPIIunlXD28Z1CwGdZw7NNnrh5ioqktbx7IcJDkcOLCqftE/3gJ46+RKyiFK8k7giKo6uXUsC5Vkt6r6r7nahijJ84EDgC2r6l5JtgP+dehDZUlWjPGcMuZhsr7EyTuBqWH4rwMHVdUPG4U0L1PD10n+ATi7qj6eEWy1BzMPt49kCH4Qx/kGrQNoKZ3X9t2o5wH/neTSJH/XOra5JNk/yRlJft3fVmYte6wN1AOnkjDoJr0Dgz/hAGcAr06yKsk/Jdm5dUALMNMJp/lJaJ5eSPfBeiVAVX2fbvXn0J2c5Lgkz+1XZw1akockudPUh1OS/ZJ8Psm7kmzZOr75qKofVtVeVbVVf9t76ElY76fpCgD/OXBMklsxns/o85O8Jsm2/e3VwA9aB7U2QzvOx/KfvFgOoju5P6Sqbl9VW9B1A++WZLBbBfUJ10HAy+i6VrcGXg4cOKJkbIO+FwyA/uAf/FB5VR1eVY+n23z6v4G3JBn0StUkD0vyMmBFkpdO3F7LOIb3AH5XE9sEJdmImVfGDUpVbUc3h/B+wBlJvpBkyHtPvp9uU3X61Z1vpqvV9ku64afBSvL8vqd06iL70CS/THJWkp1axzcPf063Cn6P/iJ1S+D/tg1p3p4DrKCb5vAZum0DhzwNZVDH+eA/+BbZfsBjquqyqYaqOr8/UR4HvL1ZZLP7a2CfaVd5X0m3B+InGXiRy95b6XoLPt0/firwpobxLNQf0G2FtS3w3bahzOmWdHWsNmLN6u5X0i02GIOvJXkVcJskj6H7G/j3xjHNS1/s95tJ/h54G3A48NG2Ua3VhlV1eX//acAhfVmZo5Kc2TCu+TgQ+FB//xl0FdPvSdfT/k7gj9qENT99UfFLgN3pyhBdy3jKEV1BtxBlLAZ1nC/3ROwWk0nYlKq6NN3+ZEO12Uxd7VX1w3TVgQevqj7cT3p/JN1EySdV1dATGpK8BXgS8D9026a8YXKIdYj6ie5fBx4wVel9hF5BV2z5bLqJtscwgi13+r/HfYCn0+0M8Fm63tSh2jDJRlV1Ld2ClAMmnhv658W1tXp/4P8FfLi6LXe+nGToKyan9j3cGbgPcBjdavKPsnqu22AlOR546rQ5v5+sqse1jWytBnWcD/0Pa7Fds47PtfabdXxuUPriuZfS73qf5G5V9ePGYc3lB8DDZkrgh6yqrhvLHJ9JU8dEVV1PtwprDLsvTPo2XbmQ11fVKa2DmYdP0PU+XkZ3LvlPgCR/wPC3sLk+3U4jV9B9uE72sN+mTUgLsg9d790ZAFV1Ybq6XGOw1fQ5v0mGPIdzUMf5ck/Edkhy5QztoU8OBuoPM/OekmEk+8Gl26z8rXRz3C4B7k5XluN+LeNamyT3rarz6AqK3i3J3SafH0OtH+BbSY4GPgXcsO/hwOuIfY6unhJJjqqqJzeOZ6HuOaZtmarqTUlOAO5MV6h4cvPsF7eLbF7+jq6kz4bA0VM1rJL8Cd0WPEN3TVVVkoJuf9LWAS3A9ZMX0ulKzQz2uB/acW75ihHqD/K1qqofra9Y1lWSb9Pt1/jlfsn2I4FnVNUBc7y1iSSHVNUB6Sq8T1dV9aj1HtQCJTlshuYacsmQrFmRfhRL+WG8xaLHrl/EsWk/Z2mqbWO6z7pBV3tP8n+A7YDHAP9ANwH+40MorzCXJHvQTXKf2ufzj4EDqurYdlGNh4mYmkiysqp27hOyB1XV9Um+WVVDnj+j9SzJGVW10/T7Q5fkwVV1et8bcyM1so2ptX70C1EeSze6cWxVHd84pHlLshWwK13sp4xt+kZLJmJqIsmXgb3prvy2ohuefEhVDbUS8w3SbVmzLRND+1U1+JWqSbahqxu2G10vzdfpiupe0DSwWSS5jm4YNXTzfK6eeophbuAsLUiSg4D/Ar7VTx4fpX6C/nZMTOupqpPaRTQeJmJqoh8u+A3dmPwzgdsBH+tXOQ1Wko/QrX47k9XbplQNe39P4IaVTR9n9dZe+wLPrKrHtItqaUuyG90+mXenS9ynEshRzOXU4kvyz3RbAd0XOAs4mS4xO2WixMKgJXkeXfmQbejOjbvSxT/4KRtDYCI2ckluA9ytqr7XOpZ11Xdp/3wMk5qTnAtsP4ZYp0tyZlXtOFebbj5JzgP+hm4/uxv2OxzBBceTgLfQ7V4QRtQDmeSEmrb11UxtQ5NuU/id6ZKyh/W3X1TV9k0Dm4ckZwMPAU6tqh2T3Bd4XVU9rXFosxrKcb6sK+snuSrJlTPcrlrLaspBSfIEuquPL/WPd+xXxQ1Wkl2TfDXJZ5I8KMk5wDnAxf2Ez6E7B7hT6yDW0WVJ9k2yYX/bFxh0QrAE/LKqvlhVl1TVz6durYOah38EnlhVt6uqzapq06EnYUlu3Zdo2SrJFkm27G/b0q3OHrrbAJvRjQ7cDrgQ+EbTiObvt1X1W4Akt+pXmN+ncUzzMYjjfFmXr6iqsdRoWZvX0hWH/CpAVZ3Zn3SG7D3Aq+hONF8B9qyqU/srqE/QJ5UDthXw3STfBH431TiSVXDPofv3fzvdHLGT+zYtnhOT/BPdti+Tx8vQy51cXFXntg5igV5At/XbXehrcfWuBN7bJKJ5SHIIXdmeq+gSr5OBt02u/ByBC9Ltpfo54PgkV9AlkkM3iOPcockJfQG6yYmGgy4umuQbVbXLtCX+Z1XVA1vHtjaTQ2FJzq2qP5x4bvDlCVwFp4UYW7mTfqgG4E/oen4/x5oJ5JBrzgGQ5MVjKPkwJcmX6C7wzqFLwk4Bzhnj9Ae44Rx5O+BLNbE/7JAM7Thf1j1iU8ZWXHTCOUn+N912DdvR7fV1cuOY5nL9xP3puwAM/sQzxoQryd/N8nRV1RvWWzDLTFU9snUMC/SEiftX05VSmFJ0PXtDd2iSV9PNnT2gPzfep6q+0DqwmVTVHklC93nzcOBlwP2TXE434f3gpgHOU5Ldge2q6rAkK4Ct6XYiGaJBHef2iDG+4qJTktwW+FtWH0THAm+cGqsfojnKEdy6qga5x2eSq1gzUSzgMuBE4P8Ned5PkpfN0Lwx3d6Nt6+qTdZzSEtekpdOa5o6Xr5eVUP9cLpBkt2q6r/mahuiJEfQLY7Yr6ru3y9oOmUMi1L6EjO70SVk/4vu73PztlHNLRP7ZFbVvZPcBfhUVQ16n8yhHOcmYoyzuGiSDYE3V9X/bR3LctXXzfkL4OFV9dTG4cxLur3rDqRLwo4E3lpVl7SNaunpP5im2xJ4HPDaqvrkeg5pQWYqnjuWgroT5/PJKRvfrqodWsc2kyQvoUu8dgN+T1+6ov96dnX7rA5akjPp98kcyzQZGM5x7tBk5xdJNgFOAj6W5BJg0IX1qtvE+cGt41jO+sm0b0/yrNaxzKVfTfZSuppthwM7jWwy8KhU1etmau//H74MDDIRS/IwuqRgxbRevc3o9nAcg2v6XrCpPRvvxcT8nwHaFvg08DdVdVHjWNbVqPbJHNpxbiLW2Qv4LV29n6nioq9vGtH8jHET5yUlyS0Y+N9Rv2rvSXR7wT2gBr7n3lJWVZf384GG6pbAJnTH9OSq8iuBpzSJaOEOplt9fdckH6PrafqLphHNoqqmD2OP0ZFJ3g9snuT5dKuxP9A4ptkM6jh3aHLEMsJNnMdqYpXNpC2Ap9HN+xls4p7keroegWtZc57baIp0LhVJHgW8eqirJqckuXtV/ah1HOsqye1Zve/hqeW+h4suI9sns5/ec0RVNb/AWNaJWJKvV9XuM0zEHsUH1FAmGi4HMyS9RVcM9atV9R8NQtKApas0Pv3kuiVdbaX9+oKXg9WX3bjRh8PQE0iAJK+vqr+beLwB8JGqembDsJaNjGunlK8M4Zhe1onY2A1loqGkNSW5+7Smovtw+vVMrx+aafNPbw08Gbi2ql7eKKR5S/Ih4HtV9Q9JbkU3deOMqnpt08CWoCS7Am8GLgfeQLeP7VZ0u/bsV1WDLtCd5K10G5U3nd5jIsYNB9N3quqq/vEmwP2qapDbS0xMNDyIrkr6lM2AfYa6OkjSeCX5WlXNWNB4SPo5eB8DzgYeCXyxqt4++7u0LpKsZPVOKYcwbaeUERToHsT0nkFPMl6P3gdM9iJdPUPbkAxqoqGkpaVf3TllA+DBDHyP1SST5+t3Au+nKwHxtSQ7jWBbqTHaqKqOgxuGhE8FqKrzhr0mpVNVz24dA5iITcnkeHZfR2yw/zZ9dfevJfnQmCfUShqs0+mGU0O3yOMHdLXnhuyt0x5fAWzftxdd0W7dvEa9U0pfQPfddCtrC/g6cGBVXbBe43BoEpJ8hm7j7Pf1TX8NPLKq9m4W1Dz020i8nG5rjMk9Mj3hLJJ+zsmT6Wr/3JCsD3nVpLRc9BPzn1pVR7SOZTkY604pU5IcD3ycbm4bwL7AM6vqMeszjg3W5w8bsL+km3P1U+ACYBdg0Nsb9T4GnAfcA3gd8EPgtJYBLQOfp6s7dy3dCWjqJt1Ikicl+X6SXya5MslVSa5sHddcktwiyUuSfLq/vaivmTdofRX6F7aOY7moqg2rarOq2rSqNurvTz0e/PECrKiqw6rq2v72IWDF+g7CHrERS3J6VT14ciuJsUyoHask51TV/VvHoXFIsgp4QlWd2zqWhUjyb8At6HZhAHgWcF1VPa9dVPOT5DV0w2RHsOZKuMubBaVBSvJl4EPAJ/qmZwDPrqpHr884BjsPan3qh/iez42Hm4ZeGPX3/deLkvwZXY2ibRrGsxycnOQBVXV260A0ChePLQnrPWTa6uuv9HvxjsHUeXuyZ6yAezaIRcP2HOA9dNUHCjiZ1cfPemMi1vk88J90e8Bd1ziWhXhjktsBL6ObcLgZ3TZNWjy7A3+R5Ad01eqniv8OenNbNbMyyRHA55jY73AE25Bdl+ReVfU/AEnuyUjOjVV1j9YxaByq6sfAE1vH4dAk3c7xVbVj6zg0fDMU6gTA1auayVDqFC1UkkcDhwHn011s3J1uyObEpoHNU5L7062YnFzE9OF2EWlIkvzdLE9XVb1hvQWDiRgASd4InFxVx7SOZSGS3Jtupecdq+r+SR4IPLGq3tg4tCUtyQ7AH/UP/7OqxjJko/Wo38vuJWMtJtqvEL4PXSJ2XlX9bo63DEKSg4FH0CVixwB70u0Ha41FAZDkZTM0b0xXouX2VbXJeo3HRAz6vSY3phs6+D3j2Wvya8D/Bd4/VcHYyeSLK8mBdPMJp4aW9gEOqap3t4tKQ5XkxKp6ZOs45ivJQ4CfVNXP+sf70ZVr+RHw2jFMeO/3+dwB+FZV7ZDkjsC/VdUTGoemAUqyKXAgXRJ2JPDWqrpkfcbgHDGgqjad+1WDdNuq+ua0CsbXtgpmmXgusMvUnoFJ3gKcQjdHT5ru5CTv4cYr+IZa5f39wJ8CJPljun0EXwzsSLeFzRh6lX7TF+W+NslmwCU4UV/T9LtHvBR4Jt3q4J2q6ooWsZiI9ZJsQbf55+ScgpPaRTQvlyW5F30F4yRPAS5qG9KSF9actHxd3ybN5OH918mCv0Ou8r7hRK/X0+h6e48CjkpyZsO4FmJlks2BD9DtEPAr4JttQ9KQJPkn4El0FxcPqKpfNY3HoUlI8jy6rsltgDOBXYFThl6hvl/JdAjdyf4Kum1I9q2qH7aMaylL8lJgf+CzfdPewOFjnQckTUpyDrBjVV2b5DzggKkL0jFOe0iyLbBZVZ3VOBQNSJLr6aYiXcuaWzE1mZZkIsYNcwoeApxaVTv2O8e/rqqe1ji0eUmyMbBBVV3VOpbloN9ceHe6P9qTqupbjUPSQPXzk/4euEtV7Zlke+BhVfXBxqHNKMnfAo8HLgPuRjdcU0n+gO6CY7emAc5Tv3BpW9asCzn0kiFapkzEgCSnVdVD+q73Xarqd2MoaeG+h+tfko9U1bPmapMAknyRrgzE3/YTxzeim0T+gMahrVWSXYE7A8dNzIW8N7DJgOe23SDJocADge+welPqwZcM0fLlHLHOBf2cgs8Bxye5gq5K/dB9Hvgl3TyIUSwtXwLuN/mgL1Hw4EaxaPi2qqojk7wSoB/yG3Rh1Ko6dYa2/24Ryzrataq2bx2ENF8mYkBV7dPffW2SE4HbAV9qGNJ8bVNVe7QOYjnoP0hfBdxmYtPmANfQzdOTZvLrJLdn9YKaXekunrR4TkmyfVV9t3Ug0nw4NNnrezbuyJpDfD9uF9HckhwCvNt9D9efJP9QVa9sHYfGoZ9P+G7g/sA5wArgqRYBXjx92Y1/B36G25BpBEzEgCQvBg4GLmbNOQWD/MPtVzZdT5c0bke3DYknnPUgyW7AmVX16yT7AjsB73SLI82kn8d5Hasr1H+PbmGNUwkWSZJVdPWhzmb1+dxtyDRYJmLc8Ie7S1X9vHUs89HPYVvrQgJPOIsnyVl0VbsfCHwE+CDwpKr6k6aBaZCSnFFVO83VpptPkq8MvfSQNMk5Yp2fMK55Gz8w2Wrm2n45/150PWEfTLJ/66A0LEnuBGxNN6fwQawu+rsZcNtmgS0P5yX5ON3w5A09j5av0FCZiHXOB76a5D9Y8w/3be1CmtUd+sKiMxpw3EvBVf3E/WcBf9TPLbxF45g0PI8D/oKuSPRbWZ2IXUW36EOL5zZ05/HHTrQVq/eHlQbFRKzz4/52y/42dBsCm+DWOi08DfjfwHOq6mdJ7gb8U+OYNDBVdThweJIn91sEaf152Rg2J5emOEdshJxj0lZfLf0h/cNvVtUlLePRcCU5kK6g61V0ex/uBLyiqo5rGtgSluT7dFvVHQZ8sfyQ08Bt0DqAlpK8o//670mOnn5rHd8s7AlrJMmf020g/FTgz4Fv9JutSzN5TlVdSTdMdgfg2cCb24a05N2brrbfs4BVSf6+3xlAGqRl3SOW5MFVdXqSGVe8VdXX1ndM85FkS7ve20jybeAxU71gSVYAX66qHdpGpiFKclZVPTDJO4GvVtVnk3yrqh7UOrblIMkjgY8CGwPfpuuNPKVtVNKalvscsUthuAnX2piENbXBtKHIn7PMe5Y1q9OTHAfcA3hlkk2ZqG2lm1+/k8G+dD1iFwMvBo6mK/nzKbr/C2kwlnsi9jm6ORskOaqqntw4Hg3fl5IcC3yif/w04JiG8WjYnkuXAJxfVVf3ScKzG8e01J1CV+Nv76q6YKJ9ZZJ/bRSTtFbLfWjyhiEChws0X0meBOxON1fvpKr6bOOQNGBJtqDbAePWU21VdVK7iJa2JHGCvsZkufeI1VruS2vVF4b8TJKt6IYmpRkleR5wIF09sTOBXel6bKz8vni2SvJy4H6smfz6b65BWu5zW3ZIcmWSq4AH9vevTHJVkitbB6fhSLJrkq8m+UySB/X7fZ4DXJxkj9bxabAOpCt18qOqeiTwIPq5qVo0HwPOo5sL9jrgh8BpLQOSZrOse8SqasPWMWg03kNXEf12wFeAPavq1CT3pZsv9qWWwWmwfltVv01CkltV1XlJ7tM6qCXu9v3WYwf2C7G+lmRUC7K0vCzrRExagI2minAmeX1VnQrQf7C2jUxDdkGSzekWBh2f5ArgwsYxLXW/779elOTP6P69t2kYjzQrEzFpfiZLDvxm2nPOL9SMqmqf/u5rk5xI16Nq7+niemOS2wEvA95Nt9H637QNSVq7Zb1qUpqvJNcBv6ZbKXkb4Oqpp4BbV5Ubf2tGSXYHtquqw/oCwJtU1Q9axyVpGEzEJGmRJDkY2Bm4T1XdO8ldgE9V1W6NQ1tykrybWXqnq+ol6zEcad4cmpSkxbMP3UrJMwCq6sK+ur5ufisn7r8OOLhVINJCmIhJ0uK5pqoqSQEk2bh1QEtVVR0+dT/JQZOPpSFb7nXEJGkxHZnk/cDmSZ4PfBn4QOOYlgPn3Gg0nCMmSYsoyWOAx9It7Di2qo5vHNKSl+SMqtqpdRzSfJiISZJGr98hZeoD7basubK5qmqzJoFJczARk6RF0m8Q/xbgDnQJgUmBpDWYiEnSIkmyCnhCVZ3bOhZJw+RkfUlaPBebhEmajT1iknQz64ckAf4EuBPdXpO/m3q+qj7TIi5Jw2MiJkk3sySHzfJ0VdVz1lswkgbNREySFkmS3arqv+Zqk7R8mYhJ0iKZqZ6VNa4kTXKLI0m6mSV5GPBwYEWSl048tRmwYZuoJA2RiZgk3fxuCWxCd46d3OT7SuApTSKSNEgOTUrSIkiyIXBEVZl4SVor64hJ0iKoquuALVvHIWnYHJqUpMXzrSRHA58Cfj3VaB0xSVNMxCRp8WwJ/Bx41ERbASZikgDniEmSJDXjHDFJWiRJtkny2SSXJLk4yVFJtmkdl6ThMBGTpMVzGHA0cBdga+Df+zZJAhyalKRFk+TMqtpxrjZJy5c9YpK0eC5Lsm+SDfvbvnST9yUJsEdMkhZNkrsB7wEeRrda8mTgwKr6UdPAJA2GiZgkSVIj1hGTpJtZkr+b5emqqjest2AkDZo9YpJ0M0vyshmaNwaeC9y+qjZZzyFJGigTMUlaREk2BQ6kS8KOBN5aVZe0jUrSUDg0KUmLIMmWwEuBZwKHAztV1RVto5I0NCZiknQzS/JPwJOAQ4AHVNWvGockaaAcmpSkm1mS64HfAdfSla244Sm6yfqbNQlM0uCYiEmSJDViZX1JkqRGTMQkSZIaMRGTJElqxERMkiSpERMxSZKkRv4/EAcdHyKqnTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uber_dataset['source'].value_counts().plot(kind='bar', figsize=(10,5), color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "id": "PoyZzStKdM1a",
    "outputId": "8847ae8b-c257-407e-e131-ddec1db6e925"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1d27a6cc8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uber_dataset['name'].value_counts().plot(kind='bar', figsize=(10,5), color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "FnccWsNkdM1a",
    "outputId": "ad8758bb-c770-4be8-e4f3-d6b157a3c173"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1d218c9c8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAGKCAYAAABXSdZjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de5RlZXnn8e+Pm+AFaKBNFNBG09Gg0QglombUoIuLN4jRDC4vvZAZEoOjZmZiyMXBiGsmJpMQcZQMERDUCRIkgXhJpwNEJEGkGhIIIqGDCh1RGhuQyFKBPPPH2aWH7urq6jpVtd9T9f2sVavOfvc+p57e3ef0r/be77NTVUiSJKktO/VdgCRJkrZmSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElq0C59FzDf9ttvv1q1alXfZUiSJG3X+vXr766qldOtW3IhbdWqVUxOTvZdhiRJ0nYl+fq21nm6U5IkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElqkCFNkiSpQYY0SZKkBi2520LNu6TvCmZW1XcFkiRpAXgkTZIkqUHbDWlJzklyV5J/GhrbJ8m6JLd231d040lyRpINSW5IcsjQc9Z029+aZM3Q+KFJbuyec0YyOHS1rZ8hSZK0HMzmSNpHgaO3GDsFuKyqVgOXdcsAxwCru6+TgDNhELiAU4HnAYcBpw6FrjO7baeed/R2foYkSdKSt92QVlVXApu3GD4WOK97fB5w3ND4+TXwRWDvJE8AjgLWVdXmqroHWAcc3a3bs6qurqoCzt/itab7GZIkSUveXK9J+7GquhOg+/74bnx/4I6h7TZ2YzONb5xmfKafsZUkJyWZTDK5adOmOf6RJEmS2jHfEwemmwpZcxjfIVV1VlVNVNXEypUrd/TpkiRJzZlrSPtWd6qS7vtd3fhG4MCh7Q4AvrGd8QOmGZ/pZ0iSJC15cw1plwJTMzTXAJcMjb+5m+V5OHBfd6pyLXBkkhXdhIEjgbXduvuTHN7N6nzzFq813c+QJEla8rbbzDbJnwIvAfZLspHBLM3fBS5MciJwO/C6bvPPAi8HNgAPACcAVNXmJKcB13bbvbeqpiYjvJXBDNI9gM91X8zwMyRJkpa81BLrWD8xMVGTk5Pz94LecUCSJC2QJOuramK6dd5xQJIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElqkCFNkiSpQYY0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElqkCFNkiSpQYY0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElqkCFNkiSpQYY0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGjRTSkvxqkpuS/FOSP02ye5KDklyT5NYkn0yyW7fto7rlDd36VUOv8xvd+C1JjhoaP7ob25DklFFqlSRJGidzDmlJ9gfeDkxU1TOBnYHjgfcDp1fVauAe4MTuKScC91TVTwCnd9uR5ODuec8AjgY+nGTnJDsDHwKOAQ4GXt9tK0mStOSNerpzF2CPJLsAjwbuBI4ALurWnwcc1z0+tlumW//SJOnGL6iq71fVV4ENwGHd14aquq2qfgBc0G0rSZK05M05pFXVvwL/G7idQTi7D1gP3FtVD3WbbQT27x7vD9zRPfehbvt9h8e3eM62xreS5KQkk0kmN23aNNc/kiRJUjNGOd25gsGRrYOAJwKPYXBqcks19ZRtrNvR8a0Hq86qqomqmli5cuX2SpckSWreKKc7XwZ8tao2VdWDwMXAC4C9u9OfAAcA3+gebwQOBOjW7wVsHh7f4jnbGpckSVryRglptwOHJ3l0d23ZS4EvA1cAr+22WQNc0j2+tFumW395VVU3fnw3+/MgYDXwJeBaYHU3W3Q3BpMLLh2hXkmSpLGxy/Y3mV5VXZPkIuA64CHgeuAs4DPABUne142d3T3lbOBjSTYwOIJ2fPc6NyW5kEHAewg4uaoeBkjyNmAtg5mj51TVTXOtV5IkaZxkcDBr6ZiYmKjJycn5e8FMd2lcQ5bY358kSctJkvVVNTHdOu84IEmS1CBDmiRJUoMMaZIkSQ0ypEmSJDXIkCZJktQgQ5okSVKDDGmSJEkNMqRJkiQ1yJAmSZLUIEOaJElSg+Z8705pu7ylliRJc+aRNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElqkCFNkiSpQYY0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElqkCFNkiSpQYY0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElq0EghLcneSS5K8pUkNyd5fpJ9kqxLcmv3fUW3bZKckWRDkhuSHDL0Omu67W9NsmZo/NAkN3bPOSNJRqlXkiRpXIx6JO0DwF9V1dOBZwM3A6cAl1XVauCybhngGGB193UScCZAkn2AU4HnAYcBp04Fu26bk4aed/SI9UqSJI2FOYe0JHsCLwLOBqiqH1TVvcCxwHndZucBx3WPjwXOr4EvAnsneQJwFLCuqjZX1T3AOuDobt2eVXV1VRVw/tBrSZIkLWmjHEl7CrAJODfJ9Uk+kuQxwI9V1Z0A3ffHd9vvD9wx9PyN3dhM4xunGd9KkpOSTCaZ3LRp0wh/JEmSpDaMEtJ2AQ4Bzqyq5wDf5UenNqcz3fVkNYfxrQerzqqqiaqaWLly5cxVS5IkjYFRQtpGYGNVXdMtX8QgtH2rO1VJ9/2uoe0PHHr+AcA3tjN+wDTjkiRJS96cQ1pVfRO4I8nTuqGXAl8GLgWmZmiuAS7pHl8KvLmb5Xk4cF93OnQtcGSSFd2EgSOBtd26+5Mc3s3qfPPQa0mSJC1pu4z4/P8CfCLJbsBtwAkMgt+FSU4Ebgde1237WeDlwAbggW5bqmpzktOAa7vt3ltVm7vHbwU+CuwBfK77kiRJWvIymDi5dExMTNTk5OT8vWDrrdla/vtz30mSNKMk66tqYrp13nFAkiSpQYY0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGjXrvTkkLxdtqSdKy5pE0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElqkPfulLQ0ee9TSWPOI2mSJEkNMqRJkiQ1yJAmSZLUIEOaJElSgwxpkiRJDTKkSZIkNciQJkmS1CBDmiRJUoMMaZIkSQ0ypEmSJDXIkCZJktQgQ5okSVKDDGmSJEkNMqRJkiQ1yJAmSZLUIEOaJElSgwxpkiRJDTKkSZIkNWjkkJZk5yTXJ/l0t3xQkmuS3Jrkk0l268Yf1S1v6NavGnqN3+jGb0ly1ND40d3YhiSnjFqrJEnSuJiPI2nvAG4eWn4/cHpVrQbuAU7sxk8E7qmqnwBO77YjycHA8cAzgKOBD3fBb2fgQ8AxwMHA67ttJUmSlryRQlqSA4BXAB/plgMcAVzUbXIecFz3+NhumW79S7vtjwUuqKrvV9VXgQ3AYd3Xhqq6rap+AFzQbStJkrTkjXok7Y+AdwH/3i3vC9xbVQ91yxuB/bvH+wN3AHTr7+u2/+H4Fs/Z1rgkSdKSN+eQluSVwF1VtX54eJpNazvrdnR8ulpOSjKZZHLTpk0zVC1JkjQeRjmS9kLg1Um+xuBU5BEMjqztnWSXbpsDgG90jzcCBwJ06/cCNg+Pb/GcbY1vparOqqqJqppYuXLlCH8kSZKkNsw5pFXVb1TVAVW1isGF/5dX1RuAK4DXdputAS7pHl/aLdOtv7yqqhs/vpv9eRCwGvgScC2wupstulv3My6da72SJEnjZJftb7LDfh24IMn7gOuBs7vxs4GPJdnA4Aja8QBVdVOSC4EvAw8BJ1fVwwBJ3gasBXYGzqmqmxagXkmSpOZkcDBr6ZiYmKjJycn5e8FMd2lcQ1r++3Pfjcb9Nxr3n6QxkGR9VU1Mt847DkiSJDXIkCZJktQgQ5okSVKDDGmSJEkNMqRJkiQ1yJAmSZLUIEOaJElSgwxpkiRJDTKkSZIkNciQJkmS1CBDmiRJUoMMaZIkSQ0ypEmSJDXIkCZJktQgQ5okSVKDDGmSJEkNMqRJkiQ1yJAmSZLUIEOaJElSgwxpkiRJDTKkSZIkNciQJkmS1CBDmiRJUoMMaZIkSQ0ypEmSJDVol74LkCQ1KOm7gm2r6rsCaVF4JE2SJKlBhjRJkqQGGdIkSZIaZEiTJElqkCFNkiSpQYY0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElq0JxDWpIDk1yR5OYkNyV5Rze+T5J1SW7tvq/oxpPkjCQbktyQ5JCh11rTbX9rkjVD44cmubF7zhlJMsofVpIkaVyMciTtIeC/VdVPAYcDJyc5GDgFuKyqVgOXdcsAxwCru6+TgDNhEOqAU4HnAYcBp04Fu26bk4aed/QI9UqSJI2NOYe0qrqzqq7rHt8P3AzsDxwLnNdtdh5wXPf4WOD8GvgisHeSJwBHAeuqanNV3QOsA47u1u1ZVVdXVQHnD72WJEnSkjYv16QlWQU8B7gG+LGquhMGQQ54fLfZ/sAdQ0/b2I3NNL5xmvHpfv5JSSaTTG7atGnUP44kSVLvRg5pSR4LfAp4Z1V9Z6ZNpxmrOYxvPVh1VlVNVNXEypUrt1eyJElS80YKaUl2ZRDQPlFVF3fD3+pOVdJ9v6sb3wgcOPT0A4BvbGf8gGnGJUmSlrxRZncGOBu4uar+cGjVpcDUDM01wCVD42/uZnkeDtzXnQ5dCxyZZEU3YeBIYG237v4kh3c/681DryVJkrSk7TLCc18IvAm4Mck/dGO/CfwucGGSE4Hbgdd16z4LvBzYADwAnABQVZuTnAZc22333qra3D1+K/BRYA/gc92XJEnSkpfBxMmlY2JioiYnJ+fvBVtvzdby35/7bjTuv9G4/0bT8v5rfd9JOyDJ+qqamG6ddxyQJElqkCFNkiSpQYY0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElq0C59FyBJ0pKS9F3BzKr6rkCz5JE0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElqkCFNkiSpQYY0SZKkBhnSJEmSGmRIkyRJapAhTZIkqUGGNEmSpAYZ0iRJkhpkSJMkSWqQIU2SJKlBu/RdgCRJ0g8lfVcws6pF+1EeSZMkSWqQIU2SJKlBhjRJkqQGGdIkSZIaZEiTJElqkCFNkiSpQYY0SZKkBjUf0pIcneSWJBuSnNJ3PZIkSYuh6ZCWZGfgQ8AxwMHA65Mc3G9VkiRJC6/pkAYcBmyoqtuq6gfABcCxPdckSZK04Fq/LdT+wB1DyxuB5225UZKTgJO6xX9Lcssi1DZX+wF3z9urtX77jPnlvhuN+2807r/RzN/+c9+Nxv03mvnff0/e1orWQ9p0e2Krm2ZV1VnAWQtfzuiSTFbVRN91jCP33Wjcf6Nx/43G/Td37rvRjPP+a/1050bgwKHlA4Bv9FSLJEnSomk9pF0LrE5yUJLdgOOBS3uuSZIkacE1fbqzqh5K8jZgLbAzcE5V3dRzWaMai9OyjXLfjcb9Nxr332jcf3PnvhvN2O6/VG11iZckSZJ61vrpTkmSpGXJkCZJktQgQ5okSVKDDGmSJEkNMqRJkiQ1yJAmSZp3SQ6azZikbTOkLbAkn0ryiiTu6x3kh/xokrwtyYq+69Cy9alpxi5a9CrGkO/d0SQ5ZJqvpyZpujfsdMau4DF0JnACcEaSPwM+WlVf6bmmcfEp4JAtxi4CDu2hlnH048C1Sa4DzgHWlo0RZy3JTwK/xuDmxz/8rKyqI3oragwkeTrwDGCvJK8ZWrUnsHs/VY0d37uj+TCD/ztuYHAP8Gd2j/dN8stV9dd9FrcjbGa7SJLsBbwe+C3gDuBPgI9X1YO9FtagoQ/532Pwn+SUPYFfq6pn9FLYGEoS4EgGvyhMABcCZ1fVv/Ra2BhI8o/AHwPrgYenxqtqfW9FjYEkxwLHAa/mkbfxux+4oKr+vpfCxozv3blLcgFw2tQdipIczOD/ktOAi6vqZ/qsb0d4JG0RJNkXeCPwJuB64BPAzwJrgJf0V1mznga8EtgbeNXQ+P3Af+6lojFVVZXkm8A3gYeAFcBFSdZV1bv6ra55D1XVmX0XMW6q6hLgkiTPr6qr+65nXPneHcnTh28hWVVfTvKcqrptkH3Hh0fSFliSi4GnAx9jcKrzzqF1k1U10VtxjfNDfjRJ3s7gF4G7gY8Af1FVD3bXR95aVU/ttcDGJXkPcBfw58D3p8aranNfNY2TJCsZ/FK1ikeeLn5LXzWNC9+7o0nySWAzcEE39B+B/RgcKLmqqp7bV207ypC2wJIcUVWX913HOPJDfjRJ3svg9MjXp1n3U1V1cw9ljY0kX51muKrqKYtezBhK8vfAF9j6dPF0Ewo0xPfuaJLsAfwKgzNWAa5icJ3a94BHV9W/9VjeDjGkLZAtLpjdSlVdvFi1jCs/5OdHksczdMF2Vd3eYzlaJpL8wzhd+9Mi37tzl2Q3BpfOFHDLuF7/7TVpC2fqWqrHAy8Apo6m/Rzwt4AhbfseXVW/3ncR4yrJq4A/BJ7I4LTdk4GbGUzK0DZMHf3e1i9a/oI1a59O8vKq+mzfhYwb37ujSfIS4DzgawyOpB2YZE1VXdlnXXNhSFsgVXUCQJJPAwdPXYuW5AnAh/qsbYz4IT+a9wGHA39TVc9J8nMMZhhrZi9m8EvVq6ZZV/gL1oyS3M9gPwX4zSTfBx7slquq9uyzvjHhe3c0fwAcWVW3wA/b6fwpY9i+yZC28FYNTxYAvgX8ZF/FjAM/5OfNg1X17SQ7Jdmpqq5I8v6+i2pdVZ3afT+h71rGUVU9ru8algDfu6PZdSqgAVTVPyfZtc+C5sqQtvD+NslaBim+gOOBK/otqW1+yM+be5M8FrgS+ESSuxhM5dcsJXkFg1NMw9cFvbe/isZHki0bUQPcB3y9qvx3ODPfu6OZTHI2g64KAG9gcG3z2HHiwCLorm35D93ilVX1533WMy78kB9NkscwmM0UBh9SewGfqKpv91rYmEjyx8CjGVxH+hHgtcCXqurEXgsbE0m+yKDr+43d0E8D/wjsC4xV1/fF5nt3NEkeBZzMj2Z3Xgl8uKq+P+MTG2RIU7P8kFefktxQVc8a+v5YBt3Kj+y7tnGwlLq+azwkuayqXprk/Utl0pmnOxfY0PVVALsBuwLf9bqqWfkacOK2PuQBQ9o0tvg3txX/7c3a97rvDyR5IvBt4KAe6xk3S6br+2LxvTuyJyR5MfDq7peER/xDq6rr+ilr7gxpC2zL66uSHAcc1lM548YP+TmY+jfXNcT8JoPrMqZOm3i93+z9ZZK9gd8HrmPwn+ef9FvSWLklyZk8suv7P3enosayZ9VC8707sv8BnAIcwKCFybACjlj0ikbk6c4eJPliVR3edx2tW0q39uhDkmuq6nnbG9PWutvvHD51M/AuWOxeVff1W9n4WEpd3xeb793RJHl3VZ3Wdx3zwZC2wLZoiLkTMAG8uKqe31NJY8MP+dF0d2z4EIOQWwz6LJ1cVS/otbAxkeRq36fqg+9dTTGkLbAk5w4tPsTgOqs/qaq7+qlIy0WSVcAHgBd2Q1cB76yqr/VU0lhJ8jvADQwucveDcpaSXFhVv5jkRqa5vqqqntVDWWPF966mGNLUHD/k1YLuIu7HMPjlaqodgs2UtyPJE6rqziRPnm79dDcNlzQ9Q9oCS3IA8EEGvxEVg9+I3lFVG3strGF+yM+/JNdV1XR95yQ1zPfu8rZT3wUsA+cClzK4Ue7+wF92Y9qGqdtoVdXXp/vqu74x5XRYLaokr0lya5L7knwnyf1JvtN3XWPI9+4yZkhbeCur6tyqeqj7+iiwsu+ixoEf8vPqM30XoGXn94BXV9VeVbVnVT3OU8Vz4nt3GTOkLby7k7wxyc7d1xsZNMXU9vkhP4Ikb0uyAqCqfrvverTsfKuqbu67iHHT/T/xN1PLvneXN5vZLry3AP8HOJ3BNWl/341p+/yQH82PA9cmuQ44B1jrLEUtosmu1+FfAD+8Z2JVXdxfSe2rqoeTPJBkL/vyyYkDalaSDzAIGn7Iz1EGt2Y4EjiBQY++C4Gzq+pfei1MS94W7YemVFX5S+p2JLkQOBxYB3x3aryq3t5bUeqFR9IWSJIPMvM92Hyzbd+ewAMMQsaUYnDfTs1CVVWSbzK4xcxDwArgoiTrqupd/VanpayqTui7hjH2GbwWTXgkbcEkWTPT+qo6b7Fq0fKU5O3AGuBu4CPAX1TVg90tj26tqqf2WqCWDdtISHPjkbQFYgibX37Iz8l+wGu2bFtSVf+e5JU91aTlyTYSOyDJauB/AQcDu0+NV9VTeitKvXB25wJLsi7J3kPLK5Ks7bOmMeWH/Cwl2SfJPsAfAfdPLQ+N44QMLTJP3e2Yc4EzGVyi8HPA+cDHeq1IvTCkLbyVVXXv1EJV3QM8vsd6xpUf8rO3Hpjsvm8C/hm4tXu8vse6tEzYRmJke1TVZQwuSfp6Vb0HOKLnmtQDQ9rCezjJk6YWulsdeSHgLNjna26q6qDutMha4FVVtV9V7Qu8EiddaBFU1cPAA0n26ruWMfW9qWtHu8/Bn8df7pclr0lbeL8FXJXk893yi4CTeqxnnNjnazTPrapfnlqoqs8lOa3PgrSsfA+4MYltJHbcO4FHA28HTmNwynPGyWhampzduQiS7Meg502Aq6vq7p5LGhv2+Zq77trHLwAfZ3D09o3Ai6rqqF4L07KwrRnuTqqavSSPqarvbn9LLVWGNDUvybMZhLSjgSvomjza52tm3SSBUxkcvQW4EvidqtrcX1WStifJ84GzgcdW1ZO6z8Bfqqpf6bk0LTJDmpplny9pfNlGYu6SXAO8Fri0qp7Tjf1TVT2z38q02LwmTS2zz9cIklzBNJNUqspZYloM5zI4kns6g2uqTsBWOrNWVXcMrvb4oYf7qkX9MaSpOVO9vBj0+RpeBqCqNtvna1b++9Dj3YFfYNB3SVoMe1TVZUnS/aL1niRfYBDcNLM7krwAqCS7MZhA4GfeMmRIU4vWMzgCFOBJwD3d472B24GD+ittfFTVlj3R/m5olrG00B7RRgL4V2wjMVu/DHwA2B/YCPw1cHKvFakXhjQ1p6oOAkjyxwyuyfhst3wM8LI+axsnWxyB3Ak4lEFbE2kx2EZijroOAG/ouw71z4kDalaS9VV16BZjk1U10VdN4yTJV/nREcmHgK8C762qq3otTMuKbSRmL8kHmaHZuT3mlh+PpKlldyf5bR7Z5+vb/ZY0PqaOSEp9GG4jAdhGYnYm+y5AbfFImppln6/RJNkVeCs/2n9/C/zfqnqwt6K0bNhGQhqdR9LUrC6MvaPvOsbYmcCuwIe75Td1Y/+pt4q0rNhGYm66W2m9rqru7ZZXABd4t5Dlx5CmZtnna2TPrapnDy1fnuQfe6tGy41tJOZu5VRAA6iqe5I4M3YZMqSpZfb5Gs3DSZ46dZ/TJE/BIxlaPLaRmLuHkzypqm4HSPJkZphQoKXLa9I0VpJ8vqpe3Hcd4yDJSxl0fb+NwQzPJwMnVNUVvRYmaUZJjgbOAqb6Gr4IOKmq1vZXlfpgSFOzttHn64yqelpPJY2dJI8CnsYgpH2lqr7fc0la4mwjMT+S7AcczuC9e3XXO03LjKc71bLhOw9M9fk6sdeKxkCS12xj1VOTUFUXL2pBWm5sIzEPulD26b7rUL88kiYtMUnOnWF1VdVbFq0YSdKcGdLULPt8SePLNhLS6HbquwBpBmcyuA7tw93Xod2YZiHJ/0yy99DyiiTv67MmLStbtZHAG6xLO8SQppY9t6rWVNXl3dcJwHP7LmqMHDPNf5Iv77EeLS8PJ3nS1IJtJKQd58QBtcw+X6PZOcmjpmZ0JtkDeFTPNWn5+C3gqiSPaCPRYz3S2DGkqWW/BlyR5BF9vvotaax8HLism0hQwFuA8/otSctFVf1VkkP4URuJX7WNhLRjnDigptnnazRdU8yXMdh/f20zTEkaH4Y0NWeGPl8A9vmagySvrCp7LknSGDGkqTn2+Zp/Sa6rqkP6rkOSNHtek6bmdLM4Nb/SdwGSpB1jCw41yz5f8+qX+i5AkrRjDGlqmX2+RpBkMsnJSVZU1Zf6rkeStGMMaWrZzt3sTsA+X3NwPPBE4NokFyQ5KomnPSVpTDhxQM1K8i7g1cBwn69Lq+r3ei1szCTZCXglg1tq/TtwDvCBqtrca2GSpBkZ0tQ0+3yNJsmzGDQAfjmwFvgE8LPAm6rqZ/qsTZI0M0OaxoJ9vnZckvXAvcDZwKeGGwEnubiqZuxHJ0nqlyFNY8E+XzsuyVOq6ra+65AkzY190jQuvOB9lpL816HHW62vqj9c1IIkSXNiSNO4sM/X7D1uhnUeOpekMeHpTjUrySSDmZ3/r+uRph2Q5IVV9XfbG5Mktck+aWqZfb5G88FZjkmSGuSRNDXPPl87JsnzgRcA7wROH1q1J/DzVfXsXgqTJO0Qj6SpaV2frz8Afh/4FPBa4DvA5X3W1bjdgMcyuOb0cUNf32Gw/yRJY8AjaWqWfb7mLsnOwCerylAmSWPKkKZm2edrNEaWgQMAAAJGSURBVEkur6oj+q5DkjQ3tuBQc+zzNW+uT3Ip8GfAd6cGq+ri/kqSJM2WIU0tss/X/NgH+DYwfDStAEOaJI0BT3eqWfb5kiQtZ4Y0NWu6+3V6D8/ZS7I7cCLwDGD3qfGqektvRUmSZs3TnWrOUJ+vlcPXpzHo87VzP1WNpY8BXwGOAt4LvAG4udeKJEmzZp80tcg+X/PjJ6rq3cB3q+o84BXAT/dckyRpljySpuZU1eeTXAX8dFX9Tt/1jLEHu+/3Jnkm8E1gVX/lSJJ2hCFNTaqqh5Ps03cdY+6sJCuAdwOXMjg6+e5+S5IkzZYTB9SsJH8ArMY+X5KkZcgjaWqZfb5GkGRf4D3ACxnsty8Ap1XVt/usS5I0Ox5Jk5aoJOuAK4GPd0NvAF5SVS/rrypJ0mwZ0tQs+3yNJsn6qjp0i7HJqproqyZJ0uzZgkMt+xjw4wz6fH0eOAC4v9eKxssVSY5PslP39YvAZ/ouSpI0Ox5JU7OSXF9Vz0lyQ1U9K8muwNqqOmK7TxZJ7gceAzzcDe3MjyZgVFXt2UthkqRZceKAWmafrxFU1Uw3qpckNc6QppbZ50uStGx5ulOSJKlBThxQs5Lsm+SDSa5Lsj7JH3W9vyRJWvIMaWrZBcBdwC8wuLH63cAne61IkqRF4ulONcs+X5Kk5cwjaWqZfb4kScuWR9LULPt8SZKWM0OaJElSgzzdKUmS1CBDmiRJUoMMaZIkSQ0ypEmSJDXIkCZJktQgQ5okSVKDDGmSJEkNMqRJkiQ16P8DG4BJu1bNFYYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uber_dataset['icon'].value_counts().plot(kind='bar', figsize=(10,5), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "id": "n_R2K2icdM1a",
    "outputId": "77116a09-5ab0-46c5-ca61-457ba1a4bbfd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1d27c8b08>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uber_dataset['uvIndex'].value_counts().plot(kind='bar', figsize=(10,5), color='brown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "id": "NuEyhvgadM1b",
    "outputId": "545c5871-a840-4dd4-e3d6-4a2c68d95810"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1d2828048>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uber_dataset['moonPhase'].value_counts().plot(kind='bar', figsize=(10,5), color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "id": "br6awwc4dM1b",
    "outputId": "5abfb5a4-56ac-4af3-bd51-f812c6149143"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1d28ce508>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uber_dataset['precipProbability'].value_counts().plot(kind='bar', figsize=(10,5), color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JXZS2rP2dM1b"
   },
   "source": [
    "### Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "jifvbsGzdM1b"
   },
   "outputs": [],
   "source": [
    "# Import label encoder \n",
    "from sklearn import preprocessing \n",
    "  \n",
    "# label_encoder object knows how to understand word labels. \n",
    "label_encoder = preprocessing.LabelEncoder() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1_sciw5SdM1b",
    "outputId": "34da9e9d-76b0-46b2-9b8b-76af0175e2a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                              object\n",
       "timestamp                        int64\n",
       "hour                             int64\n",
       "day                              int64\n",
       "month                            int64\n",
       "datetime                        object\n",
       "timezone                        object\n",
       "source                          object\n",
       "destination                     object\n",
       "product_id                      object\n",
       "name                            object\n",
       "distance                       float64\n",
       "surge_multiplier               float64\n",
       "latitude                       float64\n",
       "longitude                      float64\n",
       "temperature                    float64\n",
       "apparentTemperature            float64\n",
       "short_summary                   object\n",
       "long_summary                    object\n",
       "precipIntensity                float64\n",
       "precipProbability              float64\n",
       "humidity                       float64\n",
       "windSpeed                      float64\n",
       "windGust                       float64\n",
       "windGustTime                     int64\n",
       "visibility                     float64\n",
       "temperatureHigh                float64\n",
       "temperatureHighTime              int64\n",
       "temperatureLow                 float64\n",
       "temperatureLowTime               int64\n",
       "apparentTemperatureHigh        float64\n",
       "apparentTemperatureHighTime      int64\n",
       "apparentTemperatureLow         float64\n",
       "apparentTemperatureLowTime       int64\n",
       "icon                            object\n",
       "dewPoint                       float64\n",
       "pressure                       float64\n",
       "windBearing                      int64\n",
       "cloudCover                     float64\n",
       "uvIndex                          int64\n",
       "visibility.1                   float64\n",
       "ozone                          float64\n",
       "sunriseTime                      int64\n",
       "sunsetTime                       int64\n",
       "moonPhase                      float64\n",
       "precipIntensityMax             float64\n",
       "uvIndexTime                      int64\n",
       "temperatureMin                 float64\n",
       "temperatureMinTime               int64\n",
       "temperatureMax                 float64\n",
       "temperatureMaxTime               int64\n",
       "apparentTemperatureMin         float64\n",
       "apparentTemperatureMinTime       int64\n",
       "apparentTemperatureMax         float64\n",
       "apparentTemperatureMaxTime       int64\n",
       "price                          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "uber_dataset['id']= label_encoder.fit_transform(uber_dataset['id']) \n",
    "uber_dataset['datetime']= label_encoder.fit_transform(uber_dataset['datetime']) \n",
    "uber_dataset['timezone']= label_encoder.fit_transform(uber_dataset['timezone'])\n",
    "uber_dataset['destination']= label_encoder.fit_transform(uber_dataset['destination']) \n",
    "uber_dataset['product_id']= label_encoder.fit_transform(uber_dataset['product_id'])\n",
    "uber_dataset['short_summary']= label_encoder.fit_transform(uber_dataset['short_summary'])\n",
    "uber_dataset['long_summary']= label_encoder.fit_transform(uber_dataset['long_summary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class mapping of Name: \n",
      "Black SUV --> 0\n",
      "Lux --> 1\n",
      "Shared --> 2\n",
      "Taxi --> 3\n",
      "UberPool --> 4\n",
      "UberX --> 5\n"
     ]
    }
   ],
   "source": [
    "uber_dataset['name']= label_encoder.fit_transform(uber_dataset['name'])\n",
    "\n",
    "print(\"Class mapping of Name: \")\n",
    "for i, item in enumerate(label_encoder.classes_):\n",
    "    print(item, \"-->\", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class mapping of Source: \n",
      "Back Bay --> 0\n",
      "Beacon Hill --> 1\n",
      "Boston University --> 2\n",
      "Fenway --> 3\n",
      "Financial District --> 4\n",
      "Haymarket Square --> 5\n",
      "North End --> 6\n",
      "North Station --> 7\n",
      "Northeastern University --> 8\n",
      "South Station --> 9\n",
      "Theatre District --> 10\n",
      "West End --> 11\n"
     ]
    }
   ],
   "source": [
    "uber_dataset['source']= label_encoder.fit_transform(uber_dataset['source'])\n",
    "\n",
    "print(\"Class mapping of Source: \")\n",
    "for i, item in enumerate(label_encoder.classes_):\n",
    "    print(item, \"-->\", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "z5JEPXQ9dM1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class mapping of Icon: \n",
      " clear-day  --> 0\n",
      " clear-night  --> 1\n",
      " cloudy  --> 2\n",
      " fog  --> 3\n",
      " partly-cloudy-day  --> 4\n",
      " partly-cloudy-night  --> 5\n",
      " rain  --> 6\n"
     ]
    }
   ],
   "source": [
    "uber_dataset['icon']= label_encoder.fit_transform(uber_dataset['icon'])\n",
    "\n",
    "print(\"Class mapping of Icon: \")\n",
    "for i, item in enumerate(label_encoder.classes_):\n",
    "    print(item, \"-->\", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kFeWRoYTdM1c",
    "outputId": "ffa6fdd2-17d5-4d07-85d8-19bedff646a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                               int32\n",
       "timestamp                        int64\n",
       "hour                             int64\n",
       "day                              int64\n",
       "month                            int64\n",
       "datetime                         int32\n",
       "timezone                         int32\n",
       "source                           int32\n",
       "destination                      int32\n",
       "product_id                       int32\n",
       "name                             int32\n",
       "distance                       float64\n",
       "surge_multiplier               float64\n",
       "latitude                       float64\n",
       "longitude                      float64\n",
       "temperature                    float64\n",
       "apparentTemperature            float64\n",
       "short_summary                    int32\n",
       "long_summary                     int32\n",
       "precipIntensity                float64\n",
       "precipProbability              float64\n",
       "humidity                       float64\n",
       "windSpeed                      float64\n",
       "windGust                       float64\n",
       "windGustTime                     int64\n",
       "visibility                     float64\n",
       "temperatureHigh                float64\n",
       "temperatureHighTime              int64\n",
       "temperatureLow                 float64\n",
       "temperatureLowTime               int64\n",
       "apparentTemperatureHigh        float64\n",
       "apparentTemperatureHighTime      int64\n",
       "apparentTemperatureLow         float64\n",
       "apparentTemperatureLowTime       int64\n",
       "icon                             int32\n",
       "dewPoint                       float64\n",
       "pressure                       float64\n",
       "windBearing                      int64\n",
       "cloudCover                     float64\n",
       "uvIndex                          int64\n",
       "visibility.1                   float64\n",
       "ozone                          float64\n",
       "sunriseTime                      int64\n",
       "sunsetTime                       int64\n",
       "moonPhase                      float64\n",
       "precipIntensityMax             float64\n",
       "uvIndexTime                      int64\n",
       "temperatureMin                 float64\n",
       "temperatureMinTime               int64\n",
       "temperatureMax                 float64\n",
       "temperatureMaxTime               int64\n",
       "apparentTemperatureMin         float64\n",
       "apparentTemperatureMinTime       int64\n",
       "apparentTemperatureMax         float64\n",
       "apparentTemperatureMaxTime       int64\n",
       "price                          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "eZiAMOtUdM1c",
    "outputId": "440c921d-695e-4fa6-d081-27f65a8697ec"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>datetime</th>\n",
       "      <th>timezone</th>\n",
       "      <th>source</th>\n",
       "      <th>destination</th>\n",
       "      <th>product_id</th>\n",
       "      <th>...</th>\n",
       "      <th>uvIndexTime</th>\n",
       "      <th>temperatureMin</th>\n",
       "      <th>temperatureMinTime</th>\n",
       "      <th>temperatureMax</th>\n",
       "      <th>temperatureMaxTime</th>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <th>apparentTemperatureMinTime</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "      <th>apparentTemperatureMaxTime</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>83667</td>\n",
       "      <td>1544952608</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>4127</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1544979600</td>\n",
       "      <td>39.89</td>\n",
       "      <td>1545012000</td>\n",
       "      <td>43.68</td>\n",
       "      <td>1544968800</td>\n",
       "      <td>33.73</td>\n",
       "      <td>1545012000</td>\n",
       "      <td>38.07</td>\n",
       "      <td>1544958000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>95698</td>\n",
       "      <td>1543284024</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>674</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1543251600</td>\n",
       "      <td>40.49</td>\n",
       "      <td>1543233600</td>\n",
       "      <td>47.30</td>\n",
       "      <td>1543251600</td>\n",
       "      <td>36.20</td>\n",
       "      <td>1543291200</td>\n",
       "      <td>43.92</td>\n",
       "      <td>1543251600</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>100484</td>\n",
       "      <td>1543818483</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1543852800</td>\n",
       "      <td>43.09</td>\n",
       "      <td>1543896000</td>\n",
       "      <td>57.02</td>\n",
       "      <td>1543852800</td>\n",
       "      <td>39.90</td>\n",
       "      <td>1543896000</td>\n",
       "      <td>56.35</td>\n",
       "      <td>1543852800</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>181550</td>\n",
       "      <td>1543594384</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1543593600</td>\n",
       "      <td>28.64</td>\n",
       "      <td>1543579200</td>\n",
       "      <td>42.32</td>\n",
       "      <td>1543600800</td>\n",
       "      <td>29.29</td>\n",
       "      <td>1543579200</td>\n",
       "      <td>40.48</td>\n",
       "      <td>1543611600</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>67474</td>\n",
       "      <td>1544728504</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>3118</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1544716800</td>\n",
       "      <td>18.29</td>\n",
       "      <td>1544688000</td>\n",
       "      <td>33.83</td>\n",
       "      <td>1544731200</td>\n",
       "      <td>13.79</td>\n",
       "      <td>1544688000</td>\n",
       "      <td>32.85</td>\n",
       "      <td>1544734800</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id   timestamp  hour  day  month  datetime  timezone  source  \\\n",
       "0   83667  1544952608     9   16     12      4127         0       5   \n",
       "1   95698  1543284024     2   27     11       674         0       5   \n",
       "2  100484  1543818483     6    3     12      5720         0       0   \n",
       "3  181550  1543594384    16   30     11      2008         0       0   \n",
       "4   67474  1544728504    19   13     12      3118         0       6   \n",
       "\n",
       "   destination  product_id  ...  uvIndexTime  temperatureMin  \\\n",
       "0            7           4  ...   1544979600           39.89   \n",
       "1            7           5  ...   1543251600           40.49   \n",
       "2            8           4  ...   1543852800           43.09   \n",
       "3            8           5  ...   1543593600           28.64   \n",
       "4           11           0  ...   1544716800           18.29   \n",
       "\n",
       "   temperatureMinTime  temperatureMax  temperatureMaxTime  \\\n",
       "0          1545012000           43.68          1544968800   \n",
       "1          1543233600           47.30          1543251600   \n",
       "2          1543896000           57.02          1543852800   \n",
       "3          1543579200           42.32          1543600800   \n",
       "4          1544688000           33.83          1544731200   \n",
       "\n",
       "   apparentTemperatureMin  apparentTemperatureMinTime  apparentTemperatureMax  \\\n",
       "0                   33.73                  1545012000                   38.07   \n",
       "1                   36.20                  1543291200                   43.92   \n",
       "2                   39.90                  1543896000                   56.35   \n",
       "3                   29.29                  1543579200                   40.48   \n",
       "4                   13.79                  1544688000                   32.85   \n",
       "\n",
       "   apparentTemperatureMaxTime  price  \n",
       "0                  1544958000    5.0  \n",
       "1                  1543251600   11.0  \n",
       "2                  1543852800    3.0  \n",
       "3                  1543611600   13.5  \n",
       "4                  1544734800    7.5  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p17aW6tHdM1c"
   },
   "source": [
    "### Filling NAN Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2eeT-9fJdM1c",
    "outputId": "ebdba671-fbd4-4e70-b738-e50669732113"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                 0\n",
       "timestamp                          0\n",
       "hour                               0\n",
       "day                                0\n",
       "month                              0\n",
       "datetime                           0\n",
       "timezone                           0\n",
       "source                             0\n",
       "destination                        0\n",
       "product_id                         0\n",
       "name                               0\n",
       "distance                           0\n",
       "surge_multiplier                   0\n",
       "latitude                           0\n",
       "longitude                          0\n",
       "temperature                        0\n",
       "apparentTemperature                0\n",
       "short_summary                      0\n",
       "long_summary                       0\n",
       "precipIntensity                    0\n",
       "precipProbability                  0\n",
       "humidity                           0\n",
       "windSpeed                          0\n",
       "windGust                           0\n",
       "windGustTime                       0\n",
       "visibility                         0\n",
       "temperatureHigh                    0\n",
       "temperatureHighTime                0\n",
       "temperatureLow                     0\n",
       "temperatureLowTime                 0\n",
       "apparentTemperatureHigh            0\n",
       "apparentTemperatureHighTime        0\n",
       "apparentTemperatureLow             0\n",
       "apparentTemperatureLowTime         0\n",
       "icon                               0\n",
       "dewPoint                           0\n",
       "pressure                           0\n",
       "windBearing                        0\n",
       "cloudCover                         0\n",
       "uvIndex                            0\n",
       "visibility.1                       0\n",
       "ozone                              0\n",
       "sunriseTime                        0\n",
       "sunsetTime                         0\n",
       "moonPhase                          0\n",
       "precipIntensityMax                 0\n",
       "uvIndexTime                        0\n",
       "temperatureMin                     0\n",
       "temperatureMinTime                 0\n",
       "temperatureMax                     0\n",
       "temperatureMaxTime                 0\n",
       "apparentTemperatureMin             0\n",
       "apparentTemperatureMinTime         0\n",
       "apparentTemperatureMax             0\n",
       "apparentTemperatureMaxTime         0\n",
       "price                          55095\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qS-MXbVKdM1c",
    "outputId": "13859e69-346c-4759-ee97-8207241af37b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset['price'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "FA5TXwDqdM1d"
   },
   "outputs": [],
   "source": [
    "uber_dataset[\"price\"].fillna(10.5, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mhBj24RTdM1d",
    "outputId": "0b9f7d6f-4cbb-4299-c91b-2f2e63691478"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                             0\n",
       "timestamp                      0\n",
       "hour                           0\n",
       "day                            0\n",
       "month                          0\n",
       "datetime                       0\n",
       "timezone                       0\n",
       "source                         0\n",
       "destination                    0\n",
       "product_id                     0\n",
       "name                           0\n",
       "distance                       0\n",
       "surge_multiplier               0\n",
       "latitude                       0\n",
       "longitude                      0\n",
       "temperature                    0\n",
       "apparentTemperature            0\n",
       "short_summary                  0\n",
       "long_summary                   0\n",
       "precipIntensity                0\n",
       "precipProbability              0\n",
       "humidity                       0\n",
       "windSpeed                      0\n",
       "windGust                       0\n",
       "windGustTime                   0\n",
       "visibility                     0\n",
       "temperatureHigh                0\n",
       "temperatureHighTime            0\n",
       "temperatureLow                 0\n",
       "temperatureLowTime             0\n",
       "apparentTemperatureHigh        0\n",
       "apparentTemperatureHighTime    0\n",
       "apparentTemperatureLow         0\n",
       "apparentTemperatureLowTime     0\n",
       "icon                           0\n",
       "dewPoint                       0\n",
       "pressure                       0\n",
       "windBearing                    0\n",
       "cloudCover                     0\n",
       "uvIndex                        0\n",
       "visibility.1                   0\n",
       "ozone                          0\n",
       "sunriseTime                    0\n",
       "sunsetTime                     0\n",
       "moonPhase                      0\n",
       "precipIntensityMax             0\n",
       "uvIndexTime                    0\n",
       "temperatureMin                 0\n",
       "temperatureMinTime             0\n",
       "temperatureMax                 0\n",
       "temperatureMaxTime             0\n",
       "apparentTemperatureMin         0\n",
       "apparentTemperatureMinTime     0\n",
       "apparentTemperatureMax         0\n",
       "apparentTemperatureMaxTime     0\n",
       "price                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset['price'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "uber_dataset['price'] = uber_dataset['price'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1    11\n",
       "2     3\n",
       "3    13\n",
       "4     7\n",
       "Name: price, dtype: int32"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_dataset['price'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U5UVpEBndM1d"
   },
   "source": [
    "### RFE (Recursive Feature Elimination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "phS20dKJdM1d"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "1Yq3zRhydM1d"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "VijCz7WxdM1d"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "MFRqNu2hdM1k"
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "PKmO97t6dM1d"
   },
   "outputs": [],
   "source": [
    "X = uber_dataset.drop('price', axis = 1)\n",
    "y = uber_dataset['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "ggSQPNlFdM1e",
    "outputId": "2f81fddb-9826-43f2-daee-0bd306516970"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>datetime</th>\n",
       "      <th>timezone</th>\n",
       "      <th>source</th>\n",
       "      <th>destination</th>\n",
       "      <th>product_id</th>\n",
       "      <th>...</th>\n",
       "      <th>precipIntensityMax</th>\n",
       "      <th>uvIndexTime</th>\n",
       "      <th>temperatureMin</th>\n",
       "      <th>temperatureMinTime</th>\n",
       "      <th>temperatureMax</th>\n",
       "      <th>temperatureMaxTime</th>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <th>apparentTemperatureMinTime</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "      <th>apparentTemperatureMaxTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>83667</td>\n",
       "      <td>1544952608</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>4127</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>1544979600</td>\n",
       "      <td>39.89</td>\n",
       "      <td>1545012000</td>\n",
       "      <td>43.68</td>\n",
       "      <td>1544968800</td>\n",
       "      <td>33.73</td>\n",
       "      <td>1545012000</td>\n",
       "      <td>38.07</td>\n",
       "      <td>1544958000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>95698</td>\n",
       "      <td>1543284024</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>674</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>1543251600</td>\n",
       "      <td>40.49</td>\n",
       "      <td>1543233600</td>\n",
       "      <td>47.30</td>\n",
       "      <td>1543251600</td>\n",
       "      <td>36.20</td>\n",
       "      <td>1543291200</td>\n",
       "      <td>43.92</td>\n",
       "      <td>1543251600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>100484</td>\n",
       "      <td>1543818483</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>1543852800</td>\n",
       "      <td>43.09</td>\n",
       "      <td>1543896000</td>\n",
       "      <td>57.02</td>\n",
       "      <td>1543852800</td>\n",
       "      <td>39.90</td>\n",
       "      <td>1543896000</td>\n",
       "      <td>56.35</td>\n",
       "      <td>1543852800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>181550</td>\n",
       "      <td>1543594384</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>1543593600</td>\n",
       "      <td>28.64</td>\n",
       "      <td>1543579200</td>\n",
       "      <td>42.32</td>\n",
       "      <td>1543600800</td>\n",
       "      <td>29.29</td>\n",
       "      <td>1543579200</td>\n",
       "      <td>40.48</td>\n",
       "      <td>1543611600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>67474</td>\n",
       "      <td>1544728504</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>3118</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>1544716800</td>\n",
       "      <td>18.29</td>\n",
       "      <td>1544688000</td>\n",
       "      <td>33.83</td>\n",
       "      <td>1544731200</td>\n",
       "      <td>13.79</td>\n",
       "      <td>1544688000</td>\n",
       "      <td>32.85</td>\n",
       "      <td>1544734800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id   timestamp  hour  day  month  datetime  timezone  source  \\\n",
       "0   83667  1544952608     9   16     12      4127         0       5   \n",
       "1   95698  1543284024     2   27     11       674         0       5   \n",
       "2  100484  1543818483     6    3     12      5720         0       0   \n",
       "3  181550  1543594384    16   30     11      2008         0       0   \n",
       "4   67474  1544728504    19   13     12      3118         0       6   \n",
       "\n",
       "   destination  product_id  ...  precipIntensityMax  uvIndexTime  \\\n",
       "0            7           4  ...              0.1276   1544979600   \n",
       "1            7           5  ...              0.1300   1543251600   \n",
       "2            8           4  ...              0.0004   1543852800   \n",
       "3            8           5  ...              0.0004   1543593600   \n",
       "4           11           0  ...              0.0001   1544716800   \n",
       "\n",
       "   temperatureMin  temperatureMinTime  temperatureMax  temperatureMaxTime  \\\n",
       "0           39.89          1545012000           43.68          1544968800   \n",
       "1           40.49          1543233600           47.30          1543251600   \n",
       "2           43.09          1543896000           57.02          1543852800   \n",
       "3           28.64          1543579200           42.32          1543600800   \n",
       "4           18.29          1544688000           33.83          1544731200   \n",
       "\n",
       "   apparentTemperatureMin  apparentTemperatureMinTime  apparentTemperatureMax  \\\n",
       "0                   33.73                  1545012000                   38.07   \n",
       "1                   36.20                  1543291200                   43.92   \n",
       "2                   39.90                  1543896000                   56.35   \n",
       "3                   29.29                  1543579200                   40.48   \n",
       "4                   13.79                  1544688000                   32.85   \n",
       "\n",
       "   apparentTemperatureMaxTime  \n",
       "0                  1544958000  \n",
       "1                  1543251600  \n",
       "2                  1543852800  \n",
       "3                  1543611600  \n",
       "4                  1544734800  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fGI0HiS7dM1e",
    "outputId": "078dba9d-23af-4867-9433-3aef975707a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1    11\n",
       "2     3\n",
       "3    13\n",
       "4     7\n",
       "Name: price, dtype: int32"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "USgYIlhUdM1e",
    "outputId": "34e5f7ac-dc83-4ca8-bc7a-762a1861742f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(322844, 55)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iaHGgGuRdM1e",
    "outputId": "12f00315-7b5d-4eca-e4b8-42080357981a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(322844,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "id": "fb_OfkjxdM1f",
    "outputId": "4ed0eee7-fe13-4aed-afe8-7dd888733bd6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x159ccd59648>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.value_counts().plot(kind='bar',figsize=(30,8),color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VnOoeBJBdM1h"
   },
   "source": [
    "#### Training accuracy in 56 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "8jbdiPe6dM1h"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZUBi6xh2dM1i",
    "outputId": "70c00aa2-7923-4b89-aabb-bb5ef98fe14d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258275, 55)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HPf6X636dM1i",
    "outputId": "7bae348e-82f1-494b-ea51-23ae6ce48533"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64569, 55)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PXsH6kstdM1i",
    "outputId": "d5726e98-f787-4730-e7e4-5eb33a5f9bc8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258275,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z6PjvSAodM1i",
    "outputId": "3a761646-07e9-444b-dff2-eee73f827724"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64569,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "QDXhk-s4dM1j"
   },
   "outputs": [],
   "source": [
    "#Creating model\n",
    "reg = LinearRegression()\n",
    "#Fitting training data\n",
    "reg = reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "emmuRAZXuaUe",
    "outputId": "8b8cb52b-295b-4adb-9047-ba50fbb4b29a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8054834220130731"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NQfX7mOJdM1k"
   },
   "source": [
    "#### Training accuracy in 40 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PwJBW2TSdM1k",
    "outputId": "422fd599-5a54-4a6b-c843-8c6c2e95d2d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 55 features.\n",
      "Fitting estimator with 54 features.\n",
      "Fitting estimator with 53 features.\n",
      "Fitting estimator with 52 features.\n",
      "Fitting estimator with 51 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 49 features.\n",
      "Fitting estimator with 48 features.\n",
      "Fitting estimator with 47 features.\n",
      "Fitting estimator with 46 features.\n",
      "Fitting estimator with 45 features.\n",
      "Fitting estimator with 44 features.\n",
      "Fitting estimator with 43 features.\n",
      "Fitting estimator with 42 features.\n",
      "Fitting estimator with 41 features.\n"
     ]
    }
   ],
   "source": [
    "rfe = RFE(reg, 40, verbose=1)\n",
    "rfe = rfe.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iAmcmLvbdM1l",
    "outputId": "75f6b5bc-71d9-4709-99d1-9632d43dfd33"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True,  True,  True, False, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "       False,  True, False,  True, False,  True, False,  True,  True,\n",
       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True, False,  True, False,  True, False,  True, False,  True,\n",
       "       False])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "id": "OCLPpCOJdM1l"
   },
   "outputs": [],
   "source": [
    "XX = X[X.columns[rfe.support_]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "id": "3aj1xRtHdM1l",
    "outputId": "ac089cc8-84f5-4e54-a98c-2cf276515f12"
   },
   "outputs": [
    {
     "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>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>source</th>\n",
       "      <th>destination</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>distance</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>visibility.1</th>\n",
       "      <th>ozone</th>\n",
       "      <th>sunriseTime</th>\n",
       "      <th>sunsetTime</th>\n",
       "      <th>moonPhase</th>\n",
       "      <th>precipIntensityMax</th>\n",
       "      <th>temperatureMin</th>\n",
       "      <th>temperatureMax</th>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.2148</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000</td>\n",
       "      <td>303.8</td>\n",
       "      <td>1544962084</td>\n",
       "      <td>1544994864</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>39.89</td>\n",
       "      <td>43.68</td>\n",
       "      <td>33.73</td>\n",
       "      <td>38.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.2148</td>\n",
       "      <td>...</td>\n",
       "      <td>4.786</td>\n",
       "      <td>291.1</td>\n",
       "      <td>1543232969</td>\n",
       "      <td>1543266992</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>40.49</td>\n",
       "      <td>47.30</td>\n",
       "      <td>36.20</td>\n",
       "      <td>43.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3503</td>\n",
       "      <td>...</td>\n",
       "      <td>1.413</td>\n",
       "      <td>290.9</td>\n",
       "      <td>1543838259</td>\n",
       "      <td>1543871628</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>43.09</td>\n",
       "      <td>57.02</td>\n",
       "      <td>39.90</td>\n",
       "      <td>56.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3503</td>\n",
       "      <td>...</td>\n",
       "      <td>9.920</td>\n",
       "      <td>269.9</td>\n",
       "      <td>1543578871</td>\n",
       "      <td>1543612479</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>28.64</td>\n",
       "      <td>42.32</td>\n",
       "      <td>29.29</td>\n",
       "      <td>40.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3647</td>\n",
       "      <td>...</td>\n",
       "      <td>9.959</td>\n",
       "      <td>330.8</td>\n",
       "      <td>1544702792</td>\n",
       "      <td>1544735599</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>18.29</td>\n",
       "      <td>33.83</td>\n",
       "      <td>13.79</td>\n",
       "      <td>32.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hour  day  month  source  destination  product_id  name  distance  \\\n",
       "0     9   16     12       5            7           4     2      0.44   \n",
       "1     2   27     11       5            7           5     1      0.44   \n",
       "2     6    3     12       0            8           4     2      1.08   \n",
       "3    16   30     11       0            8           5     1      1.08   \n",
       "4    19   13     12       6           11           0     5      1.11   \n",
       "\n",
       "   surge_multiplier  latitude  ...  visibility.1  ozone  sunriseTime  \\\n",
       "0               1.0   42.2148  ...        10.000  303.8   1544962084   \n",
       "1               1.0   42.2148  ...         4.786  291.1   1543232969   \n",
       "2               1.0   42.3503  ...         1.413  290.9   1543838259   \n",
       "3               1.0   42.3503  ...         9.920  269.9   1543578871   \n",
       "4               1.0   42.3647  ...         9.959  330.8   1544702792   \n",
       "\n",
       "   sunsetTime  moonPhase  precipIntensityMax  temperatureMin  temperatureMax  \\\n",
       "0  1544994864       0.30              0.1276           39.89           43.68   \n",
       "1  1543266992       0.64              0.1300           40.49           47.30   \n",
       "2  1543871628       0.89              0.0004           43.09           57.02   \n",
       "3  1543612479       0.79              0.0004           28.64           42.32   \n",
       "4  1544735599       0.21              0.0001           18.29           33.83   \n",
       "\n",
       "   apparentTemperatureMin  apparentTemperatureMax  \n",
       "0                   33.73                   38.07  \n",
       "1                   36.20                   43.92  \n",
       "2                   39.90                   56.35  \n",
       "3                   29.29                   40.48  \n",
       "4                   13.79                   32.85  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "id": "c2kjryu8dM1m"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(XX, y, test_size = 0.3, random_state = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UuoCsIBedM1m",
    "outputId": "fcaf0214-08ce-40aa-9630-951010c47453"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225990, 40)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "id": "bGrDwcGGdM1n"
   },
   "outputs": [],
   "source": [
    "#Creating model\n",
    "reg1 = LinearRegression()\n",
    "#Fitting training data\n",
    "reg1 = reg1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7FBGsIIHuzQN",
    "outputId": "32071a61-ed39-45b1-f04c-d8fd236074e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8050662132748471"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mh22ZUP-dM1q"
   },
   "source": [
    "#### Training accuracy in 15 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vYWk7nzQdM1q",
    "outputId": "78f77ee1-d3fa-44b4-88de-5df5632be224"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 55 features.\n",
      "Fitting estimator with 54 features.\n",
      "Fitting estimator with 53 features.\n",
      "Fitting estimator with 52 features.\n",
      "Fitting estimator with 51 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 49 features.\n",
      "Fitting estimator with 48 features.\n",
      "Fitting estimator with 47 features.\n",
      "Fitting estimator with 46 features.\n",
      "Fitting estimator with 45 features.\n",
      "Fitting estimator with 44 features.\n",
      "Fitting estimator with 43 features.\n",
      "Fitting estimator with 42 features.\n",
      "Fitting estimator with 41 features.\n",
      "Fitting estimator with 40 features.\n",
      "Fitting estimator with 39 features.\n",
      "Fitting estimator with 38 features.\n",
      "Fitting estimator with 37 features.\n",
      "Fitting estimator with 36 features.\n",
      "Fitting estimator with 35 features.\n",
      "Fitting estimator with 34 features.\n",
      "Fitting estimator with 33 features.\n",
      "Fitting estimator with 32 features.\n",
      "Fitting estimator with 31 features.\n",
      "Fitting estimator with 30 features.\n",
      "Fitting estimator with 29 features.\n",
      "Fitting estimator with 28 features.\n",
      "Fitting estimator with 27 features.\n",
      "Fitting estimator with 26 features.\n",
      "Fitting estimator with 25 features.\n",
      "Fitting estimator with 24 features.\n",
      "Fitting estimator with 23 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n"
     ]
    }
   ],
   "source": [
    "rfe = RFE(reg, 15, verbose=1)\n",
    "rfe = rfe.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "id": "m8ui7o4RdM1r"
   },
   "outputs": [],
   "source": [
    "XX = X[X.columns[rfe.support_]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "id": "DhzD1Ef4dM1r",
    "outputId": "ca44d44f-1300-4e46-bfed-2e34010833ce"
   },
   "outputs": [
    {
     "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>source</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>distance</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>precipIntensity</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperatureHigh</th>\n",
       "      <th>apparentTemperatureHigh</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>precipIntensityMax</th>\n",
       "      <th>temperatureMax</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.2148</td>\n",
       "      <td>-71.0330</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.68</td>\n",
       "      <td>43.68</td>\n",
       "      <td>37.95</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>43.68</td>\n",
       "      <td>38.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.2148</td>\n",
       "      <td>-71.0330</td>\n",
       "      <td>0.1299</td>\n",
       "      <td>0.94</td>\n",
       "      <td>47.30</td>\n",
       "      <td>43.92</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>47.30</td>\n",
       "      <td>43.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3503</td>\n",
       "      <td>-71.0810</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>57.02</td>\n",
       "      <td>56.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>57.02</td>\n",
       "      <td>56.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3503</td>\n",
       "      <td>-71.0810</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.62</td>\n",
       "      <td>42.32</td>\n",
       "      <td>40.48</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>42.32</td>\n",
       "      <td>40.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3647</td>\n",
       "      <td>-71.0542</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>33.83</td>\n",
       "      <td>32.85</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>33.83</td>\n",
       "      <td>32.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source  product_id  name  distance  surge_multiplier  latitude  longitude  \\\n",
       "0       5           4     2      0.44               1.0   42.2148   -71.0330   \n",
       "1       5           5     1      0.44               1.0   42.2148   -71.0330   \n",
       "2       0           4     2      1.08               1.0   42.3503   -71.0810   \n",
       "3       0           5     1      1.08               1.0   42.3503   -71.0810   \n",
       "4       6           0     5      1.11               1.0   42.3647   -71.0542   \n",
       "\n",
       "   precipIntensity  humidity  temperatureHigh  apparentTemperatureHigh  \\\n",
       "0           0.0000      0.68            43.68                    37.95   \n",
       "1           0.1299      0.94            47.30                    43.92   \n",
       "2           0.0000      0.96            57.02                    56.35   \n",
       "3           0.0000      0.62            42.32                    40.48   \n",
       "4           0.0000      0.56            33.83                    32.85   \n",
       "\n",
       "   cloudCover  precipIntensityMax  temperatureMax  apparentTemperatureMax  \n",
       "0        0.72              0.1276           43.68                   38.07  \n",
       "1        1.00              0.1300           47.30                   43.92  \n",
       "2        1.00              0.0004           57.02                   56.35  \n",
       "3        0.12              0.0004           42.32                   40.48  \n",
       "4        0.64              0.0001           33.83                   32.85  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "id": "ae9WHnCjdM1s"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(XX, y, test_size = 0.3, random_state = 10,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CjBN3eXPdM1s",
    "outputId": "8643fe85-a5e0-4b53-e581-b0df11478405"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225990, 15)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "id": "Stovz796dM1t"
   },
   "outputs": [],
   "source": [
    "#Creating model\n",
    "reg1 = LinearRegression()\n",
    "#Fitting training data\n",
    "reg1 = reg1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aVUbWQxldM1t",
    "outputId": "23be0e83-3cba-46b0-ff62-fbd0f90f0a42"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8050457819854595"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r5gtEk3WdM1n"
   },
   "source": [
    "#### Training accuracy in 25 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fEsXKaUidM1o",
    "outputId": "2c19cd37-1150-443d-ef19-525f4d2c50db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 55 features.\n",
      "Fitting estimator with 54 features.\n",
      "Fitting estimator with 53 features.\n",
      "Fitting estimator with 52 features.\n",
      "Fitting estimator with 51 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 49 features.\n",
      "Fitting estimator with 48 features.\n",
      "Fitting estimator with 47 features.\n",
      "Fitting estimator with 46 features.\n",
      "Fitting estimator with 45 features.\n",
      "Fitting estimator with 44 features.\n",
      "Fitting estimator with 43 features.\n",
      "Fitting estimator with 42 features.\n",
      "Fitting estimator with 41 features.\n",
      "Fitting estimator with 40 features.\n",
      "Fitting estimator with 39 features.\n",
      "Fitting estimator with 38 features.\n",
      "Fitting estimator with 37 features.\n",
      "Fitting estimator with 36 features.\n",
      "Fitting estimator with 35 features.\n",
      "Fitting estimator with 34 features.\n",
      "Fitting estimator with 33 features.\n",
      "Fitting estimator with 32 features.\n",
      "Fitting estimator with 31 features.\n",
      "Fitting estimator with 30 features.\n",
      "Fitting estimator with 29 features.\n",
      "Fitting estimator with 28 features.\n",
      "Fitting estimator with 27 features.\n",
      "Fitting estimator with 26 features.\n"
     ]
    }
   ],
   "source": [
    "rfe = RFE(reg, 25, verbose=1)\n",
    "rfe = rfe.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "okh284U_dM1o"
   },
   "outputs": [],
   "source": [
    "XX = X[X.columns[rfe.support_]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "id": "dCrDntLidM1o",
    "outputId": "9612f86d-2ca6-4536-b69c-25ad0d1b915e"
   },
   "outputs": [
    {
     "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>month</th>\n",
       "      <th>source</th>\n",
       "      <th>destination</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>distance</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>apparentTemperature</th>\n",
       "      <th>...</th>\n",
       "      <th>temperatureHigh</th>\n",
       "      <th>apparentTemperatureHigh</th>\n",
       "      <th>icon</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>uvIndex</th>\n",
       "      <th>moonPhase</th>\n",
       "      <th>precipIntensityMax</th>\n",
       "      <th>temperatureMax</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.2148</td>\n",
       "      <td>-71.0330</td>\n",
       "      <td>37.12</td>\n",
       "      <td>...</td>\n",
       "      <td>43.68</td>\n",
       "      <td>37.95</td>\n",
       "      <td>5</td>\n",
       "      <td>32.70</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>43.68</td>\n",
       "      <td>38.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.2148</td>\n",
       "      <td>-71.0330</td>\n",
       "      <td>37.35</td>\n",
       "      <td>...</td>\n",
       "      <td>47.30</td>\n",
       "      <td>43.92</td>\n",
       "      <td>6</td>\n",
       "      <td>41.83</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>47.30</td>\n",
       "      <td>43.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3503</td>\n",
       "      <td>-71.0810</td>\n",
       "      <td>45.58</td>\n",
       "      <td>...</td>\n",
       "      <td>57.02</td>\n",
       "      <td>56.35</td>\n",
       "      <td>3</td>\n",
       "      <td>44.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>57.02</td>\n",
       "      <td>56.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3503</td>\n",
       "      <td>-71.0810</td>\n",
       "      <td>38.00</td>\n",
       "      <td>...</td>\n",
       "      <td>42.32</td>\n",
       "      <td>40.48</td>\n",
       "      <td>0</td>\n",
       "      <td>27.99</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>42.32</td>\n",
       "      <td>40.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3647</td>\n",
       "      <td>-71.0542</td>\n",
       "      <td>32.85</td>\n",
       "      <td>...</td>\n",
       "      <td>33.83</td>\n",
       "      <td>32.85</td>\n",
       "      <td>4</td>\n",
       "      <td>18.66</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>33.83</td>\n",
       "      <td>32.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  source  destination  product_id  name  distance  surge_multiplier  \\\n",
       "0     12       5            7           4     2      0.44               1.0   \n",
       "1     11       5            7           5     1      0.44               1.0   \n",
       "2     12       0            8           4     2      1.08               1.0   \n",
       "3     11       0            8           5     1      1.08               1.0   \n",
       "4     12       6           11           0     5      1.11               1.0   \n",
       "\n",
       "   latitude  longitude  apparentTemperature  ...  temperatureHigh  \\\n",
       "0   42.2148   -71.0330                37.12  ...            43.68   \n",
       "1   42.2148   -71.0330                37.35  ...            47.30   \n",
       "2   42.3503   -71.0810                45.58  ...            57.02   \n",
       "3   42.3503   -71.0810                38.00  ...            42.32   \n",
       "4   42.3647   -71.0542                32.85  ...            33.83   \n",
       "\n",
       "   apparentTemperatureHigh  icon  dewPoint  cloudCover  uvIndex  moonPhase  \\\n",
       "0                    37.95     5     32.70        0.72        0       0.30   \n",
       "1                    43.92     6     41.83        1.00        0       0.64   \n",
       "2                    56.35     3     44.50        1.00        0       0.89   \n",
       "3                    40.48     0     27.99        0.12        2       0.79   \n",
       "4                    32.85     4     18.66        0.64        0       0.21   \n",
       "\n",
       "   precipIntensityMax  temperatureMax  apparentTemperatureMax  \n",
       "0              0.1276           43.68                   38.07  \n",
       "1              0.1300           47.30                   43.92  \n",
       "2              0.0004           57.02                   56.35  \n",
       "3              0.0004           42.32                   40.48  \n",
       "4              0.0001           33.83                   32.85  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "a4bIu5BBdM1p"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(XX, y, test_size = 0.3, random_state = 20,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B5mo6yv5dM1p",
    "outputId": "7b1de43d-fdb3-4409-c94f-86139825eb58"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225990, 25)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "R9iOX2L3dM1p"
   },
   "outputs": [],
   "source": [
    "#Creating model\n",
    "reg1 = LinearRegression()\n",
    "#Fitting training data\n",
    "reg1 = reg1.fit(X_train, y_train)\n",
    "#Y prediction\n",
    "Y_pred = reg1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yyphrzfLdM1q",
    "outputId": "d431bb63-e9f9-40a4-d2ed-4eb88d4695b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.805535515159816"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6GT_96u2dM1u"
   },
   "source": [
    "- Since we find the accuracy for  k = 56 , 40 , 25 and 15. \n",
    "- Hence we noticed that the when k = 25 we get the maximum training accuracy in Linear Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pHzg3qKjdM1u"
   },
   "source": [
    "### 25 Columns After RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zVDqBK1DdM1u",
    "outputId": "21465058-853f-4de2-ce4a-cf9eb079807d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['month', 'source', 'destination', 'product_id', 'name', 'distance',\n",
       "       'surge_multiplier', 'latitude', 'longitude', 'apparentTemperature',\n",
       "       'long_summary', 'precipIntensity', 'humidity', 'windSpeed', 'windGust',\n",
       "       'temperatureHigh', 'apparentTemperatureHigh', 'icon', 'dewPoint',\n",
       "       'cloudCover', 'uvIndex', 'moonPhase', 'precipIntensityMax',\n",
       "       'temperatureMax', 'apparentTemperatureMax'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ktWXBUOFdM1v",
    "outputId": "9448de0d-532d-4f0b-b6c1-c965c6de5c57"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(322844, 25)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "id": "mVNmkqETdM1v",
    "outputId": "646ad93a-cee4-4ed8-d87d-8016c63c5d7e"
   },
   "outputs": [
    {
     "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>month</th>\n",
       "      <th>source</th>\n",
       "      <th>destination</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>distance</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>apparentTemperature</th>\n",
       "      <th>...</th>\n",
       "      <th>temperatureHigh</th>\n",
       "      <th>apparentTemperatureHigh</th>\n",
       "      <th>icon</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>uvIndex</th>\n",
       "      <th>moonPhase</th>\n",
       "      <th>precipIntensityMax</th>\n",
       "      <th>temperatureMax</th>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.2148</td>\n",
       "      <td>-71.0330</td>\n",
       "      <td>37.12</td>\n",
       "      <td>...</td>\n",
       "      <td>43.68</td>\n",
       "      <td>37.95</td>\n",
       "      <td>5</td>\n",
       "      <td>32.70</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>43.68</td>\n",
       "      <td>38.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.2148</td>\n",
       "      <td>-71.0330</td>\n",
       "      <td>37.35</td>\n",
       "      <td>...</td>\n",
       "      <td>47.30</td>\n",
       "      <td>43.92</td>\n",
       "      <td>6</td>\n",
       "      <td>41.83</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>47.30</td>\n",
       "      <td>43.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3503</td>\n",
       "      <td>-71.0810</td>\n",
       "      <td>45.58</td>\n",
       "      <td>...</td>\n",
       "      <td>57.02</td>\n",
       "      <td>56.35</td>\n",
       "      <td>3</td>\n",
       "      <td>44.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>57.02</td>\n",
       "      <td>56.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3503</td>\n",
       "      <td>-71.0810</td>\n",
       "      <td>38.00</td>\n",
       "      <td>...</td>\n",
       "      <td>42.32</td>\n",
       "      <td>40.48</td>\n",
       "      <td>0</td>\n",
       "      <td>27.99</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>42.32</td>\n",
       "      <td>40.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.3647</td>\n",
       "      <td>-71.0542</td>\n",
       "      <td>32.85</td>\n",
       "      <td>...</td>\n",
       "      <td>33.83</td>\n",
       "      <td>32.85</td>\n",
       "      <td>4</td>\n",
       "      <td>18.66</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>33.83</td>\n",
       "      <td>32.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  source  destination  product_id  name  distance  surge_multiplier  \\\n",
       "0     12       5            7           4     2      0.44               1.0   \n",
       "1     11       5            7           5     1      0.44               1.0   \n",
       "2     12       0            8           4     2      1.08               1.0   \n",
       "3     11       0            8           5     1      1.08               1.0   \n",
       "4     12       6           11           0     5      1.11               1.0   \n",
       "\n",
       "   latitude  longitude  apparentTemperature  ...  temperatureHigh  \\\n",
       "0   42.2148   -71.0330                37.12  ...            43.68   \n",
       "1   42.2148   -71.0330                37.35  ...            47.30   \n",
       "2   42.3503   -71.0810                45.58  ...            57.02   \n",
       "3   42.3503   -71.0810                38.00  ...            42.32   \n",
       "4   42.3647   -71.0542                32.85  ...            33.83   \n",
       "\n",
       "   apparentTemperatureHigh  icon  dewPoint  cloudCover  uvIndex  moonPhase  \\\n",
       "0                    37.95     5     32.70        0.72        0       0.30   \n",
       "1                    43.92     6     41.83        1.00        0       0.64   \n",
       "2                    56.35     3     44.50        1.00        0       0.89   \n",
       "3                    40.48     0     27.99        0.12        2       0.79   \n",
       "4                    32.85     4     18.66        0.64        0       0.21   \n",
       "\n",
       "   precipIntensityMax  temperatureMax  apparentTemperatureMax  \n",
       "0              0.1276           43.68                   38.07  \n",
       "1              0.1300           47.30                   43.92  \n",
       "2              0.0004           57.02                   56.35  \n",
       "3              0.0004           42.32                   40.48  \n",
       "4              0.0001           33.83                   32.85  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop Useless Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "j9GdXVpAdM1w"
   },
   "outputs": [],
   "source": [
    "features_drop = ['latitude', 'longitude', 'apparentTemperature',\n",
    "       'long_summary', 'precipIntensity', 'humidity', 'windSpeed', 'windGust',\n",
    "       'temperatureHigh', 'apparentTemperatureHigh', 'dewPoint','precipIntensityMax',\n",
    "       'temperatureMax', 'apparentTemperatureMax', 'distance', 'cloudCover', 'moonPhase']\n",
    "new_uber = XX.drop(features_drop, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "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>month</th>\n",
       "      <th>source</th>\n",
       "      <th>destination</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>icon</th>\n",
       "      <th>uvIndex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  source  destination  product_id  name  surge_multiplier  icon  \\\n",
       "0     12       5            7           4     2               1.0     5   \n",
       "1     11       5            7           5     1               1.0     6   \n",
       "2     12       0            8           4     2               1.0     3   \n",
       "3     11       0            8           5     1               1.0     0   \n",
       "4     12       6           11           0     5               1.0     4   \n",
       "\n",
       "   uvIndex  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        2  \n",
       "4        0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_uber.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_mapping = {11: 0, 12: 1}\n",
    "new_uber['month'] = new_uber['month'].map(month_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "surge_multiplier_mapping = {1.: 0, 1.25: 1, 1.5: 2, 1.75: 3, 2.:4}\n",
    "new_uber['surge_multiplier'] = new_uber['surge_multiplier'].map(surge_multiplier_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x_pzLDEQdM1w"
   },
   "source": [
    "### Final Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "wyWua_W-dM1w",
    "outputId": "302f32f2-d5ab-45a2-87db-3dedd5fc2c19"
   },
   "outputs": [
    {
     "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>month</th>\n",
       "      <th>source</th>\n",
       "      <th>destination</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>icon</th>\n",
       "      <th>uvIndex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  source  destination  product_id  name  surge_multiplier  icon  \\\n",
       "0      1       5            7           4     2                 0     5   \n",
       "1      0       5            7           5     1                 0     6   \n",
       "2      1       0            8           4     2                 0     3   \n",
       "3      0       0            8           5     1                 0     0   \n",
       "4      1       6           11           0     5                 0     4   \n",
       "\n",
       "   uvIndex  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        2  \n",
       "4        0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_uber.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4301z2lmdM1w",
    "outputId": "c9f307c5-b8ae-4876-a4e3-0e2695bec404"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1    11\n",
       "2     3\n",
       "3    13\n",
       "4     7\n",
       "Name: price, dtype: int32"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JF9cUd1cdM1x"
   },
   "source": [
    "## 5. Modeling\n",
    "-----------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z2ycv_I1dM1x",
    "outputId": "786d6860-d986-4e64-8905-23bc8551b795"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(322844, 8)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_uber.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l6g4DuvHdM1y",
    "outputId": "70212ec8-a27d-4377-b166-63ea4c339cd8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(322844,)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "Cs4mLXOJdM1y"
   },
   "outputs": [],
   "source": [
    "# Using Skicit-learn to split data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Split the data into training and testing sets\n",
    "xx_train, xx_test, yy_train, yy_test = train_test_split(new_uber, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NJUgsh3MdM1y",
    "outputId": "5af56cce-9d2d-4e00-e8e8-d092f0b46cb9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258275, 8)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yilLNU7xdM1z",
    "outputId": "0929bcea-2132-439d-f5b5-c73acb3de2d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64569, 8)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yxfu38ltdM1z",
    "outputId": "37d29ced-a0e9-4c82-84a5-c57308855877"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258275,)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uLPsI8SbdM10",
    "outputId": "f03238dd-b013-4bee-8f53-7ee613c577dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64569,)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "hl_HaXAQdM12"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SGNZ4cfPdM13"
   },
   "source": [
    "#### 5.1 Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oNFKta4rdM14",
    "outputId": "818cb515-1fe4-4ccc-9ae6-dd1d34a15445"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7475450731641595"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear = LinearRegression()\n",
    "linear.fit(xx_train, yy_train)\n",
    "linear.score(xx_test, yy_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rkcejW5zdM16"
   },
   "source": [
    "#### 5.2 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rrsV9YTGdM16",
    "outputId": "dfda8878-a4d8-4cfb-9af3-d1cb848fd7ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9617917299993272"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision = DecisionTreeRegressor(random_state = 0)  \n",
    "decision.fit(xx_train , yy_train) \n",
    "decision.score(xx_test, yy_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KdJrUIRadM17"
   },
   "source": [
    "#### 5.3 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "91iuwQ8AdM17",
    "outputId": "05694275-b827-45d9-d68e-9f5ad943a1ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9622694743419838"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random = RandomForestRegressor(n_estimators = 100, random_state = 0) \n",
    "random.fit(xx_train , yy_train)  \n",
    "random.score(xx_test, yy_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.4 Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble\n",
    "clf = ensemble.GradientBoostingRegressor(n_estimators = 400, max_depth = 5)\n",
    "clf.fit(xx_train, yy_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.963187213130919"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(xx_test, yy_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K fold Crossvalidation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75028011, 0.75047377, 0.74819024, 0.75061918, 0.74745602])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cv=ShuffleSplit(n_splits=5,test_size=0.2,random_state=0)\n",
    "cross_val_score(LinearRegression(),xx_test,yy_test,cv=cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-AH2Uz7odM2F"
   },
   "source": [
    "## 6. Testing\n",
    "-----------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.31668826e-02, -3.88218032e-02, -8.15543686e-02, -2.78093598e+00,\n",
       "       -4.52948453e+00,  5.84661636e+00,  5.63253668e-03,  1.57160771e-02])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.65732287, 12.90624268, 11.44487949, ..., 28.68756688,\n",
       "       29.18862538, 12.89669938])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = linear.predict(xx_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=  prediction.astype(int)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "plt.scatter(yy_test,prediction)\n",
    "plt.xlabel('Y Test')\n",
    "plt.ylabel('Predicted Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE :   3.4060772197184406\n",
      "MSE :   20.033437098297945\n",
      "RMAE :   4.475872775034825\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('MAE :',\" \", metrics.mean_absolute_error(yy_test,prediction))\n",
    "print('MSE :',\" \", metrics.mean_squared_error(yy_test,prediction))\n",
    "print('RMAE :',\" \", np.sqrt(metrics.mean_squared_error(yy_test,prediction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x159ce9909c8>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(yy_test - prediction,bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U093z2lEdM18"
   },
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = random.predict(xx_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Y')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(yy_test,predictions)\n",
    "plt.xlabel('Y Test')\n",
    "plt.ylabel('Predicted Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE :   0.9981370098530818\n",
      "MSE :   2.944653619230457\n",
      "RMAE :   1.7159993063024404\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('MAE :',\" \", metrics.mean_absolute_error(yy_test,predictions))\n",
    "print('MSE :',\" \", metrics.mean_squared_error(yy_test,predictions))\n",
    "print('RMAE :',\" \", np.sqrt(metrics.mean_squared_error(yy_test,predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x159d48edc08>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(yy_test - predictions,bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Price prediction function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "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>month</th>\n",
       "      <th>source</th>\n",
       "      <th>destination</th>\n",
       "      <th>product_id</th>\n",
       "      <th>name</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>icon</th>\n",
       "      <th>uvIndex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  source  destination  product_id  name  surge_multiplier  icon  \\\n",
       "0      1       5            7           4     2                 0     5   \n",
       "1      0       5            7           5     1                 0     6   \n",
       "2      1       0            8           4     2                 0     3   \n",
       "3      0       0            8           5     1                 0     0   \n",
       "4      1       6           11           0     5                 0     4   \n",
       "\n",
       "   uvIndex  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        2  \n",
       "4        0  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_uber.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_price(name,source,surge_multiplier,icon):    \n",
    "    loc_index = np.where(new_uber.columns==name)[0]\n",
    "\n",
    "    x = np.zeros(len(new_uber.columns))\n",
    "    x[0] = source\n",
    "    x[1] = surge_multiplier\n",
    "    x[2] = icon\n",
    "    if loc_index >= 0:\n",
    "        x[loc_index] = 1\n",
    "\n",
    "    return random.predict([x])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "oCT_frHKdM2F"
   },
   "outputs": [],
   "source": [
    "pre= random.predict(xx_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  <span style='background:yellow'>Follow  these instructions before predicting the price:</span> \n",
    "<hr>\n",
    "\n",
    "-  **For cab_name**:  <font color = 'red'>Black SUV --> 0 , Lux --> 1 , Shared --> 2 , Taxi --> 3 , UberPool --> 4 , UberX --> 5</font>\n",
    "\n",
    "\n",
    "- **For Source**:  <font color = 'blue'>Back Bay --> 0 , Beacon Hill --> 1 , Boston University --> 2 , Fenway --> 3 , Financial District --> 4 , Haymarket Square --> 5 , North End --> 6 , North Station --> 7 , Northeastern University --> 8 , South Station --> 9 , Theatre District --> 10 , West End --> 11</font>\n",
    "\n",
    "\n",
    "- **For Surge_multiplier** : <font color = 'red'>Enter Surge Multiplier value from 0 to 4</font>\n",
    "\n",
    "\n",
    "- **for Icon**:  <font color = 'blue'>clear-day  --> 0 , clear-night  --> 1 , cloudy  --> 2 , fog  --> 3 , partly-cloudy-day  --> 4 , partly-cloudy-night  --> 5 , rain  --> 6</font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='background:yellow'>**predict_price(cab_name , source , surge_multiplier , icon)**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gupta\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "26.440184991891492"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price(1 , 3, 2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "qi1G0xr8dM1Y",
    "JXZS2rP2dM1b",
    "p17aW6tHdM1c",
    "r5gtEk3WdM1n",
    "Mh22ZUP-dM1q",
    "Nrg22on_qgPa",
    "pHzg3qKjdM1u",
    "x_pzLDEQdM1w"
   ],
   "name": "new uber.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}