PYTN_KampusMerdeka_fp1_Muhammad Arrafi Aziiz Syam.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec758bbe",
   "metadata": {},
   "source": [
    "# Introduce"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a436dd72",
   "metadata": {},
   "source": [
    "## In this notebook we will do some analysis and simple regression model based on our data which is uber and lyft fare dataset. The data is from kaggle where the main purpose and objective of this large dataset is to model how price or cab fare varies with all the features that've been given."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a5fd3b1",
   "metadata": {},
   "source": [
    "## Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41242043",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e6ba9d",
   "metadata": {},
   "source": [
    "This cell contains library that needed for this final project,it contain such as pandas,matplotlib,seaborn,Sklear,and we using Linear Regression as our model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "820dc370",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1d55974",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <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>cab_type</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",
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       "      <th>0</th>\n",
       "      <td>424553bb-7174-41ea-aeb4-fe06d4f4b9d7</td>\n",
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       "      <td>Haymarket Square</td>\n",
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       "      <td>America/New_York</td>\n",
       "      <td>Haymarket Square</td>\n",
       "      <td>North Station</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1064</td>\n",
       "      <td>1543338000</td>\n",
       "      <td>35.36</td>\n",
       "      <td>1543377600</td>\n",
       "      <td>47.55</td>\n",
       "      <td>1543320000</td>\n",
       "      <td>31.04</td>\n",
       "      <td>1543377600</td>\n",
       "      <td>44.12</td>\n",
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       "      <td>Haymarket Square</td>\n",
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       "      <td>34.67</td>\n",
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       "      <td>30.30</td>\n",
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       "      <td>38.53</td>\n",
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       "      <td>Lyft</td>\n",
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      "text/plain": [
       "                                          id     timestamp  hour  day  month  \\\n",
       "0       424553bb-7174-41ea-aeb4-fe06d4f4b9d7  1.544953e+09     9   16     12   \n",
       "1       4bd23055-6827-41c6-b23b-3c491f24e74d  1.543284e+09     2   27     11   \n",
       "2       981a3613-77af-4620-a42a-0c0866077d1e  1.543367e+09     1   28     11   \n",
       "3       c2d88af2-d278-4bfd-a8d0-29ca77cc5512  1.543554e+09     4   30     11   \n",
       "4       e0126e1f-8ca9-4f2e-82b3-50505a09db9a  1.543463e+09     3   29     11   \n",
       "...                                      ...           ...   ...  ...    ...   \n",
       "693066  616d3611-1820-450a-9845-a9ff304a4842  1.543708e+09    23    1     12   \n",
       "693067  633a3fc3-1f86-4b9e-9d48-2b7132112341  1.543708e+09    23    1     12   \n",
       "693068  64d451d0-639f-47a4-9b7c-6fd92fbd264f  1.543708e+09    23    1     12   \n",
       "693069  727e5f07-a96b-4ad1-a2c7-9abc3ad55b4e  1.543708e+09    23    1     12   \n",
       "693070  e7fdc087-fe86-40a5-a3c3-3b2a8badcbda  1.543708e+09    23    1     12   \n",
       "\n",
       "                   datetime          timezone            source  \\\n",
       "0       2018-12-16 09:30:07  America/New_York  Haymarket Square   \n",
       "1       2018-11-27 02:00:23  America/New_York  Haymarket Square   \n",
       "2       2018-11-28 01:00:22  America/New_York  Haymarket Square   \n",
       "3       2018-11-30 04:53:02  America/New_York  Haymarket Square   \n",
       "4       2018-11-29 03:49:20  America/New_York  Haymarket Square   \n",
       "...                     ...               ...               ...   \n",
       "693066  2018-12-01 23:53:05  America/New_York          West End   \n",
       "693067  2018-12-01 23:53:05  America/New_York          West End   \n",
       "693068  2018-12-01 23:53:05  America/New_York          West End   \n",
       "693069  2018-12-01 23:53:05  America/New_York          West End   \n",
       "693070  2018-12-01 23:53:05  America/New_York          West End   \n",
       "\n",
       "          destination cab_type  ... precipIntensityMax uvIndexTime  \\\n",
       "0       North Station     Lyft  ...             0.1276  1544979600   \n",
       "1       North Station     Lyft  ...             0.1300  1543251600   \n",
       "2       North Station     Lyft  ...             0.1064  1543338000   \n",
       "3       North Station     Lyft  ...             0.0000  1543507200   \n",
       "4       North Station     Lyft  ...             0.0001  1543420800   \n",
       "...               ...      ...  ...                ...         ...   \n",
       "693066      North End     Uber  ...             0.0000  1543683600   \n",
       "693067      North End     Uber  ...             0.0000  1543683600   \n",
       "693068      North End     Uber  ...             0.0000  1543683600   \n",
       "693069      North End     Uber  ...             0.0000  1543683600   \n",
       "693070      North End     Uber  ...             0.0000  1543683600   \n",
       "\n",
       "        temperatureMin  temperatureMinTime  temperatureMax  \\\n",
       "0                39.89          1545012000           43.68   \n",
       "1                40.49          1543233600           47.30   \n",
       "2                35.36          1543377600           47.55   \n",
       "3                34.67          1543550400           45.03   \n",
       "4                33.10          1543402800           42.18   \n",
       "...                ...                 ...             ...   \n",
       "693066           31.42          1543658400           44.76   \n",
       "693067           31.42          1543658400           44.76   \n",
       "693068           31.42          1543658400           44.76   \n",
       "693069           31.42          1543658400           44.76   \n",
       "693070           31.42          1543658400           44.76   \n",
       "\n",
       "        temperatureMaxTime  apparentTemperatureMin  \\\n",
       "0               1544968800                   33.73   \n",
       "1               1543251600                   36.20   \n",
       "2               1543320000                   31.04   \n",
       "3               1543510800                   30.30   \n",
       "4               1543420800                   29.11   \n",
       "...                    ...                     ...   \n",
       "693066          1543690800                   27.77   \n",
       "693067          1543690800                   27.77   \n",
       "693068          1543690800                   27.77   \n",
       "693069          1543690800                   27.77   \n",
       "693070          1543690800                   27.77   \n",
       "\n",
       "        apparentTemperatureMinTime  apparentTemperatureMax  \\\n",
       "0                       1545012000                   38.07   \n",
       "1                       1543291200                   43.92   \n",
       "2                       1543377600                   44.12   \n",
       "3                       1543550400                   38.53   \n",
       "4                       1543392000                   35.75   \n",
       "...                            ...                     ...   \n",
       "693066                  1543658400                   44.09   \n",
       "693067                  1543658400                   44.09   \n",
       "693068                  1543658400                   44.09   \n",
       "693069                  1543658400                   44.09   \n",
       "693070                  1543658400                   44.09   \n",
       "\n",
       "       apparentTemperatureMaxTime  \n",
       "0                      1544958000  \n",
       "1                      1543251600  \n",
       "2                      1543320000  \n",
       "3                      1543510800  \n",
       "4                      1543420800  \n",
       "...                           ...  \n",
       "693066                 1543690800  \n",
       "693067                 1543690800  \n",
       "693068                 1543690800  \n",
       "693069                 1543690800  \n",
       "693070                 1543690800  \n",
       "\n",
       "[693071 rows x 57 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('dataset/rideshare_kaggle.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f839528",
   "metadata": {},
   "source": [
    "This cell contains the code for importing dataset from the datasets folder and the dataset uses csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "307f4684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 693071 entries, 0 to 693070\n",
      "Data columns (total 57 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   id                           693071 non-null  object \n",
      " 1   timestamp                    693071 non-null  float64\n",
      " 2   hour                         693071 non-null  int64  \n",
      " 3   day                          693071 non-null  int64  \n",
      " 4   month                        693071 non-null  int64  \n",
      " 5   datetime                     693071 non-null  object \n",
      " 6   timezone                     693071 non-null  object \n",
      " 7   source                       693071 non-null  object \n",
      " 8   destination                  693071 non-null  object \n",
      " 9   cab_type                     693071 non-null  object \n",
      " 10  product_id                   693071 non-null  object \n",
      " 11  name                         693071 non-null  object \n",
      " 12  price                        637976 non-null  float64\n",
      " 13  distance                     693071 non-null  float64\n",
      " 14  surge_multiplier             693071 non-null  float64\n",
      " 15  latitude                     693071 non-null  float64\n",
      " 16  longitude                    693071 non-null  float64\n",
      " 17  temperature                  693071 non-null  float64\n",
      " 18  apparentTemperature          693071 non-null  float64\n",
      " 19  short_summary                693071 non-null  object \n",
      " 20  long_summary                 693071 non-null  object \n",
      " 21  precipIntensity              693071 non-null  float64\n",
      " 22  precipProbability            693071 non-null  float64\n",
      " 23  humidity                     693071 non-null  float64\n",
      " 24  windSpeed                    693071 non-null  float64\n",
      " 25  windGust                     693071 non-null  float64\n",
      " 26  windGustTime                 693071 non-null  int64  \n",
      " 27  visibility                   693071 non-null  float64\n",
      " 28  temperatureHigh              693071 non-null  float64\n",
      " 29  temperatureHighTime          693071 non-null  int64  \n",
      " 30  temperatureLow               693071 non-null  float64\n",
      " 31  temperatureLowTime           693071 non-null  int64  \n",
      " 32  apparentTemperatureHigh      693071 non-null  float64\n",
      " 33  apparentTemperatureHighTime  693071 non-null  int64  \n",
      " 34  apparentTemperatureLow       693071 non-null  float64\n",
      " 35  apparentTemperatureLowTime   693071 non-null  int64  \n",
      " 36  icon                         693071 non-null  object \n",
      " 37  dewPoint                     693071 non-null  float64\n",
      " 38  pressure                     693071 non-null  float64\n",
      " 39  windBearing                  693071 non-null  int64  \n",
      " 40  cloudCover                   693071 non-null  float64\n",
      " 41  uvIndex                      693071 non-null  int64  \n",
      " 42  visibility.1                 693071 non-null  float64\n",
      " 43  ozone                        693071 non-null  float64\n",
      " 44  sunriseTime                  693071 non-null  int64  \n",
      " 45  sunsetTime                   693071 non-null  int64  \n",
      " 46  moonPhase                    693071 non-null  float64\n",
      " 47  precipIntensityMax           693071 non-null  float64\n",
      " 48  uvIndexTime                  693071 non-null  int64  \n",
      " 49  temperatureMin               693071 non-null  float64\n",
      " 50  temperatureMinTime           693071 non-null  int64  \n",
      " 51  temperatureMax               693071 non-null  float64\n",
      " 52  temperatureMaxTime           693071 non-null  int64  \n",
      " 53  apparentTemperatureMin       693071 non-null  float64\n",
      " 54  apparentTemperatureMinTime   693071 non-null  int64  \n",
      " 55  apparentTemperatureMax       693071 non-null  float64\n",
      " 56  apparentTemperatureMaxTime   693071 non-null  int64  \n",
      "dtypes: float64(29), int64(17), object(11)\n",
      "memory usage: 301.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e24278bf",
   "metadata": {},
   "source": [
    "This cell contains the code for showing the info of the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ba4b75",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee4cf294",
   "metadata": {},
   "source": [
    "calculating missing values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb768207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55095"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee285af5",
   "metadata": {},
   "source": [
    "This cell contain the code for showing the missing value in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "99fd251a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffab2187",
   "metadata": {},
   "source": [
    "This cell contain the code for deleting the missing value in dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "840882be",
   "metadata": {},
   "source": [
    "# EDA and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e7534139",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>price</th>\n",
       "      <th>distance</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>temperature</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",
       "      <th>count</th>\n",
       "      <td>6.379760e+05</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>6.379760e+05</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>6.379760e+05</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>6.379760e+05</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>6.379760e+05</td>\n",
       "      <td>637976.000000</td>\n",
       "      <td>6.379760e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.544046e+09</td>\n",
       "      <td>11.618528</td>\n",
       "      <td>17.797674</td>\n",
       "      <td>11.586550</td>\n",
       "      <td>16.545125</td>\n",
       "      <td>2.189261</td>\n",
       "      <td>1.015068</td>\n",
       "      <td>42.338161</td>\n",
       "      <td>-71.066144</td>\n",
       "      <td>39.582406</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037369</td>\n",
       "      <td>1.544044e+09</td>\n",
       "      <td>33.456171</td>\n",
       "      <td>1.544042e+09</td>\n",
       "      <td>45.259945</td>\n",
       "      <td>1.544047e+09</td>\n",
       "      <td>29.729028</td>\n",
       "      <td>1.544048e+09</td>\n",
       "      <td>41.995876</td>\n",
       "      <td>1.544048e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.892028e+05</td>\n",
       "      <td>6.948776</td>\n",
       "      <td>9.982083</td>\n",
       "      <td>0.492452</td>\n",
       "      <td>9.324359</td>\n",
       "      <td>1.135413</td>\n",
       "      <td>0.095422</td>\n",
       "      <td>0.047856</td>\n",
       "      <td>0.020303</td>\n",
       "      <td>6.725500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055216</td>\n",
       "      <td>6.912268e+05</td>\n",
       "      <td>6.466349</td>\n",
       "      <td>6.902087e+05</td>\n",
       "      <td>5.644414</td>\n",
       "      <td>6.901597e+05</td>\n",
       "      <td>7.109907</td>\n",
       "      <td>6.872011e+05</td>\n",
       "      <td>6.935538</td>\n",
       "      <td>6.911036e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.543204e+09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.500000</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>...</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.543444e+09</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.350300</td>\n",
       "      <td>-71.081000</td>\n",
       "      <td>36.450000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.543737e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>2.160000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.351900</td>\n",
       "      <td>-71.063100</td>\n",
       "      <td>40.490000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000400</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.544828e+09</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>2.930000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>42.364700</td>\n",
       "      <td>-71.054200</td>\n",
       "      <td>43.580000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.091600</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.545161e+09</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>97.500000</td>\n",
       "      <td>7.860000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>42.366100</td>\n",
       "      <td>-71.033000</td>\n",
       "      <td>57.220000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145900</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          timestamp           hour            day          month  \\\n",
       "count  6.379760e+05  637976.000000  637976.000000  637976.000000   \n",
       "mean   1.544046e+09      11.618528      17.797674      11.586550   \n",
       "std    6.892028e+05       6.948776       9.982083       0.492452   \n",
       "min    1.543204e+09       0.000000       1.000000      11.000000   \n",
       "25%    1.543444e+09       6.000000      13.000000      11.000000   \n",
       "50%    1.543737e+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",
       "               price       distance  surge_multiplier       latitude  \\\n",
       "count  637976.000000  637976.000000     637976.000000  637976.000000   \n",
       "mean       16.545125       2.189261          1.015068      42.338161   \n",
       "std         9.324359       1.135413          0.095422       0.047856   \n",
       "min         2.500000       0.020000          1.000000      42.214800   \n",
       "25%         9.000000       1.270000          1.000000      42.350300   \n",
       "50%        13.500000       2.160000          1.000000      42.351900   \n",
       "75%        22.500000       2.930000          1.000000      42.364700   \n",
       "max        97.500000       7.860000          3.000000      42.366100   \n",
       "\n",
       "           longitude    temperature  ...  precipIntensityMax   uvIndexTime  \\\n",
       "count  637976.000000  637976.000000  ...       637976.000000  6.379760e+05   \n",
       "mean      -71.066144      39.582406  ...            0.037369  1.544044e+09   \n",
       "std         0.020303       6.725500  ...            0.055216  6.912268e+05   \n",
       "min       -71.105400      18.910000  ...            0.000000  1.543162e+09   \n",
       "25%       -71.081000      36.450000  ...            0.000000  1.543421e+09   \n",
       "50%       -71.063100      40.490000  ...            0.000400  1.543770e+09   \n",
       "75%       -71.054200      43.580000  ...            0.091600  1.544807e+09   \n",
       "max       -71.033000      57.220000  ...            0.145900  1.545152e+09   \n",
       "\n",
       "       temperatureMin  temperatureMinTime  temperatureMax  temperatureMaxTime  \\\n",
       "count   637976.000000        6.379760e+05   637976.000000        6.379760e+05   \n",
       "mean        33.456171        1.544042e+09       45.259945        1.544047e+09   \n",
       "std          6.466349        6.902087e+05        5.644414        6.901597e+05   \n",
       "min         15.630000        1.543122e+09       33.510000        1.543154e+09   \n",
       "25%         30.170000        1.543399e+09       42.570000        1.543439e+09   \n",
       "50%         34.240000        1.543727e+09       44.660000        1.543788e+09   \n",
       "75%         38.880000        1.544789e+09       46.910000        1.544814e+09   \n",
       "max         43.100000        1.545192e+09       57.870000        1.545109e+09   \n",
       "\n",
       "       apparentTemperatureMin  apparentTemperatureMinTime  \\\n",
       "count           637976.000000                6.379760e+05   \n",
       "mean                29.729028                1.544048e+09   \n",
       "std                  7.109907                6.872011e+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  \n",
       "count           637976.000000                6.379760e+05  \n",
       "mean                41.995876                1.544048e+09  \n",
       "std                  6.935538                6.911036e+05  \n",
       "min                 28.950000                1.543187e+09  \n",
       "25%                 36.570000                1.543439e+09  \n",
       "50%                 40.950000                1.543788e+09  \n",
       "75%                 44.120000                1.544818e+09  \n",
       "max                 57.200000                1.545109e+09  \n",
       "\n",
       "[8 rows x 46 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e86a06",
   "metadata": {},
   "source": [
    "This cell contain the code for showing the descriptive statistic in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96a5bc72",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.39</td>\n",
       "      <td>12.717054</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.40</td>\n",
       "      <td>12.353333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.41</td>\n",
       "      <td>12.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.42</td>\n",
       "      <td>13.042683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.43</td>\n",
       "      <td>12.361111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>6.09</td>\n",
       "      <td>30.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>6.12</td>\n",
       "      <td>32.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>6.13</td>\n",
       "      <td>28.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>6.14</td>\n",
       "      <td>30.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>6.33</td>\n",
       "      <td>28.916667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>513 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     distance      price\n",
       "0        0.39  12.717054\n",
       "1        0.40  12.353333\n",
       "2        0.41  12.416667\n",
       "3        0.42  13.042683\n",
       "4        0.43  12.361111\n",
       "..        ...        ...\n",
       "508      6.09  30.083333\n",
       "509      6.12  32.708333\n",
       "510      6.13  28.166667\n",
       "511      6.14  30.083333\n",
       "512      6.33  28.916667\n",
       "\n",
       "[513 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_lyft = data[data['cab_type'] == 'Lyft']\n",
    "avg_price_lyft = data_lyft.groupby('distance')['price'].mean().reset_index()\n",
    "avg_price_lyft"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eadcc77",
   "metadata": {},
   "source": [
    "This cell contain the code for showing the mean in cab type lyft based on distance and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd4053db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(\n",
    "    x='distance',\n",
    "    y='price',\n",
    "    data=avg_price_lyft\n",
    ")\n",
    "plt.title('Average Lyft Price by Distance')\n",
    "plt.xlabel('distance')\n",
    "plt.ylabel('price')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c83a7b9c",
   "metadata": {},
   "source": [
    "This cell contain the code for visualing the mean in cab type lyft based on distance and price with line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0de848f",
   "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>distance</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.02</td>\n",
       "      <td>11.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.03</td>\n",
       "      <td>11.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.12</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.17</td>\n",
       "      <td>11.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>7.45</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>7.46</td>\n",
       "      <td>27.374444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>7.50</td>\n",
       "      <td>28.472222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>7.62</td>\n",
       "      <td>28.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>7.86</td>\n",
       "      <td>27.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>422 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     distance      price\n",
       "0        0.02  11.850000\n",
       "1        0.03  11.666667\n",
       "2        0.04  12.000000\n",
       "3        0.12  12.000000\n",
       "4        0.17  11.583333\n",
       "..        ...        ...\n",
       "417      7.45  26.000000\n",
       "418      7.46  27.374444\n",
       "419      7.50  28.472222\n",
       "420      7.62  28.250000\n",
       "421      7.86  27.833333\n",
       "\n",
       "[422 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_uber = data[data['cab_type'] == 'Uber']\n",
    "avg_price_uber = data_uber.groupby('distance')['price'].mean().reset_index()\n",
    "avg_price_uber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c041bd2",
   "metadata": {},
   "source": [
    "This cell contain the code for showing the mean in cab type uber based on distance and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a256aea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(\n",
    "    x='distance',\n",
    "    y='price',\n",
    "    data=avg_price_uber\n",
    ")\n",
    "plt.title('Average Uber Price by Distance')\n",
    "plt.xlabel('distance')\n",
    "plt.ylabel('price')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaa0e139",
   "metadata": {},
   "source": [
    "This cell contain the code for visualing the mean in cab type uber based on distance and price with line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bef1c3b0",
   "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>distance</th>\n",
       "      <th>cab_type</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.02</td>\n",
       "      <td>Uber</td>\n",
       "      <td>11.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.03</td>\n",
       "      <td>Uber</td>\n",
       "      <td>11.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04</td>\n",
       "      <td>Uber</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.12</td>\n",
       "      <td>Uber</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.17</td>\n",
       "      <td>Uber</td>\n",
       "      <td>11.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>7.45</td>\n",
       "      <td>Uber</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>7.46</td>\n",
       "      <td>Uber</td>\n",
       "      <td>27.374444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>7.50</td>\n",
       "      <td>Uber</td>\n",
       "      <td>28.472222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>7.62</td>\n",
       "      <td>Uber</td>\n",
       "      <td>28.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>7.86</td>\n",
       "      <td>Uber</td>\n",
       "      <td>27.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>935 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     distance cab_type      price\n",
       "0        0.02     Uber  11.850000\n",
       "1        0.03     Uber  11.666667\n",
       "2        0.04     Uber  12.000000\n",
       "3        0.12     Uber  12.000000\n",
       "4        0.17     Uber  11.583333\n",
       "..        ...      ...        ...\n",
       "930      7.45     Uber  26.000000\n",
       "931      7.46     Uber  27.374444\n",
       "932      7.50     Uber  28.472222\n",
       "933      7.62     Uber  28.250000\n",
       "934      7.86     Uber  27.833333\n",
       "\n",
       "[935 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_prices = data.groupby(['distance', 'cab_type'])['price'].mean().reset_index()\n",
    "avg_prices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5dcb071",
   "metadata": {},
   "source": [
    "This cell contain the code for showing the mean price based on distance and cab type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05655532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='distance', y='price', hue='cab_type', data=avg_prices)\n",
    "plt.title('Average Uber vs Lyft Price by Distance')\n",
    "plt.xlabel('Distance')\n",
    "plt.ylabel('Average Price')\n",
    "plt.legend(title='Cab Type')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393f3708",
   "metadata": {},
   "source": [
    "This cell contain the code for visualing the average price of uber and lyft by distance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87837c4f",
   "metadata": {},
   "source": [
    "# Data Prepocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9acfe3a4",
   "metadata": {},
   "source": [
    "# 1. Removing Unnecessary Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "728d998b",
   "metadata": {},
   "source": [
    "First, i want to check the correlation between numeric col and target (price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "32ae92af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                      0.000808\n",
       "hour                           0.000561\n",
       "day                           -0.000722\n",
       "month                          0.000913\n",
       "distance                       0.345061\n",
       "surge_multiplier               0.240458\n",
       "latitude                       0.002088\n",
       "longitude                     -0.001417\n",
       "temperature                   -0.000084\n",
       "apparentTemperature           -0.000193\n",
       "precipIntensity                0.000166\n",
       "precipProbability             -0.000243\n",
       "humidity                      -0.001238\n",
       "windSpeed                      0.000990\n",
       "windGust                       0.001218\n",
       "windGustTime                   0.000851\n",
       "visibility                     0.001497\n",
       "temperatureHigh               -0.000516\n",
       "temperatureHighTime            0.000759\n",
       "temperatureLow                 0.000182\n",
       "temperatureLowTime             0.000754\n",
       "apparentTemperatureHigh       -0.000261\n",
       "apparentTemperatureHighTime    0.000741\n",
       "apparentTemperatureLow         0.000831\n",
       "apparentTemperatureLowTime     0.000761\n",
       "dewPoint                      -0.000615\n",
       "pressure                       0.000866\n",
       "windBearing                   -0.001382\n",
       "cloudCover                     0.000883\n",
       "uvIndex                       -0.000424\n",
       "visibility.1                   0.001497\n",
       "ozone                          0.000460\n",
       "sunriseTime                    0.000760\n",
       "sunsetTime                     0.000760\n",
       "moonPhase                     -0.001602\n",
       "precipIntensityMax             0.001114\n",
       "uvIndexTime                    0.000763\n",
       "temperatureMin                -0.000428\n",
       "temperatureMinTime             0.000769\n",
       "temperatureMax                -0.000550\n",
       "temperatureMaxTime             0.000766\n",
       "apparentTemperatureMin        -0.000580\n",
       "apparentTemperatureMinTime     0.000814\n",
       "apparentTemperatureMax        -0.000290\n",
       "apparentTemperatureMaxTime     0.000746\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_col = []\n",
    "\n",
    "for col in data.columns:\n",
    "    if col != 'price' and (data[col].dtype == 'int64' or data[col].dtype == 'float64'):\n",
    "        numeric_col.append(col)\n",
    "\n",
    "data[numeric_col].corrwith(data['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12c83c8",
   "metadata": {},
   "source": [
    "Second, i want to check the correlation of our categorical features with our target feature (price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "14ff7bab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id : ['424553bb-7174-41ea-aeb4-fe06d4f4b9d7'\n",
      " '4bd23055-6827-41c6-b23b-3c491f24e74d'\n",
      " '981a3613-77af-4620-a42a-0c0866077d1e' ...\n",
      " '633a3fc3-1f86-4b9e-9d48-2b7132112341'\n",
      " '727e5f07-a96b-4ad1-a2c7-9abc3ad55b4e'\n",
      " 'e7fdc087-fe86-40a5-a3c3-3b2a8badcbda']\n",
      "\n",
      "datetime : ['2018-12-16 09:30:07' '2018-11-27 02:00:23' '2018-11-28 01:00:22' ...\n",
      " '2018-12-17 05:00:04' '2018-12-03 16:48:07' '2018-12-02 05:07:57']\n",
      "\n",
      "timezone : ['America/New_York']\n",
      "\n",
      "source : ['Haymarket Square' 'Back Bay' 'North End' 'North Station' 'Beacon Hill'\n",
      " 'Boston University' 'Fenway' 'South Station' 'Theatre District'\n",
      " 'West End' 'Financial District' 'Northeastern University']\n",
      "\n",
      "destination : ['North Station' 'Northeastern University' 'West End' 'Haymarket Square'\n",
      " 'South Station' 'Fenway' 'Theatre District' 'Beacon Hill' 'Back Bay'\n",
      " 'North End' 'Financial District' 'Boston University']\n",
      "\n",
      "cab_type : ['Lyft' 'Uber']\n",
      "\n",
      "product_id : ['lyft_line' 'lyft_premier' 'lyft' 'lyft_luxsuv' 'lyft_plus' 'lyft_lux'\n",
      " '6f72dfc5-27f1-42e8-84db-ccc7a75f6969'\n",
      " '6c84fd89-3f11-4782-9b50-97c468b19529'\n",
      " '55c66225-fbe7-4fd5-9072-eab1ece5e23e'\n",
      " '9a0e7b09-b92b-4c41-9779-2ad22b4d779d'\n",
      " '6d318bcc-22a3-4af6-bddd-b409bfce1546'\n",
      " '997acbb5-e102-41e1-b155-9df7de0a73f2']\n",
      "\n",
      "name : ['Shared' 'Lux' 'Lyft' 'Lux Black XL' 'Lyft XL' 'Lux Black' 'UberXL'\n",
      " 'Black' 'UberX' 'WAV' 'Black SUV' 'UberPool']\n",
      "\n",
      "short_summary : [' Mostly Cloudy ' ' Rain ' ' Clear ' ' Partly Cloudy ' ' Overcast '\n",
      " ' Light Rain ' ' Foggy ' ' Possible Drizzle ' ' Drizzle ']\n",
      "\n",
      "long_summary : [' Rain throughout the day. '\n",
      " ' Rain until morning, starting again in the evening. '\n",
      " ' Light rain in the morning. ' ' Partly cloudy throughout the day. '\n",
      " ' Mostly cloudy throughout the day. '\n",
      " ' Light rain in the morning and overnight. '\n",
      " ' Light rain until evening. ' ' Foggy in the morning. '\n",
      " ' Overcast throughout the day. ' ' Possible drizzle in the morning. '\n",
      " ' Rain in the morning and afternoon. ']\n",
      "\n",
      "icon : [' partly-cloudy-night ' ' rain ' ' clear-night ' ' cloudy ' ' fog '\n",
      " ' clear-day ' ' partly-cloudy-day ']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "categorical_col = []\n",
    "\n",
    "for col in data.select_dtypes(include='object'):\n",
    "    print(f'{col} : {data[col].unique()}')\n",
    "    print()\n",
    "    categorical_col.append(col)\n",
    "\n",
    "data_cat = data[categorical_col]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06e3efa",
   "metadata": {},
   "source": [
    "We can see that 'timezone' feature has only 1 value and 'product_id' feature contains many unidentified values. So we can remove or drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f82d658b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cat = data_cat.drop(['timezone', 'product_id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "caddba8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "encode = LabelEncoder()\n",
    "\n",
    "for col in data_cat.columns:\n",
    "    data_cat[col] = encode.fit_transform(data_cat[col])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5e786c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id               0.001137\n",
       "datetime         0.000403\n",
       "source          -0.025366\n",
       "destination     -0.046320\n",
       "cab_type        -0.083385\n",
       "name            -0.586644\n",
       "short_summary    0.001245\n",
       "long_summary     0.000396\n",
       "icon             0.002100\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cat.corrwith(data['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d98de1",
   "metadata": {},
   "source": [
    "As we can see that only the cab type, name, distance, and surge_multiplier columns have a strong correlation with the target (price). so I'm going to use that as a feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a1e33820",
   "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>cab_type</th>\n",
       "      <th>name</th>\n",
       "      <th>distance</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Shared</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lux</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lyft</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lux Black XL</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lyft XL</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693065</th>\n",
       "      <td>Uber</td>\n",
       "      <td>WAV</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693066</th>\n",
       "      <td>Uber</td>\n",
       "      <td>UberXL</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693067</th>\n",
       "      <td>Uber</td>\n",
       "      <td>UberX</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693069</th>\n",
       "      <td>Uber</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693070</th>\n",
       "      <td>Uber</td>\n",
       "      <td>UberPool</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>637976 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cab_type          name  distance  surge_multiplier  price\n",
       "0          Lyft        Shared      0.44               1.0    5.0\n",
       "1          Lyft           Lux      0.44               1.0   11.0\n",
       "2          Lyft          Lyft      0.44               1.0    7.0\n",
       "3          Lyft  Lux Black XL      0.44               1.0   26.0\n",
       "4          Lyft       Lyft XL      0.44               1.0    9.0\n",
       "...         ...           ...       ...               ...    ...\n",
       "693065     Uber           WAV      1.00               1.0    9.5\n",
       "693066     Uber        UberXL      1.00               1.0   13.0\n",
       "693067     Uber         UberX      1.00               1.0    9.5\n",
       "693069     Uber     Black SUV      1.00               1.0   27.0\n",
       "693070     Uber      UberPool      1.00               1.0   10.0\n",
       "\n",
       "[637976 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature = ['cab_type', 'name', 'distance', 'surge_multiplier', 'price']\n",
    "new_data = data[feature]\n",
    "new_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca38ba3",
   "metadata": {},
   "source": [
    "# 2. Removing Outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e424bf",
   "metadata": {},
   "source": [
    "Use IQR method for checking lower and upper bound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a31cfb02",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = new_data['price'].quantile(0.25)\n",
    "Q3 = new_data['price'].quantile(0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "lower_bound = Q1 - (1.5 * IQR)\n",
    "upper_bound = Q3 + (1.5 * IQR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5a344f",
   "metadata": {},
   "source": [
    "Checking lower bound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "76ae1b35",
   "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>cab_type</th>\n",
       "      <th>name</th>\n",
       "      <th>distance</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [cab_type, name, distance, surge_multiplier, price]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[new_data['price'] < lower_bound]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e6c2074",
   "metadata": {},
   "source": [
    "Checking upper bound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b6adf60d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cab_type</th>\n",
       "      <th>name</th>\n",
       "      <th>distance</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lux Black</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>52.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lux Black XL</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>67.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lux Black XL</td>\n",
       "      <td>4.76</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lux Black XL</td>\n",
       "      <td>4.31</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lux Black XL</td>\n",
       "      <td>5.33</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692439</th>\n",
       "      <td>Uber</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>5.56</td>\n",
       "      <td>1.00</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692698</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lux Black XL</td>\n",
       "      <td>4.58</td>\n",
       "      <td>1.25</td>\n",
       "      <td>52.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692891</th>\n",
       "      <td>Lyft</td>\n",
       "      <td>Lux Black XL</td>\n",
       "      <td>5.42</td>\n",
       "      <td>1.00</td>\n",
       "      <td>47.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692962</th>\n",
       "      <td>Uber</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>7.36</td>\n",
       "      <td>1.00</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693007</th>\n",
       "      <td>Uber</td>\n",
       "      <td>Black SUV</td>\n",
       "      <td>7.36</td>\n",
       "      <td>1.00</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5589 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cab_type          name  distance  surge_multiplier  price\n",
       "706        Lyft     Lux Black      3.25              2.00   52.5\n",
       "707        Lyft  Lux Black XL      3.25              2.00   67.5\n",
       "769        Lyft  Lux Black XL      4.76              1.00   45.5\n",
       "1094       Lyft  Lux Black XL      4.31              1.00   45.5\n",
       "1318       Lyft  Lux Black XL      5.33              1.00   45.5\n",
       "...         ...           ...       ...               ...    ...\n",
       "692439     Uber     Black SUV      5.56              1.00   47.0\n",
       "692698     Lyft  Lux Black XL      4.58              1.25   52.5\n",
       "692891     Lyft  Lux Black XL      5.42              1.00   47.5\n",
       "692962     Uber     Black SUV      7.36              1.00   51.0\n",
       "693007     Uber     Black SUV      7.36              1.00   49.5\n",
       "\n",
       "[5589 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[new_data['price'] > upper_bound]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3b8bf5e",
   "metadata": {},
   "source": [
    "We have 5589 outlier, so we can remove them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2834e17d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(632387, 5)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = new_data[new_data['price'] < upper_bound]\n",
    "new_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cdcd554",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0502f6d2",
   "metadata": {},
   "source": [
    "# 1. Encode categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9e6ec766",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col = ['cab_type', 'name']\n",
    "encoded_data = pd.get_dummies(new_data[cat_col], prefix=cat_col)\n",
    "new_data_encoded = pd.concat([new_data, encoded_data], axis=1)\n",
    "new_data_encoded = new_data_encoded.drop(cat_col, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "951883ce",
   "metadata": {},
   "source": [
    "This cell contains the code for encode cab type and name with one hot encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0fe056d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>price</th>\n",
       "      <th>cab_type_Lyft</th>\n",
       "      <th>cab_type_Uber</th>\n",
       "      <th>name_Black</th>\n",
       "      <th>name_Black SUV</th>\n",
       "      <th>name_Lux</th>\n",
       "      <th>name_Lux Black</th>\n",
       "      <th>name_Lux Black XL</th>\n",
       "      <th>name_Lyft</th>\n",
       "      <th>name_Lyft XL</th>\n",
       "      <th>name_Shared</th>\n",
       "      <th>name_UberPool</th>\n",
       "      <th>name_UberX</th>\n",
       "      <th>name_UberXL</th>\n",
       "      <th>name_WAV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.44</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693065</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>693066</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>693067</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>693069</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693070</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>632387 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        distance  surge_multiplier  price  cab_type_Lyft  cab_type_Uber  \\\n",
       "0           0.44               1.0    5.0           True          False   \n",
       "1           0.44               1.0   11.0           True          False   \n",
       "2           0.44               1.0    7.0           True          False   \n",
       "3           0.44               1.0   26.0           True          False   \n",
       "4           0.44               1.0    9.0           True          False   \n",
       "...          ...               ...    ...            ...            ...   \n",
       "693065      1.00               1.0    9.5          False           True   \n",
       "693066      1.00               1.0   13.0          False           True   \n",
       "693067      1.00               1.0    9.5          False           True   \n",
       "693069      1.00               1.0   27.0          False           True   \n",
       "693070      1.00               1.0   10.0          False           True   \n",
       "\n",
       "        name_Black  name_Black SUV  name_Lux  name_Lux Black  \\\n",
       "0            False           False     False           False   \n",
       "1            False           False      True           False   \n",
       "2            False           False     False           False   \n",
       "3            False           False     False           False   \n",
       "4            False           False     False           False   \n",
       "...            ...             ...       ...             ...   \n",
       "693065       False           False     False           False   \n",
       "693066       False           False     False           False   \n",
       "693067       False           False     False           False   \n",
       "693069       False            True     False           False   \n",
       "693070       False           False     False           False   \n",
       "\n",
       "        name_Lux Black XL  name_Lyft  name_Lyft XL  name_Shared  \\\n",
       "0                   False      False         False         True   \n",
       "1                   False      False         False        False   \n",
       "2                   False       True         False        False   \n",
       "3                    True      False         False        False   \n",
       "4                   False      False          True        False   \n",
       "...                   ...        ...           ...          ...   \n",
       "693065              False      False         False        False   \n",
       "693066              False      False         False        False   \n",
       "693067              False      False         False        False   \n",
       "693069              False      False         False        False   \n",
       "693070              False      False         False        False   \n",
       "\n",
       "        name_UberPool  name_UberX  name_UberXL  name_WAV  \n",
       "0               False       False        False     False  \n",
       "1               False       False        False     False  \n",
       "2               False       False        False     False  \n",
       "3               False       False        False     False  \n",
       "4               False       False        False     False  \n",
       "...               ...         ...          ...       ...  \n",
       "693065          False       False        False      True  \n",
       "693066          False       False         True     False  \n",
       "693067          False        True        False     False  \n",
       "693069          False       False        False     False  \n",
       "693070           True       False        False     False  \n",
       "\n",
       "[632387 rows x 17 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da5f4223",
   "metadata": {},
   "source": [
    "This cell contain the code for showing the new data that has been encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed8f46b",
   "metadata": {},
   "source": [
    "# 2. Split Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03f676ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_data_encoded.drop(columns='price', axis=1).values\n",
    "y = new_data_encoded['price'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab1b52e",
   "metadata": {},
   "source": [
    "This cell contain the code for splitting the dataset for our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6f4b031c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f0759f",
   "metadata": {},
   "source": [
    "This cell contain the code for defining split dataset as X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56f1242b",
   "metadata": {},
   "source": [
    "# Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a1dac74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb24f9b",
   "metadata": {},
   "source": [
    "This cell contain the code to train the model with calling the linear regression and using it for split dataset from library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f7ca7890",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca20612",
   "metadata": {},
   "source": [
    "This cell contain the code for defining the y_pred with predict model based on X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c2582a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9337785333729759"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988a9283",
   "metadata": {},
   "source": [
    "This cell contain the code for showing a result from modelling the dataset with Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93f06979",
   "metadata": {},
   "source": [
    "## We got our regression model with accuracy or score 93.37% based on our data which is uber and lyft fare dataset."
   ]
  }
 ],
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