AE19B019_PIYUSH_BHUJBAL_prediction_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "725736be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn import metrics\n",
    "import calendar\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc4ded2",
   "metadata": {},
   "source": [
    "# Fare Price Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d55be3e5",
   "metadata": {},
   "source": [
    "### Data Loading\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fdec5f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "training = pd.read_csv('train_data_iitm.csv')\n",
    "test = pd.read_csv('test_data_iitm.csv')\n",
    "taxi_mapping = pd.read_csv('taxi_zone_mapping.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0378822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c093b26",
   "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>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2020-06-10 13:36:49</td>\n",
       "      <td>2020-06-10 13:51:08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>224</td>\n",
       "      <td>148</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-06-13 06:19:00</td>\n",
       "      <td>2020-06-13 06:37:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.81</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2020-06-20 07:59:23</td>\n",
       "      <td>2020-06-20 08:05:39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>68</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-06-27 15:11:32</td>\n",
       "      <td>2020-06-27 15:15:02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-06-26 16:00:36</td>\n",
       "      <td>2020-06-26 16:02:28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>90</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0       2.0  2020-06-10 13:36:49   2020-06-10 13:51:08              1.0   \n",
       "1       NaN  2020-06-13 06:19:00   2020-06-13 06:37:00              NaN   \n",
       "2       2.0  2020-06-20 07:59:23   2020-06-20 08:05:39              1.0   \n",
       "3       1.0  2020-06-27 15:11:32   2020-06-27 15:15:02              1.0   \n",
       "4       1.0  2020-06-26 16:00:36   2020-06-26 16:02:28              1.0   \n",
       "\n",
       "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "0           2.73         1.0                  N           224           148   \n",
       "1          11.58         NaN                NaN            56            42   \n",
       "2           2.05         1.0                  N            79            68   \n",
       "3           1.40         1.0                  N           236            75   \n",
       "4           0.60         1.0                  N           186            90   \n",
       "\n",
       "   payment_type  fare_amount  congestion_surcharge  \n",
       "0           2.0        12.00                   2.5  \n",
       "1           NaN        28.81                   0.0  \n",
       "2           2.0         8.00                   2.5  \n",
       "3           1.0         6.00                   2.5  \n",
       "4           2.0         4.00                   2.5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "632ff746",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60000 entries, 0 to 59999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   VendorID               54365 non-null  float64\n",
      " 1   tpep_pickup_datetime   60000 non-null  object \n",
      " 2   tpep_dropoff_datetime  60000 non-null  object \n",
      " 3   passenger_count        54365 non-null  float64\n",
      " 4   trip_distance          60000 non-null  float64\n",
      " 5   RatecodeID             54365 non-null  float64\n",
      " 6   store_and_fwd_flag     54365 non-null  object \n",
      " 7   PULocationID           60000 non-null  int64  \n",
      " 8   DOLocationID           60000 non-null  int64  \n",
      " 9   payment_type           54365 non-null  float64\n",
      " 10  fare_amount            60000 non-null  float64\n",
      " 11  congestion_surcharge   60000 non-null  float64\n",
      "dtypes: float64(7), int64(2), object(3)\n",
      "memory usage: 5.5+ MB\n"
     ]
    }
   ],
   "source": [
    "training.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7abdb78a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorID                 5635\n",
       "tpep_pickup_datetime        0\n",
       "tpep_dropoff_datetime       0\n",
       "passenger_count          5635\n",
       "trip_distance               0\n",
       "RatecodeID               5635\n",
       "store_and_fwd_flag       5635\n",
       "PULocationID                0\n",
       "DOLocationID                0\n",
       "payment_type             5635\n",
       "fare_amount                 0\n",
       "congestion_surcharge        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b5453c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "training= training.dropna() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67aa166e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2.0\n",
       "2        2.0\n",
       "3        1.0\n",
       "4        1.0\n",
       "5        1.0\n",
       "        ... \n",
       "59995    1.0\n",
       "59996    2.0\n",
       "59997    1.0\n",
       "59998    2.0\n",
       "59999    1.0\n",
       "Name: VendorID, Length: 54365, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.pop('VendorID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4883993c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06-10 13:36:49</td>\n",
       "      <td>2020-06-10 13:51:08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>224</td>\n",
       "      <td>148</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-20 07:59:23</td>\n",
       "      <td>2020-06-20 08:05:39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>68</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-06-27 15:11:32</td>\n",
       "      <td>2020-06-27 15:15:02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-06-26 16:00:36</td>\n",
       "      <td>2020-06-26 16:02:28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>90</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-06-10 13:00:08</td>\n",
       "      <td>2020-06-10 13:13:58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>137</td>\n",
       "      <td>236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  trip_distance  \\\n",
       "0  2020-06-10 13:36:49   2020-06-10 13:51:08              1.0           2.73   \n",
       "2  2020-06-20 07:59:23   2020-06-20 08:05:39              1.0           2.05   \n",
       "3  2020-06-27 15:11:32   2020-06-27 15:15:02              1.0           1.40   \n",
       "4  2020-06-26 16:00:36   2020-06-26 16:02:28              1.0           0.60   \n",
       "5  2020-06-10 13:00:08   2020-06-10 13:13:58              1.0           4.00   \n",
       "\n",
       "   RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  payment_type  \\\n",
       "0         1.0                  N           224           148           2.0   \n",
       "2         1.0                  N            79            68           2.0   \n",
       "3         1.0                  N           236            75           1.0   \n",
       "4         1.0                  N           186            90           2.0   \n",
       "5         1.0                  N           137           236           1.0   \n",
       "\n",
       "   fare_amount  congestion_surcharge  \n",
       "0         12.0                   2.5  \n",
       "2          8.0                   2.5  \n",
       "3          6.0                   2.5  \n",
       "4          4.0                   2.5  \n",
       "5         15.0                   2.5  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d50496f",
   "metadata": {},
   "source": [
    "### Trimming Data and throwing useless information out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "283e62e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>pickup_date</th>\n",
       "      <th>pickup_day</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <th>pickup_month</th>\n",
       "      <th>pickup_year</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06-10 13:36:49</td>\n",
       "      <td>2020-06-10 13:51:08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>224</td>\n",
       "      <td>148</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2020-06-10 13:36:49</td>\n",
       "      <td>2020-06-10 13:51:08</td>\n",
       "      <td>2020-06-10</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>14.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-20 07:59:23</td>\n",
       "      <td>2020-06-20 08:05:39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>68</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2020-06-20 07:59:23</td>\n",
       "      <td>2020-06-20 08:05:39</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>6.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-06-27 15:11:32</td>\n",
       "      <td>2020-06-27 15:15:02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2020-06-27 15:11:32</td>\n",
       "      <td>2020-06-27 15:15:02</td>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-06-26 16:00:36</td>\n",
       "      <td>2020-06-26 16:02:28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>90</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2020-06-26 16:00:36</td>\n",
       "      <td>2020-06-26 16:02:28</td>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>Friday</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-06-10 13:00:08</td>\n",
       "      <td>2020-06-10 13:13:58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>137</td>\n",
       "      <td>236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2020-06-10 13:00:08</td>\n",
       "      <td>2020-06-10 13:13:58</td>\n",
       "      <td>2020-06-10</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>13.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  trip_distance  \\\n",
       "0  2020-06-10 13:36:49   2020-06-10 13:51:08              1.0           2.73   \n",
       "2  2020-06-20 07:59:23   2020-06-20 08:05:39              1.0           2.05   \n",
       "3  2020-06-27 15:11:32   2020-06-27 15:15:02              1.0           1.40   \n",
       "4  2020-06-26 16:00:36   2020-06-26 16:02:28              1.0           0.60   \n",
       "5  2020-06-10 13:00:08   2020-06-10 13:13:58              1.0           4.00   \n",
       "\n",
       "   RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  payment_type  \\\n",
       "0         1.0                  N           224           148           2.0   \n",
       "2         1.0                  N            79            68           2.0   \n",
       "3         1.0                  N           236            75           1.0   \n",
       "4         1.0                  N           186            90           2.0   \n",
       "5         1.0                  N           137           236           1.0   \n",
       "\n",
       "   fare_amount  congestion_surcharge     pickup_datetime    dropoff_datetime  \\\n",
       "0         12.0                   2.5 2020-06-10 13:36:49 2020-06-10 13:51:08   \n",
       "2          8.0                   2.5 2020-06-20 07:59:23 2020-06-20 08:05:39   \n",
       "3          6.0                   2.5 2020-06-27 15:11:32 2020-06-27 15:15:02   \n",
       "4          4.0                   2.5 2020-06-26 16:00:36 2020-06-26 16:02:28   \n",
       "5         15.0                   2.5 2020-06-10 13:00:08 2020-06-10 13:13:58   \n",
       "\n",
       "  pickup_date  pickup_day  pickup_hour pickup_day_of_week  pickup_month  \\\n",
       "0  2020-06-10          10           13          Wednesday             6   \n",
       "2  2020-06-20          20            7           Saturday             6   \n",
       "3  2020-06-27          27           15           Saturday             6   \n",
       "4  2020-06-26          26           16             Friday             6   \n",
       "5  2020-06-10          10           13          Wednesday             6   \n",
       "\n",
       "   pickup_year       time  \n",
       "0         2020  14.316667  \n",
       "2         2020   6.266667  \n",
       "3         2020   3.500000  \n",
       "4         2020   1.866667  \n",
       "5         2020  13.833333  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training['pickup_datetime']=pd.to_datetime(training['tpep_pickup_datetime'],format='%Y-%m-%d %H:%M:%S')\n",
    "training['dropoff_datetime']=pd.to_datetime(training['tpep_dropoff_datetime'],format='%Y-%m-%d %H:%M:%S')\n",
    "training['pickup_date']= training['pickup_datetime'].dt.date\n",
    "training['pickup_day']=training['pickup_datetime'].apply(lambda x:x.day)\n",
    "training['pickup_hour']=training['pickup_datetime'].apply(lambda x:x.hour)\n",
    "training['pickup_day_of_week']=training['pickup_datetime'].apply(lambda x:calendar.day_name[x.weekday()])\n",
    "training['pickup_month']=training['pickup_datetime'].apply(lambda x:x.month)\n",
    "training['pickup_year']=training['pickup_datetime'].apply(lambda x:x.year)\n",
    "training['time'] = (training.dropoff_datetime - training.pickup_datetime) / pd.Timedelta(minutes=1)\n",
    "training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b3d14f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    54364\n",
       "2009        1\n",
       "Name: pickup_year, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training['pickup_year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f4fd2f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    54362\n",
       "1        1\n",
       "5        1\n",
       "7        1\n",
       "Name: pickup_month, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training['pickup_month'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e9f5b691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    53933\n",
       "Y      432\n",
       "Name: store_and_fwd_flag, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training['store_and_fwd_flag'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b0717a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "training = training.drop(['tpep_pickup_datetime','tpep_dropoff_datetime','pickup_datetime','dropoff_datetime','pickup_date','pickup_day','pickup_month','pickup_year','payment_type'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea2432b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     53357\n",
       "2.0       610\n",
       "5.0       264\n",
       "4.0        66\n",
       "3.0        62\n",
       "99.0        6\n",
       "Name: RatecodeID, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training['RatecodeID'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "30b6af7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "training.replace({'store_and_fwd_flag':{\"N\":0,\"Y\":1}}, inplace=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b437e3b",
   "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>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>pickup_day_of_week</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>224</td>\n",
       "      <td>148</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>13</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>14.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>68</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>6.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>236</td>\n",
       "      <td>75</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>15</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>16</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>236</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>13</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>13.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  trip_distance  RatecodeID  store_and_fwd_flag  \\\n",
       "0              1.0           2.73         1.0                   0   \n",
       "2              1.0           2.05         1.0                   0   \n",
       "3              1.0           1.40         1.0                   0   \n",
       "4              1.0           0.60         1.0                   0   \n",
       "5              1.0           4.00         1.0                   0   \n",
       "\n",
       "   PULocationID  DOLocationID  fare_amount  congestion_surcharge  pickup_hour  \\\n",
       "0           224           148         12.0                   2.5           13   \n",
       "2            79            68          8.0                   2.5            7   \n",
       "3           236            75          6.0                   2.5           15   \n",
       "4           186            90          4.0                   2.5           16   \n",
       "5           137           236         15.0                   2.5           13   \n",
       "\n",
       "  pickup_day_of_week       time  \n",
       "0          Wednesday  14.316667  \n",
       "2           Saturday   6.266667  \n",
       "3           Saturday   3.500000  \n",
       "4             Friday   1.866667  \n",
       "5          Wednesday  13.833333  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b1e63d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "training.replace({'pickup_day_of_week':{\"Sunday\":0,\"Monday\":1,\"Tuesday\":2,\"Wednesday\":3,\"Thursday\":4,\"Friday\":5,\"Saturday\":6}}, inplace=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "69054cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "training['day_of_week_sin'] = np.sin(training['pickup_day_of_week'] * (2 * np.pi / 7))\n",
    "training['day_of_week_cos'] = np.cos(training['pickup_day_of_week'] * (2 * np.pi / 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "266ffb07",
   "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>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>time</th>\n",
       "      <th>day_of_week_sin</th>\n",
       "      <th>day_of_week_cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>224</td>\n",
       "      <td>148</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>13</td>\n",
       "      <td>14.316667</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>-0.900969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>68</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7</td>\n",
       "      <td>6.266667</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.623490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>236</td>\n",
       "      <td>75</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>15</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.623490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>16</td>\n",
       "      <td>1.866667</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>236</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>13</td>\n",
       "      <td>13.833333</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>-0.900969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  trip_distance  RatecodeID  store_and_fwd_flag  \\\n",
       "0              1.0           2.73         1.0                   0   \n",
       "2              1.0           2.05         1.0                   0   \n",
       "3              1.0           1.40         1.0                   0   \n",
       "4              1.0           0.60         1.0                   0   \n",
       "5              1.0           4.00         1.0                   0   \n",
       "\n",
       "   PULocationID  DOLocationID  fare_amount  congestion_surcharge  pickup_hour  \\\n",
       "0           224           148         12.0                   2.5           13   \n",
       "2            79            68          8.0                   2.5            7   \n",
       "3           236            75          6.0                   2.5           15   \n",
       "4           186            90          4.0                   2.5           16   \n",
       "5           137           236         15.0                   2.5           13   \n",
       "\n",
       "        time  day_of_week_sin  day_of_week_cos  \n",
       "0  14.316667         0.433884        -0.900969  \n",
       "2   6.266667        -0.781831         0.623490  \n",
       "3   3.500000        -0.781831         0.623490  \n",
       "4   1.866667        -0.974928        -0.222521  \n",
       "5  13.833333         0.433884        -0.900969  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.pop('pickup_day_of_week')\n",
    "training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f614ccad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "passenger_count         0\n",
       "trip_distance           0\n",
       "RatecodeID              0\n",
       "store_and_fwd_flag      0\n",
       "PULocationID            0\n",
       "DOLocationID            0\n",
       "fare_amount             0\n",
       "congestion_surcharge    0\n",
       "pickup_hour             0\n",
       "time                    0\n",
       "day_of_week_sin         0\n",
       "day_of_week_cos         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9697a4d",
   "metadata": {},
   "source": [
    "## Trimming Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f54f32e4",
   "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>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>time</th>\n",
       "      <th>day_of_week_sin</th>\n",
       "      <th>day_of_week_cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48</td>\n",
       "      <td>90</td>\n",
       "      <td>2.5</td>\n",
       "      <td>18</td>\n",
       "      <td>5.683333</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48</td>\n",
       "      <td>246</td>\n",
       "      <td>2.5</td>\n",
       "      <td>23</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.623490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237</td>\n",
       "      <td>234</td>\n",
       "      <td>2.5</td>\n",
       "      <td>12</td>\n",
       "      <td>7.166667</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>2.5</td>\n",
       "      <td>9</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.623490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186</td>\n",
       "      <td>238</td>\n",
       "      <td>2.5</td>\n",
       "      <td>18</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  trip_distance  RatecodeID  store_and_fwd_flag  \\\n",
       "0              1.0           1.66         1.0                 0.0   \n",
       "1              3.0           1.50         1.0                 0.0   \n",
       "2              1.0           1.90         1.0                 0.0   \n",
       "3              1.0           0.27         1.0                 0.0   \n",
       "4              2.0           4.06         1.0                 0.0   \n",
       "\n",
       "   PULocationID  DOLocationID  congestion_surcharge  pickup_hour       time  \\\n",
       "0            48            90                   2.5           18   5.683333   \n",
       "1            48           246                   2.5           23   7.000000   \n",
       "2           237           234                   2.5           12   7.166667   \n",
       "3           170           170                   2.5            9   2.550000   \n",
       "4           186           238                   2.5           18  16.800000   \n",
       "\n",
       "   day_of_week_sin  day_of_week_cos  \n",
       "0         0.974928        -0.222521  \n",
       "1        -0.781831         0.623490  \n",
       "2         0.974928        -0.222521  \n",
       "3        -0.781831         0.623490  \n",
       "4         0.974928        -0.222521  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['tpep_pickup_datetime']=pd.to_datetime(test['tpep_pickup_datetime'],format='%Y-%m-%d %H:%M:%S')\n",
    "test['tpep_dropoff_datetime']=pd.to_datetime(test['tpep_dropoff_datetime'],format='%Y-%m-%d %H:%M:%S')\n",
    "test['pickup_hour']=test['tpep_pickup_datetime'].apply(lambda x:x.hour)\n",
    "test['pickup_day_of_week']=test['tpep_pickup_datetime'].apply(lambda x:calendar.day_name[x.weekday()])\n",
    "test['time'] = (test.tpep_dropoff_datetime - test.tpep_pickup_datetime) / pd.Timedelta(minutes=1)\n",
    "test =  test.drop(['tpep_pickup_datetime','tpep_dropoff_datetime','VendorID','payment_type'],axis = 1)\n",
    "test.replace({'store_and_fwd_flag':{\"N\":0,\"Y\":1}}, inplace=True )\n",
    "test.replace({'pickup_day_of_week':{\"Sunday\":0,\"Monday\":1,\"Tuesday\":2,\"Wednesday\":3,\"Thursday\":4,\"Friday\":5,\"Saturday\":6}}, inplace=True )\n",
    "test['day_of_week_sin'] = np.sin(test['pickup_day_of_week'] * (2 * np.pi / 7))\n",
    "test['day_of_week_cos'] = np.cos(test['pickup_day_of_week'] * (2 * np.pi / 7))\n",
    "test = test.drop(['pickup_day_of_week'],axis = 1)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "550daff2",
   "metadata": {},
   "source": [
    "## Splitting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d96a76f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = training.drop('fare_amount',axis = 1)\n",
    "Y = training['fare_amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d227aa61",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, Y_train, Y_valid = train_test_split(X,Y,test_size = 0.2, random_state = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8673ef",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "59b928be",
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg_model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f96f44a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cc769d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_prediction = lin_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "653743b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.5955034286791389\n",
      "Mean Squared Error: 38.25868497600965\n"
     ]
    }
   ],
   "source": [
    "score_LR_train = metrics.r2_score(training_data_prediction,Y_train)\n",
    "print(f\"R2 Score: {score_LR_train}\")\n",
    "error_LR_train = metrics.mean_squared_error(training_data_prediction,Y_train)\n",
    "print(f\"Mean Squared Error: {error_LR_train}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "083f912d",
   "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": [
    "plt.scatter(Y_train,training_data_prediction)\n",
    "plt.xlabel(\"Actual Fare\")\n",
    "plt.ylabel(\"Predicted Fare\")\n",
    "plt.title(\"Actual Fare vs Predicted Fare\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8a1f4ce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.7020139376829979\n",
      "Mean Squared Error: 25.15227582066491\n"
     ]
    },
    {
     "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": [
    "test_data_prediction = lin_reg_model.predict(X_valid)\n",
    "score_LR_valid = metrics.r2_score(test_data_prediction,Y_valid)\n",
    "print(f\"R2 Score: {score_LR_valid}\")\n",
    "error_LR_valid = metrics.mean_squared_error(test_data_prediction,Y_valid)\n",
    "print(f\"Mean Squared Error: {error_LR_valid}\")\n",
    "plt.scatter(Y_valid,test_data_prediction)\n",
    "plt.xlabel(\"Actual Prices\")\n",
    "plt.ylabel(\"Predicted Prices\")\n",
    "plt.title(\"Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ef100fb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:03:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:573: \n",
      "Parameters: { \"n_estimators\", \"silent\" } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\ttrain-rmse:15.60988\tvalid-rmse:14.61519\n",
      "[50]\ttrain-rmse:2.84633\tvalid-rmse:3.09819\n",
      "[100]\ttrain-rmse:1.49920\tvalid-rmse:2.78685\n",
      "[150]\ttrain-rmse:1.14491\tvalid-rmse:2.80241\n",
      "[200]\ttrain-rmse:0.97507\tvalid-rmse:2.81724\n",
      "[213]\ttrain-rmse:0.93905\tvalid-rmse:2.82682\n"
     ]
    }
   ],
   "source": [
    "dtrain = xgb.DMatrix(X_train, label=Y_train)\n",
    "dvalid = xgb.DMatrix(X_valid, label=Y_valid)\n",
    "dtest = xgb.DMatrix(test)\n",
    "watchlist = [(dtrain, 'train'), (dvalid, 'valid')]\n",
    "\n",
    "xgb_params = {\n",
    "    'min_child_weight': 1, \n",
    "    'learning_rate': 0.05, \n",
    "    'colsample_bytree': 0.7, \n",
    "    'max_depth': 10,\n",
    "    'subsample': 0.7,\n",
    "    'n_estimators': 5000,\n",
    "    'n_jobs': -1, \n",
    "    'booster' : 'gbtree', \n",
    "    'silent': 1,\n",
    "    'eval_metric': 'rmse'}\n",
    "\n",
    "model = xgb.train(xgb_params, dtrain, 1000, watchlist, early_stopping_rounds=100, maximize=False, verbose_eval=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e9fa098c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.993219784323409\n",
      "Mean Squared Error: 0.8784559437744833\n"
     ]
    }
   ],
   "source": [
    "pred_train = model.predict(dtrain)\n",
    "score_XGB_train = metrics.r2_score(pred_train,Y_train)\n",
    "print(f\"R2 Score: {score_XGB_train}\")\n",
    "error_XGB_train = metrics.mean_squared_error(pred_train,Y_train)\n",
    "print(f\"Mean Squared Error: {error_XGB_train}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "689451f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.9235718515612115\n",
      "Mean Squared Error: 7.988051728323083\n"
     ]
    },
    {
     "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": [
    "pred_valid = model.predict(dvalid)\n",
    "score_XGB_valid = metrics.r2_score(pred_valid,Y_valid)\n",
    "print(f\"R2 Score: {score_XGB_valid}\")\n",
    "error_XGB_valid = metrics.mean_squared_error(pred_valid,Y_valid)\n",
    "print(f\"Mean Squared Error: {error_XGB_valid}\")\n",
    "plt.scatter(Y_valid,pred_valid)\n",
    "plt.xlabel(\"Actual Fare\")\n",
    "plt.ylabel(\"Predicted Fare\")\n",
    "plt.title(\"Actual Fare vs Predicted Fare\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8c0abe28",
   "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>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>time</th>\n",
       "      <th>day_of_week_sin</th>\n",
       "      <th>day_of_week_cos</th>\n",
       "      <th>fare_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48</td>\n",
       "      <td>90</td>\n",
       "      <td>2.5</td>\n",
       "      <td>18</td>\n",
       "      <td>5.683333</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>7.104831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48</td>\n",
       "      <td>246</td>\n",
       "      <td>2.5</td>\n",
       "      <td>23</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>6.977403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237</td>\n",
       "      <td>234</td>\n",
       "      <td>2.5</td>\n",
       "      <td>12</td>\n",
       "      <td>7.166667</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>8.064060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>2.5</td>\n",
       "      <td>9</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>3.817357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186</td>\n",
       "      <td>238</td>\n",
       "      <td>2.5</td>\n",
       "      <td>18</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>15.286615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  trip_distance  RatecodeID  store_and_fwd_flag  \\\n",
       "0              1.0           1.66         1.0                 0.0   \n",
       "1              3.0           1.50         1.0                 0.0   \n",
       "2              1.0           1.90         1.0                 0.0   \n",
       "3              1.0           0.27         1.0                 0.0   \n",
       "4              2.0           4.06         1.0                 0.0   \n",
       "\n",
       "   PULocationID  DOLocationID  congestion_surcharge  pickup_hour       time  \\\n",
       "0            48            90                   2.5           18   5.683333   \n",
       "1            48           246                   2.5           23   7.000000   \n",
       "2           237           234                   2.5           12   7.166667   \n",
       "3           170           170                   2.5            9   2.550000   \n",
       "4           186           238                   2.5           18  16.800000   \n",
       "\n",
       "   day_of_week_sin  day_of_week_cos  fare_amount  \n",
       "0         0.974928        -0.222521     7.104831  \n",
       "1        -0.781831         0.623490     6.977403  \n",
       "2         0.974928        -0.222521     8.064060  \n",
       "3        -0.781831         0.623490     3.817357  \n",
       "4         0.974928        -0.222521    15.286615  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_test = model.predict(dtest)\n",
    "test['fare_amount'] = pred_test\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f5ffa8",
   "metadata": {},
   "source": [
    "## Final Output and creating .csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "53bc5f51",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We get 70.20% accuracy on Validation set for Linear Regression\n",
      "We get 92.36% accuracy on Validation set for XGBoost\n"
     ]
    }
   ],
   "source": [
    "print(f\"We get {score_LR_valid * 100:0.2f}% accuracy on Validation set for Linear Regression\")\n",
    "print(f\"We get {score_XGB_valid * 100:0.2f}% accuracy on Validation set for XGBoost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "84cd69df",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('test_data_iitm.csv')\n",
    "data['fare_amount'] = pred_test\n",
    "data.to_csv('AE19B019_PIYUSH_BHUJBAL_predictions.csv', index=False)"
   ]
  }
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