Taxi Fare Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c9c572a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial Python environment setup...\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # CSV file I/O (e.g. pd.read_csv)\n",
    "import os # reading the input files we have access to\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c986e560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key                   object\n",
       "fare_amount          float64\n",
       "pickup_datetime       object\n",
       "pickup_longitude     float64\n",
       "pickup_latitude      float64\n",
       "dropoff_longitude    float64\n",
       "dropoff_latitude     float64\n",
       "passenger_count        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df =  pd.read_csv('taxi fare dataset.csv')\n",
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d54545a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key                   object\n",
       "pickup_datetime       object\n",
       "pickup_longitude     float64\n",
       "pickup_latitude      float64\n",
       "dropoff_longitude    float64\n",
       "dropoff_latitude     float64\n",
       "passenger_count        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "        test_df = pd.read_csv('test.csv')\n",
    "test_df.dtypes    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f13164ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9914, 7)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ea948c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Given a dataframe, add two new features 'abs_diff_longitude' and\n",
    "# 'abs_diff_latitude' reprensenting the \"Manhattan vector\" from\n",
    "# the pickup location to the dropoff location.\n",
    "def add_travel_vector_features(df):\n",
    "    df['abs_diff_longitude'] = (df.dropoff_longitude - df.pickup_longitude).abs()\n",
    "    df['abs_diff_latitude'] = (df.dropoff_latitude - df.pickup_latitude).abs()\n",
    "\n",
    "add_travel_vector_features(train_df)\n",
    "add_travel_vector_features(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ab3d4aee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key                   0\n",
      "fare_amount           0\n",
      "pickup_datetime       0\n",
      "pickup_longitude      0\n",
      "pickup_latitude       0\n",
      "dropoff_longitude     0\n",
      "dropoff_latitude      0\n",
      "passenger_count       0\n",
      "abs_diff_longitude    0\n",
      "abs_diff_latitude     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e8f055d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old size: 50000\n",
      "New size: 50000\n"
     ]
    }
   ],
   "source": [
    "print('Old size: %d' % len(train_df))\n",
    "train_df = train_df.dropna(how = 'any', axis = 'rows')\n",
    "print('New size: %d' % len(train_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "afee42a7",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot = train_df.iloc[:2000].plot.scatter('abs_diff_longitude', 'abs_diff_latitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7e494d55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old size: 50000\n",
      "New size: 49906\n"
     ]
    }
   ],
   "source": [
    "print('Old size: %d' % len(train_df))\n",
    "train_df = train_df[(train_df.abs_diff_longitude < 5.0) & (train_df.abs_diff_latitude < 5.0)]\n",
    "print('New size: %d' % len(train_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "68f55cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls1 = list(train_df['pickup_datetime'])\n",
    "for i in range(len(ls1)):\n",
    "    ls1[i] = ls1[i][11:-7:]\n",
    "train_df['pickuptime'] = ls1\n",
    "\n",
    "ls1 = list(test_df['pickup_datetime'])\n",
    "for i in range(len(ls1)):\n",
    "    ls1[i] = ls1[i][11:-7:]\n",
    "test_df['pickuptime'] = ls1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "408911de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .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>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>abs_diff_longitude</th>\n",
       "      <th>abs_diff_latitude</th>\n",
       "      <th>pickuptime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>17:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.036780</td>\n",
       "      <td>0.070701</td>\n",
       "      <td>16:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>0.010708</td>\n",
       "      <td>00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004437</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>04:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011440</td>\n",
       "      <td>0.015754</td>\n",
       "      <td>07:51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  abs_diff_longitude  abs_diff_latitude pickuptime  \n",
       "0                1            0.002701           0.009041      17:26  \n",
       "1                1            0.036780           0.070701      16:52  \n",
       "2                2            0.008504           0.010708      00:35  \n",
       "3                1            0.004437           0.024949      04:30  \n",
       "4                1            0.011440           0.015754      07:51  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "391bb71b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls1 = list(train_df['pickup_datetime'])\n",
    "for i in range(len(ls1)):\n",
    "    ls1[i] = ls1[i][:-4:]\n",
    "    ls1[i] = pd.Timestamp(ls1[i])\n",
    "    ls1[i] = ls1[i].weekday()\n",
    "train_df['weekday'] = ls1\n",
    "\n",
    "\n",
    "ls1 = list(test_df['pickup_datetime'])\n",
    "for i in range(len(ls1)):\n",
    "    ls1[i] = ls1[i][:-4:]\n",
    "    ls1[i] = pd.Timestamp(ls1[i])\n",
    "    ls1[i] = ls1[i].weekday()\n",
    "test_df['weekday'] = ls1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b3ab1939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>abs_diff_longitude</th>\n",
       "      <th>abs_diff_latitude</th>\n",
       "      <th>pickuptime</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>17:26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.036780</td>\n",
       "      <td>0.070701</td>\n",
       "      <td>16:52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>0.010708</td>\n",
       "      <td>00:35</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004437</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>04:30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011440</td>\n",
       "      <td>0.015754</td>\n",
       "      <td>07:51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  abs_diff_longitude  abs_diff_latitude pickuptime  weekday  \n",
       "0                1            0.002701           0.009041      17:26        0  \n",
       "1                1            0.036780           0.070701      16:52        1  \n",
       "2                2            0.008504           0.010708      00:35        3  \n",
       "3                1            0.004437           0.024949      04:30        5  \n",
       "4                1            0.011440           0.015754      07:51        1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "49d5a51c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop('pickup_datetime', inplace = True, axis = 1)\n",
    "test_df.drop('pickup_datetime', inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a11b3c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['weekday'].replace(to_replace = [i for i in range(0,7)], value = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], inplace=True)\n",
    "test_df['weekday'].replace(to_replace = [i for i in range(0,7)], value = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d6bf256a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>abs_diff_longitude</th>\n",
       "      <th>abs_diff_latitude</th>\n",
       "      <th>pickuptime</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>17:26</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.036780</td>\n",
       "      <td>0.070701</td>\n",
       "      <td>16:52</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>0.010708</td>\n",
       "      <td>00:35</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004437</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>04:30</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011440</td>\n",
       "      <td>0.015754</td>\n",
       "      <td>07:51</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount  pickup_longitude  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5        -73.844311   \n",
       "1    2010-01-05 16:52:16.0000002         16.9        -74.016048   \n",
       "2   2011-08-18 00:35:00.00000049          5.7        -73.982738   \n",
       "3    2012-04-21 04:30:42.0000001          7.7        -73.987130   \n",
       "4  2010-03-09 07:51:00.000000135          5.3        -73.968095   \n",
       "\n",
       "   pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \\\n",
       "0        40.721319         -73.841610         40.712278                1   \n",
       "1        40.711303         -73.979268         40.782004                1   \n",
       "2        40.761270         -73.991242         40.750562                2   \n",
       "3        40.733143         -73.991567         40.758092                1   \n",
       "4        40.768008         -73.956655         40.783762                1   \n",
       "\n",
       "   abs_diff_longitude  abs_diff_latitude pickuptime   weekday  \n",
       "0            0.002701           0.009041      17:26    Monday  \n",
       "1            0.036780           0.070701      16:52   Tuesday  \n",
       "2            0.008504           0.010708      00:35  Thursday  \n",
       "3            0.004437           0.024949      04:30  Saturday  \n",
       "4            0.011440           0.015754      07:51   Tuesday  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d92234b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_one_hot = pd.get_dummies(train_df['weekday'])\n",
    "test_one_hot = pd.get_dummies(test_df['weekday'])\n",
    "train_df = pd.concat([train_df, train_one_hot], axis = 1)\n",
    "test_df = pd.concat([test_df, test_one_hot], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c9281834",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop('weekday', inplace=True, axis=1)\n",
    "test_df.drop('weekday', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bd449688",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>abs_diff_longitude</th>\n",
       "      <th>abs_diff_latitude</th>\n",
       "      <th>pickuptime</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
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       "      <td>17:26</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.036780</td>\n",
       "      <td>0.070701</td>\n",
       "      <td>16:52</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008504</td>\n",
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       "      <td>00:35</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004437</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>04:30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
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       "      <td>07:51</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                             key  fare_amount  pickup_longitude  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5        -73.844311   \n",
       "1    2010-01-05 16:52:16.0000002         16.9        -74.016048   \n",
       "2   2011-08-18 00:35:00.00000049          5.7        -73.982738   \n",
       "3    2012-04-21 04:30:42.0000001          7.7        -73.987130   \n",
       "4  2010-03-09 07:51:00.000000135          5.3        -73.968095   \n",
       "\n",
       "   pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \\\n",
       "0        40.721319         -73.841610         40.712278                1   \n",
       "1        40.711303         -73.979268         40.782004                1   \n",
       "2        40.761270         -73.991242         40.750562                2   \n",
       "3        40.733143         -73.991567         40.758092                1   \n",
       "4        40.768008         -73.956655         40.783762                1   \n",
       "\n",
       "   abs_diff_longitude  abs_diff_latitude pickuptime  Friday  Monday  Saturday  \\\n",
       "0            0.002701           0.009041      17:26       0       1         0   \n",
       "1            0.036780           0.070701      16:52       0       0         0   \n",
       "2            0.008504           0.010708      00:35       0       0         0   \n",
       "3            0.004437           0.024949      04:30       0       0         1   \n",
       "4            0.011440           0.015754      07:51       0       0         0   \n",
       "\n",
       "   Sunday  Thursday  Tuesday  Wednesday  \n",
       "0       0         0        0          0  \n",
       "1       0         0        1          0  \n",
       "2       0         1        0          0  \n",
       "3       0         0        0          0  \n",
       "4       0         0        1          0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8a3a8fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls1 = list(train_df['pickuptime'])\n",
    "for i in range(len(ls1)):\n",
    "    z = ls1[i].split(':')\n",
    "    ls1[i] = int(z[0])*100+int(z[1])\n",
    "train_df['pickuptime'] = ls1\n",
    "    \n",
    "    \n",
    "ls1 = list(test_df['pickuptime'])\n",
    "for i in range(len(ls1)):\n",
    "    z = ls1[i].split(':')\n",
    "    ls1[i] = int(z[0])*100+int(z[1])\n",
    "test_df['pickuptime'] = ls1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "49fa44fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
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       "      <th>abs_diff_latitude</th>\n",
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       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
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       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004437</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>430</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "                             key  fare_amount  pickup_longitude  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5        -73.844311   \n",
       "1    2010-01-05 16:52:16.0000002         16.9        -74.016048   \n",
       "2   2011-08-18 00:35:00.00000049          5.7        -73.982738   \n",
       "3    2012-04-21 04:30:42.0000001          7.7        -73.987130   \n",
       "4  2010-03-09 07:51:00.000000135          5.3        -73.968095   \n",
       "\n",
       "   pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \\\n",
       "0        40.721319         -73.841610         40.712278                1   \n",
       "1        40.711303         -73.979268         40.782004                1   \n",
       "2        40.761270         -73.991242         40.750562                2   \n",
       "3        40.733143         -73.991567         40.758092                1   \n",
       "4        40.768008         -73.956655         40.783762                1   \n",
       "\n",
       "   abs_diff_longitude  abs_diff_latitude  pickuptime  Friday  Monday  \\\n",
       "0            0.002701           0.009041        1726       0       1   \n",
       "1            0.036780           0.070701        1652       0       0   \n",
       "2            0.008504           0.010708          35       0       0   \n",
       "3            0.004437           0.024949         430       0       0   \n",
       "4            0.011440           0.015754         751       0       0   \n",
       "\n",
       "   Saturday  Sunday  Thursday  Tuesday  Wednesday  \n",
       "0         0       0         0        0          0  \n",
       "1         0       0         0        1          0  \n",
       "2         0       0         1        0          0  \n",
       "3         1       0         0        0          0  \n",
       "4         0       0         0        1          0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a0981832",
   "metadata": {},
   "outputs": [],
   "source": [
    "R = 6373.0\n",
    "lat1 = np.asarray(np.radians(train_df['pickup_latitude']))\n",
    "lon1 = np.asarray(np.radians(train_df['pickup_longitude']))\n",
    "lat2 = np.asarray(np.radians(train_df['dropoff_latitude']))\n",
    "lon2 = np.asarray(np.radians(train_df['dropoff_longitude']))\n",
    "\n",
    "dlon = lon2 - lon1\n",
    "dlat = lat2 - lat1\n",
    "ls1 = []\n",
    "\n",
    "a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n",
    "c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1-a))\n",
    "distance = R * c\n",
    "\n",
    "train_df['Distance'] = np.asarray(distance)*0.621\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "lat1 = np.asarray(np.radians(test_df['pickup_latitude']))\n",
    "lon1 = np.asarray(np.radians(test_df['pickup_longitude']))\n",
    "lat2 = np.asarray(np.radians(test_df['dropoff_latitude']))\n",
    "lon2 = np.asarray(np.radians(test_df['dropoff_longitude']))\n",
    "\n",
    "dlon = lon2 - lon1\n",
    "dlat = lat2 - lat1\n",
    "ls1 = []\n",
    "\n",
    "a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n",
    "c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1-a))\n",
    "distance = R * c\n",
    "\n",
    "test_df['Distance'] = np.asarray(distance)*0.621"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8b204072",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>key</th>\n",
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       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
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       "      <th>abs_diff_latitude</th>\n",
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       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
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       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.036780</td>\n",
       "      <td>0.070701</td>\n",
       "      <td>1652</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>316.933663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>0.010708</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004437</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>430</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.523055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011440</td>\n",
       "      <td>0.015754</td>\n",
       "      <td>751</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>156.215667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount  pickup_longitude  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5        -73.844311   \n",
       "1    2010-01-05 16:52:16.0000002         16.9        -74.016048   \n",
       "2   2011-08-18 00:35:00.00000049          5.7        -73.982738   \n",
       "3    2012-04-21 04:30:42.0000001          7.7        -73.987130   \n",
       "4  2010-03-09 07:51:00.000000135          5.3        -73.968095   \n",
       "\n",
       "   pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \\\n",
       "0        40.721319         -73.841610         40.712278                1   \n",
       "1        40.711303         -73.979268         40.782004                1   \n",
       "2        40.761270         -73.991242         40.750562                2   \n",
       "3        40.733143         -73.991567         40.758092                1   \n",
       "4        40.768008         -73.956655         40.783762                1   \n",
       "\n",
       "   abs_diff_longitude  abs_diff_latitude  pickuptime  Friday  Monday  \\\n",
       "0            0.002701           0.009041        1726       0       1   \n",
       "1            0.036780           0.070701        1652       0       0   \n",
       "2            0.008504           0.010708          35       0       0   \n",
       "3            0.004437           0.024949         430       0       0   \n",
       "4            0.011440           0.015754         751       0       0   \n",
       "\n",
       "   Saturday  Sunday  Thursday  Tuesday  Wednesday    Distance  \n",
       "0         0       0         0        0          0         NaN  \n",
       "1         0       0         0        1          0  316.933663  \n",
       "2         0       0         1        0          0         NaN  \n",
       "3         1       0         0        0          0  156.523055  \n",
       "4         0       0         0        1          0  156.215667  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3f4ee3f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "R = 6373.0\n",
    "lat1 =np.asarray(np.radians(train_df['pickup_latitude']))\n",
    "lon1 = np.asarray(np.radians(train_df['pickup_longitude']))\n",
    "lat2 = np.asarray(np.radians(train_df['dropoff_latitude']))\n",
    "lon2 = np.asarray(np.radians(train_df['dropoff_longitude']))\n",
    "\n",
    "lat3=np.zeros(len(train_df))+np.radians(40.6413111)\n",
    "lon3=np.zeros(len(train_df))+np.radians(-73.7781391)\n",
    "dlon_pickup = lon3 - lon1\n",
    "dlat_pickup = lat3 - lat1\n",
    "d_lon_dropoff=lon3 -lon2\n",
    "d_lat_dropoff=lat3-lat2\n",
    "a1 = np.sin(dlat_pickup/2)**2 + np.cos(lat1) * np.cos(lat3) * np.sin(dlon_pickup/ 2)**2\n",
    "c1 = 2 * np.arctan2(np.sqrt(a1), np.sqrt(1 - a1))\n",
    "distance1 = R * c1\n",
    "train_df['Pickup_Distance_airport']=np.asarray(distance1)*0.621\n",
    "\n",
    "a2=np.sin(d_lat_dropoff/2)**2 + np.cos(lat2) * np.cos(lat3) * np.sin(d_lon_dropoff/ 2)**2\n",
    "c2 = 2 * np.arctan2(np.sqrt(a2), np.sqrt(1 - a2))\n",
    "distance2 = R * c2\n",
    "\n",
    "    \n",
    "train_df['Dropoff_Distance_airport']=np.asarray(distance2)*0.621\n",
    "\n",
    "\n",
    "\n",
    "lat1 =np.asarray(np.radians(test_df['pickup_latitude']))\n",
    "lon1 = np.asarray(np.radians(test_df['pickup_longitude']))\n",
    "lat2 = np.asarray(np.radians(test_df['dropoff_latitude']))\n",
    "lon2 = np.asarray(np.radians(test_df['dropoff_longitude']))\n",
    "\n",
    "lat3=np.zeros(len(test_df))+np.radians(40.6413111)\n",
    "lon3=np.zeros(len(test_df))+np.radians(-73.7781391)\n",
    "dlon_pickup = lon3 - lon1\n",
    "dlat_pickup = lat3 - lat1\n",
    "d_lon_dropoff=lon3 -lon2\n",
    "d_lat_dropoff=lat3-lat2\n",
    "a1 = np.sin(dlat_pickup/2)**2 + np.cos(lat1) * np.cos(lat3) * np.sin(dlon_pickup/ 2)**2\n",
    "c1 = 2 * np.arctan2(np.sqrt(a1), np.sqrt(1 - a1))\n",
    "distance1 = R * c1\n",
    "test_df['Pickup_Distance_airport']=np.asarray(distance1)*0.621\n",
    "\n",
    "a2=np.sin(d_lat_dropoff/2)**2 + np.cos(lat2) * np.cos(lat3) * np.sin(d_lon_dropoff/ 2)**2\n",
    "c2 = 2 * np.arctan2(np.sqrt(a2), np.sqrt(1 - a2))\n",
    "distance2 = R * c2\n",
    "\n",
    "    \n",
    "test_df['Dropoff_Distance_airport']=np.asarray(distance2)*0.621"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e3f1b328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>abs_diff_longitude</th>\n",
       "      <th>abs_diff_latitude</th>\n",
       "      <th>pickuptime</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Pickup_Distance_airport</th>\n",
       "      <th>Dropoff_Distance_airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>1726</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.640305</td>\n",
       "      <td>6.523508</td>\n",
       "      <td>5.923206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.036780</td>\n",
       "      <td>0.070701</td>\n",
       "      <td>1652</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.249180</td>\n",
       "      <td>13.367898</td>\n",
       "      <td>14.329642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>0.010708</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.863166</td>\n",
       "      <td>13.544325</td>\n",
       "      <td>13.472164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004437</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>430</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.738893</td>\n",
       "      <td>12.651421</td>\n",
       "      <td>13.783538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011440</td>\n",
       "      <td>0.015754</td>\n",
       "      <td>751</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241866</td>\n",
       "      <td>13.248595</td>\n",
       "      <td>13.571182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount  pickup_longitude  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5        -73.844311   \n",
       "1    2010-01-05 16:52:16.0000002         16.9        -74.016048   \n",
       "2   2011-08-18 00:35:00.00000049          5.7        -73.982738   \n",
       "3    2012-04-21 04:30:42.0000001          7.7        -73.987130   \n",
       "4  2010-03-09 07:51:00.000000135          5.3        -73.968095   \n",
       "\n",
       "   pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \\\n",
       "0        40.721319         -73.841610         40.712278                1   \n",
       "1        40.711303         -73.979268         40.782004                1   \n",
       "2        40.761270         -73.991242         40.750562                2   \n",
       "3        40.733143         -73.991567         40.758092                1   \n",
       "4        40.768008         -73.956655         40.783762                1   \n",
       "\n",
       "   abs_diff_longitude  abs_diff_latitude  pickuptime  Friday  Monday  \\\n",
       "0            0.002701           0.009041        1726       0       1   \n",
       "1            0.036780           0.070701        1652       0       0   \n",
       "2            0.008504           0.010708          35       0       0   \n",
       "3            0.004437           0.024949         430       0       0   \n",
       "4            0.011440           0.015754         751       0       0   \n",
       "\n",
       "   Saturday  Sunday  Thursday  Tuesday  Wednesday  Distance  \\\n",
       "0         0       0         0        0          0  0.640305   \n",
       "1         0       0         0        1          0  5.249180   \n",
       "2         0       0         1        0          0  0.863166   \n",
       "3         1       0         0        0          0  1.738893   \n",
       "4         0       0         0        1          0  1.241866   \n",
       "\n",
       "   Pickup_Distance_airport  Dropoff_Distance_airport  \n",
       "0                 6.523508                  5.923206  \n",
       "1                13.367898                 14.329642  \n",
       "2                13.544325                 13.472164  \n",
       "3                12.651421                 13.783538  \n",
       "4                13.248595                 13.571182  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6664b9b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Distance'] = np.round(train_df['Distance'],2)\n",
    "train_df['Pickup_Distance_airport'] = np.round(train_df['Pickup_Distance_airport'],2)\n",
    "train_df['Dropoff_Distance_airport'] = np.round(train_df['Dropoff_Distance_airport'],2)\n",
    "\n",
    "test_df['Distance'] = np.round(test_df['Distance'],2)\n",
    "test_df['Pickup_Distance_airport'] = np.round(test_df['Pickup_Distance_airport'],2)\n",
    "test_df['Dropoff_Distance_airport'] = np.round(test_df['Dropoff_Distance_airport'],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "56dc4ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(['pickup_longitude','pickup_latitude','dropoff_longitude','dropoff_latitude'],axis=1,inplace=True)\n",
    "test_df.drop(['pickup_longitude','pickup_latitude','dropoff_longitude','dropoff_latitude'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eeb816fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>abs_diff_longitude</th>\n",
       "      <th>abs_diff_latitude</th>\n",
       "      <th>pickuptime</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Pickup_Distance_airport</th>\n",
       "      <th>Dropoff_Distance_airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>0.009041</td>\n",
       "      <td>1726</td>\n",
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       "      <td>0.64</td>\n",
       "      <td>6.52</td>\n",
       "      <td>5.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.036780</td>\n",
       "      <td>0.070701</td>\n",
       "      <td>1652</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>13.37</td>\n",
       "      <td>14.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008504</td>\n",
       "      <td>0.010708</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>13.54</td>\n",
       "      <td>13.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004437</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>430</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>12.65</td>\n",
       "      <td>13.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011440</td>\n",
       "      <td>0.015754</td>\n",
       "      <td>751</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.24</td>\n",
       "      <td>13.25</td>\n",
       "      <td>13.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount  passenger_count  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5                1   \n",
       "1    2010-01-05 16:52:16.0000002         16.9                1   \n",
       "2   2011-08-18 00:35:00.00000049          5.7                2   \n",
       "3    2012-04-21 04:30:42.0000001          7.7                1   \n",
       "4  2010-03-09 07:51:00.000000135          5.3                1   \n",
       "\n",
       "   abs_diff_longitude  abs_diff_latitude  pickuptime  Friday  Monday  \\\n",
       "0            0.002701           0.009041        1726       0       1   \n",
       "1            0.036780           0.070701        1652       0       0   \n",
       "2            0.008504           0.010708          35       0       0   \n",
       "3            0.004437           0.024949         430       0       0   \n",
       "4            0.011440           0.015754         751       0       0   \n",
       "\n",
       "   Saturday  Sunday  Thursday  Tuesday  Wednesday  Distance  \\\n",
       "0         0       0         0        0          0      0.64   \n",
       "1         0       0         0        1          0      5.25   \n",
       "2         0       0         1        0          0      0.86   \n",
       "3         1       0         0        0          0      1.74   \n",
       "4         0       0         0        1          0      1.24   \n",
       "\n",
       "   Pickup_Distance_airport  Dropoff_Distance_airport  \n",
       "0                     6.52                      5.92  \n",
       "1                    13.37                     14.33  \n",
       "2                    13.54                     13.47  \n",
       "3                    12.65                     13.78  \n",
       "4                    13.25                     13.57  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "99492ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['abs_diff_longitude']=np.abs(train_df['abs_diff_longitude']-np.mean(train_df['abs_diff_longitude']))\n",
    "train_df['abs_diff_longitude']=train_df['abs_diff_longitude']/np.var(train_df['abs_diff_longitude'])\n",
    "\n",
    "\n",
    "train_df['abs_diff_latitude']=np.abs(train_df['abs_diff_latitude']-np.mean(train_df['abs_diff_latitude']))\n",
    "train_df['abs_diff_latitude']=train_df['abs_diff_latitude']/np.var(train_df['abs_diff_latitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e848192d",
   "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>key</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>abs_diff_longitude</th>\n",
       "      <th>abs_diff_latitude</th>\n",
       "      <th>pickuptime</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Pickup_Distance_airport</th>\n",
       "      <th>Dropoff_Distance_airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-27 13:08:24.0000002</td>\n",
       "      <td>1</td>\n",
       "      <td>0.008110</td>\n",
       "      <td>0.019970</td>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.44</td>\n",
       "      <td>13.27</td>\n",
       "      <td>12.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-27 13:08:24.0000003</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>0.019817</td>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>12.19</td>\n",
       "      <td>13.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-10-08 11:53:44.0000002</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002870</td>\n",
       "      <td>0.005121</td>\n",
       "      <td>1153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>13.12</td>\n",
       "      <td>12.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-12-01 21:12:12.0000002</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.016172</td>\n",
       "      <td>2112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.22</td>\n",
       "      <td>13.76</td>\n",
       "      <td>13.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-12-01 21:12:12.0000003</td>\n",
       "      <td>1</td>\n",
       "      <td>0.022519</td>\n",
       "      <td>0.045348</td>\n",
       "      <td>2112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.35</td>\n",
       "      <td>14.21</td>\n",
       "      <td>13.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           key  passenger_count  abs_diff_longitude  \\\n",
       "0  2015-01-27 13:08:24.0000002                1            0.008110   \n",
       "1  2015-01-27 13:08:24.0000003                1            0.012024   \n",
       "2  2011-10-08 11:53:44.0000002                1            0.002870   \n",
       "3  2012-12-01 21:12:12.0000002                1            0.009288   \n",
       "4  2012-12-01 21:12:12.0000003                1            0.022519   \n",
       "\n",
       "   abs_diff_latitude  pickuptime  Friday  Monday  Saturday  Sunday  Thursday  \\\n",
       "0           0.019970        1308       0       0         0       0         0   \n",
       "1           0.019817        1308       0       0         0       0         0   \n",
       "2           0.005121        1153       0       0         1       0         0   \n",
       "3           0.016172        2112       0       0         1       0         0   \n",
       "4           0.045348        2112       0       0         1       0         0   \n",
       "\n",
       "   Tuesday  Wednesday  Distance  Pickup_Distance_airport  \\\n",
       "0        1          0      1.44                    13.27   \n",
       "1        1          0      1.51                    12.19   \n",
       "2        0          0      0.38                    13.12   \n",
       "3        0          0      1.22                    13.76   \n",
       "4        0          0      3.35                    14.21   \n",
       "\n",
       "   Dropoff_Distance_airport  \n",
       "0                     12.79  \n",
       "1                     13.39  \n",
       "2                     12.80  \n",
       "3                     13.48  \n",
       "4                     13.12  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a50b76b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49906, 16)\n",
      "(9914, 15)\n"
     ]
    }
   ],
   "source": [
    "print(train_df.shape)\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "41f61e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = train_df.drop(['key','fare_amount'],axis=1)\n",
    "Y = train_df['fare_amount']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.01, random_state=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4420bfcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6143654550752371\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression(normalize = True)\n",
    "lr.fit(X_train,Y_train)\n",
    "print(lr.score(X_test,Y_test))"
   ]
  }
 ],
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