HW2-Linear Regression Random Forest Regression XGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "from math import sin, cos, sqrt, atan2, radians\n",
    "import scipy.stats as sc\n",
    "from sklearn.model_selection import train_test_split, cross_val_score #set splitting and validation\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pearson_correlation of entire data (0.015583777034493661, 0.0) (-0.007416771899850075, 6.813146209097224e-129) (-0.017561136590543064, 0.0) (0.2237917755061497, 0.0)\n",
      "[17:37:59] Tree method is automatically selected to be 'approx' for faster speed. to use old behavior(exact greedy algorithm on single machine), set tree_method to 'exact'\n",
      "[ 9.797076  10.187675   4.7528615 ... 52.466763  20.51092    6.978396 ]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"/Users/sriramreddy/Downloads/DSF/HW2/all/train.csv\",nrows=11000000)\n",
    "tf = pd.read_csv(\"/Users/sriramreddy/Downloads/DSF/HW2/all/test.csv\")\n",
    "# df.describe()\n",
    "df['Date'], df['Time'],df['Zone'] = df['pickup_datetime'].str.split(' ', 2).str\n",
    "df=df.drop(['pickup_datetime'],axis=1)\n",
    "df = df.drop(df[df['passenger_count']>10].index, axis = 0)\n",
    "df = df.drop(df[df['passenger_count']==0].index, axis = 0)\n",
    "df['Euclidean_distance']=((df.pickup_longitude - df.dropoff_longitude)**2 +(df.pickup_latitude - df.dropoff_latitude)**2)**0.5\n",
    "\n",
    "from math import sin, cos, sqrt, atan2, radians\n",
    "def haversine(df):\n",
    "    R = 3959.0\n",
    "    for i,row in df.iterrows():\n",
    "        lat1 = radians(row['pickup_latitude'])\n",
    "        lon1 = radians(row['pickup_longitude'])\n",
    "        lat2 = radians(row['dropoff_latitude'])\n",
    "        lon2 = radians(row['dropoff_longitude'])\n",
    "        dlon = lon2 - lon1\n",
    "        dlat = lat2 - lat1\n",
    "        a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n",
    "        c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
    "        distance = R * c\n",
    "        df.at[i,'distance'] = distance\n",
    "\n",
    "df = df.drop(df[df['fare_amount']>200].index, axis = 0)\n",
    "df = df.drop(df[df['fare_amount']<1].index, axis = 0)\n",
    "df = df.drop(((df[df['dropoff_latitude']<-90])|(df[df['dropoff_latitude']>90])).index, axis=0)\n",
    "df = df.drop(((df[df['dropoff_longitude']<-180])|(df[df['dropoff_longitude']>180])).index, axis=0)\n",
    "df=df[df.Euclidean_distance != 0]\n",
    "df['Hour'], df['Min'], df['Sec'] = df['Time'].str.split(':', 2).str\n",
    "# I have checked the co-ordinates of the New York city and I have taken extreme points.\n",
    "df = df[df['pickup_longitude'] < -73.699215]\n",
    "df = df[df['pickup_longitude'] > -74.257159]\n",
    "df = df[(df['pickup_latitude'] > 40.495992) & (df['pickup_latitude'] < 40.916)]\n",
    "df = df[df['dropoff_longitude'] < -73.699215]\n",
    "df = df[df['dropoff_longitude'] > -74.257159]\n",
    "df = df[(df['dropoff_latitude'] > 40.495992) & (df['dropoff_latitude'] < 40.916)]\n",
    "df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d')\n",
    "df['weekday'] = df.Date.dt.weekday\n",
    "df['Month']=df.Date.dt.month\n",
    "df['Year']=df.Date.dt.year\n",
    "df=df.drop(['Date'],axis=1)\n",
    "df=df.drop(['Zone'],axis=1)\n",
    "df.drop(df.columns[0], axis=1)\n",
    "df=df.drop(['Time'],axis=1)\n",
    "haversine(df)\n",
    "df=df.dropna(axis=0)\n",
    "df.Hour = df.Hour.astype(float)\n",
    "df.Min = df.Min.astype(float)\n",
    "df.Sec = df.Sec.astype(float)\n",
    "pd.to_numeric(df.Hour, errors='coerce')\n",
    "pd.to_numeric(df.Min, errors='coerce')\n",
    "pd.to_numeric(df.Sec, errors='coerce')\n",
    "df['Hour_rate'] = ((df.Hour + (df.Min*60.0 + df.Sec)/3600))\n",
    "#pre-processing done\n",
    "import scipy.stats as sc\n",
    "A=sc.pearsonr(df.Euclidean_distance, df.fare_amount)\n",
    "B=sc.pearsonr(df.Hour_rate, df.distance)\n",
    "C=sc.pearsonr(df.Hour_rate, df.fare_amount)\n",
    "D= sc.pearsonr(df.distance, df.fare_amount)\n",
    "print(\"pearson_correlation of entire data\",A,B,C,D)\n",
    "\n",
    "# Test data cleaning\n",
    "tf = pd.read_csv(\"/Users/sriramreddy/Downloads/DSF/HW2/all/test.csv\")\n",
    "tf['Date'], tf['Time'],tf['Zone'] = tf['pickup_datetime'].str.split(' ', 2).str\n",
    "tf=tf.drop(['pickup_datetime'],axis=1)\n",
    "tf['Euclidean_distance']=((tf.pickup_longitude - tf.dropoff_longitude)**2 +(tf.pickup_latitude - tf.dropoff_latitude)**2)**0.5\n",
    "from math import sin, cos, sqrt, atan2, radians\n",
    "def haversine(tf):\n",
    "    R = 3959.0\n",
    "    for i,row in tf.iterrows():\n",
    "        lat1 = radians(row['pickup_latitude'])\n",
    "        lon1 = radians(row['pickup_longitude'])\n",
    "        lat2 = radians(row['dropoff_latitude'])\n",
    "        lon2 = radians(row['dropoff_longitude'])\n",
    "        dlon = lon2 - lon1\n",
    "        dlat = lat2 - lat1\n",
    "        a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n",
    "        c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
    "        distance = R * c\n",
    "        tf.at[i,'distance'] = distance\n",
    "tf['Hour'], tf['Min'], tf['Sec'] = tf['Time'].str.split(':', 2).str\n",
    "tf['Date'] = pd.to_datetime(tf['Date'], format='%Y-%m-%d')\n",
    "tf['weekday'] = tf.Date.dt.weekday\n",
    "tf['Month']=tf.Date.dt.month\n",
    "tf['Year']=tf.Date.dt.year\n",
    "tf=tf.drop(['Date'],axis=1)\n",
    "tf=tf.drop(['Zone'],axis=1)\n",
    "tf.drop(tf.columns[0], axis=1)\n",
    "tf=tf.drop(['Time'],axis=1)\n",
    "# tf=tf.drop(['key'])--------------------This drops the first column\n",
    "tf=tf.drop(tf.columns[0], axis=1)\n",
    "haversine(tf)\n",
    "tf.Hour = tf.Hour.astype(float)\n",
    "tf.Min = tf.Min.astype(float)\n",
    "tf.Sec = tf.Sec.astype(float)\n",
    "pd.to_numeric(tf.Hour, errors='coerce')\n",
    "pd.to_numeric(tf.Min, errors='coerce')\n",
    "pd.to_numeric(tf.Sec, errors='coerce')\n",
    "tf['Hour_rate'] = ((tf.Hour + (tf.Min*60.0 + tf.Sec)/3600))\n",
    "X = df.drop(['key','fare_amount'],axis=1)\n",
    "y = df['fare_amount']\n",
    "\n",
    "\n",
    "import xgboost as xgb \n",
    "X = df.drop(['key','fare_amount'],axis=1)\n",
    "y = df['fare_amount']\n",
    "X_train = xgb.DMatrix(X, label=y)\n",
    "X_test = xgb.DMatrix(tf)\n",
    "params = {'max_depth':7,\n",
    "          'eta':1,\n",
    "          'silent':1,\n",
    "          'objective':'reg:linear',\n",
    "          'eval_metric':'rmse',\n",
    "          'learning_rate':0.05\n",
    "         }\n",
    "num_rounds = 60\n",
    "xb = xgb.train(params, X_train, num_rounds)\n",
    "y_pred_xgb = xb.predict(X_test)\n",
    "print(y_pred_xgb)\n",
    "\n",
    "# I have taken referrence from the following link:\n",
    "# https://www.kaggle.com/madhurisivalenka/cleansing-eda-modelling-lgbm-xgboost-starters/comments\n",
    "\n",
    "\n",
    "submission = pd.read_csv('/Users/sriramreddy/Downloads/DSF/HW2/all/sample_submission.csv')\n",
    "submission['fare_amount'] = y_pred_xgb\n",
    "submission.to_csv('/Users/sriramreddy/Downloads/DSF/HW2/all/submission_xgb_first_11.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>Euclidean_distance</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Min</th>\n",
       "      <th>Sec</th>\n",
       "      <th>weekday</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>distance</th>\n",
       "      <th>Hour_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009436</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.640527</td>\n",
       "      <td>17.439167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.079696</td>\n",
       "      <td>16.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>5.250993</td>\n",
       "      <td>16.871111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.013674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.863464</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025340</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.739493</td>\n",
       "      <td>4.511667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019470</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.242295</td>\n",
       "      <td>7.850000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount  pickup_longitude  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5        -73.844311   \n",
       "1    2010-01-05 16:52:16.0000002         16.9        -74.016048   \n",
       "2   2011-08-18 00:35:00.00000049          5.7        -73.982738   \n",
       "3    2012-04-21 04:30:42.0000001          7.7        -73.987130   \n",
       "4  2010-03-09 07:51:00.000000135          5.3        -73.968095   \n",
       "\n",
       "   pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \\\n",
       "0        40.721319         -73.841610         40.712278                1   \n",
       "1        40.711303         -73.979268         40.782004                1   \n",
       "2        40.761270         -73.991242         40.750562                2   \n",
       "3        40.733143         -73.991567         40.758092                1   \n",
       "4        40.768008         -73.956655         40.783762                1   \n",
       "\n",
       "   Euclidean_distance  Hour   Min   Sec  weekday  Month  Year  distance  \\\n",
       "0            0.009436  17.0  26.0  21.0        0      6  2009  0.640527   \n",
       "1            0.079696  16.0  52.0  16.0        1      1  2010  5.250993   \n",
       "2            0.013674   0.0  35.0   0.0        3      8  2011  0.863464   \n",
       "3            0.025340   4.0  30.0  42.0        5      4  2012  1.739493   \n",
       "4            0.019470   7.0  51.0   0.0        1      3  2010  1.242295   \n",
       "\n",
       "   Hour_rate  \n",
       "0  17.439167  \n",
       "1  16.871111  \n",
       "2   0.583333  \n",
       "3   4.511667  \n",
       "4   7.850000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.060544e+07\n",
       "mean     2.011744e+03\n",
       "std      1.866653e+00\n",
       "min      2.009000e+03\n",
       "25%      2.010000e+03\n",
       "50%      2.012000e+03\n",
       "75%      2.013000e+03\n",
       "max      2.015000e+03\n",
       "Name: Year, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Year.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Years are from 2009 to 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.060544e+07\n",
       "mean     2.102594e+00\n",
       "std      8.733067e+00\n",
       "min      5.221980e-05\n",
       "25%      7.942095e-01\n",
       "50%      1.352012e+00\n",
       "75%      2.446182e+00\n",
       "max      6.351180e+03\n",
       "Name: distance, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.distance.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>Euclidean_distance</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Min</th>\n",
       "      <th>Sec</th>\n",
       "      <th>weekday</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>distance</th>\n",
       "      <th>Hour_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60442</th>\n",
       "      <td>2012-01-12 13:36:00.000000186</td>\n",
       "      <td>4.90</td>\n",
       "      <td>-736.550000</td>\n",
       "      <td>40.738230</td>\n",
       "      <td>-73.988742</td>\n",
       "      <td>40.748847</td>\n",
       "      <td>1</td>\n",
       "      <td>662.561258</td>\n",
       "      <td>13.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>2950.436085</td>\n",
       "      <td>13.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98652</th>\n",
       "      <td>2011-05-03 07:45:00.00000027</td>\n",
       "      <td>10.50</td>\n",
       "      <td>-78.083332</td>\n",
       "      <td>40.705727</td>\n",
       "      <td>-74.005315</td>\n",
       "      <td>40.728592</td>\n",
       "      <td>1</td>\n",
       "      <td>4.078081</td>\n",
       "      <td>7.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2011</td>\n",
       "      <td>213.559558</td>\n",
       "      <td>7.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243342</th>\n",
       "      <td>2012-08-02 10:38:00.000000111</td>\n",
       "      <td>7.30</td>\n",
       "      <td>-736.333333</td>\n",
       "      <td>40.766480</td>\n",
       "      <td>-73.987928</td>\n",
       "      <td>40.751742</td>\n",
       "      <td>3</td>\n",
       "      <td>662.345405</td>\n",
       "      <td>10.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2012</td>\n",
       "      <td>2960.346022</td>\n",
       "      <td>10.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351119</th>\n",
       "      <td>2012-02-03 07:53:00.000000135</td>\n",
       "      <td>4.10</td>\n",
       "      <td>-736.483332</td>\n",
       "      <td>40.766512</td>\n",
       "      <td>-73.981992</td>\n",
       "      <td>40.771672</td>\n",
       "      <td>1</td>\n",
       "      <td>662.501340</td>\n",
       "      <td>7.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>2952.198697</td>\n",
       "      <td>7.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370663</th>\n",
       "      <td>2012-05-04 23:19:00.000000261</td>\n",
       "      <td>6.10</td>\n",
       "      <td>-736.516667</td>\n",
       "      <td>40.719095</td>\n",
       "      <td>-74.003952</td>\n",
       "      <td>40.729860</td>\n",
       "      <td>1</td>\n",
       "      <td>662.512715</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>2953.716451</td>\n",
       "      <td>23.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411093</th>\n",
       "      <td>2012-08-06 11:25:00.00000043</td>\n",
       "      <td>4.50</td>\n",
       "      <td>-80.616665</td>\n",
       "      <td>40.780687</td>\n",
       "      <td>-73.970537</td>\n",
       "      <td>40.789157</td>\n",
       "      <td>2</td>\n",
       "      <td>6.646133</td>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2012</td>\n",
       "      <td>347.632103</td>\n",
       "      <td>11.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416859</th>\n",
       "      <td>2012-07-21 15:23:00.00000093</td>\n",
       "      <td>6.50</td>\n",
       "      <td>-736.416665</td>\n",
       "      <td>40.752285</td>\n",
       "      <td>-73.992565</td>\n",
       "      <td>40.742687</td>\n",
       "      <td>1</td>\n",
       "      <td>662.424100</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2012</td>\n",
       "      <td>2957.011847</td>\n",
       "      <td>15.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436142</th>\n",
       "      <td>2012-06-01 21:29:00.000000180</td>\n",
       "      <td>6.10</td>\n",
       "      <td>-128.175950</td>\n",
       "      <td>40.750000</td>\n",
       "      <td>-73.985985</td>\n",
       "      <td>40.741350</td>\n",
       "      <td>3</td>\n",
       "      <td>54.189966</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>2789.602541</td>\n",
       "      <td>21.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568830</th>\n",
       "      <td>2013-05-23 15:23:00.0000007</td>\n",
       "      <td>11.50</td>\n",
       "      <td>-735.888333</td>\n",
       "      <td>40.760863</td>\n",
       "      <td>-73.989835</td>\n",
       "      <td>40.738443</td>\n",
       "      <td>5</td>\n",
       "      <td>661.898498</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "      <td>2982.792699</td>\n",
       "      <td>15.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675500</th>\n",
       "      <td>2011-10-28 10:53:00.00000080</td>\n",
       "      <td>16.90</td>\n",
       "      <td>-1452.988333</td>\n",
       "      <td>40.776000</td>\n",
       "      <td>-73.989487</td>\n",
       "      <td>40.740667</td>\n",
       "      <td>2</td>\n",
       "      <td>1378.998846</td>\n",
       "      <td>10.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2011</td>\n",
       "      <td>3124.552641</td>\n",
       "      <td>10.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739053</th>\n",
       "      <td>2011-02-25 07:55:00.000000207</td>\n",
       "      <td>3.30</td>\n",
       "      <td>-77.183333</td>\n",
       "      <td>40.742635</td>\n",
       "      <td>-73.987172</td>\n",
       "      <td>40.740625</td>\n",
       "      <td>1</td>\n",
       "      <td>3.196162</td>\n",
       "      <td>7.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>167.317832</td>\n",
       "      <td>7.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888631</th>\n",
       "      <td>2012-04-20 13:11:00.000000143</td>\n",
       "      <td>10.50</td>\n",
       "      <td>-94.366665</td>\n",
       "      <td>40.711132</td>\n",
       "      <td>-73.997302</td>\n",
       "      <td>40.722352</td>\n",
       "      <td>5</td>\n",
       "      <td>20.369366</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>1064.379892</td>\n",
       "      <td>13.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914435</th>\n",
       "      <td>2012-03-04 00:10:00.000000131</td>\n",
       "      <td>11.70</td>\n",
       "      <td>-736.250000</td>\n",
       "      <td>40.788027</td>\n",
       "      <td>-73.990812</td>\n",
       "      <td>40.750942</td>\n",
       "      <td>1</td>\n",
       "      <td>662.259189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "      <td>2964.107441</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043169</th>\n",
       "      <td>2013-03-15 11:11:00.00000019</td>\n",
       "      <td>36.83</td>\n",
       "      <td>-736.333333</td>\n",
       "      <td>40.755922</td>\n",
       "      <td>-73.871502</td>\n",
       "      <td>40.774202</td>\n",
       "      <td>1</td>\n",
       "      <td>662.461831</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>2954.333182</td>\n",
       "      <td>11.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055960</th>\n",
       "      <td>2011-11-05 23:26:00.000000309</td>\n",
       "      <td>33.70</td>\n",
       "      <td>-735.200000</td>\n",
       "      <td>40.770092</td>\n",
       "      <td>-73.980187</td>\n",
       "      <td>40.765530</td>\n",
       "      <td>1</td>\n",
       "      <td>661.219813</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>2011</td>\n",
       "      <td>3015.298011</td>\n",
       "      <td>23.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252557</th>\n",
       "      <td>2012-12-30 16:13:00.000000196</td>\n",
       "      <td>19.00</td>\n",
       "      <td>-77.233332</td>\n",
       "      <td>40.715087</td>\n",
       "      <td>-73.983452</td>\n",
       "      <td>40.773615</td>\n",
       "      <td>6</td>\n",
       "      <td>3.250407</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2012</td>\n",
       "      <td>170.170701</td>\n",
       "      <td>16.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271731</th>\n",
       "      <td>2012-11-09 09:48:00.00000071</td>\n",
       "      <td>25.00</td>\n",
       "      <td>-84.016667</td>\n",
       "      <td>40.775817</td>\n",
       "      <td>-74.004123</td>\n",
       "      <td>40.753345</td>\n",
       "      <td>1</td>\n",
       "      <td>10.012569</td>\n",
       "      <td>9.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>523.718363</td>\n",
       "      <td>9.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497332</th>\n",
       "      <td>2011-07-18 07:10:00.00000013</td>\n",
       "      <td>5.30</td>\n",
       "      <td>-736.566667</td>\n",
       "      <td>40.755662</td>\n",
       "      <td>-73.976190</td>\n",
       "      <td>40.754715</td>\n",
       "      <td>1</td>\n",
       "      <td>662.590477</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2011</td>\n",
       "      <td>2948.453751</td>\n",
       "      <td>7.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535841</th>\n",
       "      <td>2012-04-24 09:02:00.00000096</td>\n",
       "      <td>4.90</td>\n",
       "      <td>-736.166665</td>\n",
       "      <td>40.763905</td>\n",
       "      <td>-73.956043</td>\n",
       "      <td>40.763622</td>\n",
       "      <td>1</td>\n",
       "      <td>662.210622</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>2966.765638</td>\n",
       "      <td>9.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738206</th>\n",
       "      <td>2013-01-10 14:47:00.000000147</td>\n",
       "      <td>7.50</td>\n",
       "      <td>-77.633333</td>\n",
       "      <td>40.784112</td>\n",
       "      <td>-73.967712</td>\n",
       "      <td>40.768627</td>\n",
       "      <td>2</td>\n",
       "      <td>3.665654</td>\n",
       "      <td>14.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>191.793184</td>\n",
       "      <td>14.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852898</th>\n",
       "      <td>2012-04-05 08:28:00.00000037</td>\n",
       "      <td>4.10</td>\n",
       "      <td>-797.916665</td>\n",
       "      <td>40.743922</td>\n",
       "      <td>-73.999382</td>\n",
       "      <td>40.738957</td>\n",
       "      <td>1</td>\n",
       "      <td>723.917283</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>205.063412</td>\n",
       "      <td>8.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893827</th>\n",
       "      <td>2012-05-19 21:04:00.00000033</td>\n",
       "      <td>9.70</td>\n",
       "      <td>-93.916667</td>\n",
       "      <td>40.771380</td>\n",
       "      <td>-73.991565</td>\n",
       "      <td>40.754922</td>\n",
       "      <td>2</td>\n",
       "      <td>19.925109</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>1040.537599</td>\n",
       "      <td>21.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893883</th>\n",
       "      <td>2012-02-08 12:43:00.00000033</td>\n",
       "      <td>5.30</td>\n",
       "      <td>-90.433333</td>\n",
       "      <td>40.773917</td>\n",
       "      <td>-73.954077</td>\n",
       "      <td>40.766490</td>\n",
       "      <td>1</td>\n",
       "      <td>16.479258</td>\n",
       "      <td>12.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>861.086638</td>\n",
       "      <td>12.716667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994336</th>\n",
       "      <td>2012-02-14 09:48:00.00000012</td>\n",
       "      <td>10.50</td>\n",
       "      <td>-736.333333</td>\n",
       "      <td>40.756345</td>\n",
       "      <td>-73.985257</td>\n",
       "      <td>40.775435</td>\n",
       "      <td>5</td>\n",
       "      <td>662.348076</td>\n",
       "      <td>9.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>2959.897760</td>\n",
       "      <td>9.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031648</th>\n",
       "      <td>2012-10-03 12:38:00.000000106</td>\n",
       "      <td>5.00</td>\n",
       "      <td>-77.050000</td>\n",
       "      <td>40.739347</td>\n",
       "      <td>-73.977028</td>\n",
       "      <td>40.738925</td>\n",
       "      <td>2</td>\n",
       "      <td>3.072972</td>\n",
       "      <td>12.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2012</td>\n",
       "      <td>160.875585</td>\n",
       "      <td>12.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046408</th>\n",
       "      <td>2012-06-25 15:10:00.00000063</td>\n",
       "      <td>4.10</td>\n",
       "      <td>-77.283332</td>\n",
       "      <td>40.715342</td>\n",
       "      <td>-73.996350</td>\n",
       "      <td>40.715770</td>\n",
       "      <td>1</td>\n",
       "      <td>3.286982</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>172.139118</td>\n",
       "      <td>15.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110338</th>\n",
       "      <td>2013-01-12 01:22:00.000000134</td>\n",
       "      <td>13.50</td>\n",
       "      <td>-80.533332</td>\n",
       "      <td>40.731727</td>\n",
       "      <td>-73.949583</td>\n",
       "      <td>40.776465</td>\n",
       "      <td>5</td>\n",
       "      <td>6.583901</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>344.544096</td>\n",
       "      <td>1.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2134687</th>\n",
       "      <td>2011-12-20 19:39:00.00000035</td>\n",
       "      <td>4.90</td>\n",
       "      <td>-91.866665</td>\n",
       "      <td>40.644807</td>\n",
       "      <td>-73.963682</td>\n",
       "      <td>40.765075</td>\n",
       "      <td>1</td>\n",
       "      <td>17.903387</td>\n",
       "      <td>19.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>936.188149</td>\n",
       "      <td>19.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354586</th>\n",
       "      <td>2013-02-01 09:08:00.000000139</td>\n",
       "      <td>16.00</td>\n",
       "      <td>-96.650000</td>\n",
       "      <td>40.704785</td>\n",
       "      <td>-73.973857</td>\n",
       "      <td>40.743597</td>\n",
       "      <td>1</td>\n",
       "      <td>22.676176</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "      <td>1184.141062</td>\n",
       "      <td>9.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2701329</th>\n",
       "      <td>2012-01-06 12:46:00.00000057</td>\n",
       "      <td>21.30</td>\n",
       "      <td>-736.283332</td>\n",
       "      <td>40.740510</td>\n",
       "      <td>-73.978643</td>\n",
       "      <td>40.654830</td>\n",
       "      <td>3</td>\n",
       "      <td>662.304695</td>\n",
       "      <td>12.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>2965.228073</td>\n",
       "      <td>12.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7755471</th>\n",
       "      <td>2012-02-27 08:56:00.000000136</td>\n",
       "      <td>3.70</td>\n",
       "      <td>-765.583333</td>\n",
       "      <td>40.690602</td>\n",
       "      <td>-73.989467</td>\n",
       "      <td>40.690557</td>\n",
       "      <td>2</td>\n",
       "      <td>691.593866</td>\n",
       "      <td>8.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>1481.710084</td>\n",
       "      <td>8.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7878777</th>\n",
       "      <td>2012-06-25 13:12:00.000000162</td>\n",
       "      <td>14.10</td>\n",
       "      <td>-740.050000</td>\n",
       "      <td>40.726942</td>\n",
       "      <td>-74.000038</td>\n",
       "      <td>40.753362</td>\n",
       "      <td>1</td>\n",
       "      <td>666.049963</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>2777.927209</td>\n",
       "      <td>13.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7958736</th>\n",
       "      <td>2013-01-10 19:46:00.00000084</td>\n",
       "      <td>5.50</td>\n",
       "      <td>-79.450000</td>\n",
       "      <td>40.725545</td>\n",
       "      <td>-74.008190</td>\n",
       "      <td>40.730327</td>\n",
       "      <td>3</td>\n",
       "      <td>5.441812</td>\n",
       "      <td>19.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>284.905575</td>\n",
       "      <td>19.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8263803</th>\n",
       "      <td>2011-07-27 12:17:00.00000024</td>\n",
       "      <td>49.80</td>\n",
       "      <td>-79.800000</td>\n",
       "      <td>40.759755</td>\n",
       "      <td>-73.799232</td>\n",
       "      <td>40.645172</td>\n",
       "      <td>5</td>\n",
       "      <td>6.001862</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2011</td>\n",
       "      <td>314.378528</td>\n",
       "      <td>12.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8375699</th>\n",
       "      <td>2012-02-04 19:14:00.000000167</td>\n",
       "      <td>5.70</td>\n",
       "      <td>-736.650000</td>\n",
       "      <td>40.684515</td>\n",
       "      <td>-73.977682</td>\n",
       "      <td>40.680952</td>\n",
       "      <td>2</td>\n",
       "      <td>662.672318</td>\n",
       "      <td>19.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>2947.782965</td>\n",
       "      <td>19.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8375956</th>\n",
       "      <td>2012-11-12 13:27:00.00000024</td>\n",
       "      <td>11.00</td>\n",
       "      <td>-98.450000</td>\n",
       "      <td>40.733915</td>\n",
       "      <td>-74.014650</td>\n",
       "      <td>40.711485</td>\n",
       "      <td>5</td>\n",
       "      <td>24.435360</td>\n",
       "      <td>13.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2012</td>\n",
       "      <td>1275.449708</td>\n",
       "      <td>13.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8699188</th>\n",
       "      <td>2013-07-01 21:00:00.00000057</td>\n",
       "      <td>10.50</td>\n",
       "      <td>-100.616667</td>\n",
       "      <td>40.771107</td>\n",
       "      <td>-73.983080</td>\n",
       "      <td>40.754525</td>\n",
       "      <td>1</td>\n",
       "      <td>26.633592</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "      <td>1388.480374</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8717348</th>\n",
       "      <td>2011-10-16 19:51:00.000000211</td>\n",
       "      <td>10.50</td>\n",
       "      <td>-736.333333</td>\n",
       "      <td>40.762032</td>\n",
       "      <td>-74.000562</td>\n",
       "      <td>40.762330</td>\n",
       "      <td>2</td>\n",
       "      <td>662.332771</td>\n",
       "      <td>19.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2011</td>\n",
       "      <td>2960.824653</td>\n",
       "      <td>19.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8791431</th>\n",
       "      <td>2012-04-10 09:17:00.000000200</td>\n",
       "      <td>19.70</td>\n",
       "      <td>-736.416665</td>\n",
       "      <td>40.774592</td>\n",
       "      <td>-74.014332</td>\n",
       "      <td>40.703042</td>\n",
       "      <td>5</td>\n",
       "      <td>662.402337</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>2958.492385</td>\n",
       "      <td>9.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8822353</th>\n",
       "      <td>2012-07-31 16:40:00.00000099</td>\n",
       "      <td>9.30</td>\n",
       "      <td>-740.000000</td>\n",
       "      <td>40.740135</td>\n",
       "      <td>-73.987280</td>\n",
       "      <td>40.750772</td>\n",
       "      <td>1</td>\n",
       "      <td>666.012720</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2012</td>\n",
       "      <td>2779.545910</td>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8868439</th>\n",
       "      <td>2012-04-04 15:30:00.00000028</td>\n",
       "      <td>7.30</td>\n",
       "      <td>-84.566665</td>\n",
       "      <td>40.756757</td>\n",
       "      <td>-73.974955</td>\n",
       "      <td>40.755975</td>\n",
       "      <td>1</td>\n",
       "      <td>10.591710</td>\n",
       "      <td>15.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>554.042594</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9002880</th>\n",
       "      <td>2012-04-26 20:06:00.000000154</td>\n",
       "      <td>5.30</td>\n",
       "      <td>-743.200000</td>\n",
       "      <td>40.783482</td>\n",
       "      <td>-73.942883</td>\n",
       "      <td>40.793930</td>\n",
       "      <td>1</td>\n",
       "      <td>669.257117</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>2616.062546</td>\n",
       "      <td>20.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9146152</th>\n",
       "      <td>2012-01-15 00:54:00.000000183</td>\n",
       "      <td>4.90</td>\n",
       "      <td>-738.283332</td>\n",
       "      <td>40.764112</td>\n",
       "      <td>-73.983995</td>\n",
       "      <td>40.760172</td>\n",
       "      <td>3</td>\n",
       "      <td>664.299337</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>2863.748828</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9223770</th>\n",
       "      <td>2012-07-06 23:43:00.00000058</td>\n",
       "      <td>6.50</td>\n",
       "      <td>-737.016667</td>\n",
       "      <td>40.744075</td>\n",
       "      <td>-73.986397</td>\n",
       "      <td>40.726475</td>\n",
       "      <td>1</td>\n",
       "      <td>663.030270</td>\n",
       "      <td>23.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2012</td>\n",
       "      <td>2927.685382</td>\n",
       "      <td>23.716667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9421322</th>\n",
       "      <td>2009-08-26 11:55:00.00000023</td>\n",
       "      <td>7.30</td>\n",
       "      <td>-768.550000</td>\n",
       "      <td>40.757812</td>\n",
       "      <td>-73.997040</td>\n",
       "      <td>40.740007</td>\n",
       "      <td>1</td>\n",
       "      <td>694.552960</td>\n",
       "      <td>11.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2009</td>\n",
       "      <td>1327.357865</td>\n",
       "      <td>11.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9524328</th>\n",
       "      <td>2012-05-06 10:33:00.00000068</td>\n",
       "      <td>5.70</td>\n",
       "      <td>-736.466667</td>\n",
       "      <td>40.749735</td>\n",
       "      <td>-73.988095</td>\n",
       "      <td>40.734597</td>\n",
       "      <td>1</td>\n",
       "      <td>662.478572</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>2954.575635</td>\n",
       "      <td>10.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9531260</th>\n",
       "      <td>2013-04-12 16:46:00.000000104</td>\n",
       "      <td>57.33</td>\n",
       "      <td>-86.200000</td>\n",
       "      <td>40.645052</td>\n",
       "      <td>-73.981265</td>\n",
       "      <td>40.742112</td>\n",
       "      <td>1</td>\n",
       "      <td>12.219120</td>\n",
       "      <td>16.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>639.661176</td>\n",
       "      <td>16.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9580480</th>\n",
       "      <td>2011-12-16 23:55:00.00000066</td>\n",
       "      <td>10.90</td>\n",
       "      <td>-736.100000</td>\n",
       "      <td>40.760110</td>\n",
       "      <td>-74.005497</td>\n",
       "      <td>40.726050</td>\n",
       "      <td>1</td>\n",
       "      <td>662.094504</td>\n",
       "      <td>23.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>2973.453759</td>\n",
       "      <td>23.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9821748</th>\n",
       "      <td>2012-04-02 07:24:00.0000009</td>\n",
       "      <td>18.10</td>\n",
       "      <td>-736.250000</td>\n",
       "      <td>40.756272</td>\n",
       "      <td>-73.953623</td>\n",
       "      <td>40.756272</td>\n",
       "      <td>1</td>\n",
       "      <td>662.296377</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>2962.893415</td>\n",
       "      <td>7.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9893783</th>\n",
       "      <td>2013-02-21 16:21:00.0000007</td>\n",
       "      <td>5.50</td>\n",
       "      <td>-1991.186175</td>\n",
       "      <td>40.759597</td>\n",
       "      <td>-73.982463</td>\n",
       "      <td>40.761860</td>\n",
       "      <td>6</td>\n",
       "      <td>1917.203712</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "      <td>5566.554617</td>\n",
       "      <td>16.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977031</th>\n",
       "      <td>2012-04-21 19:30:00.00000065</td>\n",
       "      <td>5.70</td>\n",
       "      <td>-737.833333</td>\n",
       "      <td>40.753757</td>\n",
       "      <td>-73.967028</td>\n",
       "      <td>40.798477</td>\n",
       "      <td>3</td>\n",
       "      <td>663.866307</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>2884.528995</td>\n",
       "      <td>19.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10035662</th>\n",
       "      <td>2012-01-29 21:30:00.00000072</td>\n",
       "      <td>26.90</td>\n",
       "      <td>-736.200000</td>\n",
       "      <td>40.769035</td>\n",
       "      <td>-73.963710</td>\n",
       "      <td>40.800535</td>\n",
       "      <td>1</td>\n",
       "      <td>662.236291</td>\n",
       "      <td>21.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>2964.517883</td>\n",
       "      <td>21.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10066829</th>\n",
       "      <td>2012-06-04 07:49:00.000000123</td>\n",
       "      <td>4.10</td>\n",
       "      <td>-736.300000</td>\n",
       "      <td>40.758627</td>\n",
       "      <td>-73.973137</td>\n",
       "      <td>40.751107</td>\n",
       "      <td>5</td>\n",
       "      <td>662.326863</td>\n",
       "      <td>7.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>2961.457577</td>\n",
       "      <td>7.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10252061</th>\n",
       "      <td>2011-12-17 01:22:00.00000010</td>\n",
       "      <td>5.70</td>\n",
       "      <td>-779.283332</td>\n",
       "      <td>40.726987</td>\n",
       "      <td>-73.991775</td>\n",
       "      <td>40.726367</td>\n",
       "      <td>4</td>\n",
       "      <td>705.291557</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>769.293463</td>\n",
       "      <td>1.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10396742</th>\n",
       "      <td>2012-01-03 07:26:00.000000145</td>\n",
       "      <td>8.90</td>\n",
       "      <td>-85.733332</td>\n",
       "      <td>40.782777</td>\n",
       "      <td>-73.997735</td>\n",
       "      <td>40.751530</td>\n",
       "      <td>1</td>\n",
       "      <td>11.735639</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>613.697029</td>\n",
       "      <td>7.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10597047</th>\n",
       "      <td>2009-10-16 21:29:00.00000076</td>\n",
       "      <td>6.10</td>\n",
       "      <td>-735.633332</td>\n",
       "      <td>40.745412</td>\n",
       "      <td>-73.990650</td>\n",
       "      <td>40.756132</td>\n",
       "      <td>1</td>\n",
       "      <td>661.642682</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>2995.324639</td>\n",
       "      <td>21.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10630149</th>\n",
       "      <td>2013-01-16 19:55:00.00000039</td>\n",
       "      <td>5.50</td>\n",
       "      <td>-79.650000</td>\n",
       "      <td>40.742982</td>\n",
       "      <td>-73.993150</td>\n",
       "      <td>40.742977</td>\n",
       "      <td>1</td>\n",
       "      <td>5.656850</td>\n",
       "      <td>19.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>296.092965</td>\n",
       "      <td>19.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10654485</th>\n",
       "      <td>2012-10-06 23:57:00.000000267</td>\n",
       "      <td>9.00</td>\n",
       "      <td>-83.283332</td>\n",
       "      <td>40.742322</td>\n",
       "      <td>-73.974690</td>\n",
       "      <td>40.762352</td>\n",
       "      <td>1</td>\n",
       "      <td>9.308664</td>\n",
       "      <td>23.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>2012</td>\n",
       "      <td>487.025505</td>\n",
       "      <td>23.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10818140</th>\n",
       "      <td>2012-03-12 20:22:00.00000031</td>\n",
       "      <td>14.90</td>\n",
       "      <td>-731.733332</td>\n",
       "      <td>40.757977</td>\n",
       "      <td>-73.948365</td>\n",
       "      <td>40.809087</td>\n",
       "      <td>1</td>\n",
       "      <td>657.784969</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "      <td>3182.482973</td>\n",
       "      <td>20.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10833446</th>\n",
       "      <td>2010-08-13 06:27:00.00000028</td>\n",
       "      <td>6.90</td>\n",
       "      <td>-736.166665</td>\n",
       "      <td>40.783645</td>\n",
       "      <td>-73.961427</td>\n",
       "      <td>40.773680</td>\n",
       "      <td>1</td>\n",
       "      <td>662.205238</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "      <td>2966.332589</td>\n",
       "      <td>6.450000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    key  fare_amount  pickup_longitude  \\\n",
       "60442     2012-01-12 13:36:00.000000186         4.90       -736.550000   \n",
       "98652      2011-05-03 07:45:00.00000027        10.50        -78.083332   \n",
       "243342    2012-08-02 10:38:00.000000111         7.30       -736.333333   \n",
       "351119    2012-02-03 07:53:00.000000135         4.10       -736.483332   \n",
       "370663    2012-05-04 23:19:00.000000261         6.10       -736.516667   \n",
       "411093     2012-08-06 11:25:00.00000043         4.50        -80.616665   \n",
       "416859     2012-07-21 15:23:00.00000093         6.50       -736.416665   \n",
       "436142    2012-06-01 21:29:00.000000180         6.10       -128.175950   \n",
       "568830      2013-05-23 15:23:00.0000007        11.50       -735.888333   \n",
       "675500     2011-10-28 10:53:00.00000080        16.90      -1452.988333   \n",
       "739053    2011-02-25 07:55:00.000000207         3.30        -77.183333   \n",
       "888631    2012-04-20 13:11:00.000000143        10.50        -94.366665   \n",
       "914435    2012-03-04 00:10:00.000000131        11.70       -736.250000   \n",
       "1043169    2013-03-15 11:11:00.00000019        36.83       -736.333333   \n",
       "1055960   2011-11-05 23:26:00.000000309        33.70       -735.200000   \n",
       "1252557   2012-12-30 16:13:00.000000196        19.00        -77.233332   \n",
       "1271731    2012-11-09 09:48:00.00000071        25.00        -84.016667   \n",
       "1497332    2011-07-18 07:10:00.00000013         5.30       -736.566667   \n",
       "1535841    2012-04-24 09:02:00.00000096         4.90       -736.166665   \n",
       "1738206   2013-01-10 14:47:00.000000147         7.50        -77.633333   \n",
       "1852898    2012-04-05 08:28:00.00000037         4.10       -797.916665   \n",
       "1893827    2012-05-19 21:04:00.00000033         9.70        -93.916667   \n",
       "1893883    2012-02-08 12:43:00.00000033         5.30        -90.433333   \n",
       "1994336    2012-02-14 09:48:00.00000012        10.50       -736.333333   \n",
       "2031648   2012-10-03 12:38:00.000000106         5.00        -77.050000   \n",
       "2046408    2012-06-25 15:10:00.00000063         4.10        -77.283332   \n",
       "2110338   2013-01-12 01:22:00.000000134        13.50        -80.533332   \n",
       "2134687    2011-12-20 19:39:00.00000035         4.90        -91.866665   \n",
       "2354586   2013-02-01 09:08:00.000000139        16.00        -96.650000   \n",
       "2701329    2012-01-06 12:46:00.00000057        21.30       -736.283332   \n",
       "...                                 ...          ...               ...   \n",
       "7755471   2012-02-27 08:56:00.000000136         3.70       -765.583333   \n",
       "7878777   2012-06-25 13:12:00.000000162        14.10       -740.050000   \n",
       "7958736    2013-01-10 19:46:00.00000084         5.50        -79.450000   \n",
       "8263803    2011-07-27 12:17:00.00000024        49.80        -79.800000   \n",
       "8375699   2012-02-04 19:14:00.000000167         5.70       -736.650000   \n",
       "8375956    2012-11-12 13:27:00.00000024        11.00        -98.450000   \n",
       "8699188    2013-07-01 21:00:00.00000057        10.50       -100.616667   \n",
       "8717348   2011-10-16 19:51:00.000000211        10.50       -736.333333   \n",
       "8791431   2012-04-10 09:17:00.000000200        19.70       -736.416665   \n",
       "8822353    2012-07-31 16:40:00.00000099         9.30       -740.000000   \n",
       "8868439    2012-04-04 15:30:00.00000028         7.30        -84.566665   \n",
       "9002880   2012-04-26 20:06:00.000000154         5.30       -743.200000   \n",
       "9146152   2012-01-15 00:54:00.000000183         4.90       -738.283332   \n",
       "9223770    2012-07-06 23:43:00.00000058         6.50       -737.016667   \n",
       "9421322    2009-08-26 11:55:00.00000023         7.30       -768.550000   \n",
       "9524328    2012-05-06 10:33:00.00000068         5.70       -736.466667   \n",
       "9531260   2013-04-12 16:46:00.000000104        57.33        -86.200000   \n",
       "9580480    2011-12-16 23:55:00.00000066        10.90       -736.100000   \n",
       "9821748     2012-04-02 07:24:00.0000009        18.10       -736.250000   \n",
       "9893783     2013-02-21 16:21:00.0000007         5.50      -1991.186175   \n",
       "9977031    2012-04-21 19:30:00.00000065         5.70       -737.833333   \n",
       "10035662   2012-01-29 21:30:00.00000072        26.90       -736.200000   \n",
       "10066829  2012-06-04 07:49:00.000000123         4.10       -736.300000   \n",
       "10252061   2011-12-17 01:22:00.00000010         5.70       -779.283332   \n",
       "10396742  2012-01-03 07:26:00.000000145         8.90        -85.733332   \n",
       "10597047   2009-10-16 21:29:00.00000076         6.10       -735.633332   \n",
       "10630149   2013-01-16 19:55:00.00000039         5.50        -79.650000   \n",
       "10654485  2012-10-06 23:57:00.000000267         9.00        -83.283332   \n",
       "10818140   2012-03-12 20:22:00.00000031        14.90       -731.733332   \n",
       "10833446   2010-08-13 06:27:00.00000028         6.90       -736.166665   \n",
       "\n",
       "          pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "60442           40.738230         -73.988742         40.748847   \n",
       "98652           40.705727         -74.005315         40.728592   \n",
       "243342          40.766480         -73.987928         40.751742   \n",
       "351119          40.766512         -73.981992         40.771672   \n",
       "370663          40.719095         -74.003952         40.729860   \n",
       "411093          40.780687         -73.970537         40.789157   \n",
       "416859          40.752285         -73.992565         40.742687   \n",
       "436142          40.750000         -73.985985         40.741350   \n",
       "568830          40.760863         -73.989835         40.738443   \n",
       "675500          40.776000         -73.989487         40.740667   \n",
       "739053          40.742635         -73.987172         40.740625   \n",
       "888631          40.711132         -73.997302         40.722352   \n",
       "914435          40.788027         -73.990812         40.750942   \n",
       "1043169         40.755922         -73.871502         40.774202   \n",
       "1055960         40.770092         -73.980187         40.765530   \n",
       "1252557         40.715087         -73.983452         40.773615   \n",
       "1271731         40.775817         -74.004123         40.753345   \n",
       "1497332         40.755662         -73.976190         40.754715   \n",
       "1535841         40.763905         -73.956043         40.763622   \n",
       "1738206         40.784112         -73.967712         40.768627   \n",
       "1852898         40.743922         -73.999382         40.738957   \n",
       "1893827         40.771380         -73.991565         40.754922   \n",
       "1893883         40.773917         -73.954077         40.766490   \n",
       "1994336         40.756345         -73.985257         40.775435   \n",
       "2031648         40.739347         -73.977028         40.738925   \n",
       "2046408         40.715342         -73.996350         40.715770   \n",
       "2110338         40.731727         -73.949583         40.776465   \n",
       "2134687         40.644807         -73.963682         40.765075   \n",
       "2354586         40.704785         -73.973857         40.743597   \n",
       "2701329         40.740510         -73.978643         40.654830   \n",
       "...                   ...                ...               ...   \n",
       "7755471         40.690602         -73.989467         40.690557   \n",
       "7878777         40.726942         -74.000038         40.753362   \n",
       "7958736         40.725545         -74.008190         40.730327   \n",
       "8263803         40.759755         -73.799232         40.645172   \n",
       "8375699         40.684515         -73.977682         40.680952   \n",
       "8375956         40.733915         -74.014650         40.711485   \n",
       "8699188         40.771107         -73.983080         40.754525   \n",
       "8717348         40.762032         -74.000562         40.762330   \n",
       "8791431         40.774592         -74.014332         40.703042   \n",
       "8822353         40.740135         -73.987280         40.750772   \n",
       "8868439         40.756757         -73.974955         40.755975   \n",
       "9002880         40.783482         -73.942883         40.793930   \n",
       "9146152         40.764112         -73.983995         40.760172   \n",
       "9223770         40.744075         -73.986397         40.726475   \n",
       "9421322         40.757812         -73.997040         40.740007   \n",
       "9524328         40.749735         -73.988095         40.734597   \n",
       "9531260         40.645052         -73.981265         40.742112   \n",
       "9580480         40.760110         -74.005497         40.726050   \n",
       "9821748         40.756272         -73.953623         40.756272   \n",
       "9893783         40.759597         -73.982463         40.761860   \n",
       "9977031         40.753757         -73.967028         40.798477   \n",
       "10035662        40.769035         -73.963710         40.800535   \n",
       "10066829        40.758627         -73.973137         40.751107   \n",
       "10252061        40.726987         -73.991775         40.726367   \n",
       "10396742        40.782777         -73.997735         40.751530   \n",
       "10597047        40.745412         -73.990650         40.756132   \n",
       "10630149        40.742982         -73.993150         40.742977   \n",
       "10654485        40.742322         -73.974690         40.762352   \n",
       "10818140        40.757977         -73.948365         40.809087   \n",
       "10833446        40.783645         -73.961427         40.773680   \n",
       "\n",
       "          passenger_count  Euclidean_distance  Hour   Min  Sec  weekday  \\\n",
       "60442                   1          662.561258  13.0  36.0  0.0        3   \n",
       "98652                   1            4.078081   7.0  45.0  0.0        1   \n",
       "243342                  3          662.345405  10.0  38.0  0.0        3   \n",
       "351119                  1          662.501340   7.0  53.0  0.0        4   \n",
       "370663                  1          662.512715  23.0  19.0  0.0        4   \n",
       "411093                  2            6.646133  11.0  25.0  0.0        0   \n",
       "416859                  1          662.424100  15.0  23.0  0.0        5   \n",
       "436142                  3           54.189966  21.0  29.0  0.0        4   \n",
       "568830                  5          661.898498  15.0  23.0  0.0        3   \n",
       "675500                  2         1378.998846  10.0  53.0  0.0        4   \n",
       "739053                  1            3.196162   7.0  55.0  0.0        4   \n",
       "888631                  5           20.369366  13.0  11.0  0.0        4   \n",
       "914435                  1          662.259189   0.0  10.0  0.0        6   \n",
       "1043169                 1          662.461831  11.0  11.0  0.0        4   \n",
       "1055960                 1          661.219813  23.0  26.0  0.0        5   \n",
       "1252557                 6            3.250407  16.0  13.0  0.0        6   \n",
       "1271731                 1           10.012569   9.0  48.0  0.0        4   \n",
       "1497332                 1          662.590477   7.0  10.0  0.0        0   \n",
       "1535841                 1          662.210622   9.0   2.0  0.0        1   \n",
       "1738206                 2            3.665654  14.0  47.0  0.0        3   \n",
       "1852898                 1          723.917283   8.0  28.0  0.0        3   \n",
       "1893827                 2           19.925109  21.0   4.0  0.0        5   \n",
       "1893883                 1           16.479258  12.0  43.0  0.0        2   \n",
       "1994336                 5          662.348076   9.0  48.0  0.0        1   \n",
       "2031648                 2            3.072972  12.0  38.0  0.0        2   \n",
       "2046408                 1            3.286982  15.0  10.0  0.0        0   \n",
       "2110338                 5            6.583901   1.0  22.0  0.0        5   \n",
       "2134687                 1           17.903387  19.0  39.0  0.0        1   \n",
       "2354586                 1           22.676176   9.0   8.0  0.0        4   \n",
       "2701329                 3          662.304695  12.0  46.0  0.0        4   \n",
       "...                   ...                 ...   ...   ...  ...      ...   \n",
       "7755471                 2          691.593866   8.0  56.0  0.0        0   \n",
       "7878777                 1          666.049963  13.0  12.0  0.0        0   \n",
       "7958736                 3            5.441812  19.0  46.0  0.0        3   \n",
       "8263803                 5            6.001862  12.0  17.0  0.0        2   \n",
       "8375699                 2          662.672318  19.0  14.0  0.0        5   \n",
       "8375956                 5           24.435360  13.0  27.0  0.0        0   \n",
       "8699188                 1           26.633592  21.0   0.0  0.0        0   \n",
       "8717348                 2          662.332771  19.0  51.0  0.0        6   \n",
       "8791431                 5          662.402337   9.0  17.0  0.0        1   \n",
       "8822353                 1          666.012720  16.0  40.0  0.0        1   \n",
       "8868439                 1           10.591710  15.0  30.0  0.0        2   \n",
       "9002880                 1          669.257117  20.0   6.0  0.0        3   \n",
       "9146152                 3          664.299337   0.0  54.0  0.0        6   \n",
       "9223770                 1          663.030270  23.0  43.0  0.0        4   \n",
       "9421322                 1          694.552960  11.0  55.0  0.0        2   \n",
       "9524328                 1          662.478572  10.0  33.0  0.0        6   \n",
       "9531260                 1           12.219120  16.0  46.0  0.0        4   \n",
       "9580480                 1          662.094504  23.0  55.0  0.0        4   \n",
       "9821748                 1          662.296377   7.0  24.0  0.0        0   \n",
       "9893783                 6         1917.203712  16.0  21.0  0.0        3   \n",
       "9977031                 3          663.866307  19.0  30.0  0.0        5   \n",
       "10035662                1          662.236291  21.0  30.0  0.0        6   \n",
       "10066829                5          662.326863   7.0  49.0  0.0        0   \n",
       "10252061                4          705.291557   1.0  22.0  0.0        5   \n",
       "10396742                1           11.735639   7.0  26.0  0.0        1   \n",
       "10597047                1          661.642682  21.0  29.0  0.0        4   \n",
       "10630149                1            5.656850  19.0  55.0  0.0        2   \n",
       "10654485                1            9.308664  23.0  57.0  0.0        5   \n",
       "10818140                1          657.784969  20.0  22.0  0.0        0   \n",
       "10833446                1          662.205238   6.0  27.0  0.0        4   \n",
       "\n",
       "          Month  Year     distance  Hour_rate  \n",
       "60442         1  2012  2950.436085  13.600000  \n",
       "98652         5  2011   213.559558   7.750000  \n",
       "243342        8  2012  2960.346022  10.633333  \n",
       "351119        2  2012  2952.198697   7.883333  \n",
       "370663        5  2012  2953.716451  23.316667  \n",
       "411093        8  2012   347.632103  11.416667  \n",
       "416859        7  2012  2957.011847  15.383333  \n",
       "436142        6  2012  2789.602541  21.483333  \n",
       "568830        5  2013  2982.792699  15.383333  \n",
       "675500       10  2011  3124.552641  10.883333  \n",
       "739053        2  2011   167.317832   7.916667  \n",
       "888631        4  2012  1064.379892  13.183333  \n",
       "914435        3  2012  2964.107441   0.166667  \n",
       "1043169       3  2013  2954.333182  11.183333  \n",
       "1055960      11  2011  3015.298011  23.433333  \n",
       "1252557      12  2012   170.170701  16.216667  \n",
       "1271731      11  2012   523.718363   9.800000  \n",
       "1497332       7  2011  2948.453751   7.166667  \n",
       "1535841       4  2012  2966.765638   9.033333  \n",
       "1738206       1  2013   191.793184  14.783333  \n",
       "1852898       4  2012   205.063412   8.466667  \n",
       "1893827       5  2012  1040.537599  21.066667  \n",
       "1893883       2  2012   861.086638  12.716667  \n",
       "1994336       2  2012  2959.897760   9.800000  \n",
       "2031648      10  2012   160.875585  12.633333  \n",
       "2046408       6  2012   172.139118  15.166667  \n",
       "2110338       1  2013   344.544096   1.366667  \n",
       "2134687      12  2011   936.188149  19.650000  \n",
       "2354586       2  2013  1184.141062   9.133333  \n",
       "2701329       1  2012  2965.228073  12.766667  \n",
       "...         ...   ...          ...        ...  \n",
       "7755471       2  2012  1481.710084   8.933333  \n",
       "7878777       6  2012  2777.927209  13.200000  \n",
       "7958736       1  2013   284.905575  19.766667  \n",
       "8263803       7  2011   314.378528  12.283333  \n",
       "8375699       2  2012  2947.782965  19.233333  \n",
       "8375956      11  2012  1275.449708  13.450000  \n",
       "8699188       7  2013  1388.480374  21.000000  \n",
       "8717348      10  2011  2960.824653  19.850000  \n",
       "8791431       4  2012  2958.492385   9.283333  \n",
       "8822353       7  2012  2779.545910  16.666667  \n",
       "8868439       4  2012   554.042594  15.500000  \n",
       "9002880       4  2012  2616.062546  20.100000  \n",
       "9146152       1  2012  2863.748828   0.900000  \n",
       "9223770       7  2012  2927.685382  23.716667  \n",
       "9421322       8  2009  1327.357865  11.916667  \n",
       "9524328       5  2012  2954.575635  10.550000  \n",
       "9531260       4  2013   639.661176  16.766667  \n",
       "9580480      12  2011  2973.453759  23.916667  \n",
       "9821748       4  2012  2962.893415   7.400000  \n",
       "9893783       2  2013  5566.554617  16.350000  \n",
       "9977031       4  2012  2884.528995  19.500000  \n",
       "10035662      1  2012  2964.517883  21.500000  \n",
       "10066829      6  2012  2961.457577   7.816667  \n",
       "10252061     12  2011   769.293463   1.366667  \n",
       "10396742      1  2012   613.697029   7.433333  \n",
       "10597047     10  2009  2995.324639  21.483333  \n",
       "10630149      1  2013   296.092965  19.916667  \n",
       "10654485     10  2012   487.025505  23.950000  \n",
       "10818140      3  2012  3182.482973  20.366667  \n",
       "10833446      8  2010  2966.332589   6.450000  \n",
       "\n",
       "[117 rows x 16 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pickup_longitude.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.060544e+07\n",
       "mean     4.075125e+01\n",
       "std      3.062038e-02\n",
       "min      4.049619e+01\n",
       "25%      4.073561e+01\n",
       "50%      4.075388e+01\n",
       "75%      4.076839e+01\n",
       "max      4.091592e+01\n",
       "Name: dropoff_latitude, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropoff_latitude.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop(((df[df['pickup_latitude']<-90])|(df[df['pickup_latitude']>90])).index, axis=0)\n",
    "df = df.drop(((df[df['pickup_longitude']<-180])|(df[df['pickup_longitude']>180])).index, axis=0)\n",
    "df = df.drop(((df[df['dropoff_latitude']<-90])|(df[df['dropoff_latitude']>90])).index, axis=0)\n",
    "df = df.drop(((df[df['dropoff_longitude']<-180])|(df[df['dropoff_longitude']>180])).index, axis=0)\n",
    "\n",
    "\n",
    "df = df.drop(((df[df['pickup_latitude']<-90])|(df[df['pickup_latitude']>90])).index, axis=0)\n",
    "df = df.drop(((df[df['pickup_longitude']<-180])|(df[df['pickup_longitude']>180])).index, axis=0)\n",
    "df = df.drop(((df[df['dropoff_latitude']<-90])|(df[df['dropoff_latitude']>90])).index, axis=0)\n",
    "df = df.drop(((df[df['dropoff_longitude']<-180])|(df[df['dropoff_longitude']>180])).index, axis=0)\n",
    "\n",
    "df = df[df['pickup_longitude'] < -73.699215]\n",
    "df = df[df['pickup_longitude'] > -74.257159]\n",
    "df = df[(df['pickup_latitude'] > 40.495992) & (df['pickup_latitude'] < 40.916)]\n",
    "df = df[df['dropoff_longitude'] < -73.699215]\n",
    "df = df[df['dropoff_longitude'] > -74.257159]\n",
    "df = df[(df['dropoff_latitude'] > 40.495992) & (df['dropoff_latitude'] < 40.916)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[df['pickup_longitude'] < -73.699215]\n",
    "df = df[df['pickup_longitude'] > -74.257159]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1ElLNfZPYV5KkKZto2HwlyZokr0/yeuCLwJfG2yHJE5N8J8n3k9yc5K9a/Ygk1yQZTvLp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\nUEmSOs2gkiR12v8HI0oFJbTbXj4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10adbf630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pylab\n",
    "bins=[-50,0,10,20,30,50,70,80,90,100,150,200,250,300,800,1000,10000]\n",
    "plt.hist(df['fare_amount'],bins,histtype='bar')\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Count')\n",
    "plt.xlim(-50,1000)\n",
    "plt.title('Histogram of Fare')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning\n",
    "\n",
    "\n",
    "Dropped NA cells\n",
    "\n",
    "Dropped latitude and Longitude outside the New York Region i.e Latitude Range (40.495992,40.916) and Longitude Range (-74.257159,-73.699215)\n",
    "\n",
    "Dropped Fare < 0 and Fare > 200\n",
    "\n",
    "Dropped passenger count > 10\n",
    "\n",
    "Dropped cells with Euclidean distance 0\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>Euclidean_distance</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Min</th>\n",
       "      <th>Sec</th>\n",
       "      <th>weekday</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>distance</th>\n",
       "      <th>Hour_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009436</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.640527</td>\n",
       "      <td>17.439167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.079696</td>\n",
       "      <td>16.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>5.250993</td>\n",
       "      <td>16.871111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.013674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.863464</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025340</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.739493</td>\n",
       "      <td>4.511667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019470</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.242295</td>\n",
       "      <td>7.850000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount  pickup_longitude  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5        -73.844311   \n",
       "1    2010-01-05 16:52:16.0000002         16.9        -74.016048   \n",
       "2   2011-08-18 00:35:00.00000049          5.7        -73.982738   \n",
       "3    2012-04-21 04:30:42.0000001          7.7        -73.987130   \n",
       "4  2010-03-09 07:51:00.000000135          5.3        -73.968095   \n",
       "\n",
       "   pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \\\n",
       "0        40.721319         -73.841610         40.712278                1   \n",
       "1        40.711303         -73.979268         40.782004                1   \n",
       "2        40.761270         -73.991242         40.750562                2   \n",
       "3        40.733143         -73.991567         40.758092                1   \n",
       "4        40.768008         -73.956655         40.783762                1   \n",
       "\n",
       "   Euclidean_distance  Hour   Min   Sec  weekday  Month  Year  distance  \\\n",
       "0            0.009436  17.0  26.0  21.0        0      6  2009  0.640527   \n",
       "1            0.079696  16.0  52.0  16.0        1      1  2010  5.250993   \n",
       "2            0.013674   0.0  35.0   0.0        3      8  2011  0.863464   \n",
       "3            0.025340   4.0  30.0  42.0        5      4  2012  1.739493   \n",
       "4            0.019470   7.0  51.0   0.0        1      3  2010  1.242295   \n",
       "\n",
       "   Hour_rate  \n",
       "0  17.439167  \n",
       "1  16.871111  \n",
       "2   0.583333  \n",
       "3   4.511667  \n",
       "4   7.850000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pearson Correlation\n",
    "\n",
    "Correlation is calcualted for the requested things. \n",
    "\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pearson_correlation of entire data (0.889261602906746, 0.0) (-0.02992658338056006, 0.0) (-0.017581156528025905, 0.0) (0.8815326938132544, 0.0)\n"
     ]
    }
   ],
   "source": [
    "#pre-processing done\n",
    "import scipy.stats as sc\n",
    "A=sc.pearsonr(df.Euclidean_distance, df.fare_amount)\n",
    "B=sc.pearsonr(df.Hour_rate, df.distance)\n",
    "C=sc.pearsonr(df.Hour_rate, df.fare_amount)\n",
    "D= sc.pearsonr(df.distance, df.fare_amount)\n",
    "print(\"pearson_correlation of entire data\",A,B,C,D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation for Euclidean distance and fare_amount 0.8815326938132544\n",
      "Correlation for Euclidean distance and Time of the day -0.02992658338056006\n",
      "Correlation for Hour_rate and fare_amount -0.017581156528025905\n",
      "Correlation for distance and fare_amount 0.889261602906746\n"
     ]
    }
   ],
   "source": [
    "print(\"Correlation for Euclidean distance and fare_amount\", D[0])\n",
    "print(\"Correlation for Euclidean distance and Time of the day\", B[0])\n",
    "print(\"Correlation for Time of the day and fare_amount\", C[0])\n",
    "print(\"Correlation for distance and fare_amount\", A[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the relation between the variables\n",
    "Distance and fare are highly correlated with the value 0.881. \n",
    "\n",
    "Distance and Time of the day are negatively correlated with -0.029.\n",
    "\n",
    "Fare and Time of the day are negatively correlated with -0.017.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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mIc7MUbYVt/JgGkll+JhqlrYPNZtIDSJr28o6ytnJ8t62znHOYDfthS9fVNKz\nyfPcSYQDA/XzOLKazJLavdn5EdL3IMNppcAoYjDUqu+4QshbQHwZwMKQc53rZggIAH8M4D8ADCRc\ndxuAsbTymxIQvb0zmUkRZptGygxR78tSrzzv26gAkgxAahDN1DfuWp8ZzN4rVINw017Y40k2/cnJ\neoZqz5PahzXXSOGTtQ2S3oFP08kC+3zWMW7Lmj279t6KYOKNaKMdjrwFxGoAzwK4BcDNlgKuqxMQ\nAF4H4FEABzrnHQigJ9p+CYANABaklZ/LehA2u2RRiLNXN1pOs53bNV+1MptmGrNyTXxpZSSZ4uJ8\nJm45cQwprr3TTGSzZ5vr+vrizSVxGmNaH/FpGnbf6Kj5tQMfuX7F5GR4ksOk5/P5SrJAaiBSI5Jl\n5m3aYlYNwoO8BcRpPkq55iYAGwG8AGA9gLcBeDwyH9WFswL4AwCPRPvuA3BOSL1yMzG5QiKEUYXC\nltVs7vuQTp7F3kwUXqdmnJO+e8fZyOUoOW4FN1mGFQy9vTPTeMjImImJmQ5ZOZqVI24LlxmPj5tz\nBwaSNTuX6UnmZ8+xg5PRUb/dPc2X4mt/dwa0FJqSGbvO3zhNJ+4dhwj5NA3RhtfKOsn7JjnPs6AI\ni0AV7h2A3KOYyki5rkntc9DlNZKRKm6zHSZphBWSsVOOrOWINYkpufdsZHQdd9xnq7eM28Iyu76+\net+By+RlGfLZXAYpj1tHs20zKxjsfteE45qQXO1FMr64Pubut/ewvgzJ7GWfSTJFSaevz8QVp0H4\n2r/RkXyo1mbbyIa5xvW3ZoNDitBGqnDvAOStQWwHsC2iXQCmAWwLubZIylVAuIurZJ1EZFGU/yBE\ngwjN+Z/EqEMYf4h9PutzJTEy16FpbeC+Nolr/7hRaZwGY5mXdWi79nK5VrUUrG50ji+239UgbJ3k\n+xscrNdupBbqC69tJH26vc46tptdYEj2oaTvJs3ZLd9rM34D1SBiUZgGAYAALAXwsazX5k25RjGF\njpLTkOW6LJ0opNxGTEHyY0wLEW3GvOAijkkkCViZFrqZCC+fCVGOfq0gsMd80Uous7b7rA9AmqJC\n+0Tc+/NpFKFMNMTkk5fzNk2DyFK3LOeE1q3EDLvVKNzEBOD+Rq/Ni3IVEDb7ZlwETGgHy5vpN1Ju\nlvKz1CFPtdmdEJVkPpHH3dF2FvODaxaS5pg4U5K8TvYLqRX4rpf1T5o/4tNofMLLze3krpRn/SOu\n3yxJqwzRSrOikf4f55zPk6mX3OTTauRtYjpX0JsAfAzAf4RcWyTluuRo2khKdrC4jpu1Qyedn6dZ\nKusIzcek8qiTvN5lmpaRDQzkNhsvAAAgAElEQVSEma9CR6vyWazwsWYs68uQGsDAgNECiGqRQe5C\nRb57W3ONjCJK0iDcdRGsoJPagsvUXSEqI4LkHAN7Hxl1JJ3+7ruw93W1xyRtLqnP2jr5hJ+8j+0D\nVhtz65gnU1cNog55C4h/FLQymuh2UMi1RVJuAsKaLnp6kpeblMLB13Ht/kbNH/IeWZaIzBuuPyKv\nj8snZC0TcZ3FEiEMLE7jk9qCHanKUbWtg2SwSY5maVKyTD0ufNOd5yAZpu8e0u9g//v6nc8HIR2/\ndr87czmuD7vv231f8tmS5mq4Ay1XiMr7uAsF2bq7712ZeiHQKKb0FvIzBJ+6G2pucplSVm1DfrRZ\nFplv5ENKeiZXg/AJxKxt4jJxySzcdnfLihPIcQxPMkxrNnRNNi6zk8yqry9ZUNgyrd9G9iNXIE1O\nztQWrEPY1SDktdZRL/uBz9Tkmtvk6F2W7w46fGuEuyYqWYZtn/7+eAdznAYhf6X2ZssdHTXbMty8\nE4RDiZ8hbw1iJEqNsSmirwIYCbm2SMp1HoRMIhY3kgpVdeWHHJdCIGk07XMYJ3W2RlRx95qkMnwj\n4LjrJXNLKleaaVw/gm+msY8x+kwtbtSRrYtPmMn90gHe2ztT+7BMTDJMN7rKmpfs/cfHZwoHKfRt\nXa1D2zfAcNvC1X7sMfs8Pl+Ka8aT1yb1c58GEZISw21fKYhtmVZI+FJ/u3UpMaNNRJ4mspyRt4D4\nHoCLAPRG9McAvhdybZGUa5irZHppKa3TIJlfnLlJlulqCyEfb5J5JbSOIRqARNJHK5/ZtmHafeLu\nadtDzgaW70auRyDvaR3HvlE/YJiRHBVLc5ar0dj6SSewFDr2/lKQu8xOTkZ0maBPyPpmSrvtZcsb\nHKwJtfFx/zNYCu0zIX0iZG1x19QkTXmuGc0l64fwme1KyGj3IaQ9S4S8BcSakH2tptwnyslRZFLi\nNOawEb1vkpPv/LjcPL7OFmILztIxk87NKkSsOUVqEK75Iq3eNppMzra1ZUoGa8/zjdDliN4d5bsC\nRJqLrGlJ1k/6p4Calukm+pucjK+HKzSkkJXtlZbqWgoAdzaynC0un0MKpDwgmbXPDGrft32H0mQl\nAxHihERVNYgqCDGBvAXErQAmAPRENIFOWpPajhiTQh1dhJhkZIfOer673zU/JOXtydJZk5zhjZjW\n4p5bMra4zKXyXMksfTNv40bLLrOR5/X3z7TNy/fsM6m4TmP73wr/JNNOnNCSzy2fM2QCmbyPFI6u\nVhPaj7NCvmNf33G1B3lfGUUW1z5JS8PmjTzvUQUhJpC3gHhxlKBvc+SD+AaARSHXFkm5CYi+PrNP\nMo/R0fqRYiMmmWbNQJJBu/bmuHq4du00uKPAuDpnNUHJNvBpED4GItNwW5v/+Lj/mWy50lnszgi2\no2rrY3JnMs+aNdPhLJ3aLjOz58k5B652YJP1JQkt2TZxM/ZdjdH33PK9uOG1VmDIPFWhCO2rcZqu\n6/B2n8UVEFKjaOUIvGKj/jyhUUzpLVT/YVtmZOFOpopj0ElI64AhJps0Bu3eI+uKXO7HKwWGZZTS\n9Jb0MWWxa/sYnxQacuTu05ykScW2jysMXAY+MBDfRrYdXdOHZPjWJ+IbuVtm7KbasOf4JrHZe/q0\nwaSRuK9t7X1tmKs9Zv0icfNLfGiUcdp7umZEtzxfG1nNstG1JhpBM6P+imkMLvLWIA4D8HEAX8uS\n7rtoytXEJEd4LgPwmXhCPp44ZmgR9yE240NodFEXVwDGmQFCfBVJ5caZ0SQz90X/WCYrTTzynfls\n2r44+8nJmo3e5kKS73fWLP9zx9nMXce0q3VIIecKfKsJ+Pwyvr4jfTRWgMuyfZP3pHkua79NMnkm\n9aG4XFG+iYaWknxTeSFPpl5x7SNvAfEAgHcBeHVouu9WUCECwmUwbmcKYcBuR3Q7U6OCo0i4dZYj\nUpvqOm4FtqT6+nwn0jQkI10kM5YRPb5IIju6l4nzksidVyDLk/MCXEafZjJKEgauv0C2gXTAp+XA\ncttZMlWXEVsBIkOs3TDgJC0vRNjH9eOklB1SQ3ODDqzmlTXZYFbEmUAbERqqQdQJiLtCzms1FSog\nfCYB5jDmHfchuQJDTjqSH1vIrGFfKow0NKOZyGeKMxtJLcYmrhsaqhcw7sSypPa3v/ZZ4+YU2HOt\nhmDPc8uPEyRuuKwcALhM2Scg7HuTGV7te3KFlGWIvjqkvQdXg/BpjbK+Pq3N13/lvqSghbg+IQVt\nUn+Kc+RLQRY6MGqEQfuuqbgm0CjyFhBvAfBhAK8E8ApLAdddHzm15apyC6J5FT+Lfoei/QTg2mhR\noQdDys893bcvbXNcPpwkv0FoVlRpLkmzOcuOLJlAlo6d9jEkPVuWuPSkiB43iihk9O8yVd9IXzJ1\nW9ek0b+878TEzDkFUoNJq5frB7BM0xdNJO8vBZgv/l++syTflzvh0OebcR3GcluO/BuZwW8FtJy3\n4oMcOEhfkf1m3ICBpG8oL8ZecU2gUeQtIP4aZlW4f4dZfnQ1gB8EXHdqJEykgLgawOXR9uUAroq2\nzwLwb5GgOClEa8lNQAwPzzwuo1iyjNZDOq7tlG5GUPux+u7XiAaRNtLPUvckDcL3XHaSmE16J0e1\nvklngGHovjDUJIHjprcA4v0IcTQ4OFMwu/Wy2zZxn28UnDYBTAoby/xcARGX5dYyYTenEnOt3OHh\nmf1J1jVp0GHvFbo0qe+9p/nn5P3cyLSkAZCvv2XVhkPNeCHP2QHCJG8B8TiA2SHneq4ddQTEWgAL\no+2FANZG258BsMx3XhzlqkG4cDu9HBVmYco+pNnsfVpEmt8i6T6hKbGT6h7yXEkzwn3taesmn9dn\nlpEjfHd2svWRZBEILklH8+ioqXucZjMw4HdMJ5Fl2pJZ29G2dZjHaRAW0vRj29U6g32ZYeU50i/h\nag4+n0XaACep/8TlvZqcrL0nuzJgnBBwGbo03TVSJznAcJ8r1P/C3FHmqLwFxDfQYPZWj4B4TmyT\n/Q/gWwBOFsduBTCWVHauAmLu3Hq7ueyorqmit7dWTloHcz94OUIeH4+367oLw7ijVl+kiCtA5L2S\nJta5cD9Q3zNaLaG3t/bBWzNQ3Ixwy0Dcmb5JI29LclRs792MUEgiqVEURT4/hO0Tvv7k27Z9wm2L\nuXNnjph9I3Q3QaCdp9LMSNsdFEjh72tTNw+Vrz/LftJofaym6WrgvjxRjZhgK4a8BcRtALYAuCVr\nmGuSgIj+Pxv9BgkIABcDmAIwtWjRomZaKJnkx+Pr2BZJydPc0Yv7X+YVimOq0tQlzQ9uemyfD0Pu\ny5KaQ6r49t5umXHkCin5oTfDdIlmppGuOiW1pcuwfGYSd1AQV5abN8ldmS+ruTJU03QHO24iRdkX\n4wSLfNeNzJHw1VW2lRw4dZAASEPeAuI0HwVeWw0Tk2REMgRRpoWwtnS5lrCM4PDZYt3kcK4GkeQU\nlOYWm2jOaiNxM6B9zDmt80vBZmfByhGuTHExOTkzRNFu9/XV/08a5WYlGerarEkpD/IJqdmzZ2Z9\nTbp+cDBZk/AxW5exytGwvbfb1u7I2x20hMCngaSZWuJ8Bz4/R5xgSTOpNsLUfaawLkOuAqIZ8giI\naxwn9dXR9tmOk/rutLILERAhqY3d0Xpa9lfJzOXoKE2ll0xB2p7jHHcu4j4EV5i4z+syGXfk5oZz\nupMLLUlncV+feW7XgRwy16BIcs1d7ruxx+MYeZ4k28EKZd9aCjJfkeuP8QliuXKeHclLs6d9h25K\nErcv5THS9o3eZdRSXJBGWnnt8AvkrXG0UIPJW4M4CcA9AHYA2A1gGsC2gOtuArARwAswUVBvAzAc\nmY9+BuD7ABZE5xKATwH4OYCH0vwPzDkKCHdClu1scS9MMtgQm60vRFSasJJsnpIpZB35uPZlWxeX\nGdpnlOf7Qhbtc1thIM1qaWYfX+SPjR5rp4Bw+4E05WR1RidRnGYRN0HQ3lsKLLd/xpmV5ORGdzJh\nUlJG2Q5x/bFRBub6j3y5rNw+maXcPDWMUOQtnFoo7PIWEFMAXgrgfphsrhcB+OuQa4uk3DUI2zlt\nJIuMhXcZtWtXd1N7S8FhX7wdafvKiWPGaXltLHzRL26iOeZ6Ri4donHOPN+sZ5mRVR6PMyPZhHu+\nY3H720G+BWwaoTRtwwpE+0587072vZGR+nr55jhIs5vU0nyz0H2TMK0GYdshSaOVZs6kwUrcd+K2\ng6xXlii9EBTJdFWDqAmI6PdBse/+kGuLpEJnUrsOZfnBxY2afSM7yYDliEk6x9xOLO/thtZapu3O\n9PYloHMTuDHXM2R5f/kRJ41QpcPbp3H52lLmG2qEio5acplpnsLBV28ZxBDHFKRfSGptbk4lV/OL\nq5MbJOBeb/tSmk/Mx+jjTJ4+U6w7IJBpyouAbxDWaL6yDkLeAuJ2ALMBfB5mott7ATwQcm2RVKiA\nkCMqOSLzjfzTUjJbSJORjAyKmz0rqa+v3hEu68ns7/RpJjJZlgzn7eurj5GXCd+so13OfHWfMS6c\nUbZpHLVCEBQlXNy5BLa/2Hds21cmCZTLlLp9SPqFrGYnJ1fG9RWfOSuuX7pzH5JSbdgybL+QgxTf\nSD0ucML2fRt4kdZHsyJJWI2M+AdTXYa8BcSLAewHYH+YlBsfB/DSkGuLpMIEhAwjtR+LtZ+6zj3f\nR53ksJbmpiQNwn5Ybt3sNSH5b+LqIBGXlsL1T7hO+yTVfe5cf7uWyZzko7yEU1weL5/G6Xu/7vuR\n/VD6JCyjlkLXCh+f8PL1Uxll5poV45hzkjBIYvTu+7eDjSQNqhFfRFr9VIPIV0DUXRCQI6lVVKgG\nIUf69sORHxxz/Yc4MFCLeLHrDshzkhzfSfukbdk1FblpFeI+aMvY7ZoA0n4cZ56wtm+rLchoHjmS\ntaG68mNvN6MvA8UJY6KaNugTmHGMUg5arJNX9gu73dtb67vuGt12oCMFfdpoWyJOI0jqe7KsJAEc\nd69QDULep4W2/KqiSAFxX9ZriqJCBMTw8Mwkau45vb0zhYavw8sPSJpl4kaYloHIrKdypChNOtI0\nJEeVcpRky4rLbupjMJZkRE/oNSHHupHknAZ3fol7ruv8tQOB0PkVce9BRjK5EWqSoUozqBucIYWV\nqxHHjfQnJ2vreEhTrV3b2y0vCUlaii/LrSIWRQqItjunLRW+HkQS2Q/CNwq05hqZ6M/HaKX9Ns78\n4ks8JxmGHdm7s6vd+9h7hDBvaV5yNQybvTRtXeE0x2m3UZKT3p1dL7fzaEfrp7JlWeZs1+j2zb73\n1UX6l2z/txqunDzqRjfFBXtkjViKE0auMzzULNUsKqypFCkglma9pihqiYBIWllNMnc56or7SF3m\nbMNF3Y8xK/lmV8vj0uEcx7ziMsvKa2T4Y9n9CWWi4eHaO3En5tn35U4Ukz4ma2qU/cdmbg3pN25Y\ndVw23d7e2uBmYKBea7FakLuGtnt/+d+awmyuLnewk5WRp/k5fMEiRTLwrH6SEiFvJzUBmABwRfR/\nEYATQ64tkgoREHY2qSVftIhdM8DXQVy/hBxNWZONNTHJcFE58pLM19qU3XqOjtZrDa7tVcbR+5iI\nXMeAeWbcvRstZetp0UzYajfS6Gh9QkOgZuqR/cD2Ezm/wbeaX5qvZ3g4/t3FhZom7XO1C1eDkM/p\nq487aInzueU5D6IoBl5EXVuMvAXEddEs559E/4cA3BNybZFUWC4maW+1HUAyabnPag3uiHtgYGbU\nE3OtXJl8zhU49hqZSM2tp3R6x0WduIzAt9a2hStELEOSPg67XoMKh+ZJCgO3b0lN0B2Ru6Njn/D3\nDShs1lQLaS4CzCjf+sdc4WADE3zmozRB5Q6GAH/0UDNmopAIqpD9oaiw5mCRt4C4L/q9X+zr7HkQ\ndsTnS1znrgYmnW3yQ/fFW7sfpr3ejViSIzabNNDWSUZWSYbiznCW9/Cl2pCagk/TcGdQ+0gd0uEk\nI48mJvxmOmmCsj4C951k6cfyHSWF0MrcT3KNCPn+rXCQzmDbf3ymMxmCnRZa2syoPCvDboTBu478\nimoOFnkLiLuiFBtWUBxYBmd1W5zUvolevb0zU0/44q3diCZrm5VrKksG4pt/IOGas+KYtVzjwtVS\n4pyOltJs3JbR5TELudPJnUmfdr59Z66wl5ppSB4rO4qXqSzcd+ZGullBlOZcj4vmizOJFTHqzsqw\nG2HwHaA1SOQtIC6AWQNiPYCPRqm4zwu5tkhqaxSTNUW55gHm+A7oS2Mg7c1yhOnLU+8mB3TnZkg/\ngjxPToaSzMWXisMVOpKh+PL+yIycdl/WJT+7kXwjbpekQI/zE6SZ+2wfSBJIso/Z+9hQ7rQ6ur4J\n91uQ/bzRUXezI/Y8RvwdoDVI5B7FBOBlAC4BcCmAo0KvK5JyExDuR+Y6E12Scdtu8jt3VrKcQObL\nqCkZgJwjIUd0PuZtBYB1qluntdVWpLBw625/JTOwkCYwGYooVX+phbj2bjU5ZaO49hoerhe61rwo\nha9rgvL5HnxmKpekZpGlrq5TXc7VydOBm5b+w/bbOMe3THjZYYy+UeQmICLT0k9DCms1Fa5BJE1q\nChkB+j4wKwiSFncZHJw5WS9JYEkBZwVK3Lmu/8SS/WDSFuPp6alpOq1YI6HTKSn8OE3Yukw97t3F\nzUNIO0fWQwoCe67VMiykGSbEJBPKrJMSCPru7e5LSmvTpcjbxPQvABaFnNtKaquJqQhqZPTtMnwb\nOpm1nKS0G0r5kfU7yYCDpEmHSf3DpyFmIZ9p0zcQkpFTvnBVizRHrrsvlFmHCBJpqk2aB6EaBDMz\nF5HNdXu00E+mNamLpMoJiNHR5PUGfDOd5eSlEAbiyzbrxsSH0sSEagmtoJCBQVwfaCQ4wC4iFBeq\nKusjI/Dc82bNymZKcgVC3sw6SeCoYKhD3gLiNB+FXOsp60gAawRtA/AeAFcC2CD2n5VWVssEhGtO\nkkuFTkzMZPpxH63r0HNTKbjzK6w/QZqQfHZ/oH7CHHN9BFVcrLzP3BQ3QdCOVjUHU/XJRp75liiV\nJkR5PrNfg3Aj7JJG7SHCpJlw0qTzfeamLkZhqTbypMi/8csonfiVAN6f5fq2aBBu/hrfIiiuJjA8\nXC9Q7Ki8t7feRGDLth+THL3PmlVj3L56yaR+cctY2n3WdjwyMnPmq5tG3E03HRLZotQaKkog+wIP\nJFOVfUbm+ZIh2TKQQyYJdOEKBF9eqDTncqgJynVYdzHy1iAaWpM6oNwzAdwZbZdXQLiRI75Rt/wf\nmhQv60fvTqaTH2ncPbOYINwlSicnZ5ob2s0Uq0I2JXq765FGSf3NRsbJgYprcnQn0/nWzpbnu3OD\npIbqSzkS4lz2HUuaRR2yjnyHI28BUcia1ACuB3BptH0lgCcAPBjtH4q55uKoPlOLFi1qpoXyo0Yi\nmuIo1N8gyTUL+NYCaIRZhDpPlapBjfRTO4s/6RypTftMQ26KF/vrDnjseiVxWkLcGtg+YZBVoHQZ\nchcQ0W9ua1LDLGH6KwAHR/8PjoTPrGgy3vVpZbTNSW1DUS1THhrKZ7H7vMjmhpIfZDPPOnv2TGFh\no3Da/aydTFVpX5ng0cI1HdlnmT273o/mLmiUhJBwV9/9sxzrEhQRxZTrmtQA3gDguzHHRgE8nFZG\nywWE9SXYyWzWFmtTIbsfdEhyO5siQy6gYpmDHWE1wigavc5H1s7cbkakVE7yre3gjtLdyW5x2QHk\nMZeBh0yYUwQhbwHxYuS8JjWALwK4SPxfKLbfC+CLaWW0XED41nQoiuS6znJClFX5GzEh+SjUpBVq\nT6/KqLfTqZEV6PIgVwBYDSKL3T/OBKQj/9yQi4AoanIcgLkAngHwIrFvFYCHIh/EzVJgxFFLBUTc\nugxKSko1ctPIMyfPf9DJbG1BqIAgc64fRHQfM78i2v4qM/9B7MltwNjYGE9NTTV2MVG+lVEoOhGj\no8CTTwL77We+mV//unaMyIgFAJg1C9i712yPjABPPVU7b+VKYMUK4IorgNtvB268sXYeAKxfP/Ma\nRaEgonuZeSztvFlp5YjtlzRXJYVCUTls2mSEwM6dhiyGhoALLgAGB822HGhecYURCgMDRnDcfrth\n/suXAzfdVDtvxw7g9NONcLjiipY9kiIcvSnHOWZbUVbIUZ1C0QyGhoBnn639P/FEYO1a08fOPtsw\n++lpc15/vxEgF1xgBMGhh9YEyo03AqeeavaPjQF33QX09gLPPQfcdptqDiVGmgaxmIi2EdF2AMdG\n29uIaDsRbWtFBRUZ0U7hMDgIzJ7dvvsr8sP4ODB3LtDXV9u3di2wfbsRGjfeaIQDYDSBX/8aeNGL\ngG9/2wiMQw6pL2/FCvO7YYP5nT9fNYcKIFFAMHMPM+/PzPOZuTfatv/3b1UlFRXBc88Bu3e3uxaK\nPHDXXcY38MIL5n9vL7B1qxEKPT315+7ZY5g9kREezz0H3H23OTY4WC8IrrjC/L/qqprZaeVKo3Gs\nXNm651MEIU2DUCgUilqcUk+PMRNJnHiiYfZXXVXb19NjBMHrXw88/zzwgQ8YAbB8eU0wWKxYYYSR\n1TIUpYEKCIVCEY7Zs2vagcXUFHDhhYbB9/ebffPnG23hpptqWkWcALBahZqbSgcVEAqFIh3W37Bz\nZ71fwh678UajBRAZ7eHss41AmJ42+wYHawLANSn5tApFKaACQqFQZENPj38eUU+PMUNNT5voJKsZ\nfOYzRouwAkBNSpWBCgiFQuHH4CAwMTFz/86d/mi56Wlgzpx0c5GalCqDxJnUZYfOpFYoCsKsWSZs\n9YgjTERTHPr7geFhM+HNag2A0Q527DC+B50lXTqEzqROmyinUCi6EXv3GrNQknAAgE98Yqbv4NBD\njQlpaChZU5ApONT/UEqoiUmhUGRDbzSuHBjwH/fNdfChCF9EFedUlLnOIRn9ykptWzBISanbyaZ1\nb2ZVtiKytpZltbgsz9aGOiMwm6tqEAqFIh7WVzc6Wu+3sxPhkhzNaSPjIsJby+IAz6IdlaXOPoRI\nkbKSahBKSgWQu7IhUBsN2/928Z+kUXJZRvPtQMnXtECeK8qVlVRAKCkVSL6lRCXjSxMAJWeS3YxQ\nAdG2KCYiegLAdgDTAPYw8xgRLQDwzzBrUj8B4M3M/GxcGQqFokAsWwasWlW/b/nymXmU4kwjcmKc\n/K+oDNo2DyISEGPM/Cux72oAW5j5Y0R0OYAhZv7zuDJ0HoRC0QRsNNKePeZ3dBR4+unaOg55zF+w\nIa86F6JUyGtFuVbjDQBuiLZvALC0jXVRKDobe/bUhMPQELBunVnXYXKyeaepdVCnrRhX5hBPBVJt\nUEURgHUA7gNwL4CLo33PieMk/4v9FwOYAjC1aNGiZoxwSkpKgPE1DA7m6ysYHDRl23J9foyJCXNv\noH2O7Li6dbjfBGV3UgM4JPo9CMADAE51BQKAZ5PKUCe1klKOlCeTHhoyZQ4NzXRm2/9WOPT0tI8h\nx9WtwyOvQgVE20xMzLwh+t0E4OsATgTwNBEtBIDod1O76qdQdCQmJozJRybh881paNb0c9VVtdnU\nbpy//b9smfm97rr2ObDj6lbGOQltQFuc1EQ0F8AsZt4ebX8PwAoAZwB4hmtO6gXM/IG4ctRJrVAE\nYHAQmDevPueRdR739PgZtDqXOxpld1IfDOCHRPQAgLsBfJuZvwPgYwBeS0Q/A/C70X+FQtEoenuB\nI48ENmwA3v1uoxGsXGmWAe3tNUn5br+9pjFceGFrnMvqnK4ENN23QtHJGBkBNm6srQg3NARs21b7\nDxgtYuHCmkYxPZ2uOTSrYaiG0laUXYNQKBRFo7/faADLlpkBUX+/cUdPTxtBMD5ufpctq9nex8bM\nvtNPTy67WVu92vorAdUgFIoqYHTULL5DZBy/APCOd8zUBOy8hjhceCFw003+WdKAjuy7BKpBKBSd\nhKeeMgv4bNlSS3dx3XWGkUtNIA233VZbMxqY6QvQkb1CIiQWtqyk8yCUuoZGR2vJ85qBOxGsS+L+\nS4s2TcxD4DwINTEpFGXHxIQxC1nfQZoZKQt02c/2ok0mPTUxKRRVhh3AjI8bX8GyZeFmpFCocGg/\nSm7SUw1CoSgz7ES2Ihi5OqS7FqpBKBRlxuhobXvWLGBgwMx4tqkwxsfNIGbOHOADHwhfvlLCnfzm\nTkor+ehV0X6oBqFQtAJExt0MmBnM8+YBV1+drBHYEf7QEDB3bnYNQqbTCJn8pugaqAahULQTvb1m\nXYXJSaMZWOFAZJzMzz1XrxHIcFN3LYWrrjKMPat5yU2KV1VNQdNytA8hoU5lJQ1zVSotybBRG0oK\nMI+PmxTY/f3m1xduKrfzDoMs63oHSfVqNBS3rM9aAqDs60HkQSoglEpFfX3MRIb5T0zUmNPk5MyF\ncSzTGxysLZ4jz5fCQZ7XLLMr67yHpHo1yujL+qwlgAqI9BZSUsqPXCbvMqe4lcvswjpxTMyuutbf\nn3xeCCYnjaCRmktZUMRoXzWIWKiASG8hJaV8qL+/XkuwNDBQz5wss5+YqG2PjyczMStABgb852VZ\nMlNH1IoIKiDSW0hJqXkiMkzcrsEsqaenvs9ZAUIUf44LubZzyPEiTDVFoEx16UKECgiNYlIoktDT\nU/+/r8+kze7vN+Gng4MmiR6RiRTq76+dK2c9r1wJzJ5tzttvv9r+MSfS0I1mAsx9rr56ZjTPypXA\n1q1m24ZtJ81tWL68sWioIrBiRWNzOxStRYgUyZMAHApgNYBHATwC4N3R/isBbACwJqKz0spSDUKp\nUOrpMUny3P1xDmVmc4yIubfXmIVstJId6dtzXae1hS+aqafH79dwj1dpVF62upatPgUDZTUxAVgI\n4BXR9nwAjwE4OhIQ789SlgoIpbaS9T1Yx68VDr5zZ8+uRTglMXO53xUkaf4G9TE0ji5ru9IKiBkV\nAP4FwGtVQCiVhlxnsymEUs4AAAu8SURBVCQ5pyHtXElScGSZ35BlZNuKUXCnjrQ79bliUAkBAWAU\nwH8C2D8SEE8AeBDA9QCGYq65GMAUgKlFixY100JKSmFEVD/6tyGnPurvrzctWXPUxETN1OQzF2WB\nvV6aulqFLhtpdypKLyAAzANwL4Bzo/8HA+iBSf/xUQDXp5WhGoRSIvX21raTmHoa9ffXNAA5+rcM\nWvop0hinNUn198+cTe2al9LCVa32Yn0QrUCXjbQ7FaUWEAD6ANwC4LKY46MAHk4rRwWEkpfGxw0T\nHhioMfY430AWss5nwPxa5u46i5lrgmBgoKY1MNebqOJmV7vpNlxIAeWWoQxcEYDSCggABODzAP7O\n2b9QbL8XwBfTylIBodR2snmViOqXA3V9FZaBW0e2m46DOV2D8Dmp3ZnRagJSBKDMAuJkABz5GvaF\ntAJYBeChaP/NUmDEkQoIpUQaGvKHqeZJ0owlmfLERP154+Nmv0/bYE6OULLbMlSWuTbLemgovpwQ\nqNbRdSitgMiTVEB0Oc2enXzcjtB9x/ISHFYbsBqEmzvJkp0xLUNXpZBwR/6S+dtjdttekzbLOhSa\nLbXroAIivYWUlJonKWhcP4fULqwGweyfKCfzNDHXmH9/f3yCvbwYdNHZUlWQlA4qINJbSEmpdWRz\nNlmTUZrvIM6sxDxTmMShaMYcWr76RUoHFRDpLaSkVDzJEFnACAUZWZWWhdW3X07OS7o+hDHr5Lqu\nhAqI9BZSUmoNjY7WNAY362sjo2rpAPeFyFqEMOZWjO5VQJQOoQJCs7kqFEXjiSdMxtctW0xWVpnx\n9fTTa9u+tZd9+1atMmtdy6ytp59uMs/K8kKytyZlf82KuLWjNXNrdREiRcpKqkEoVYaIaosDSee1\njEDyjeblvqR5Elk0AZ8PpFkkZahVDaJ0gJqYUltISan9JOcw+JzPkrnKsNY4p3YIE5ahv82GyLpl\ntjLth6JhhAoINTEpFO3CwABw9tk1s8zXvgZMT5tfC2kmsosCbd0KHHKIMSkdcoi5HphpTvItMHTo\nocYMZcuyv0nnuyYjH6yp6rrryrEgkSIfhEiRspJqEEqVpeFh0w/tyNudWMfsX4zILcddL0KajuIW\nGIpLN550vossUVeK0gFqYkptISWl9pLMx+QeGxmpzaYmqjF+eY41R0mGb49JIZEWSmuR5Xxfmg/m\n9s95UAEVBBUQ6S2kpNR+sozUd2xwsH7Og3RuA7X1KazvYny8Nseit7e2LwvDlJli5foVLuLSfGRl\n0Hkz9HYLqIpABUR6CykplYP6+w0jl/uIzPyJtDTl8rjVKHyr3IXOj3DXmpDlZtFGQpE3Q5+cTBZs\nCmZmVgGR3kJKSuUhy9BClzD1kc3bZP/L0Fq5sp0VKr4IJsv4pTYCxIewNosiTEKqRaRCBUR6Cykp\nlYcGBky/tCNgN0WHj/r6zK/1UQDmWsvMZf4nn2bg+g8spKCyGWRDlzctgw+gDHUoOVRApLeQklL7\nyGX+/f2mX7pJ+uy5NrX57Nk1bSNu4pyriUgT0cSEf6U7iWbmNOjovRJQAZHeQkpK7aGJiZqmYPfZ\n0bzdNzBQ0yLcNa+TBIN0MLtraUs0EsIagiJmaStyR2UFBIDXAVgL4HEAlyedqwJCqZKU5DD2zZaW\n5M5v8G27cyNaPV9BtYjSo5ICAkAPgJ8DeAmA2QAeAHB03PkqIJRKRa6DeXy83pwTYsf3aQTj4/Up\nOEI0iHba4NUHUHpUVUC8EsAt4v8HAXww7vzcBITbmbWDtw6NjjbzGqWGhHzqSFjRYQgVEGXLxXQI\ngKfE//XRvn0goouJaIqIpjZv3tz4nWy65MnJmTlsQtIkK/JBo+mm80pTnfSu80yFrVBUEGSESTlA\nRG8C8Dpm/pPo/4UAxpn5Ut/5Y2NjPDU11coqKhQKReVBRPcy81jaeWXTIDYAOFT8H4n2KRQKhaLF\nKJuAuAfA4UR0GBHNBvCHAG5uc50UCoWiK9Hb7gpIMPMeIroUwC0wEU3XM/Mjba6WQqFQdCVKJSAA\ngJn/FcC/trseCoVC0e0om4lJoVAoFCWBCgiFQqFQeKECQqFQKBRelGoeRFYQ0WYATzZRxAEAfpVT\ndcqCTnwmQJ+rSujEZwI667lezMwHpp1UaQHRLIhoKmSySJXQic8E6HNVCZ34TEDnPlcS1MSkUCgU\nCi9UQCgUCoXCi24XEJPtrkAB6MRnAvS5qoROfCagc58rFl3tg1AoFApFPLpdg1AoFApFDFRAKBQK\nhcKLrhQQRPQ6IlpLRI8T0eXtrk9eIKIniOghIlpDRJVdKIOIrieiTUT0sNi3gIi+R0Q/i36H2lnH\nrIh5piuJaEP0vtYQ0VntrGMjIKJDiWg1ET1KRI8Q0buj/ZV9XwnPVPn3lRVd54Mgoh4AjwF4LcyK\ndfcAWMbMj7a1YjmAiJ4AMMbMlZ7MQ0SnAtgB4PPM/NvRvqsBbGHmj0VCfYiZ/7yd9cyCmGe6EsAO\nZv6bdtatGRDRQgALmfk+IpoP4F4ASwH8MSr6vhKe6c2o+PvKim7UIE4E8Dgz/4KZdwP4IoA3tLlO\nCgFmvh3AFmf3GwDcEG3fAPPBVgYxz1R5MPNGZr4v2t4O4CcwywRX9n0lPFPXoRsFROq61xUGA/gu\nEd1LRBe3uzI542Bm3hht/xLAwe2sTI64lIgejExQlTHD+EBEowCWALgLHfK+nGcCOuh9haAbBUQn\n42RmfgWA3wdwSWTW6DiwsYt2gm30OgC/BeA4ABsB/N/2VqdxENE8AF8F8B5m3iaPVfV9eZ6pY95X\nKLpRQHTsutfMvCH63QTg6zDmtE7B05Ft2NqIN7W5Pk2DmZ9m5mlm3gtgJSr6voioD4aR/hMzfy3a\nXen35XumTnlfWdCNAqIj170mormRQw1ENBfAmQAeTr6qUrgZwFuj7bcC+Jc21iUXWAYa4Y2o4Psi\nIgLwWQA/YeaPi0OVfV9xz9QJ7ysrui6KCQCi8LS/Q23d64+2uUpNg4heAqM1AGYp2S9U9bmI6CYA\np8OkV34awIcBfAPAlwAsgknx/mZmrozTN+aZTocxVzCAJwD8qbDbVwJEdDKAOwA8BGBvtPsvYGz2\nlXxfCc+0DBV/X1nRlQJCoVAoFOnoRhOTQqFQKAKgAkKhUCgUXqiAUCgUCoUXKiAUCoVC4YUKCIVC\noVB4oQJCoQBARNNRhs5HiOgBInofEc2Kjo0R0bUJ144S0VtaV1uFojXQMFeFAgAR7WDmedH2QQC+\nAOBOZv5wwLWnA3g/M7++2FoqFK2FahAKhYMoVcnFMInZiIhOJ6JvAQARnSbWA7g/mr3+MQCnRPve\nG2kUdxDRfRG9Krr2dCK6jYi+QkQ/JaJ/imbtgohOIKIfRdrL3UQ0n4h6iOgaIronShD3p+1qE0V3\norfdFVAoyghm/kW0dshBzqH3A7iEme+MkrntAnA5hAZBRAMAXsvMu4jocAA3ARiLrl8C4OUA/gvA\nnQB+h4juBvDPAM5n5nuIaH8AOwG8DcBWZj6BiOYAuJOIvsvM64p8doXCQgWEQpENdwL4OBH9E4Cv\nMfP6SAmQ6APw90R0HIBpAEeIY3cz83oAIKI1AEYBbAWwkZnvAQCbDZWIzgRwLBG9Kbr2RQAOB6AC\nQtESqIBQKDyIcltNw2QhPcruj1ZI+zaAs2BG9L/nufy9MPmWFsOYcXeJY/8ttqeR/A0SgHcy8y0N\nPYRC0STUB6FQOCCiAwF8GsDfsxPFQUS/xcwPMfNVMJmBXwZgO4D54rQXwWgEewFcCJMUMglrASwk\nohOie8wnol4AtwB4R5R6GkR0RJSpV6FoCVSDUCgM+iOTTx+APQBWAfi457z3ENGrYbJ8PgLg36Lt\naSJ6AMDnAPwDgK8S0R8B+A6A55NuzMy7ieh8AJ8kon4Y/8PvAvh/MCao+yJn9mZUaOlORfWhYa4K\nhUKh8EJNTAqFQqHwQgWEQqFQKLxQAaFQKBQKL1RAKBQKhcILFRAKhUKh8EIFhEKhUCi8UAGhUCgU\nCi/+P50J4vZHHDnnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a5573a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZTtxBSZxOLTzKb7q3qeCIdOaamcoKdOaamZHXCp6zGFldpxpcT5KvvgZqcBN8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\nbObDBhG5EtgK7AB6vJ//noydvBraPlf5F1JE+w9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a854da4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ir8Z1xr8nxF+cezg0kbJC0WMSZVMPSyJPXqoed94VtSEmvbnGNUAoO6COjsGC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\n9QAR/RmR5g9EWxYYa+CtAPaPtrwBAOxARNsx80s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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a854e52b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualisation for the question 3\n",
    "plt.scatter(df.distance, df.fare_amount, s=3, c='r', label='Fare-Distance')\n",
    "# plt.scatter(subset_b.col1, subset_b.col2, s=60, c='r', label='col3 <= 300') \n",
    "plt.legend()\n",
    "plt.ylabel('Fare-amount')\n",
    "plt.xlabel('Distance')\n",
    "plt.title('Fare-amount v/s Distance')\n",
    "plt.show()\n",
    "\n",
    "# plt.scatter(df.Euclidean_distance, df.fare_amount, s=3, c='y', label='Fare-Distance')\n",
    "# # plt.scatter(subset_b.col1, subset_b.col2, s=60, c='r', label='col3 <= 300') \n",
    "# plt.legend()\n",
    "# plt.ylabel('Fare-amount')\n",
    "# plt.xlabel('Euclidean_Distance')\n",
    "# plt.title('Fare-amount v/s Distance')\n",
    "# plt.show()\n",
    "\n",
    "plt.scatter(df.Hour_rate, df.distance, s=3, c='g', label='Fare-Distance')\n",
    "# plt.scatter(subset_b.col1, subset_b.col2, s=60, c='r', label='col3 <= 300') \n",
    "plt.xlabel('Time of the day')\n",
    "plt.ylabel('Distance')\n",
    "plt.legend()\n",
    "plt.title('Distance v/s Time of the day')\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(df.Hour_rate, df.fare_amount, s=3, c='b', label='Fare-Distance')\n",
    "# plt.scatter(subset_b.col1, subset_b.col2, s=60, c='r', label='col3 <= 300') \n",
    "plt.legend()\n",
    "plt.xlabel('Time of the day')\n",
    "plt.ylabel('Fare-amount')\n",
    "plt.title('Fare-amount v/s Time of the day')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zUy8r8tYgVgP4BYAvAvhcRJcHnJckIL4HYFSUex7AnQB+CuDYhDrPBjAOYHzu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a92f199b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.Euclidean_distance, df.fare_amount, s=3, c='r', label='Fare-Distance')\n",
    "# plt.scatter(subset_b.col1, subset_b.col2, s=60, c='r', label='col3 <= 300') \n",
    "plt.legend()\n",
    "plt.ylabel('Fare-amount')\n",
    "plt.xlabel('Euclidean_Distance')\n",
    "plt.title('Fare-amount v/s Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YlVClzvUGTu55dJ1npWdy7cJ1Pu36GQBVwiszddWUY0D4A4+/EsCPKON1icC7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bvU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\n0nGSfhLdUooYKelCSRtKahPdMuhR9ucvJiuX78SaQDB0BUFSD+9eaSvpsNj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i6Xrgt7i0\nkhG4/n8Xmdn9NU4sLWu3UuOWsFHumkQwdAXDf5Gey+OHUtKHwD6Ra0SuaemzZrZFlkocktajctmj\nxM1O65MuCWQn1ieq0CFpH+As4NfAfSFazqWC4Fx+IkNxcuVYrDovogo6kg7FuTIvxnW5r7NO5WsC\nwXVZPAYCP5O0CJecWs6X+hLgFUmfejmdgbMltcQVjk6EpINw9QI3wjWH7ISLpkzT7LQ+6VIbaa4e\no725/YF7zWySpDU+Wk5SC5wR6GRmp0vqKqm7mf0zqQzLsVh1jjT2fw8AHjOzOVnf7ip5lE287PkN\nMX9yVRMMXcEws9a1PyuxrOGSugJRZNeUWNDH71OI+i3QD7fS3FbSAFwZpgapS85MkDQSZ7h/Iams\n6vgF4m5cBZud/PH/4QpvJzZ09ZSnJE3GuS4HSupAxqa98e+6vzg6GPfZDlQhuC4LiE9g7kpl11yW\nNj1I2hm3ib/8osjM7k0pY7yZbS/pHWBbM1umhEV166sutZwjjesy6gI/1cy+9QEYG6fZiyoisfdp\n+WuZ9/tUV/h0lDk+crIFrmFqphZEJWQXqrh3XoQVXcHwJYYuwIWtv427wnuNFRGCaWTdB2zu5USh\n3QakMi64un6tgNG4Qr3TiUWJNSRd8spfktTDzCbjjBy4aNCkaqwJLJbUnBVRl5sTS5dpaEjaw8xe\niOfQVXm/U3UY9/Pj+XhRM+JMq8OiE1Z0BUOuxFVf4HW/6d0DGGJmh9UytZSsD4Ge5SSfezktcV9A\n4boptAUeMLPEFf/rmS5PAOeVE8Aiaajfe6rzDuP1EUl74QJzegIjcU1uTzazl+pSr6xIusrMrpJr\nP2T4vfPor2UrHxfvVr4E12x5qJlNz0HlQhEMXcGIhdy/DexoZouShN9XI+sx4PxYVZE6o57pMhrY\nFgj5S6sISfcD7+L2sqYCb5jZjLrVKjuSLmFlA4e/n6mgQyA5wXVZPL6Q1A54HBglaTZQW9+q6lgX\n+MAnpMarrCT6Qa8SFVbpIdJHgtYnXWrtLJBAnxpX2GaW2pVVMP4C/BBXyWZz4C1Jo83s5rpVKzOt\n/N/uOI/LE7jP3kG4C6bUSNoEl2vY3w+NwRUb/6I8VYtHWNEVGJ9Q2hYYYWaL/VjirsOrKyE1iU71\nSRf/vE5AVzN7zgcUVJjZvBTnidxO6wE7Ay/44wHAWDPLo/h0g8bvh/bFvSZnAQtr2Rut93hvwAHR\nZ8VH2T5tZrtmkDUKeJDKDWWPN7O98tK3KARDt4aRpmqHf/76uB8bgHGrwv+fVKf6oouk04EzgPZm\ntrlPe7jdzFIX0fWpBSdFLllJGwLDzGyfDOoXBknPAy1xgVRjgFeKsPckaQqu+0W8NdO7ZtY9g6y3\nzWyb2sYCLlInsGaROLRP0lE4t8qRwFHAG5KOqAud6pMuuA7u/YG5AGb2Mdm7RHessu/4DbBpRllF\n4l1gMa6EV2+gl4/CbOjcC4yTdJWkq3Dtg4ZllDVT0gmSKvztBFyH+kAVwh7dmkeaJfyvgL7RlbRP\nbn0O1z9rdetUn3RZZGaLo/BwuVJVWV0jz0t6lsrFqp/LKKswmNlFsNy1dzIugXwDoGkdqlU2ZjZY\n0jO4/UeAU8zsrYziTsXt0d2E+/yNBU4pX8viEQxdoCYaVXEXzaTuvAD1SZeXJf0S10tuL1xXhKey\nCDKzc+XqHkZ7NHeY2Wqvrl/fkHQuzhhshwubvwvnwmzwmNlEYGIOcj4HQqRvAoKhW/NIk5U8osRq\nY3j+KiXSqSxdJHU2s2k56XIZcBrwHnCm1+POpLpUxRu2ksZN0mtmtlOpxwpOM1xN0glmtqSulalP\nSLqVGjwIZnb+alSnQRCCUQqIpF1wEYF3exdfq+hHXlJ7S9GWRtLhxMKXs6425Nqu7IL7gr7qr2qj\nxxLpVI4ukiaY2XaSnq8paCTt67OqCSWdAlWRdFLscBBwZfxxc70OAzGCoSsYkq7ElQLqbmbdJG2E\nq5Lev5apq1KnK3BBJFFu2CFep9+uRh3ewhUFHojb06hEmoRdX32mum7nv01TZSXBuVJFyQbWLMKF\nUDKC67J4HIqr2jERwMy+9Bv6ick5uRpcqa2tzXcbkHQtrmZlrYYuR12OwRnYtXBdxcvhGVy9zQdj\nslsAX+Mi6A4qU34gkJSwUklAMHTFY7GZmaSoGG7LtAIsYaufFMnnX+L2XKKCs01xbVdWpy77mtl1\nkpqa2dVJZNbAj6qsst6LVl4+xDtPQqXnQKBMQh5d8XhU0p+Bdj6x+Tlg6Co61/MJnzcHmCRpmK8I\n8j6ui8Atkm5ZTbpEYdeH5HCuCkk7RAeS+gIV/jDvwIkTc5YXaOBImidprqS5QO/ofjRe1/rVR8KK\nrmCY2Q0+5H0urq7eFWY2ahWdLulqo2pU4Uv5q1KrLh9K+hjYSFK811vkAu2d4lw/Be6Sa/cj3Gv9\nU796viaV0q7m5XW4hHNRxSVrZu+nkRcoPkm9HIEVhGCUAuFrAz5nZgNW0/nqTaBEwtJdGwDPUiL3\nyOckpT1nWz93Ttq5MRmfAAeZ2YdZZQQCgZoJK7oCYa5j8TJJbcv58c0bSdMosWluZl1Wpx7mujhv\nLakJ0M0PTzGz79PI8fUJD8d3O48qpGTc+/smGLlAYNUSDF3x+C8uOGIUlXulrYok0qSuy+1j95vh\nUg3aJzpBvoneUReEe3HVNgR0lHSSmY1OMt/zBG7fcQLld70eL+kRXFulePuhNb1NTyCQG8F1WTCq\nJJMuJ2sSaR6J3tXInWBm2yV9Xl6J3pImAMeZ2RR/3A14KIkuMRnvm1mvpM+vRdbdJYbNMnScDgQC\npQkruoKRZ1WEEoned0tanuid1Mh5YxnRCLfCS/rZa+TrSnaTdHHVB6NE7xQGt3Fk5Py8jyQ1Tjg3\nYqykrczsvZTzVsLMQhHeQGAVE1Z0BcP3RrsG6IlzEwLZ9sN876x4ondz4O20vbMkvRg7XIJzG94Q\nNzg1zO2OSwm4ELi96uNmNiilLncBy4D7/dDxuKapiVdQkj4AfgBMw7kbs0RuRrKa4epmbknl9yus\n6AKBnAgruuJxN6723U24zsynkD1fMnOidxX2i4xlhKREe3Tkm+gNrgTYOUC0ZzkG+GNKGfvloEfE\nfcBkYB/gapzhDcEpgUCOhBVdwYjtab1nZlvFxzLIehzX0XsUbo9uL1zz0y8geYCLpKeBg6Mq9D7M\n/+mEe3Rvm9k2eaYy+KjLLXAruylmtjiDjGoLZ6eU85aZbSvpXTPr7d2oY8ysX1pZgUCgNGFFVzwW\nSWoEfOx7ev0f0CqjrLwSvR8HHpPrCN4ReBL4WcK5eSZ6I+kAnAv0Uy+js6QzzeyZFDKWF87GraAb\n41yhWQpnR6kN30rqhauXmbVbeSAQKEEwdAVB0n1mdiLOqLTAueZ+A+wBlIzErI28AlvMbKhfRT2O\nyz0708zGJpx7bE2J3hm4ERhgZp8ASNoceBpXqDkpZRfOjnGHpLWBy3EXAK2AKzLKCgQCJQiGrjhs\n51vyHI+rbbkAuKQcgeUmeleJkhSwKa5rQT9J/ZK2xskr0dszLzJynqnAvJQyyi6cHWFmUcPWl4HV\nmkAfCKwpBENXHG7HFTbugktkFs5IRX+z/IhmTvT2VF3l/L2a8VrJKdEbXIL2cOBR3OtyJPCmrzmZ\nNFG7auHsU8lYOFvS+sAQYCMz209ST2AnM/tLFnmBQGBlQjBKwZD0JzMbuArlZwpsyeO8lJno7eeV\nStCOSJyo7Qtn740zus9mLZwt6RncPt+vzGxrSWsBb0WBRIFAoHzCiq5g5Gnkykz0jssZBRxpZt/6\n47WBh81snxRi8kj0ziVB27sqXzCzUT7Pr7ukxhldqeua2aOSfuH1WyJpabk6BgKBFQRDF6iJG2P3\no0TvozLI6RAZOQAzmy0pbWTheEl3UjnRe3xaRSRtAtzKigjJMcAFZvZFCjGjgR96gz3C63G01ykt\n8yWtg98LldQPV0czEAjkRDB0gZooJ9E7zlJJm5rZv7yMTpQIcqmFPBK9wbkJH8TtzQGc4Mf2SiFD\nZrZA0mnAn8zseklvZ9AF4GJctOXmkl4FOgBHZJQVCARKEPboAtVSTqJ3FTn7AnfgIgsF/BA4w8ye\nTSknj0Tvt81sm9rGapHxFnA2rvrMaWY2KZ6gn0GntXA5eSJ7NGkgEKiGsKIL1EQ5id7LMbMRfr8v\nqvZxoZnNSCMjj0Rvz0xJJwAP+eNjgZkpZVwI/AL4hzdyXYAXa5lTiSjKswTdJIU2PYFAjoQVXaBG\nJJ0D7EvKRG8/t4eZTa4S1LKceMufBLImAwdWTfQ2sx5JZfh5nXB7dDvh3KdjgfMjt2pafBWaVmY2\nN+W8KPpzPWBn4AV/PAAYa2YHZtEnEAisTFjRBVYir0Rv3P7TGbiglvgVVZTbt0cKtcpO9JZUARxm\nZmVVWJH0IHAWsBR4E2gj6WYz+9+kMqLoT0kjgZ5m9pU/3hAYVo5+gUCgMlmr2geKTevYrRUu0fuT\n2FgizOwMf3d/XJmtOcC3OBfo/il1Gi9puKST5ZrLPoVP9K7BDVhVn6U4V2W59PQruENwpcM6Aydm\nlNUxMnKeb3AXFoFAICeC6zKwypH0KDAXeMAPHQe0NbPEqQo5JnrfhCvC/AgwPyYgjRt1ErANLnrz\nD2b2sqR3zGzrpDJisv4AdGXFnuHRwCdmdl5aWYFAoDTB0AWqJadEbyR9YGY9axtbHWhFE9jogx91\nQUjsRpV0PnAp8A5wAG4Fdr+Z/TCjTofhIlEBRpvZP2p6fiAQSEcwdIFqqSYU/y0z2zalnPtxK5/X\n/fGOwDlm9pMUMspK9I7tO8ZrgEZYin3H6uSvFaVhBAKB+kUIRgnURB6J3gDbAWMlRZGNmwJTJL1H\n8p5y5SZ6R3uL3XHNZJ/AGbuDcM1kU+HTHbbEFbuOSNwBXdIrZraLpHmUCNQxszZpdQoEAqUJK7pA\nteSY6N2ppsfN7PMEMspO9PZzRgMHmNk8f9wal6awawoZt+N6/g0A7sRVMhlnZqel0SUQCKwewoou\nUC15JHp7ObUasgTkkegNsD4Qr6iy2I+lYWcz6y3pXTMbJOlG0jVurbWUmpnNSqlTIBCohmDoAitR\nItH7S/93U+/KTByhmCOn4vbobmJFoneWTgT3AuMkRQEfh5A+b22h/7vAN7udCWyYUsYEVt4rjMja\nPzAQCJQgGLpAKfJM9C6bvBK9AcxssO8BF0U5nmJmb6UU809J7YDrcQYLnAszjR6dU54zEAhkJOzR\nBapFUnNc8eJdcAZuDK5a/3c1Tlw1uowzsx1W93lL4V+XgThjWdbrIulQXG+7Of64HbC7mT2eo8qB\nwBpNMHSBaskj0TtHXcpO9M5Rl0dx5cei3niZX5e8UjgCgUD1BNdloCZ6VUnqflHSB3WkS2QMBvm/\ndeJG9eT5upQqwxe+l4FAjoQvVKAmJvoizvFE79Rdvcshluj9T0okeq9OXWLk+bqMl/Q74DZ/fA4r\n9v0CgUAOBNdloFokfYhLsK6U6A0sIXmid7k6XOnvlkz0NrMTVrUOJXTK7XWR1BK4HPiRHxoF/NbM\n5lc/KxAIpCEYukC15JHonaMuZSd656hLvXldAoFA7QTXZaBa6tkPdh6J3rmQ5+vii0yvdLWZpsh0\nIBComWDoAg2FPBK96yM/i91vBhyOc4EGAoGcCK7LQIPBV2qJt7NJm+jdIKhPOYOBQBEIK7pAg8Hn\nzNVF+bFVRpWal41wnR7a1pE6gUAhCYYuEKhb4jUvlwDTgNAFIRDIkeC6DAQCgUChKVWVIRAIrGIk\n/Tx2/8gqjw1Z/RoFAsUlGLpAoG44Jnb/F1Ue23d1KhIIFJ1g6AKBukHV3C91HAgEyiAYukCgbrBq\n7pc6DgQCZRCCUQKBOkDSUly7IQHNgQXRQ0AzM2tcV7oFAkUjGLpAIBAIFJrgugwEAoFAoQmGLhAI\nBAKFJhi6QCAQCBSaYOgCgUAgUGiCoQsEAoFEbc8nAAAACElEQVRAofl/JKW9XkJNc5sAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104ffde80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(df.drop(['key'],axis=1).corr(),annot=True,fmt=\"0.4f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exciting plot \n",
    "I have added Year, Month, Hour, Weekday, Distance as a part of 7. \n",
    "\n",
    "The dataset think reveals very interesting features. \n",
    "\n",
    "I have presented below with the plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.060482e+07\n",
       "mean     2.011744e+03\n",
       "std      1.866695e+00\n",
       "min      2.009000e+03\n",
       "25%      2.010000e+03\n",
       "50%      2.012000e+03\n",
       "75%      2.013000e+03\n",
       "max      2.015000e+03\n",
       "Name: Year, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Year.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8Zoeh6u5l9wAAAMAmd6R95xIAAIAFEC45LFTViVX1/qq6sapuqKofX3ZPHFxV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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a6bb1e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KfC85WA6322LfneTEqjqhqu6R5OlJLltyTwAAACvvsJq57O47qur5Sa5McmSS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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a728610b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3n7KaDQEAALB4lnvm8m+3LFTV21ehFwAAABbUcuGyppbvPe9GAAAAWFzLhcve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\nlwAAAIz2fwCdqQVO5Er4TAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8cfaba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Interesting observations which you can check from the data\n",
    "# Passenger count v/s Frequency of trips\n",
    "# Rush hour trips v/s Non Rush hour trips\n",
    "# Weekday trips v/s Weekend trips\n",
    "# This clearly shows that New York has night life or (late hours at work) as it has so much traffic/more trips at night.\n",
    "\n",
    "bins=[]\n",
    "for i in range(15):\n",
    "    bins.append(i+1)\n",
    "plt.figure(figsize=(15,7))\n",
    "plt.hist(df['passenger_count'], bins)\n",
    "plt.xlabel('Passenger Count')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,7))\n",
    "bins=[]\n",
    "for i in range(24):\n",
    "    bins.append(i)\n",
    "plt.hist(df['Hour'], bins)\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Frequency of trips - Hour')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "bins=[]\n",
    "for i in range(7):\n",
    "    bins.append(i)\n",
    "plt.figure(figsize=(15,7))\n",
    "plt.hist(df['weekday'], bins)\n",
    "plt.xlabel('Day of the week')\n",
    "plt.ylabel('Frequency of trips - Day of the week')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "bins=[]\n",
    "for i in range(12):\n",
    "    bins.append(i+1)\n",
    "plt.figure(figsize=(15,7))\n",
    "plt.hist(df['Month'], bins)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Frequency of trips - Month')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ysrl1BAAAwMJZNVx29zeydN/li+ffDgAAAItolnsuAQAAYEXCJQAAAMNWDZdV9e/XoxEA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\nYJhwCQAAwLD/D8nF0+95SGowAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3d949470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D+Q7t7puH7S8kOXTYPizJjRPjdg+11eq7l6k/mDluDgAAAKOsGC67+/2znLi7u6pmuvLs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\n0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a5beeac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[]\n",
    "for i in range(2009,2016):\n",
    "    bins.append(i)\n",
    "plt.figure(figsize=(15,7))\n",
    "plt.hist(df['Year'], bins)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Frequency of trips - Year')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Interesting observations\n",
    "\n",
    "Based on the plots, you can see that there is night life or Late office work style in New York City.\n",
    "\n",
    "In the month of december, there are too many trips.\n",
    "\n",
    "This might be because of the holiday season and tourists might have come to New York from all over the world.\n",
    "\n",
    "Lot many trips in weekends because of tourists and holidays.\n",
    "\n",
    "Particularly in the year 2015, there are lot number of trips than any other year in our data base.\n",
    "\n",
    "Weather implication on the number of the trips. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional Features Generated from the data set\n",
    "\n",
    "I have generated the following features:\n",
    "\n",
    "1)Day of the Week\n",
    "\n",
    "2)Month of the year\n",
    "\n",
    "3)Year\n",
    "\n",
    "4)Time of the day\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8050426181483947"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score #set splitting and validation\n",
    "from sklearn.linear_model import LinearRegression \n",
    "\n",
    "X = df.drop(['key','fare_amount'],axis=1)\n",
    "y = df['fare_amount']\n",
    "\n",
    "X_train,X_test,Y_train,Y_test =train_test_split(X,y, test_size=0.2)\n",
    "\n",
    "#Linear Regression\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X_train, Y_train)\n",
    "Y_pred_lin = linreg.predict(X_test)\n",
    "linreg.score(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean Square Error on the test data 4.141296300685397\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(\"Root mean Square Error on the test data\", np.sqrt(mean_squared_error(Y_test, Y_pred_lin)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}\n"
     ]
    }
   ],
   "source": [
    "print(linreg.get_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean Square Error on the test data 4.141296300685397\n"
     ]
    }
   ],
   "source": [
    "linreg_normalize = LinearRegression()\n",
    "linreg_normalize.fit(X_train, Y_train)\n",
    "Y_pred_lin_norm = linreg_normalize.predict(X_test)\n",
    "linreg_normalize.score(X_train, Y_train)\n",
    "\n",
    "print(\"Root mean Square Error on the test data\", np.sqrt(mean_squared_error(Y_test, Y_pred_lin_norm)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients before normalize: \n",
      " [ 5.77379285e+00  7.98961692e+00 -1.11358864e+01 -1.36205207e+01\n",
      "  2.25874860e-02  2.51127377e+02 -7.03771525e+10 -1.17295254e+09\n",
      " -1.95492090e+07 -3.83453369e-02  6.91146851e-02  5.13336182e-01\n",
      " -5.57379961e-01  7.03771525e+10]\n"
     ]
    }
   ],
   "source": [
    "print('Coefficients before normalize: \\n', linreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients after normalize: \n",
      " [ 5.77379285e+00  7.98961692e+00 -1.11358864e+01 -1.36205207e+01\n",
      "  2.25874860e-02  2.51127377e+02 -7.03771525e+10 -1.17295254e+09\n",
      " -1.95492090e+07 -3.83453369e-02  6.91146851e-02  5.13336182e-01\n",
      " -5.57379961e-01  7.03771525e+10]\n"
     ]
    }
   ],
   "source": [
    "print('Coefficients after normalize: \\n', linreg_normalize.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Euclidean_distance</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Min</th>\n",
       "      <th>Sec</th>\n",
       "      <th>weekday</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>distance</th>\n",
       "      <th>Hour_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009436</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.640527</td>\n",
       "      <td>17.439167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.079696</td>\n",
       "      <td>16.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>5.250993</td>\n",
       "      <td>16.871111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.013674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.863464</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025340</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.739493</td>\n",
       "      <td>4.511667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019470</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.242295</td>\n",
       "      <td>7.850000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  Euclidean_distance  Hour   Min   Sec  weekday  Month  \\\n",
       "0                1            0.009436  17.0  26.0  21.0        0      6   \n",
       "1                1            0.079696  16.0  52.0  16.0        1      1   \n",
       "2                2            0.013674   0.0  35.0   0.0        3      8   \n",
       "3                1            0.025340   4.0  30.0  42.0        5      4   \n",
       "4                1            0.019470   7.0  51.0   0.0        1      3   \n",
       "\n",
       "   Year  distance  Hour_rate  \n",
       "0  2009  0.640527  17.439167  \n",
       "1  2010  5.250993  16.871111  \n",
       "2  2011  0.863464   0.583333  \n",
       "3  2012  1.739493   4.511667  \n",
       "4  2010  1.242295   7.850000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train, Y_train)\n",
    "Y_pred_rf = rf.predict(X_test)\n",
    "rf.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extreme Gradient Boosting XGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb \n",
    "X = df.drop(['key','fare_amount'],axis=1)\n",
    "y = df['fare_amount']\n",
    "X_train = xgb.DMatrix(X, label=y)\n",
    "X_test = xgb.DMatrix(tf)\n",
    "params = {'max_depth':7,\n",
    "          'eta':1,\n",
    "          'silent':1,\n",
    "          'objective':'reg:linear',\n",
    "          'eval_metric':'rmse',\n",
    "          'learning_rate':0.05\n",
    "         }\n",
    "num_rounds = 60\n",
    "xb = xgb.train(params, X_train, num_rounds)\n",
    "y_pred_xgb = xb.predict(X_test)\n",
    "print(y_pred_xgb)\n",
    "\n",
    "output = pd.read_csv('/Users/sriramreddy/Downloads/DSF/HW2/all/sample_submission.csv')\n",
    "output['fare_amount'] = y_pred_xgb\n",
    "output.to_csv('/Users/sriramreddy/Downloads/DSF/HW2/all/submission_xgb_first_11.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding data from external data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>Euclidean_distance</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Min</th>\n",
       "      <th>Sec</th>\n",
       "      <th>weekday</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>distance</th>\n",
       "      <th>Hour_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-73.844311</td>\n",
       "      <td>40.721319</td>\n",
       "      <td>-73.841610</td>\n",
       "      <td>40.712278</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009436</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.640527</td>\n",
       "      <td>17.439167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>-74.016048</td>\n",
       "      <td>40.711303</td>\n",
       "      <td>-73.979268</td>\n",
       "      <td>40.782004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.079696</td>\n",
       "      <td>16.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>5.250993</td>\n",
       "      <td>16.871111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-73.982738</td>\n",
       "      <td>40.761270</td>\n",
       "      <td>-73.991242</td>\n",
       "      <td>40.750562</td>\n",
       "      <td>2</td>\n",
       "      <td>0.013674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.863464</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-73.987130</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991567</td>\n",
       "      <td>40.758092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025340</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.739493</td>\n",
       "      <td>4.511667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-73.968095</td>\n",
       "      <td>40.768008</td>\n",
       "      <td>-73.956655</td>\n",
       "      <td>40.783762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019470</td>\n",
       "      <td>7.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.242295</td>\n",
       "      <td>7.850000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount  pickup_longitude  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5        -73.844311   \n",
       "1    2010-01-05 16:52:16.0000002         16.9        -74.016048   \n",
       "2   2011-08-18 00:35:00.00000049          5.7        -73.982738   \n",
       "3    2012-04-21 04:30:42.0000001          7.7        -73.987130   \n",
       "4  2010-03-09 07:51:00.000000135          5.3        -73.968095   \n",
       "\n",
       "   pickup_latitude  dropoff_longitude  dropoff_latitude  passenger_count  \\\n",
       "0        40.721319         -73.841610         40.712278                1   \n",
       "1        40.711303         -73.979268         40.782004                1   \n",
       "2        40.761270         -73.991242         40.750562                2   \n",
       "3        40.733143         -73.991567         40.758092                1   \n",
       "4        40.768008         -73.956655         40.783762                1   \n",
       "\n",
       "   Euclidean_distance  Hour   Min   Sec  weekday  Month  Year  distance  \\\n",
       "0            0.009436  17.0  26.0  21.0        0      6  2009  0.640527   \n",
       "1            0.079696  16.0  52.0  16.0        1      1  2010  5.250993   \n",
       "2            0.013674   0.0  35.0   0.0        3      8  2011  0.863464   \n",
       "3            0.025340   4.0  30.0  42.0        5      4  2012  1.739493   \n",
       "4            0.019470   7.0  51.0   0.0        1      3  2010  1.242295   \n",
       "\n",
       "   Hour_rate  \n",
       "0  17.439167  \n",
       "1  16.871111  \n",
       "2   0.583333  \n",
       "3   4.511667  \n",
       "4   7.850000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>Euclidean_distance</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Min</th>\n",
       "      <th>Sec</th>\n",
       "      <th>weekday</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>distance</th>\n",
       "      <th>Hour_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10999995</th>\n",
       "      <td>2014-06-15 04:14:00.000000100</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-73.987888</td>\n",
       "      <td>40.748692</td>\n",
       "      <td>-73.979342</td>\n",
       "      <td>40.744388</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009569</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.537202</td>\n",
       "      <td>4.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10999996</th>\n",
       "      <td>2009-09-10 09:01:40.0000004</td>\n",
       "      <td>7.3</td>\n",
       "      <td>-73.987770</td>\n",
       "      <td>40.728529</td>\n",
       "      <td>-74.000935</td>\n",
       "      <td>40.720227</td>\n",
       "      <td>1</td>\n",
       "      <td>0.015564</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.896852</td>\n",
       "      <td>9.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10999997</th>\n",
       "      <td>2011-03-10 10:55:00.000000155</td>\n",
       "      <td>11.7</td>\n",
       "      <td>-73.999375</td>\n",
       "      <td>40.731172</td>\n",
       "      <td>-73.978368</td>\n",
       "      <td>40.745368</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025354</td>\n",
       "      <td>10.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.473702</td>\n",
       "      <td>10.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10999998</th>\n",
       "      <td>2012-08-07 13:32:33.0000001</td>\n",
       "      <td>6.5</td>\n",
       "      <td>-73.981785</td>\n",
       "      <td>40.765401</td>\n",
       "      <td>-73.991161</td>\n",
       "      <td>40.751840</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016487</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.057756</td>\n",
       "      <td>13.542500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10999999</th>\n",
       "      <td>2015-01-17 02:38:03.0000001</td>\n",
       "      <td>10.5</td>\n",
       "      <td>-74.002556</td>\n",
       "      <td>40.749977</td>\n",
       "      <td>-73.967850</td>\n",
       "      <td>40.759953</td>\n",
       "      <td>1</td>\n",
       "      <td>0.036111</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.942960</td>\n",
       "      <td>2.634167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    key  fare_amount  pickup_longitude  \\\n",
       "10999995  2014-06-15 04:14:00.000000100          5.0        -73.987888   \n",
       "10999996    2009-09-10 09:01:40.0000004          7.3        -73.987770   \n",
       "10999997  2011-03-10 10:55:00.000000155         11.7        -73.999375   \n",
       "10999998    2012-08-07 13:32:33.0000001          6.5        -73.981785   \n",
       "10999999    2015-01-17 02:38:03.0000001         10.5        -74.002556   \n",
       "\n",
       "          pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "10999995        40.748692         -73.979342         40.744388   \n",
       "10999996        40.728529         -74.000935         40.720227   \n",
       "10999997        40.731172         -73.978368         40.745368   \n",
       "10999998        40.765401         -73.991161         40.751840   \n",
       "10999999        40.749977         -73.967850         40.759953   \n",
       "\n",
       "          passenger_count  Euclidean_distance  Hour   Min   Sec  weekday  \\\n",
       "10999995                1            0.009569   4.0  14.0   0.0        6   \n",
       "10999996                1            0.015564   9.0   1.0  40.0        3   \n",
       "10999997                1            0.025354  10.0  55.0   0.0        3   \n",
       "10999998                1            0.016487  13.0  32.0  33.0        1   \n",
       "10999999                1            0.036111   2.0  38.0   3.0        5   \n",
       "\n",
       "          Month  Year  distance  Hour_rate  \n",
       "10999995      6  2014  0.537202   4.233333  \n",
       "10999996      9  2009  0.896852   9.027778  \n",
       "10999997      3  2011  1.473702  10.916667  \n",
       "10999998      8  2012  1.057756  13.542500  \n",
       "10999999      1  2015  1.942960   2.634167  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I got to know that there is rush hour from 4pm to 8pm i.e from 16 to 20.\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(hour):\n",
    "    return 1 if 16 <= hour <= 20 else 0\n",
    "c=df.head()\n",
    "\n",
    "df[\"peak_hrs\"] = df['Hour'].apply(distance) \n",
    "tf[\"peak_hrs\"] = tf['Hour'].apply(distance) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[02:39:01] Tree method is automatically selected to be 'approx' for faster speed. to use old behavior(exact greedy algorithm on single machine), set tree_method to 'exact'\n",
      "[10.203294  10.309223   4.6396675 ... 54.00065   20.091354   6.986351 ]\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb \n",
    "X = df.drop(['key','fare_amount'],axis=1)\n",
    "y = df['fare_amount']\n",
    "X_train = xgb.DMatrix(X, label=y)\n",
    "X_test = xgb.DMatrix(tf)\n",
    "params = {'max_depth':12,\n",
    "          'eta':1,\n",
    "          'silent':1,\n",
    "          'objective':'reg:linear',\n",
    "          'eval_metric':'rmse',\n",
    "          'learning_rate':0.05\n",
    "         }\n",
    "num_rounds = 90\n",
    "xb = xgb.train(params, X_train, num_rounds)\n",
    "y_pred_xgb = xb.predict(X_test)\n",
    "print(y_pred_xgb)\n",
    "\n",
    "submission = pd.read_csv('/Users/sriramreddy/Downloads/DSF/HW2/all/sample_submission.csv')\n",
    "submission['fare_amount'] = y_pred_xgb\n",
    "submission.to_csv('/Users/sriramreddy/Downloads/DSF/HW2/all/submission_xgb_final.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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