PYNB_WithoutFeature_DataMining.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking Models without additional Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_train = pd.read_csv('C:/Users/Raj/Downloads/Data Mining/train.csv')\n",
    "d_test = pd.read_csv('C:/Users/Raj/Downloads/Data Mining/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'vendor_id', 'pickup_datetime', 'passenger_count',\n",
       "       'pickup_longitude', 'pickup_latitude', 'dropoff_longitude',\n",
       "       'dropoff_latitude', 'store_and_fwd_flag'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>N</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>id3858529</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>N</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>id3504673</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>N</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>id2181028</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>N</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>id0801584</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-30 22:01:40</td>\n",
       "      <td>2016-01-30 22:09:03</td>\n",
       "      <td>6</td>\n",
       "      <td>-73.982857</td>\n",
       "      <td>40.742195</td>\n",
       "      <td>-73.992081</td>\n",
       "      <td>40.749184</td>\n",
       "      <td>N</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>id1813257</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-17 22:34:59</td>\n",
       "      <td>2016-06-17 22:40:40</td>\n",
       "      <td>4</td>\n",
       "      <td>-73.969017</td>\n",
       "      <td>40.757839</td>\n",
       "      <td>-73.957405</td>\n",
       "      <td>40.765896</td>\n",
       "      <td>N</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>id1324603</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-21 07:54:58</td>\n",
       "      <td>2016-05-21 08:20:49</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.969276</td>\n",
       "      <td>40.797779</td>\n",
       "      <td>-73.922470</td>\n",
       "      <td>40.760559</td>\n",
       "      <td>N</td>\n",
       "      <td>1551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>id1301050</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-05-27 23:12:23</td>\n",
       "      <td>2016-05-27 23:16:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.738400</td>\n",
       "      <td>-73.985786</td>\n",
       "      <td>40.732815</td>\n",
       "      <td>N</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>id0012891</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-10 21:45:01</td>\n",
       "      <td>2016-03-10 22:05:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.981049</td>\n",
       "      <td>40.744339</td>\n",
       "      <td>-73.973000</td>\n",
       "      <td>40.789989</td>\n",
       "      <td>N</td>\n",
       "      <td>1225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
       "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
       "1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
       "2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
       "3  id3504673          2  2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
       "4  id2181028          2  2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
       "5  id0801584          2  2016-01-30 22:01:40  2016-01-30 22:09:03   \n",
       "6  id1813257          1  2016-06-17 22:34:59  2016-06-17 22:40:40   \n",
       "7  id1324603          2  2016-05-21 07:54:58  2016-05-21 08:20:49   \n",
       "8  id1301050          1  2016-05-27 23:12:23  2016-05-27 23:16:38   \n",
       "9  id0012891          2  2016-03-10 21:45:01  2016-03-10 22:05:26   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.980415        40.738564         -73.999481   \n",
       "2                1        -73.979027        40.763939         -74.005333   \n",
       "3                1        -74.010040        40.719971         -74.012268   \n",
       "4                1        -73.973053        40.793209         -73.972923   \n",
       "5                6        -73.982857        40.742195         -73.992081   \n",
       "6                4        -73.969017        40.757839         -73.957405   \n",
       "7                1        -73.969276        40.797779         -73.922470   \n",
       "8                1        -73.999481        40.738400         -73.985786   \n",
       "9                1        -73.981049        40.744339         -73.973000   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
       "0         40.765602                  N            455  \n",
       "1         40.731152                  N            663  \n",
       "2         40.710087                  N           2124  \n",
       "3         40.706718                  N            429  \n",
       "4         40.782520                  N            435  \n",
       "5         40.749184                  N            443  \n",
       "6         40.765896                  N            341  \n",
       "7         40.760559                  N           1551  \n",
       "8         40.732815                  N            255  \n",
       "9         40.789989                  N           1225  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.534950e+00</td>\n",
       "      <td>1.664530e+00</td>\n",
       "      <td>-7.397349e+01</td>\n",
       "      <td>4.075092e+01</td>\n",
       "      <td>-7.397342e+01</td>\n",
       "      <td>4.075180e+01</td>\n",
       "      <td>9.594923e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>4.987772e-01</td>\n",
       "      <td>1.314242e+00</td>\n",
       "      <td>7.090186e-02</td>\n",
       "      <td>3.288119e-02</td>\n",
       "      <td>7.064327e-02</td>\n",
       "      <td>3.589056e-02</td>\n",
       "      <td>5.237432e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.219333e+02</td>\n",
       "      <td>3.435970e+01</td>\n",
       "      <td>-1.219333e+02</td>\n",
       "      <td>3.218114e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-7.399187e+01</td>\n",
       "      <td>4.073735e+01</td>\n",
       "      <td>-7.399133e+01</td>\n",
       "      <td>4.073588e+01</td>\n",
       "      <td>3.970000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-7.398174e+01</td>\n",
       "      <td>4.075410e+01</td>\n",
       "      <td>-7.397975e+01</td>\n",
       "      <td>4.075452e+01</td>\n",
       "      <td>6.620000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>-7.396733e+01</td>\n",
       "      <td>4.076836e+01</td>\n",
       "      <td>-7.396301e+01</td>\n",
       "      <td>4.076981e+01</td>\n",
       "      <td>1.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>-6.133553e+01</td>\n",
       "      <td>5.188108e+01</td>\n",
       "      <td>-6.133553e+01</td>\n",
       "      <td>4.392103e+01</td>\n",
       "      <td>3.526282e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          vendor_id  passenger_count  pickup_longitude  pickup_latitude  \\\n",
       "count  1.458644e+06     1.458644e+06      1.458644e+06     1.458644e+06   \n",
       "mean   1.534950e+00     1.664530e+00     -7.397349e+01     4.075092e+01   \n",
       "std    4.987772e-01     1.314242e+00      7.090186e-02     3.288119e-02   \n",
       "min    1.000000e+00     0.000000e+00     -1.219333e+02     3.435970e+01   \n",
       "25%    1.000000e+00     1.000000e+00     -7.399187e+01     4.073735e+01   \n",
       "50%    2.000000e+00     1.000000e+00     -7.398174e+01     4.075410e+01   \n",
       "75%    2.000000e+00     2.000000e+00     -7.396733e+01     4.076836e+01   \n",
       "max    2.000000e+00     9.000000e+00     -6.133553e+01     5.188108e+01   \n",
       "\n",
       "       dropoff_longitude  dropoff_latitude  trip_duration  \n",
       "count       1.458644e+06      1.458644e+06   1.458644e+06  \n",
       "mean       -7.397342e+01      4.075180e+01   9.594923e+02  \n",
       "std         7.064327e-02      3.589056e-02   5.237432e+03  \n",
       "min        -1.219333e+02      3.218114e+01   1.000000e+00  \n",
       "25%        -7.399133e+01      4.073588e+01   3.970000e+02  \n",
       "50%        -7.397975e+01      4.075452e+01   6.620000e+02  \n",
       "75%        -7.396301e+01      4.076981e+01   1.075000e+03  \n",
       "max        -6.133553e+01      4.392103e+01   3.526282e+06  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                    0\n",
      "vendor_id             0\n",
      "pickup_datetime       0\n",
      "dropoff_datetime      0\n",
      "passenger_count       0\n",
      "pickup_longitude      0\n",
      "pickup_latitude       0\n",
      "dropoff_longitude     0\n",
      "dropoff_latitude      0\n",
      "store_and_fwd_flag    0\n",
      "trip_duration         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(d_train.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1458644, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     object\n",
       "vendor_id               int64\n",
       "pickup_datetime        object\n",
       "dropoff_datetime       object\n",
       "passenger_count         int64\n",
       "pickup_longitude      float64\n",
       "pickup_latitude       float64\n",
       "dropoff_longitude     float64\n",
       "dropoff_latitude      float64\n",
       "store_and_fwd_flag     object\n",
       "trip_duration           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24a000196d8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10,10))\n",
    "plt.title(\"Trip_duration outliers\")\n",
    "d_train.boxplot(column='trip_duration')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>id3004672</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:58</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.988129</td>\n",
       "      <td>40.732029</td>\n",
       "      <td>-73.990173</td>\n",
       "      <td>40.756680</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>id3505355</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:53</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.964203</td>\n",
       "      <td>40.679993</td>\n",
       "      <td>-73.959808</td>\n",
       "      <td>40.655403</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>id1217141</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:47</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.997437</td>\n",
       "      <td>40.737583</td>\n",
       "      <td>-73.986160</td>\n",
       "      <td>40.729523</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>id2150126</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-30 23:59:41</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.956070</td>\n",
       "      <td>40.771900</td>\n",
       "      <td>-73.986427</td>\n",
       "      <td>40.730469</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>id1598245</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:33</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.970215</td>\n",
       "      <td>40.761475</td>\n",
       "      <td>-73.961510</td>\n",
       "      <td>40.755890</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id      pickup_datetime  passenger_count  \\\n",
       "0  id3004672          1  2016-06-30 23:59:58                1   \n",
       "1  id3505355          1  2016-06-30 23:59:53                1   \n",
       "2  id1217141          1  2016-06-30 23:59:47                1   \n",
       "3  id2150126          2  2016-06-30 23:59:41                1   \n",
       "4  id1598245          1  2016-06-30 23:59:33                1   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.988129        40.732029         -73.990173         40.756680   \n",
       "1        -73.964203        40.679993         -73.959808         40.655403   \n",
       "2        -73.997437        40.737583         -73.986160         40.729523   \n",
       "3        -73.956070        40.771900         -73.986427         40.730469   \n",
       "4        -73.970215        40.761475         -73.961510         40.755890   \n",
       "\n",
       "  store_and_fwd_flag  \n",
       "0                  N  \n",
       "1                  N  \n",
       "2                  N  \n",
       "3                  N  \n",
       "4                  N  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "d_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(625134, 9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_train.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "d_train.loc[d_train['trip_duration'] < 5000, 'trip_duration'].hist();\n",
    "\n",
    "plt.title('trip_duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "np.log1p(d_train['trip_duration']).hist();\n",
    "plt.title('log_trip_duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_train['passenger_count'].hist(bins=100, log=True, figsize=(10,5));\n",
    "plt.title('passenger_count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_train['pickup_datetime'] = pd.to_datetime(d_train['pickup_datetime'])\n",
    "d_train['dropoff_datetime'] = pd.to_datetime(d_train['dropoff_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_train1 = d_train[d_train['passenger_count']>= 1] \n",
    "\n",
    "d_train1 = d_train[d_train['trip_duration']>= 200 ]\n",
    "d_train1 = d_train[d_train['trip_duration']<= 5000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=d_train1[['vendor_id','passenger_count', 'pickup_longitude', 'pickup_latitude','dropoff_longitude', 'dropoff_latitude']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=d_train1['trip_duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>N</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>id3858529</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>N</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>id3504673</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>N</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>id2181028</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>N</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>id0801584</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-30 22:01:40</td>\n",
       "      <td>2016-01-30 22:09:03</td>\n",
       "      <td>6</td>\n",
       "      <td>-73.982857</td>\n",
       "      <td>40.742195</td>\n",
       "      <td>-73.992081</td>\n",
       "      <td>40.749184</td>\n",
       "      <td>N</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>id1813257</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-17 22:34:59</td>\n",
       "      <td>2016-06-17 22:40:40</td>\n",
       "      <td>4</td>\n",
       "      <td>-73.969017</td>\n",
       "      <td>40.757839</td>\n",
       "      <td>-73.957405</td>\n",
       "      <td>40.765896</td>\n",
       "      <td>N</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>id1324603</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-05-21 07:54:58</td>\n",
       "      <td>2016-05-21 08:20:49</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.969276</td>\n",
       "      <td>40.797779</td>\n",
       "      <td>-73.922470</td>\n",
       "      <td>40.760559</td>\n",
       "      <td>N</td>\n",
       "      <td>1551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>id1301050</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-05-27 23:12:23</td>\n",
       "      <td>2016-05-27 23:16:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.738400</td>\n",
       "      <td>-73.985786</td>\n",
       "      <td>40.732815</td>\n",
       "      <td>N</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>id0012891</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-10 21:45:01</td>\n",
       "      <td>2016-03-10 22:05:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.981049</td>\n",
       "      <td>40.744339</td>\n",
       "      <td>-73.973000</td>\n",
       "      <td>40.789989</td>\n",
       "      <td>N</td>\n",
       "      <td>1225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30   \n",
       "1  id2377394          1 2016-06-12 00:43:35 2016-06-12 00:54:38   \n",
       "2  id3858529          2 2016-01-19 11:35:24 2016-01-19 12:10:48   \n",
       "3  id3504673          2 2016-04-06 19:32:31 2016-04-06 19:39:40   \n",
       "4  id2181028          2 2016-03-26 13:30:55 2016-03-26 13:38:10   \n",
       "5  id0801584          2 2016-01-30 22:01:40 2016-01-30 22:09:03   \n",
       "6  id1813257          1 2016-06-17 22:34:59 2016-06-17 22:40:40   \n",
       "7  id1324603          2 2016-05-21 07:54:58 2016-05-21 08:20:49   \n",
       "8  id1301050          1 2016-05-27 23:12:23 2016-05-27 23:16:38   \n",
       "9  id0012891          2 2016-03-10 21:45:01 2016-03-10 22:05:26   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.980415        40.738564         -73.999481   \n",
       "2                1        -73.979027        40.763939         -74.005333   \n",
       "3                1        -74.010040        40.719971         -74.012268   \n",
       "4                1        -73.973053        40.793209         -73.972923   \n",
       "5                6        -73.982857        40.742195         -73.992081   \n",
       "6                4        -73.969017        40.757839         -73.957405   \n",
       "7                1        -73.969276        40.797779         -73.922470   \n",
       "8                1        -73.999481        40.738400         -73.985786   \n",
       "9                1        -73.981049        40.744339         -73.973000   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
       "0         40.765602                  N            455  \n",
       "1         40.731152                  N            663  \n",
       "2         40.710087                  N           2124  \n",
       "3         40.706718                  N            429  \n",
       "4         40.782520                  N            435  \n",
       "5         40.749184                  N            443  \n",
       "6         40.765896                  N            341  \n",
       "7         40.760559                  N           1551  \n",
       "8         40.732815                  N            255  \n",
       "9         40.789989                  N           1225  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>vendor_id</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "      <td>1.458644e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.534950e+00</td>\n",
       "      <td>1.664530e+00</td>\n",
       "      <td>-7.397349e+01</td>\n",
       "      <td>4.075092e+01</td>\n",
       "      <td>-7.397342e+01</td>\n",
       "      <td>4.075180e+01</td>\n",
       "      <td>9.594923e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>4.987772e-01</td>\n",
       "      <td>1.314242e+00</td>\n",
       "      <td>7.090186e-02</td>\n",
       "      <td>3.288119e-02</td>\n",
       "      <td>7.064327e-02</td>\n",
       "      <td>3.589056e-02</td>\n",
       "      <td>5.237432e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.219333e+02</td>\n",
       "      <td>3.435970e+01</td>\n",
       "      <td>-1.219333e+02</td>\n",
       "      <td>3.218114e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-7.399187e+01</td>\n",
       "      <td>4.073735e+01</td>\n",
       "      <td>-7.399133e+01</td>\n",
       "      <td>4.073588e+01</td>\n",
       "      <td>3.970000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-7.398174e+01</td>\n",
       "      <td>4.075410e+01</td>\n",
       "      <td>-7.397975e+01</td>\n",
       "      <td>4.075452e+01</td>\n",
       "      <td>6.620000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>-7.396733e+01</td>\n",
       "      <td>4.076836e+01</td>\n",
       "      <td>-7.396301e+01</td>\n",
       "      <td>4.076981e+01</td>\n",
       "      <td>1.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>-6.133553e+01</td>\n",
       "      <td>5.188108e+01</td>\n",
       "      <td>-6.133553e+01</td>\n",
       "      <td>4.392103e+01</td>\n",
       "      <td>3.526282e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          vendor_id  passenger_count  pickup_longitude  pickup_latitude  \\\n",
       "count  1.458644e+06     1.458644e+06      1.458644e+06     1.458644e+06   \n",
       "mean   1.534950e+00     1.664530e+00     -7.397349e+01     4.075092e+01   \n",
       "std    4.987772e-01     1.314242e+00      7.090186e-02     3.288119e-02   \n",
       "min    1.000000e+00     0.000000e+00     -1.219333e+02     3.435970e+01   \n",
       "25%    1.000000e+00     1.000000e+00     -7.399187e+01     4.073735e+01   \n",
       "50%    2.000000e+00     1.000000e+00     -7.398174e+01     4.075410e+01   \n",
       "75%    2.000000e+00     2.000000e+00     -7.396733e+01     4.076836e+01   \n",
       "max    2.000000e+00     9.000000e+00     -6.133553e+01     5.188108e+01   \n",
       "\n",
       "       dropoff_longitude  dropoff_latitude  trip_duration  \n",
       "count       1.458644e+06      1.458644e+06   1.458644e+06  \n",
       "mean       -7.397342e+01      4.075180e+01   9.594923e+02  \n",
       "std         7.064327e-02      3.589056e-02   5.237432e+03  \n",
       "min        -1.219333e+02      3.218114e+01   1.000000e+00  \n",
       "25%        -7.399133e+01      4.073588e+01   3.970000e+02  \n",
       "50%        -7.397975e+01      4.075452e+01   6.620000e+02  \n",
       "75%        -7.396301e+01      4.076981e+01   1.075000e+03  \n",
       "max        -6.133553e+01      4.392103e+01   3.526282e+06  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1163896, 6), (1163896,), (290975, 6), (290975,))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=42)\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking Model performance without additional features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the performance of all the models is lower if compared to models trained with additional features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
      "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
      "           min_impurity_split=None, min_samples_leaf=1,\n",
      "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "           presort=False, random_state=None, splitter='best')\n",
      "\n",
      "Decision Tree Regression Performance Metrics\n",
      "R^2= 0.40538736591282687\n"
     ]
    }
   ],
   "source": [
    "#  Import model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Instantiate model object\n",
    "dtree = DecisionTreeRegressor()\n",
    "\n",
    "# Fit to training data\n",
    "dtree.fit(X_train,y_train)\n",
    "print(dtree)\n",
    "\n",
    "# Predict\n",
    "y_pred_dtree = dtree.predict(X_test)\n",
    "\n",
    "# Score It\n",
    "from sklearn import metrics\n",
    "print('\\nDecision Tree Regression Performance Metrics')\n",
    "print('R^2=',metrics.explained_variance_score(y_test,y_pred_dtree))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
      "         normalize=False)\n",
      "\n",
      "Linear Regression Performance Metrics\n",
      "R^2= 0.04871890156588854\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#  Instantiate model object\n",
    "lreg = LinearRegression()\n",
    "\n",
    "# Fit to training data\n",
    "lreg.fit(X_train,y_train)\n",
    "print(lreg)\n",
    "\n",
    "# Predict\n",
    "y_pred_lreg = lreg.predict(X_test)\n",
    "\n",
    "# Score It\n",
    "from sklearn import metrics\n",
    "print('\\nLinear Regression Performance Metrics')\n",
    "print('R^2=',metrics.explained_variance_score(y_test,y_pred_lreg))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "           max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=20, n_jobs=-1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n",
      "\n",
      "Random Forest Regression Performance Metrics\n",
      "R^2 = 0.6824756793444842\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor \n",
    "\n",
    "# Instantiate model object\n",
    "rforest = RandomForestRegressor(n_estimators = 20, n_jobs = -1)\n",
    "\n",
    "# Fit to training data\n",
    "rforest = rforest.fit(X_train,y_train)\n",
    "print(rforest)\n",
    "\n",
    "# Predict\n",
    "y_pred_rforest = rforest.predict(X_test)\n",
    "\n",
    "# Score It\n",
    "from sklearn import metrics\n",
    "print('\\nRandom Forest Regression Performance Metrics')\n",
    "print('R^2 =',metrics.explained_variance_score(y_test,y_pred_rforest))\n",
    "\n"
   ]
  }
 ],
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