Assignment2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# used in Data processing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "\n",
    "# Visualization libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from math import sin, cos, sqrt, atan2, radians\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    " # Machine Learning\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "\n",
    "# loads data frame\n",
    "df = pd.read_csv(\"train.csv\",nrows = 50_00_000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>4.999964e+06</td>\n",
       "      <td>4.999964e+06</td>\n",
       "      <td>5.000000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.134080e+01</td>\n",
       "      <td>-7.250678e+01</td>\n",
       "      <td>3.991974e+01</td>\n",
       "      <td>-7.250652e+01</td>\n",
       "      <td>3.991725e+01</td>\n",
       "      <td>1.684695e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.820175e+00</td>\n",
       "      <td>1.280970e+01</td>\n",
       "      <td>8.963509e+00</td>\n",
       "      <td>1.284777e+01</td>\n",
       "      <td>9.486767e+00</td>\n",
       "      <td>1.331854e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000e+02</td>\n",
       "      <td>-3.426609e+03</td>\n",
       "      <td>-3.488080e+03</td>\n",
       "      <td>-3.412653e+03</td>\n",
       "      <td>-3.488080e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>-7.399206e+01</td>\n",
       "      <td>4.073491e+01</td>\n",
       "      <td>-7.399139e+01</td>\n",
       "      <td>4.073404e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000e+00</td>\n",
       "      <td>-7.398181e+01</td>\n",
       "      <td>4.075263e+01</td>\n",
       "      <td>-7.398016e+01</td>\n",
       "      <td>4.075315e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.250000e+01</td>\n",
       "      <td>-7.396711e+01</td>\n",
       "      <td>4.076712e+01</td>\n",
       "      <td>-7.396367e+01</td>\n",
       "      <td>4.076811e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.273310e+03</td>\n",
       "      <td>3.439426e+03</td>\n",
       "      <td>3.310364e+03</td>\n",
       "      <td>3.457622e+03</td>\n",
       "      <td>3.345917e+03</td>\n",
       "      <td>2.080000e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count  5.000000e+06      5.000000e+06     5.000000e+06       4.999964e+06   \n",
       "mean   1.134080e+01     -7.250678e+01     3.991974e+01      -7.250652e+01   \n",
       "std    9.820175e+00      1.280970e+01     8.963509e+00       1.284777e+01   \n",
       "min   -1.000000e+02     -3.426609e+03    -3.488080e+03      -3.412653e+03   \n",
       "25%    6.000000e+00     -7.399206e+01     4.073491e+01      -7.399139e+01   \n",
       "50%    8.500000e+00     -7.398181e+01     4.075263e+01      -7.398016e+01   \n",
       "75%    1.250000e+01     -7.396711e+01     4.076712e+01      -7.396367e+01   \n",
       "max    1.273310e+03      3.439426e+03     3.310364e+03       3.457622e+03   \n",
       "\n",
       "       dropoff_latitude  passenger_count  \n",
       "count      4.999964e+06     5.000000e+06  \n",
       "mean       3.991725e+01     1.684695e+00  \n",
       "std        9.486767e+00     1.331854e+00  \n",
       "min       -3.488080e+03     0.000000e+00  \n",
       "25%        4.073404e+01     1.000000e+00  \n",
       "50%        4.075315e+01     1.000000e+00  \n",
       "75%        4.076811e+01     2.000000e+00  \n",
       "max        3.345917e+03     2.080000e+02  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check statistics of the given parameters\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000000, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  \n",
       "0                1  \n",
       "1                1  \n",
       "2                2  \n",
       "3                1  \n",
       "4                1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the first 5 rows of our training data\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key                   0\n",
       "fare_amount           0\n",
       "pickup_datetime       0\n",
       "pickup_longitude      0\n",
       "pickup_latitude       0\n",
       "dropoff_longitude    36\n",
       "dropoff_latitude     36\n",
       "passenger_count       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking how many rows contain atleast a blank entry in it.\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data set contains some null entries. Removing them from the dataset\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now remove some of the outliers\n",
    "# Plot the histogram of passenger_count\n",
    "#plt.hist(df['passenger_count'], bins=np.arange(0, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Passengers count should range from 1 to 7, rest all are outliers let's remove them\n",
    "df = df[(df.passenger_count > 0) & (df.passenger_count < 8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4982350, 8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a141b46a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets consider the histogram of fare_amount\n",
    "df[df.fare_amount<500].fare_amount.hist(bins=100, figsize=(14,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4982350, 8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can see we have outliers for fare_amount > 60. Remove them from data set\n",
    "df = df[(df.fare_amount > 0) & (df.fare_amount <= 60)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4968604, 8)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we now that new york city had latitude around 40.71 and longitude around -74.00 (reference : http://latitudelongitude.org/us/new-york-city/).\n",
    "# So taking a rough estimate remove outliers out of range\n",
    "df = df[(df.pickup_longitude > -75) & (df.pickup_longitude < -70)]\n",
    "df = df[(df.dropoff_longitude > -75) & (df.dropoff_longitude < -70)]\n",
    "df = df[(df.pickup_latitude > 38) & (df.pickup_latitude < 43)]\n",
    "df = df[(df.dropoff_latitude > 38) & (df.dropoff_latitude < 43)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4865432, 8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16127e48>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting scatter plot of longitude and latitude\n",
    "df.plot(kind='scatter',x='pickup_longitude',y='pickup_latitude',c='blue',s=0.2,alpha=.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove more outliers of latitude and longitude\n",
    "df = df[(df.pickup_longitude > -74.30) & (df.pickup_longitude < -73.00)]\n",
    "df = df[(df.dropoff_longitude > -74.30) & (df.dropoff_longitude < -73.00)]\n",
    "df = df[(df.pickup_latitude > 40.40) & (df.pickup_latitude < 41.71)]\n",
    "df = df[(df.dropoff_latitude > 40.40) & (df.dropoff_latitude < 41.71)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4863823, 8)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a14da6cc0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='pickup_longitude',y='pickup_latitude',c='blue',s=0.2,alpha=.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove more outliers of latitude and longitude\n",
    "df = df[(df.pickup_longitude > -74.05) & (df.pickup_longitude < -73.75)]\n",
    "df = df[(df.dropoff_longitude > -74.05) & (df.dropoff_longitude < -73.75)]\n",
    "df = df[(df.pickup_latitude > 40.55) & (df.pickup_latitude < 40.85)]\n",
    "df = df[(df.dropoff_latitude > 40.55) & (df.dropoff_latitude < 40.85)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112b78e10>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='pickup_longitude',y='pickup_latitude',c='blue',s=0.2,alpha=.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets calculate distance travelled in each trip using HaverSine distance\n",
    "#Reference : https://community.esri.com/groups/coordinate-reference-systems/blog/2017/10/05/haversine-formula\n",
    "def cal_haversinedistance(pickup_lat : float, pickup_long : float, dropoff_lat : float, dropoff_long : float):\n",
    "    R = 6373.0\n",
    "    long_diff = dropoff_long - pickup_long\n",
    "    lat_diff = dropoff_lat - pickup_lat\n",
    "    \n",
    "    a = sin(lat_diff / 2)**2 + cos(pickup_lat) * cos(dropoff_lat) * sin(long_diff / 2)**2\n",
    "    c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
    "\n",
    "    distance = R * c\n",
    "    return distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist_calc(df):\n",
    "    R = 6373.0\n",
    "    \n",
    "    for i,row in df.iterrows():\n",
    "\n",
    "        pickup_lat = radians(row['pickup_latitude'])\n",
    "        pickup_long = radians(row['pickup_longitude'])\n",
    "        dropoff_lat = radians(row['dropoff_latitude'])\n",
    "        dropoff_long = radians(row['dropoff_longitude'])\n",
    "\n",
    "        distance = cal_haversinedistance(pickup_lat, pickup_long, dropoff_lat, dropoff_long)\n",
    "        df.at[i,'distance'] = distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_calc(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>1.031088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>8.452786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>1.389961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>2.800149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>1.999784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844311        40.721319         -73.841610         40.712278   \n",
       "1        -74.016048        40.711303         -73.979268         40.782004   \n",
       "2        -73.982738        40.761270         -73.991242         40.750562   \n",
       "3        -73.987130        40.733143         -73.991567         40.758092   \n",
       "4        -73.968095        40.768008         -73.956655         40.783762   \n",
       "\n",
       "   passenger_count  distance  \n",
       "0                1  1.031088  \n",
       "1                1  8.452786  \n",
       "2                2  1.389961  \n",
       "3                1  2.800149  \n",
       "4                1  1.999784  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.827651e+06</td>\n",
       "      <td>4.827651e+06</td>\n",
       "      <td>4.827651e+06</td>\n",
       "      <td>4.827651e+06</td>\n",
       "      <td>4.827651e+06</td>\n",
       "      <td>4.827651e+06</td>\n",
       "      <td>4.827651e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.103789e+01</td>\n",
       "      <td>-7.397566e+01</td>\n",
       "      <td>4.075081e+01</td>\n",
       "      <td>-7.397473e+01</td>\n",
       "      <td>4.075081e+01</td>\n",
       "      <td>1.690423e+00</td>\n",
       "      <td>3.225354e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.755385e+00</td>\n",
       "      <td>3.367307e-02</td>\n",
       "      <td>2.649189e-02</td>\n",
       "      <td>3.176368e-02</td>\n",
       "      <td>2.934162e-02</td>\n",
       "      <td>1.306251e+00</td>\n",
       "      <td>3.399770e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>-7.404989e+01</td>\n",
       "      <td>4.055018e+01</td>\n",
       "      <td>-7.404997e+01</td>\n",
       "      <td>4.055046e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>-7.399228e+01</td>\n",
       "      <td>4.073657e+01</td>\n",
       "      <td>-7.399157e+01</td>\n",
       "      <td>4.073567e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.250420e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000e+00</td>\n",
       "      <td>-7.398212e+01</td>\n",
       "      <td>4.075330e+01</td>\n",
       "      <td>-7.398069e+01</td>\n",
       "      <td>4.075375e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.138349e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.250000e+01</td>\n",
       "      <td>-7.396847e+01</td>\n",
       "      <td>4.076741e+01</td>\n",
       "      <td>-7.396584e+01</td>\n",
       "      <td>4.076819e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.847439e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>-7.375001e+01</td>\n",
       "      <td>4.084999e+01</td>\n",
       "      <td>-7.375001e+01</td>\n",
       "      <td>4.085000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.878735e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count  4.827651e+06      4.827651e+06     4.827651e+06       4.827651e+06   \n",
       "mean   1.103789e+01     -7.397566e+01     4.075081e+01      -7.397473e+01   \n",
       "std    8.755385e+00      3.367307e-02     2.649189e-02       3.176368e-02   \n",
       "min    1.000000e-02     -7.404989e+01     4.055018e+01      -7.404997e+01   \n",
       "25%    6.000000e+00     -7.399228e+01     4.073657e+01      -7.399157e+01   \n",
       "50%    8.500000e+00     -7.398212e+01     4.075330e+01      -7.398069e+01   \n",
       "75%    1.250000e+01     -7.396847e+01     4.076741e+01      -7.396584e+01   \n",
       "max    6.000000e+01     -7.375001e+01     4.084999e+01      -7.375001e+01   \n",
       "\n",
       "       dropoff_latitude  passenger_count      distance  \n",
       "count      4.827651e+06     4.827651e+06  4.827651e+06  \n",
       "mean       4.075081e+01     1.690423e+00  3.225354e+00  \n",
       "std        2.934162e-02     1.306251e+00  3.399770e+00  \n",
       "min        4.055046e+01     1.000000e+00  0.000000e+00  \n",
       "25%        4.073567e+01     1.000000e+00  1.250420e+00  \n",
       "50%        4.075375e+01     1.000000e+00  2.138349e+00  \n",
       "75%        4.076819e+01     2.000000e+00  3.847439e+00  \n",
       "max        4.085000e+01     7.000000e+00  2.878735e+01  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[~((df.fare_amount > 40) & (df.distance < 5))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Engineering\n",
    "df['pickup_datetime'] = pd.to_datetime(df['pickup_datetime'])\n",
    "df['hour'] = df['pickup_datetime'].dt.hour\n",
    "df['day'] = df['pickup_datetime'].dt.day\n",
    "df['week'] = df['pickup_datetime'].dt.week\n",
    "df['month'] = df['pickup_datetime'].dt.month\n",
    "df['day_of_year'] = df['pickup_datetime'].dt.dayofyear\n",
    "df['week_of_year'] = df['pickup_datetime'].dt.weekofyear\n",
    "df['year'] = df['pickup_datetime'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There is no outlier in hour, day, week, month, day_of_year, week_of_year as evident from their min and max values\n",
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>week</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fare_amount</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.458470</td>\n",
       "      <td>-0.227462</td>\n",
       "      <td>0.393536</td>\n",
       "      <td>-0.215770</td>\n",
       "      <td>0.017260</td>\n",
       "      <td>0.914287</td>\n",
       "      <td>-0.013955</td>\n",
       "      <td>0.001369</td>\n",
       "      <td>0.026391</td>\n",
       "      <td>0.026601</td>\n",
       "      <td>0.026589</td>\n",
       "      <td>0.026391</td>\n",
       "      <td>0.126142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_longitude</th>\n",
       "      <td>0.458470</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.023924</td>\n",
       "      <td>0.272631</td>\n",
       "      <td>0.053097</td>\n",
       "      <td>0.002084</td>\n",
       "      <td>0.456312</td>\n",
       "      <td>0.019801</td>\n",
       "      <td>-0.000415</td>\n",
       "      <td>0.007803</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0.007511</td>\n",
       "      <td>0.007803</td>\n",
       "      <td>0.011172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_latitude</th>\n",
       "      <td>-0.227462</td>\n",
       "      <td>-0.023924</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.073017</td>\n",
       "      <td>0.425640</td>\n",
       "      <td>-0.008415</td>\n",
       "      <td>-0.254068</td>\n",
       "      <td>0.028354</td>\n",
       "      <td>-0.002663</td>\n",
       "      <td>-0.004228</td>\n",
       "      <td>-0.004898</td>\n",
       "      <td>-0.005081</td>\n",
       "      <td>-0.004228</td>\n",
       "      <td>-0.011886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <td>0.393536</td>\n",
       "      <td>0.272631</td>\n",
       "      <td>0.073017</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.110417</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>0.409930</td>\n",
       "      <td>-0.051818</td>\n",
       "      <td>-0.000299</td>\n",
       "      <td>0.006234</td>\n",
       "      <td>0.005917</td>\n",
       "      <td>0.005854</td>\n",
       "      <td>0.006234</td>\n",
       "      <td>0.008012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <td>-0.215770</td>\n",
       "      <td>0.053097</td>\n",
       "      <td>0.425640</td>\n",
       "      <td>0.110417</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005172</td>\n",
       "      <td>-0.211630</td>\n",
       "      <td>0.022135</td>\n",
       "      <td>-0.002865</td>\n",
       "      <td>-0.004345</td>\n",
       "      <td>-0.004856</td>\n",
       "      <td>-0.005063</td>\n",
       "      <td>-0.004345</td>\n",
       "      <td>-0.007002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>0.017260</td>\n",
       "      <td>0.002084</td>\n",
       "      <td>-0.008415</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>-0.005172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>0.016268</td>\n",
       "      <td>0.004122</td>\n",
       "      <td>0.004404</td>\n",
       "      <td>0.004594</td>\n",
       "      <td>0.004938</td>\n",
       "      <td>0.004404</td>\n",
       "      <td>0.004889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>0.914287</td>\n",
       "      <td>0.456312</td>\n",
       "      <td>-0.254068</td>\n",
       "      <td>0.409930</td>\n",
       "      <td>-0.211630</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027091</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.013376</td>\n",
       "      <td>0.013419</td>\n",
       "      <td>0.013441</td>\n",
       "      <td>0.013376</td>\n",
       "      <td>0.022895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>-0.013955</td>\n",
       "      <td>0.019801</td>\n",
       "      <td>0.028354</td>\n",
       "      <td>-0.051818</td>\n",
       "      <td>0.022135</td>\n",
       "      <td>0.016268</td>\n",
       "      <td>-0.027091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001254</td>\n",
       "      <td>-0.004915</td>\n",
       "      <td>-0.004733</td>\n",
       "      <td>-0.004608</td>\n",
       "      <td>-0.004915</td>\n",
       "      <td>-0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>0.001369</td>\n",
       "      <td>-0.000415</td>\n",
       "      <td>-0.002663</td>\n",
       "      <td>-0.000299</td>\n",
       "      <td>-0.002865</td>\n",
       "      <td>0.004122</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.001254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044756</td>\n",
       "      <td>-0.015682</td>\n",
       "      <td>0.067058</td>\n",
       "      <td>0.044756</td>\n",
       "      <td>-0.009569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week</th>\n",
       "      <td>0.026391</td>\n",
       "      <td>0.007803</td>\n",
       "      <td>-0.004228</td>\n",
       "      <td>0.006234</td>\n",
       "      <td>-0.004345</td>\n",
       "      <td>0.004404</td>\n",
       "      <td>0.013376</td>\n",
       "      <td>-0.004915</td>\n",
       "      <td>0.044756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.975111</td>\n",
       "      <td>0.976771</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.123089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0.026601</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>-0.004898</td>\n",
       "      <td>0.005917</td>\n",
       "      <td>-0.004856</td>\n",
       "      <td>0.004594</td>\n",
       "      <td>0.013419</td>\n",
       "      <td>-0.004733</td>\n",
       "      <td>-0.015682</td>\n",
       "      <td>0.975111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996549</td>\n",
       "      <td>0.975111</td>\n",
       "      <td>-0.118439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_year</th>\n",
       "      <td>0.026589</td>\n",
       "      <td>0.007511</td>\n",
       "      <td>-0.005081</td>\n",
       "      <td>0.005854</td>\n",
       "      <td>-0.005063</td>\n",
       "      <td>0.004938</td>\n",
       "      <td>0.013441</td>\n",
       "      <td>-0.004608</td>\n",
       "      <td>0.067058</td>\n",
       "      <td>0.976771</td>\n",
       "      <td>0.996549</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976771</td>\n",
       "      <td>-0.118851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_of_year</th>\n",
       "      <td>0.026391</td>\n",
       "      <td>0.007803</td>\n",
       "      <td>-0.004228</td>\n",
       "      <td>0.006234</td>\n",
       "      <td>-0.004345</td>\n",
       "      <td>0.004404</td>\n",
       "      <td>0.013376</td>\n",
       "      <td>-0.004915</td>\n",
       "      <td>0.044756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.975111</td>\n",
       "      <td>0.976771</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.123089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.126142</td>\n",
       "      <td>0.011172</td>\n",
       "      <td>-0.011886</td>\n",
       "      <td>0.008012</td>\n",
       "      <td>-0.007002</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>0.022895</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>-0.009569</td>\n",
       "      <td>-0.123089</td>\n",
       "      <td>-0.118439</td>\n",
       "      <td>-0.118851</td>\n",
       "      <td>-0.123089</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   fare_amount  pickup_longitude  pickup_latitude  \\\n",
       "fare_amount           1.000000          0.458470        -0.227462   \n",
       "pickup_longitude      0.458470          1.000000        -0.023924   \n",
       "pickup_latitude      -0.227462         -0.023924         1.000000   \n",
       "dropoff_longitude     0.393536          0.272631         0.073017   \n",
       "dropoff_latitude     -0.215770          0.053097         0.425640   \n",
       "passenger_count       0.017260          0.002084        -0.008415   \n",
       "distance              0.914287          0.456312        -0.254068   \n",
       "hour                 -0.013955          0.019801         0.028354   \n",
       "day                   0.001369         -0.000415        -0.002663   \n",
       "week                  0.026391          0.007803        -0.004228   \n",
       "month                 0.026601          0.007588        -0.004898   \n",
       "day_of_year           0.026589          0.007511        -0.005081   \n",
       "week_of_year          0.026391          0.007803        -0.004228   \n",
       "year                  0.126142          0.011172        -0.011886   \n",
       "\n",
       "                   dropoff_longitude  dropoff_latitude  passenger_count  \\\n",
       "fare_amount                 0.393536         -0.215770         0.017260   \n",
       "pickup_longitude            0.272631          0.053097         0.002084   \n",
       "pickup_latitude             0.073017          0.425640        -0.008415   \n",
       "dropoff_longitude           1.000000          0.110417        -0.000045   \n",
       "dropoff_latitude            0.110417          1.000000        -0.005172   \n",
       "passenger_count            -0.000045         -0.005172         1.000000   \n",
       "distance                    0.409930         -0.211630         0.011905   \n",
       "hour                       -0.051818          0.022135         0.016268   \n",
       "day                        -0.000299         -0.002865         0.004122   \n",
       "week                        0.006234         -0.004345         0.004404   \n",
       "month                       0.005917         -0.004856         0.004594   \n",
       "day_of_year                 0.005854         -0.005063         0.004938   \n",
       "week_of_year                0.006234         -0.004345         0.004404   \n",
       "year                        0.008012         -0.007002         0.004889   \n",
       "\n",
       "                   distance      hour       day      week     month  \\\n",
       "fare_amount        0.914287 -0.013955  0.001369  0.026391  0.026601   \n",
       "pickup_longitude   0.456312  0.019801 -0.000415  0.007803  0.007588   \n",
       "pickup_latitude   -0.254068  0.028354 -0.002663 -0.004228 -0.004898   \n",
       "dropoff_longitude  0.409930 -0.051818 -0.000299  0.006234  0.005917   \n",
       "dropoff_latitude  -0.211630  0.022135 -0.002865 -0.004345 -0.004856   \n",
       "passenger_count    0.011905  0.016268  0.004122  0.004404  0.004594   \n",
       "distance           1.000000 -0.027091  0.001373  0.013376  0.013419   \n",
       "hour              -0.027091  1.000000  0.001254 -0.004915 -0.004733   \n",
       "day                0.001373  0.001254  1.000000  0.044756 -0.015682   \n",
       "week               0.013376 -0.004915  0.044756  1.000000  0.975111   \n",
       "month              0.013419 -0.004733 -0.015682  0.975111  1.000000   \n",
       "day_of_year        0.013441 -0.004608  0.067058  0.976771  0.996549   \n",
       "week_of_year       0.013376 -0.004915  0.044756  1.000000  0.975111   \n",
       "year               0.022895 -0.000008 -0.009569 -0.123089 -0.118439   \n",
       "\n",
       "                   day_of_year  week_of_year      year  \n",
       "fare_amount           0.026589      0.026391  0.126142  \n",
       "pickup_longitude      0.007511      0.007803  0.011172  \n",
       "pickup_latitude      -0.005081     -0.004228 -0.011886  \n",
       "dropoff_longitude     0.005854      0.006234  0.008012  \n",
       "dropoff_latitude     -0.005063     -0.004345 -0.007002  \n",
       "passenger_count       0.004938      0.004404  0.004889  \n",
       "distance              0.013441      0.013376  0.022895  \n",
       "hour                 -0.004608     -0.004915 -0.000008  \n",
       "day                   0.067058      0.044756 -0.009569  \n",
       "week                  0.976771      1.000000 -0.123089  \n",
       "month                 0.996549      0.975111 -0.118439  \n",
       "day_of_year           1.000000      0.976771 -0.118851  \n",
       "week_of_year          0.976771      1.000000 -0.123089  \n",
       "year                 -0.118851     -0.123089  1.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's compute correlation coefficient \n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a161a37b8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's check some relationships:\n",
    "# (1) fare_amount and distance\n",
    "df.plot(kind='scatter',x='distance',y='fare_amount',c='blue',s=0.2,alpha=.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9142873503796988"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fare_amount'].corr(df['distance'])\n",
    "# Linear Plot and correlation factor of 0.88 signifies linear relationship between fare_amount and distance travelled in a trip. The fare gets increased with the distance travelled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b12e8e400>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (2) fare_amount and day of time\n",
    "df[['fare_amount', 'hour']].boxplot(by = 'hour', showfliers = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.013955057562161444"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fare_amount'].corr(df['hour'])\n",
    "# Findings : 1) Correlation factor = -0.01607561723255814 which does not implies any relation betweem fare_amount and hour.\n",
    "# max fare_amount is in the morning at 5 am which keeps on decreasing till 7 am and it remains almost constant theroughout the day. Morning high fares \n",
    "#may be due to the fact of people going to/from airport."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a14724278>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (3) distance and day of time\n",
    "df[['distance', 'hour']].boxplot(by = 'hour', showfliers = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.027091010165026273"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['distance'].corr(df['hour'])\n",
    "# Findings : 1) Correlation factor = -0.029914726054103426 which does not implies any relation betweem distance and hour.\n",
    "# max_distance travelled is in the morning at 5 am may be due to the same fact of taxi going to/from airport. So there is no significant relation betweem distance and day of time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a19163b38>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['fare_amount', 'month']].boxplot(by = 'month', showfliers = False)\n",
    "#It shows the maximum fare received in the months of may and June due to summer vacations. Also the maximum fare is received in the months of October\n",
    "#to december may be to the new year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a148d70b8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df['day'], y=df['fare_amount'], s=1.5)\n",
    "#No correlation between date and fare_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "    def populateAirportDataSet(data):\n",
    "        jfk_coord = (40.639722, -73.778889)\n",
    "        ewr_coord = (40.6925, -74.168611)\n",
    "        lga_coord = (40.77725, -73.872611)\n",
    "        mylist = []\n",
    "        for i,row in data.iterrows():\n",
    "            pickup_lat = radians(row['pickup_latitude'])\n",
    "            dropoff_lat = radians(row['dropoff_latitude'])\n",
    "            pickup_lon = radians(row['pickup_longitude'])\n",
    "            dropoff_lon = radians(row['dropoff_longitude'])\n",
    "            #print('vivek')\n",
    "            pickup_jfk = cal_haversinedistance(pickup_lat, pickup_lon, jfk_coord[0], jfk_coord[1]) \n",
    "            dropoff_jfk = cal_haversinedistance(jfk_coord[0], jfk_coord[1], dropoff_lat, dropoff_lon) \n",
    "            pickup_ewr = cal_haversinedistance(pickup_lat, pickup_lon, ewr_coord[0], ewr_coord[1])\n",
    "            dropoff_ewr = cal_haversinedistance(ewr_coord[0], ewr_coord[1], dropoff_lat, dropoff_lon) \n",
    "            pickup_lga = cal_haversinedistance(pickup_lat, pickup_lon, lga_coord[0], lga_coord[1]) \n",
    "            dropoff_lga = cal_haversinedistance(lga_coord[0], lga_coord[1], dropoff_lat, dropoff_lon) \n",
    "            if(pickup_jfk < 2 or dropoff_jfk < 2 or pickup_ewr < 2 or dropoff_ewr < 2 or pickup_lga < 2 or dropoff_lga < 2):\n",
    "                mylist.append(1)\n",
    "            else:\n",
    "                mylist.append(0)\n",
    "        return mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['isAirportRide'] = populateAirportDataSet(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>week</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>year</th>\n",
       "      <th>isAirportRide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4.816588e+06</td>\n",
       "      <td>4816588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.094843e+01</td>\n",
       "      <td>-7.397574e+01</td>\n",
       "      <td>4.075086e+01</td>\n",
       "      <td>-7.397480e+01</td>\n",
       "      <td>4.075085e+01</td>\n",
       "      <td>1.690647e+00</td>\n",
       "      <td>3.230506e+00</td>\n",
       "      <td>1.351978e+01</td>\n",
       "      <td>1.571701e+01</td>\n",
       "      <td>2.546496e+01</td>\n",
       "      <td>6.269116e+00</td>\n",
       "      <td>1.753048e+02</td>\n",
       "      <td>2.546496e+01</td>\n",
       "      <td>2.011739e+03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.560810e+00</td>\n",
       "      <td>3.343625e-02</td>\n",
       "      <td>2.640386e-02</td>\n",
       "      <td>3.150210e-02</td>\n",
       "      <td>2.926771e-02</td>\n",
       "      <td>1.306435e+00</td>\n",
       "      <td>3.401233e+00</td>\n",
       "      <td>6.504249e+00</td>\n",
       "      <td>8.684374e+00</td>\n",
       "      <td>1.494863e+01</td>\n",
       "      <td>3.436639e+00</td>\n",
       "      <td>1.048094e+02</td>\n",
       "      <td>1.494863e+01</td>\n",
       "      <td>1.865292e+00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>-7.404989e+01</td>\n",
       "      <td>4.055018e+01</td>\n",
       "      <td>-7.404996e+01</td>\n",
       "      <td>4.055046e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>-7.399228e+01</td>\n",
       "      <td>4.073660e+01</td>\n",
       "      <td>-7.399156e+01</td>\n",
       "      <td>4.073572e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.253646e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>2.010000e+03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000e+00</td>\n",
       "      <td>-7.398213e+01</td>\n",
       "      <td>4.075332e+01</td>\n",
       "      <td>-7.398070e+01</td>\n",
       "      <td>4.075377e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.141089e+00</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.670000e+02</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>2.012000e+03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.250000e+01</td>\n",
       "      <td>-7.396850e+01</td>\n",
       "      <td>4.076743e+01</td>\n",
       "      <td>-7.396586e+01</td>\n",
       "      <td>4.076821e+01</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.851992e+00</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>3.900000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>2.670000e+02</td>\n",
       "      <td>3.900000e+01</td>\n",
       "      <td>2.013000e+03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>-7.375001e+01</td>\n",
       "      <td>4.084999e+01</td>\n",
       "      <td>-7.375001e+01</td>\n",
       "      <td>4.085000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.878735e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>3.660000e+02</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>2.015000e+03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count  4.816588e+06      4.816588e+06     4.816588e+06       4.816588e+06   \n",
       "mean   1.094843e+01     -7.397574e+01     4.075086e+01      -7.397480e+01   \n",
       "std    8.560810e+00      3.343625e-02     2.640386e-02       3.150210e-02   \n",
       "min    1.000000e-02     -7.404989e+01     4.055018e+01      -7.404996e+01   \n",
       "25%    6.000000e+00     -7.399228e+01     4.073660e+01      -7.399156e+01   \n",
       "50%    8.500000e+00     -7.398213e+01     4.075332e+01      -7.398070e+01   \n",
       "75%    1.250000e+01     -7.396850e+01     4.076743e+01      -7.396586e+01   \n",
       "max    6.000000e+01     -7.375001e+01     4.084999e+01      -7.375001e+01   \n",
       "\n",
       "       dropoff_latitude  passenger_count      distance          hour  \\\n",
       "count      4.816588e+06     4.816588e+06  4.816588e+06  4.816588e+06   \n",
       "mean       4.075085e+01     1.690647e+00  3.230506e+00  1.351978e+01   \n",
       "std        2.926771e-02     1.306435e+00  3.401233e+00  6.504249e+00   \n",
       "min        4.055046e+01     1.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%        4.073572e+01     1.000000e+00  1.253646e+00  9.000000e+00   \n",
       "50%        4.075377e+01     1.000000e+00  2.141089e+00  1.400000e+01   \n",
       "75%        4.076821e+01     2.000000e+00  3.851992e+00  1.900000e+01   \n",
       "max        4.085000e+01     7.000000e+00  2.878735e+01  2.300000e+01   \n",
       "\n",
       "                day          week         month   day_of_year  week_of_year  \\\n",
       "count  4.816588e+06  4.816588e+06  4.816588e+06  4.816588e+06  4.816588e+06   \n",
       "mean   1.571701e+01  2.546496e+01  6.269116e+00  1.753048e+02  2.546496e+01   \n",
       "std    8.684374e+00  1.494863e+01  3.436639e+00  1.048094e+02  1.494863e+01   \n",
       "min    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "25%    8.000000e+00  1.300000e+01  3.000000e+00  8.500000e+01  1.300000e+01   \n",
       "50%    1.600000e+01  2.400000e+01  6.000000e+00  1.670000e+02  2.400000e+01   \n",
       "75%    2.300000e+01  3.900000e+01  9.000000e+00  2.670000e+02  3.900000e+01   \n",
       "max    3.100000e+01  5.300000e+01  1.200000e+01  3.660000e+02  5.300000e+01   \n",
       "\n",
       "               year  isAirportRide  \n",
       "count  4.816588e+06      4816588.0  \n",
       "mean   2.011739e+03            0.0  \n",
       "std    1.865292e+00            0.0  \n",
       "min    2.009000e+03            0.0  \n",
       "25%    2.010000e+03            0.0  \n",
       "50%    2.012000e+03            0.0  \n",
       "75%    2.013000e+03            0.0  \n",
       "max    2.015000e+03            0.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b1efc5128>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='hour',y='fare_amount',c='blue',s=0.2,alpha=.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('test.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "USFederal_Holidays = USFederalHolidayCalendar().holidays(start='2005-01-01', end='2017-12-31').to_pydatetime()\n",
    "\n",
    "USFederal_Holidays = USFederal_Holidays.tolist()\n",
    "\n",
    "def IsUSFederalHoliday(data):\n",
    "    date = data['pickup_datetime']\n",
    "    if date in USFederal_Holidays:\n",
    "        return 1\n",
    "    else:\n",
    "        \n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_holiday'] = df.apply(IsUSFederalHoliday,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modelling of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = ['pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude', 'passenger_count', 'distance', 'hour', 'year', 'isAirportRide', 'is_holiday']\n",
    "label_name = 'fare_amount'\n",
    "x_train = df[feature_names]\n",
    "y_train = df[label_name]\n",
    "test = pd.read_csv('test.csv')\n",
    "dist_calc(test)\n",
    "test['hour'] = pd.to_datetime(test['pickup_datetime']).dt.hour\n",
    "test['year'] = pd.to_datetime(test['pickup_datetime']).dt.year\n",
    "test['isAirportRide'] = populateAirportDataSet(test)\n",
    "test['is_holiday'] = test.apply(IsUSFederalHoliday,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root mean Squared Error : 2.740465470517641\n"
     ]
    }
   ],
   "source": [
    "#Random Forest Model\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_train,y_train, test_size=0.2)\n",
    "\n",
    "rfr = RandomForestRegressor()\n",
    "rfr.fit(x_train1, y_train1)\n",
    "rfr_prediction = rfr.predict(x_test1)\n",
    "\n",
    "#rfr_submission = pd.DataFrame({\"key\": test['key'],\"fare_amount\": rfr_prediction},columns = ['key','fare_amount'])\n",
    "rmse = np.sqrt(mean_squared_error(rfr_prediction,y_test1))\n",
    "print (\"root mean Squared Error : {}\".format(rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtest = test[feature_names]\n",
    "rfr.fit(x_train, y_train)\n",
    "rfr_prediction = rfr.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('sample_submission.csv')\n",
    "submission['fare_amount'] = rfr_prediction\n",
    "submission.to_csv('vivek_submission.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x_train,y_train, test_size=0.2)\n",
    "linear_reg = LinearRegression()\n",
    "linear_reg.fit(x_train1, y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.42244609e+01  3.94576281e+00  6.75630186e+00 -1.22194397e+01\n",
      "  3.94864599e-02  2.19223575e+00  1.35045531e-02  4.83529209e-01\n",
      "  0.00000000e+00 -6.60865977e-01]\n"
     ]
    }
   ],
   "source": [
    "print(linear_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = linear_reg.predict(x_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root mean Squared Error : 3.3033718199477162\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(y_pred, y_test1))\n",
    "print (\"root mean Squared Error : {}\".format(rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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