taxi_fare_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading 1M rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv',nrows=1000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore and Analyse the DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key                   object\n",
       "fare_amount          float64\n",
       "pickup_datetime       object\n",
       "pickup_longitude     float64\n",
       "pickup_latitude      float64\n",
       "dropoff_longitude    float64\n",
       "dropoff_latitude     float64\n",
       "passenger_count        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We don't need such big datatypes to represent our dataset efficiently\n",
    "types1 = {'fare_amount': 'float32',\n",
    "         'pickup_longitude': 'float32',\n",
    "         'pickup_latitude': 'float32',\n",
    "         'dropoff_longitude': 'float32',\n",
    "         'dropoff_latitude': 'float32',\n",
    "         'passenger_count': 'uint8'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.astype(types1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key                   object\n",
       "fare_amount          float32\n",
       "pickup_datetime       object\n",
       "pickup_longitude     float32\n",
       "pickup_latitude      float32\n",
       "dropoff_longitude    float32\n",
       "dropoff_latitude     float32\n",
       "passenger_count        uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21 UTC</td>\n",
       "      <td>-73.844315</td>\n",
       "      <td>40.721317</td>\n",
       "      <td>-73.841614</td>\n",
       "      <td>40.712276</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16 UTC</td>\n",
       "      <td>-74.016045</td>\n",
       "      <td>40.711304</td>\n",
       "      <td>-73.979271</td>\n",
       "      <td>40.782005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00 UTC</td>\n",
       "      <td>-73.982735</td>\n",
       "      <td>40.761269</td>\n",
       "      <td>-73.991241</td>\n",
       "      <td>40.750561</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42 UTC</td>\n",
       "      <td>-73.987129</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991570</td>\n",
       "      <td>40.758091</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00 UTC</td>\n",
       "      <td>-73.968094</td>\n",
       "      <td>40.768009</td>\n",
       "      <td>-73.956657</td>\n",
       "      <td>40.783764</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount          pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5  2009-06-15 17:26:21 UTC   \n",
       "1    2010-01-05 16:52:16.0000002         16.9  2010-01-05 16:52:16 UTC   \n",
       "2   2011-08-18 00:35:00.00000049          5.7  2011-08-18 00:35:00 UTC   \n",
       "3    2012-04-21 04:30:42.0000001          7.7  2012-04-21 04:30:42 UTC   \n",
       "4  2010-03-09 07:51:00.000000135          5.3  2010-03-09 07:51:00 UTC   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844315        40.721317         -73.841614         40.712276   \n",
       "1        -74.016045        40.711304         -73.979271         40.782005   \n",
       "2        -73.982735        40.761269         -73.991241         40.750561   \n",
       "3        -73.987129        40.733143         -73.991570         40.758091   \n",
       "4        -73.968094        40.768009         -73.956657         40.783764   \n",
       "\n",
       "   passenger_count  \n",
       "0                1  \n",
       "1                1  \n",
       "2                2  \n",
       "3                1  \n",
       "4                1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing Value Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>999990.000000</td>\n",
       "      <td>999990.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.344579</td>\n",
       "      <td>-71.745049</td>\n",
       "      <td>39.555500</td>\n",
       "      <td>-71.744743</td>\n",
       "      <td>39.546509</td>\n",
       "      <td>1.684924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.818618</td>\n",
       "      <td>12.104804</td>\n",
       "      <td>7.601978</td>\n",
       "      <td>11.410378</td>\n",
       "      <td>8.176702</td>\n",
       "      <td>1.323911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-44.900002</td>\n",
       "      <td>-3377.680908</td>\n",
       "      <td>-3116.285400</td>\n",
       "      <td>-3383.296631</td>\n",
       "      <td>-3114.338623</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>-73.992058</td>\n",
       "      <td>40.734966</td>\n",
       "      <td>-73.991386</td>\n",
       "      <td>40.734047</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>-73.981789</td>\n",
       "      <td>40.752693</td>\n",
       "      <td>-73.980133</td>\n",
       "      <td>40.753166</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>-73.967094</td>\n",
       "      <td>40.767155</td>\n",
       "      <td>-73.963654</td>\n",
       "      <td>40.768127</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>2522.271240</td>\n",
       "      <td>2621.628418</td>\n",
       "      <td>45.581619</td>\n",
       "      <td>1651.553467</td>\n",
       "      <td>208.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count  1000000.000000    1000000.000000   1000000.000000      999990.000000   \n",
       "mean        11.344579        -71.745049        39.555500         -71.744743   \n",
       "std          9.818618         12.104804         7.601978          11.410378   \n",
       "min        -44.900002      -3377.680908     -3116.285400       -3383.296631   \n",
       "25%          6.000000        -73.992058        40.734966         -73.991386   \n",
       "50%          8.500000        -73.981789        40.752693         -73.980133   \n",
       "75%         12.500000        -73.967094        40.767155         -73.963654   \n",
       "max        500.000000       2522.271240      2621.628418          45.581619   \n",
       "\n",
       "       dropoff_latitude  passenger_count  \n",
       "count     999990.000000   1000000.000000  \n",
       "mean          39.546509         1.684924  \n",
       "std            8.176702         1.323911  \n",
       "min        -3114.338623         0.000000  \n",
       "25%           40.734047         1.000000  \n",
       "50%           40.753166         1.000000  \n",
       "75%           40.768127         2.000000  \n",
       "max         1651.553467       208.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key                   0\n",
       "fare_amount           0\n",
       "pickup_datetime       0\n",
       "pickup_longitude      0\n",
       "pickup_latitude       0\n",
       "dropoff_longitude    10\n",
       "dropoff_latitude     10\n",
       "passenger_count       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check how many rows have null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop null since it is negligible in this case \n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>999990.000000</td>\n",
       "      <td>999990.000000</td>\n",
       "      <td>999990.000000</td>\n",
       "      <td>999990.000000</td>\n",
       "      <td>999990.000000</td>\n",
       "      <td>999990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.344448</td>\n",
       "      <td>-71.745125</td>\n",
       "      <td>39.555538</td>\n",
       "      <td>-71.744743</td>\n",
       "      <td>39.546509</td>\n",
       "      <td>1.684941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.818333</td>\n",
       "      <td>12.104649</td>\n",
       "      <td>7.601921</td>\n",
       "      <td>11.410378</td>\n",
       "      <td>8.176702</td>\n",
       "      <td>1.323907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-44.900002</td>\n",
       "      <td>-3377.680908</td>\n",
       "      <td>-3116.285400</td>\n",
       "      <td>-3383.296631</td>\n",
       "      <td>-3114.338623</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>-73.992058</td>\n",
       "      <td>40.734966</td>\n",
       "      <td>-73.991386</td>\n",
       "      <td>40.734047</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>-73.981789</td>\n",
       "      <td>40.752693</td>\n",
       "      <td>-73.980133</td>\n",
       "      <td>40.753166</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>-73.967094</td>\n",
       "      <td>40.767155</td>\n",
       "      <td>-73.963654</td>\n",
       "      <td>40.768127</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>2522.271240</td>\n",
       "      <td>2621.628418</td>\n",
       "      <td>45.581619</td>\n",
       "      <td>1651.553467</td>\n",
       "      <td>208.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count  999990.000000     999990.000000    999990.000000      999990.000000   \n",
       "mean       11.344448        -71.745125        39.555538         -71.744743   \n",
       "std         9.818333         12.104649         7.601921          11.410378   \n",
       "min       -44.900002      -3377.680908     -3116.285400       -3383.296631   \n",
       "25%         6.000000        -73.992058        40.734966         -73.991386   \n",
       "50%         8.500000        -73.981789        40.752693         -73.980133   \n",
       "75%        12.500000        -73.967094        40.767155         -73.963654   \n",
       "max       500.000000       2522.271240      2621.628418          45.581619   \n",
       "\n",
       "       dropoff_latitude  passenger_count  \n",
       "count     999990.000000    999990.000000  \n",
       "mean          39.546509         1.684941  \n",
       "std            8.176702         1.323907  \n",
       "min        -3114.338623         0.000000  \n",
       "25%           40.734047         1.000000  \n",
       "50%           40.753166         1.000000  \n",
       "75%           40.768127         2.000000  \n",
       "max         1651.553467       208.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlier treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 38 negative fares.\n",
      "There are 29 $0 fares.\n",
      "There are 396 fares greater than $100.\n"
     ]
    }
   ],
   "source": [
    "print(f\"There are {len(df[df['fare_amount'] < 0])} negative fares.\")\n",
    "print(f\"There are {len(df[df['fare_amount'] == 0])} $0 fares.\")\n",
    "print(f\"There are {len(df[df['fare_amount'] > 100])} fares greater than $100.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['fare_amount'] > 0) & (df['fare_amount'] < 100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ddd3430c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['passenger_count'].value_counts().plot.bar(color = 'b', edgecolor = 'k')\n",
    "plt.title('Passenger Counts'); plt.xlabel('Number of Passengers'); plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['passenger_count'] > 0) & (df['passenger_count'] < 6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PERCENTILES OF Pickup_latitude  : 2.5% = 40.64\t\t97.5% = 40.8\n",
      "\n",
      "PERCENTILES OF Pickup_longitude : 2.5% = -74.01\t\t97.5% = -73.78\n",
      "\n",
      "PERCENTILES OF Dropoff_latitude : 2.5% = 40.64\t\t97.5% = 40.81\n",
      "\n",
      "PERCENTILES OF Dropoff_longitude: 2.5% = -74.01\t\t97.5% = -73.78\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in ['pickup_latitude', 'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude']:\n",
    "    print(f'PERCENTILES OF {col.capitalize():17}: 2.5% = {round(np.percentile(df[col], 2.5), 2):5}\\t\\t97.5% = {round(np.percentile(df[col], 97.5), 2)}\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df['pickup_latitude'].between(40, 44)]\n",
    "df = df.loc[df['pickup_longitude'].between(-75, -72)]\n",
    "df = df.loc[df['dropoff_latitude'].between(40, 44)]\n",
    "df = df.loc[df['dropoff_longitude'].between(-75, -72)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>954540.00000</td>\n",
       "      <td>954540.000000</td>\n",
       "      <td>954540.000000</td>\n",
       "      <td>954540.000000</td>\n",
       "      <td>954540.000000</td>\n",
       "      <td>954540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.26715</td>\n",
       "      <td>-73.209351</td>\n",
       "      <td>40.379726</td>\n",
       "      <td>-73.206497</td>\n",
       "      <td>40.380077</td>\n",
       "      <td>1.597472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.33145</td>\n",
       "      <td>0.766847</td>\n",
       "      <td>0.372622</td>\n",
       "      <td>0.768797</td>\n",
       "      <td>0.372839</td>\n",
       "      <td>1.153686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.01000</td>\n",
       "      <td>-74.968140</td>\n",
       "      <td>40.052723</td>\n",
       "      <td>-74.964264</td>\n",
       "      <td>40.041180</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.00000</td>\n",
       "      <td>-73.992271</td>\n",
       "      <td>40.736568</td>\n",
       "      <td>-73.991570</td>\n",
       "      <td>40.735596</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.50000</td>\n",
       "      <td>-73.982086</td>\n",
       "      <td>40.753410</td>\n",
       "      <td>-73.980598</td>\n",
       "      <td>40.753876</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.50000</td>\n",
       "      <td>-73.968330</td>\n",
       "      <td>40.767567</td>\n",
       "      <td>-73.965340</td>\n",
       "      <td>40.768410</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.75000</td>\n",
       "      <td>-72.702873</td>\n",
       "      <td>43.098709</td>\n",
       "      <td>-72.694839</td>\n",
       "      <td>43.415192</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "count  954540.00000     954540.000000    954540.000000      954540.000000   \n",
       "mean       11.26715        -73.209351        40.379726         -73.206497   \n",
       "std         9.33145          0.766847         0.372622           0.768797   \n",
       "min         0.01000        -74.968140        40.052723         -74.964264   \n",
       "25%         6.00000        -73.992271        40.736568         -73.991570   \n",
       "50%         8.50000        -73.982086        40.753410         -73.980598   \n",
       "75%        12.50000        -73.968330        40.767567         -73.965340   \n",
       "max        99.75000        -72.702873        43.098709         -72.694839   \n",
       "\n",
       "       dropoff_latitude  passenger_count  \n",
       "count     954540.000000    954540.000000  \n",
       "mean          40.380077         1.597472  \n",
       "std            0.372839         1.153686  \n",
       "min           40.041180         1.000000  \n",
       "25%           40.735596         1.000000  \n",
       "50%           40.753876         1.000000  \n",
       "75%           40.768410         2.000000  \n",
       "max           43.415192         5.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sin, cos, sqrt, atan2, radians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get useable date for feature engineering\n",
    "df['pickup_datetime'] = df['pickup_datetime'].str.replace(\" UTC\", \"\")\n",
    "df['pickup_datetime'] = pd.to_datetime(df['pickup_datetime'], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From Timestamp we can get below new features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting interger numbers from the pickup_datetime\n",
    "df[\"hour\"] = df.pickup_datetime.dt.hour\n",
    "df[\"weekday\"] = df.pickup_datetime.dt.weekday\n",
    "df[\"month\"] = df.pickup_datetime.dt.month\n",
    "df[\"year\"] = df.pickup_datetime.dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distance is aslo another crucial attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Quicker but slightly less accurate\n",
    "def dist_calc(df):\n",
    "    R = 6373.0\n",
    "    for i,row in df.iterrows():\n",
    "\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",
    "\n",
    "        dlon = lon2 - lon1\n",
    "        dlat = lat2 - lat1\n",
    "\n",
    "        a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n",
    "        c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
    "\n",
    "        distance = R * c\n",
    "        df.at[i,'distance'] = distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_calc(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Another concept Hotspot proximity can serve as useful feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for distance calculation between coordinates as mapped variables\n",
    "def sphere_dist(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):\n",
    "    #Define earth radius (km)\n",
    "    R_earth = 6371\n",
    "    #Convert degrees to radians\n",
    "    pickup_lat, pickup_lon, dropoff_lat, dropoff_lon = map(np.radians,\n",
    "                                                             [pickup_lat, pickup_lon, \n",
    "                                                              dropoff_lat, dropoff_lon])\n",
    "    #Compute distances along lat, lon dimensions\n",
    "    dlat = dropoff_lat - pickup_lat\n",
    "    dlon = dropoff_lon - pickup_lon\n",
    "    \n",
    "    #Compute haversine distance\n",
    "    a = np.sin(dlat/2.0)**2 + np.cos(pickup_lat) * np.cos(dropoff_lat) * np.sin(dlon/2.0)**2\n",
    "    \n",
    "    return 2 * R_earth * np.arcsin(np.sqrt(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for calculating distance between newly obtained distances from the hotspots.\n",
    "def add_airport_dist(dataset):\n",
    "    jfk_coord = (40.639722, -73.778889)\n",
    "    ewr_coord = (40.6925, -74.168611)\n",
    "    lga_coord = (40.77725, -73.872611)\n",
    "    \n",
    "    pickup_lat = dataset['pickup_latitude']\n",
    "    dropoff_lat = dataset['dropoff_latitude']\n",
    "    pickup_lon = dataset['pickup_longitude']\n",
    "    dropoff_lon = dataset['dropoff_longitude']\n",
    "    \n",
    "    pickup_jfk = sphere_dist(pickup_lat, pickup_lon, jfk_coord[0], jfk_coord[1]) \n",
    "    dropoff_jfk = sphere_dist(jfk_coord[0], jfk_coord[1], dropoff_lat, dropoff_lon) \n",
    "    pickup_ewr = sphere_dist(pickup_lat, pickup_lon, ewr_coord[0], ewr_coord[1])\n",
    "    dropoff_ewr = sphere_dist(ewr_coord[0], ewr_coord[1], dropoff_lat, dropoff_lon) \n",
    "    pickup_lga = sphere_dist(pickup_lat, pickup_lon, lga_coord[0], lga_coord[1]) \n",
    "    dropoff_lga = sphere_dist(lga_coord[0], lga_coord[1], dropoff_lat, dropoff_lon) \n",
    "    \n",
    "    dataset['jfk_dist'] = pd.concat([pickup_jfk, dropoff_jfk], axis=1).min(axis=1)\n",
    "    dataset['ewr_dist'] = pd.concat([pickup_ewr, dropoff_ewr], axis=1).min(axis=1)\n",
    "    dataset['lga_dist'] = pd.concat([pickup_lga, dropoff_lga], axis=1).min(axis=1)\n",
    "    \n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run the functions to add the features to the dataset\n",
    "df = add_airport_dist(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key                          object\n",
       "fare_amount                 float32\n",
       "pickup_datetime      datetime64[ns]\n",
       "pickup_longitude            float32\n",
       "pickup_latitude             float32\n",
       "dropoff_longitude           float32\n",
       "dropoff_latitude            float32\n",
       "passenger_count               uint8\n",
       "hour                          int64\n",
       "weekday                       int64\n",
       "month                         int64\n",
       "year                          int64\n",
       "distance                    float64\n",
       "jfk_dist                    float64\n",
       "ewr_dist                    float64\n",
       "lga_dist                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We don't need such big datatypes to represent our dataset efficiently plus it is computationally costly\n",
    "types2 = {'hour': 'uint8',\n",
    "         'weekday': 'uint8',\n",
    "         'month': 'uint8',\n",
    "         'year': 'uint8',\n",
    "         'distance': 'float32'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key                          object\n",
       "fare_amount                 float32\n",
       "pickup_datetime      datetime64[ns]\n",
       "pickup_longitude            float32\n",
       "pickup_latitude             float32\n",
       "dropoff_longitude           float32\n",
       "dropoff_latitude            float32\n",
       "passenger_count               uint8\n",
       "hour                          uint8\n",
       "weekday                       uint8\n",
       "month                         uint8\n",
       "year                          uint8\n",
       "distance                    float32\n",
       "jfk_dist                    float64\n",
       "ewr_dist                    float64\n",
       "lga_dist                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.astype(types2)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>distance</th>\n",
       "      <th>jfk_dist</th>\n",
       "      <th>ewr_dist</th>\n",
       "      <th>lga_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-06-15 17:26:21.0000001</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-06-15 17:26:21</td>\n",
       "      <td>-73.844315</td>\n",
       "      <td>40.721317</td>\n",
       "      <td>-73.841614</td>\n",
       "      <td>40.712276</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>217</td>\n",
       "      <td>1.031066</td>\n",
       "      <td>9.646954</td>\n",
       "      <td>27.522536</td>\n",
       "      <td>6.660412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05 16:52:16.0000002</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2010-01-05 16:52:16</td>\n",
       "      <td>-74.016045</td>\n",
       "      <td>40.711304</td>\n",
       "      <td>-73.979271</td>\n",
       "      <td>40.782005</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>218</td>\n",
       "      <td>8.452653</td>\n",
       "      <td>21.525217</td>\n",
       "      <td>13.030207</td>\n",
       "      <td>8.996038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-18 00:35:00.00000049</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2011-08-18 00:35:00</td>\n",
       "      <td>-73.982735</td>\n",
       "      <td>40.761269</td>\n",
       "      <td>-73.991241</td>\n",
       "      <td>40.750561</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>219</td>\n",
       "      <td>1.390068</td>\n",
       "      <td>21.734953</td>\n",
       "      <td>16.282238</td>\n",
       "      <td>9.442261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-21 04:30:42.0000001</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2012-04-21 04:30:42</td>\n",
       "      <td>-73.987129</td>\n",
       "      <td>40.733143</td>\n",
       "      <td>-73.991570</td>\n",
       "      <td>40.758091</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>220</td>\n",
       "      <td>2.800089</td>\n",
       "      <td>20.401018</td>\n",
       "      <td>15.949670</td>\n",
       "      <td>10.242046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-09 07:51:00.000000135</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2010-03-09 07:51:00</td>\n",
       "      <td>-73.968094</td>\n",
       "      <td>40.768009</td>\n",
       "      <td>-73.956657</td>\n",
       "      <td>40.783764</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>218</td>\n",
       "      <td>1.999709</td>\n",
       "      <td>21.397717</td>\n",
       "      <td>18.867266</td>\n",
       "      <td>7.113775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount     pickup_datetime  \\\n",
       "0    2009-06-15 17:26:21.0000001          4.5 2009-06-15 17:26:21   \n",
       "1    2010-01-05 16:52:16.0000002         16.9 2010-01-05 16:52:16   \n",
       "2   2011-08-18 00:35:00.00000049          5.7 2011-08-18 00:35:00   \n",
       "3    2012-04-21 04:30:42.0000001          7.7 2012-04-21 04:30:42   \n",
       "4  2010-03-09 07:51:00.000000135          5.3 2010-03-09 07:51:00   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.844315        40.721317         -73.841614         40.712276   \n",
       "1        -74.016045        40.711304         -73.979271         40.782005   \n",
       "2        -73.982735        40.761269         -73.991241         40.750561   \n",
       "3        -73.987129        40.733143         -73.991570         40.758091   \n",
       "4        -73.968094        40.768009         -73.956657         40.783764   \n",
       "\n",
       "   passenger_count  hour  weekday  month  year  distance   jfk_dist  \\\n",
       "0                1    17        0      6   217  1.031066   9.646954   \n",
       "1                1    16        1      1   218  8.452653  21.525217   \n",
       "2                2     0        3      8   219  1.390068  21.734953   \n",
       "3                1     4        5      4   220  2.800089  20.401018   \n",
       "4                1     7        1      3   218  1.999709  21.397717   \n",
       "\n",
       "    ewr_dist   lga_dist  \n",
       "0  27.522536   6.660412  \n",
       "1  13.030207   8.996038  \n",
       "2  16.282238   9.442261  \n",
       "3  15.949670  10.242046  \n",
       "4  18.867266   7.113775  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attribute selection for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation between attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dd80d4b9b0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dd80d4b198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot heatmap of value correlations\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.heatmap(df.drop(['key','pickup_datetime'],axis=1).corr(),annot=True,fmt='.4f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter plot of 1K records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = df[['fare_amount','distance','jfk_dist','lga_dist']]\n",
    "d = d[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1dd80d51630>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dd80d51518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='whitegrid', context = 'notebook')\n",
    "sns.pairplot(d,size=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['distance','jfk_dist']]\n",
    "y = df[['fare_amount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_training, X_testing, y_training, y_testing = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinary least squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.21873311]\n",
      " [0.18256226]]\n"
     ]
    }
   ],
   "source": [
    "w_OLS = np.matmul(np.matmul(np.linalg.inv(np.matmul(X_training.T, X_training)), X_training.T), y_training)\n",
    "print(w_OLS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = np.matmul(X_testing, w_OLS).round(decimals = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error using Ordinary Least Square for two variables: 30.22\n"
     ]
    }
   ],
   "source": [
    "print('Mean Squared Error using Ordinary Least Square for two variables: %.2f' % mean_squared_error(y_testing, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict1 = np.matmul(X_training, w_OLS).round(decimals = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error using Ordinary Least Square for two variables ( Training Error ): 28.98\n"
     ]
    }
   ],
   "source": [
    "print('Mean Squared Error using Ordinary Least Square for two variables ( Training Error ): %.2f' % mean_squared_error(y_training, y_predict1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "lr_predict = lr.fit(X_training,y_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error using Linear Regression for two variables: 27.04\n"
     ]
    }
   ],
   "source": [
    "lin_predict = lr.predict(X_testing)\n",
    "print('Mean Squared Error using Linear Regression for two variables: %.2f' % mean_squared_error(y_testing, lin_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6895588023165142"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_predict.score(X_testing,y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now lets take it to other way around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = df[['fare_amount']]\n",
    "X_data = df.drop(['key','fare_amount','pickup_datetime'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-5.34216612e+00]\n",
      " [-1.49044768e+01]\n",
      " [-8.62237896e+00]\n",
      " [-1.31115682e+01]\n",
      " [ 5.46273272e-02]\n",
      " [ 9.18339983e-03]\n",
      " [-4.45569765e-02]\n",
      " [ 7.28169814e-02]\n",
      " [ 5.21789870e-01]\n",
      " [ 1.91611355e+00]\n",
      " [-2.37540192e-01]\n",
      " [ 2.82552338e-01]\n",
      " [-1.74431415e-01]]\n"
     ]
    }
   ],
   "source": [
    "w_OLS = np.matmul(np.matmul(np.linalg.inv(np.matmul(X_train.T, X_train)), X_train.T), y_train)\n",
    "print(w_OLS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.matmul(X_test, w_OLS).round(decimals = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error using Ordinary Least Square for all possible attributes: 25.80\n"
     ]
    }
   ],
   "source": [
    "print('Mean Squared Error using Ordinary Least Square for all possible attributes: %.2f' % mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_predictions = lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error using Linear Regression for all possible attributes: 25.80\n"
     ]
    }
   ],
   "source": [
    "linpred = lr.predict(X_test)\n",
    "print('Mean Squared Error using Linear Regression for all possible attributes: %.2f' % mean_squared_error(y_test, linpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7037170542907676"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_predictions.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define a XGB model and parameters\n",
    "def XGBoost(X_train,X_test,y_train,y_test):\n",
    "    dtrain = xgb.DMatrix(X_train,label=y_train)\n",
    "    dtest = xgb.DMatrix(X_test,label=y_test)\n",
    "\n",
    "    return xgb.train(params={'objective':'reg:linear','eval_metric':'mae'}\n",
    "                    ,dtrain=dtrain,num_boost_round=400, \n",
    "                    early_stopping_rounds=30,evals=[(dtest,'test')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:17:52] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[0]\ttest-mae:7.55523\n",
      "Will train until test-mae hasn't improved in 30 rounds.\n",
      "[21:17:53] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[1]\ttest-mae:5.35024\n",
      "[21:17:55] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[2]\ttest-mae:3.84826\n",
      "[21:17:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[3]\ttest-mae:2.90214\n",
      "[21:17:57] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[4]\ttest-mae:2.37174\n",
      "[21:17:59] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[5]\ttest-mae:2.10409\n",
      "[21:18:00] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[6]\ttest-mae:1.97149\n",
      "[21:18:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[7]\ttest-mae:1.90355\n",
      "[21:18:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[8]\ttest-mae:1.87518\n",
      "[21:18:04] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[9]\ttest-mae:1.85642\n",
      "[21:18:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[10]\ttest-mae:1.84064\n",
      "[21:18:07] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[11]\ttest-mae:1.81505\n",
      "[21:18:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[12]\ttest-mae:1.80775\n",
      "[21:18:10] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[13]\ttest-mae:1.79635\n",
      "[21:18:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[14]\ttest-mae:1.79143\n",
      "[21:18:13] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[15]\ttest-mae:1.78766\n",
      "[21:18:15] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[16]\ttest-mae:1.78346\n",
      "[21:18:16] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[17]\ttest-mae:1.777\n",
      "[21:18:18] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[18]\ttest-mae:1.76449\n",
      "[21:18:19] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[19]\ttest-mae:1.76004\n",
      "[21:18:21] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[20]\ttest-mae:1.75741\n",
      "[21:18:22] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[21]\ttest-mae:1.74879\n",
      "[21:18:24] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[22]\ttest-mae:1.74665\n",
      "[21:18:25] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[23]\ttest-mae:1.74105\n",
      "[21:18:26] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[24]\ttest-mae:1.73585\n",
      "[21:18:28] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[25]\ttest-mae:1.72864\n",
      "[21:18:29] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[26]\ttest-mae:1.72614\n",
      "[21:18:31] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[27]\ttest-mae:1.72429\n",
      "[21:18:32] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[28]\ttest-mae:1.72369\n",
      "[21:18:34] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[29]\ttest-mae:1.71437\n",
      "[21:18:35] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[30]\ttest-mae:1.7087\n",
      "[21:18:37] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[31]\ttest-mae:1.7069\n",
      "[21:18:38] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[32]\ttest-mae:1.70682\n",
      "[21:18:39] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[33]\ttest-mae:1.70387\n",
      "[21:18:41] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[34]\ttest-mae:1.70234\n",
      "[21:18:43] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[35]\ttest-mae:1.7002\n",
      "[21:18:44] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[36]\ttest-mae:1.69735\n",
      "[21:18:46] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[37]\ttest-mae:1.69397\n",
      "[21:18:47] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[38]\ttest-mae:1.69215\n",
      "[21:18:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[39]\ttest-mae:1.68916\n",
      "[21:18:50] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[40]\ttest-mae:1.68684\n",
      "[21:18:51] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[41]\ttest-mae:1.68709\n",
      "[21:18:53] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[42]\ttest-mae:1.68412\n",
      "[21:18:54] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[43]\ttest-mae:1.6835\n",
      "[21:18:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[44]\ttest-mae:1.68281\n",
      "[21:18:57] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[45]\ttest-mae:1.68047\n",
      "[21:18:59] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[46]\ttest-mae:1.67659\n",
      "[21:19:00] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[47]\ttest-mae:1.67551\n",
      "[21:19:01] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[48]\ttest-mae:1.6746\n",
      "[21:19:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[49]\ttest-mae:1.67269\n",
      "[21:19:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[50]\ttest-mae:1.67099\n",
      "[21:19:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[51]\ttest-mae:1.6672\n",
      "[21:19:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[52]\ttest-mae:1.66633\n",
      "[21:19:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[53]\ttest-mae:1.66444\n",
      "[21:19:11] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[54]\ttest-mae:1.66252\n",
      "[21:19:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[55]\ttest-mae:1.66137\n",
      "[21:19:14] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[56]\ttest-mae:1.66018\n",
      "[21:19:15] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[57]\ttest-mae:1.66014\n",
      "[21:19:16] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[58]\ttest-mae:1.65972\n",
      "[21:19:18] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[59]\ttest-mae:1.6583\n",
      "[21:19:19] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[60]\ttest-mae:1.65685\n",
      "[21:19:21] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[61]\ttest-mae:1.65683\n",
      "[21:19:22] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 64 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[62]\ttest-mae:1.65607\n",
      "[21:19:23] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[63]\ttest-mae:1.65436\n",
      "[21:19:25] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[64]\ttest-mae:1.65342\n",
      "[21:19:26] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[65]\ttest-mae:1.65265\n",
      "[21:19:28] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[66]\ttest-mae:1.65058\n",
      "[21:19:29] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[67]\ttest-mae:1.64894\n",
      "[21:19:31] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[68]\ttest-mae:1.64845\n",
      "[21:19:32] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[69]\ttest-mae:1.64766\n",
      "[21:19:34] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[70]\ttest-mae:1.64702\n",
      "[21:19:35] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[71]\ttest-mae:1.64698\n",
      "[21:19:36] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[72]\ttest-mae:1.64651\n",
      "[21:19:38] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[73]\ttest-mae:1.64603\n",
      "[21:19:39] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[74]\ttest-mae:1.64536\n",
      "[21:19:41] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[75]\ttest-mae:1.64541\n",
      "[21:19:42] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[76]\ttest-mae:1.64547\n",
      "[21:19:43] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[77]\ttest-mae:1.64547\n",
      "[21:19:45] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[78]\ttest-mae:1.64502\n",
      "[21:19:46] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[79]\ttest-mae:1.64468\n",
      "[21:19:48] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[80]\ttest-mae:1.64161\n",
      "[21:19:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[81]\ttest-mae:1.64131\n",
      "[21:19:50] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[82]\ttest-mae:1.64107\n",
      "[21:19:52] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[83]\ttest-mae:1.64113\n",
      "[21:19:53] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[84]\ttest-mae:1.63987\n",
      "[21:19:55] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[85]\ttest-mae:1.63977\n",
      "[21:19:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[86]\ttest-mae:1.63911\n",
      "[21:19:57] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[87]\ttest-mae:1.63852\n",
      "[21:19:59] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[88]\ttest-mae:1.6385\n",
      "[21:20:00] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[89]\ttest-mae:1.63728\n",
      "[21:20:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[90]\ttest-mae:1.63704\n",
      "[21:20:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[91]\ttest-mae:1.63705\n",
      "[21:20:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[92]\ttest-mae:1.63656\n",
      "[21:20:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[93]\ttest-mae:1.63543\n",
      "[21:20:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[94]\ttest-mae:1.63544\n",
      "[21:20:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[95]\ttest-mae:1.63565\n",
      "[21:20:10] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[96]\ttest-mae:1.63456\n",
      "[21:20:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[97]\ttest-mae:1.63435\n",
      "[21:20:13] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[98]\ttest-mae:1.63408\n",
      "[21:20:15] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[99]\ttest-mae:1.63328\n",
      "[21:20:16] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 76 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[100]\ttest-mae:1.63323\n",
      "[21:20:18] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[101]\ttest-mae:1.63327\n",
      "[21:20:19] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[102]\ttest-mae:1.63173\n",
      "[21:20:20] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[103]\ttest-mae:1.6309\n",
      "[21:20:22] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[104]\ttest-mae:1.63004\n",
      "[21:20:23] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 68 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[105]\ttest-mae:1.63029\n",
      "[21:20:25] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 50 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[106]\ttest-mae:1.63004\n",
      "[21:20:26] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[107]\ttest-mae:1.63017\n",
      "[21:20:27] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[108]\ttest-mae:1.62864\n",
      "[21:20:29] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[109]\ttest-mae:1.62891\n",
      "[21:20:30] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[110]\ttest-mae:1.62756\n",
      "[21:20:32] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 86 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[111]\ttest-mae:1.62717\n",
      "[21:20:33] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[112]\ttest-mae:1.62602\n",
      "[21:20:35] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[113]\ttest-mae:1.62505\n",
      "[21:20:36] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[114]\ttest-mae:1.62498\n",
      "[21:20:37] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[115]\ttest-mae:1.62482\n",
      "[21:20:39] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[116]\ttest-mae:1.6247\n",
      "[21:20:40] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 68 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[117]\ttest-mae:1.62469\n",
      "[21:20:42] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[118]\ttest-mae:1.6245\n",
      "[21:20:43] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[119]\ttest-mae:1.62388\n",
      "[21:20:44] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[120]\ttest-mae:1.62292\n",
      "[21:20:46] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[121]\ttest-mae:1.62263\n",
      "[21:20:47] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[122]\ttest-mae:1.62273\n",
      "[21:20:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[123]\ttest-mae:1.62245\n",
      "[21:20:50] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[124]\ttest-mae:1.62193\n",
      "[21:20:51] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[125]\ttest-mae:1.62199\n",
      "[21:20:53] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[126]\ttest-mae:1.62203\n",
      "[21:20:54] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 72 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[127]\ttest-mae:1.62172\n",
      "[21:20:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[128]\ttest-mae:1.62153\n",
      "[21:20:57] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[129]\ttest-mae:1.62148\n",
      "[21:20:58] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[130]\ttest-mae:1.62144\n",
      "[21:21:00] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[131]\ttest-mae:1.62148\n",
      "[21:21:01] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[132]\ttest-mae:1.62133\n",
      "[21:21:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[133]\ttest-mae:1.62087\n",
      "[21:21:04] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[134]\ttest-mae:1.61953\n",
      "[21:21:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[135]\ttest-mae:1.61934\n",
      "[21:21:07] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[136]\ttest-mae:1.61896\n",
      "[21:21:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[137]\ttest-mae:1.618\n",
      "[21:21:10] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[138]\ttest-mae:1.6177\n",
      "[21:21:11] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[139]\ttest-mae:1.61769\n",
      "[21:21:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 86 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[140]\ttest-mae:1.61751\n",
      "[21:21:14] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[141]\ttest-mae:1.61761\n",
      "[21:21:15] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[142]\ttest-mae:1.61727\n",
      "[21:21:17] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[143]\ttest-mae:1.6161\n",
      "[21:21:18] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[144]\ttest-mae:1.61607\n",
      "[21:21:20] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[145]\ttest-mae:1.616\n",
      "[21:21:21] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[146]\ttest-mae:1.61604\n",
      "[21:21:22] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[147]\ttest-mae:1.61539\n",
      "[21:21:24] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[148]\ttest-mae:1.61545\n",
      "[21:21:25] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[149]\ttest-mae:1.61539\n",
      "[21:21:27] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[150]\ttest-mae:1.6155\n",
      "[21:21:28] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[151]\ttest-mae:1.61553\n",
      "[21:21:29] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[152]\ttest-mae:1.61549\n",
      "[21:21:31] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[153]\ttest-mae:1.61506\n",
      "[21:21:32] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[154]\ttest-mae:1.61494\n",
      "[21:21:34] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[155]\ttest-mae:1.61503\n",
      "[21:21:35] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[156]\ttest-mae:1.61416\n",
      "[21:21:36] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[157]\ttest-mae:1.61413\n",
      "[21:21:38] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[158]\ttest-mae:1.61395\n",
      "[21:21:39] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[159]\ttest-mae:1.61378\n",
      "[21:21:41] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[160]\ttest-mae:1.61318\n",
      "[21:21:42] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[161]\ttest-mae:1.61325\n",
      "[21:21:44] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[162]\ttest-mae:1.61293\n",
      "[21:21:45] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[163]\ttest-mae:1.61298\n",
      "[21:21:46] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 68 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[164]\ttest-mae:1.613\n",
      "[21:21:48] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[165]\ttest-mae:1.61272\n",
      "[21:21:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 86 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[166]\ttest-mae:1.6127\n",
      "[21:21:51] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[167]\ttest-mae:1.61254\n",
      "[21:21:52] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[168]\ttest-mae:1.61244\n",
      "[21:21:54] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[169]\ttest-mae:1.61207\n",
      "[21:21:55] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[170]\ttest-mae:1.61204\n",
      "[21:21:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[171]\ttest-mae:1.61203\n",
      "[21:21:58] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[172]\ttest-mae:1.61178\n",
      "[21:21:59] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[173]\ttest-mae:1.61176\n",
      "[21:22:01] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 78 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[174]\ttest-mae:1.61179\n",
      "[21:22:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[175]\ttest-mae:1.61157\n",
      "[21:22:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[176]\ttest-mae:1.61103\n",
      "[21:22:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[177]\ttest-mae:1.61089\n",
      "[21:22:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[178]\ttest-mae:1.61056\n",
      "[21:22:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[179]\ttest-mae:1.60968\n",
      "[21:22:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[180]\ttest-mae:1.60964\n",
      "[21:22:11] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[181]\ttest-mae:1.60921\n",
      "[21:22:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[182]\ttest-mae:1.60902\n",
      "[21:22:14] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[183]\ttest-mae:1.60848\n",
      "[21:22:15] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[184]\ttest-mae:1.60768\n",
      "[21:22:17] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[185]\ttest-mae:1.60775\n",
      "[21:22:18] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[186]\ttest-mae:1.60789\n",
      "[21:22:19] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[187]\ttest-mae:1.60801\n",
      "[21:22:21] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[188]\ttest-mae:1.60754\n",
      "[21:22:22] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[189]\ttest-mae:1.60759\n",
      "[21:22:24] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 78 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[190]\ttest-mae:1.6076\n",
      "[21:22:25] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[191]\ttest-mae:1.60734\n",
      "[21:22:27] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[192]\ttest-mae:1.60752\n",
      "[21:22:28] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[193]\ttest-mae:1.60754\n",
      "[21:22:29] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[194]\ttest-mae:1.60754\n",
      "[21:22:31] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[195]\ttest-mae:1.60754\n",
      "[21:22:32] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[196]\ttest-mae:1.60762\n",
      "[21:22:33] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[197]\ttest-mae:1.60728\n",
      "[21:22:35] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[198]\ttest-mae:1.60703\n",
      "[21:22:36] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[199]\ttest-mae:1.60678\n",
      "[21:22:38] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[200]\ttest-mae:1.60676\n",
      "[21:22:39] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[201]\ttest-mae:1.6067\n",
      "[21:22:41] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[202]\ttest-mae:1.60656\n",
      "[21:22:42] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[203]\ttest-mae:1.60629\n",
      "[21:22:44] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 68 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[204]\ttest-mae:1.60643\n",
      "[21:22:45] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[205]\ttest-mae:1.60655\n",
      "[21:22:47] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[206]\ttest-mae:1.60648\n",
      "[21:22:48] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[207]\ttest-mae:1.60632\n",
      "[21:22:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 76 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[208]\ttest-mae:1.60639\n",
      "[21:22:51] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[209]\ttest-mae:1.60651\n",
      "[21:22:52] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[210]\ttest-mae:1.6065\n",
      "[21:22:53] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[211]\ttest-mae:1.60638\n",
      "[21:22:55] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[212]\ttest-mae:1.60618\n",
      "[21:22:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[213]\ttest-mae:1.60616\n",
      "[21:22:58] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[214]\ttest-mae:1.60604\n",
      "[21:22:59] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[215]\ttest-mae:1.60573\n",
      "[21:23:00] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[216]\ttest-mae:1.60524\n",
      "[21:23:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[217]\ttest-mae:1.60482\n",
      "[21:23:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[218]\ttest-mae:1.60446\n",
      "[21:23:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 68 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[219]\ttest-mae:1.60464\n",
      "[21:23:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[220]\ttest-mae:1.60472\n",
      "[21:23:07] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[221]\ttest-mae:1.6047\n",
      "[21:23:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[222]\ttest-mae:1.60443\n",
      "[21:23:10] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[223]\ttest-mae:1.604\n",
      "[21:23:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[224]\ttest-mae:1.604\n",
      "[21:23:13] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[225]\ttest-mae:1.60403\n",
      "[21:23:14] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[226]\ttest-mae:1.6037\n",
      "[21:23:16] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 78 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[227]\ttest-mae:1.60373\n",
      "[21:23:17] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[228]\ttest-mae:1.60367\n",
      "[21:23:18] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[229]\ttest-mae:1.60347\n",
      "[21:23:20] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[230]\ttest-mae:1.60343\n",
      "[21:23:21] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[231]\ttest-mae:1.60299\n",
      "[21:23:23] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[232]\ttest-mae:1.60297\n",
      "[21:23:24] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[233]\ttest-mae:1.60283\n",
      "[21:23:25] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[234]\ttest-mae:1.60283\n",
      "[21:23:27] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[235]\ttest-mae:1.60292\n",
      "[21:23:28] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[236]\ttest-mae:1.60258\n",
      "[21:23:30] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[237]\ttest-mae:1.60213\n",
      "[21:23:31] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[238]\ttest-mae:1.60177\n",
      "[21:23:32] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 76 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[239]\ttest-mae:1.60177\n",
      "[21:23:34] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[240]\ttest-mae:1.60171\n",
      "[21:23:35] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 72 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[241]\ttest-mae:1.60157\n",
      "[21:23:37] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[242]\ttest-mae:1.60146\n",
      "[21:23:38] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[243]\ttest-mae:1.60132\n",
      "[21:23:39] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[244]\ttest-mae:1.60113\n",
      "[21:23:41] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[245]\ttest-mae:1.60078\n",
      "[21:23:42] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[246]\ttest-mae:1.60082\n",
      "[21:23:44] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 78 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[247]\ttest-mae:1.60058\n",
      "[21:23:45] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[248]\ttest-mae:1.60042\n",
      "[21:23:46] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[249]\ttest-mae:1.60053\n",
      "[21:23:48] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[250]\ttest-mae:1.60067\n",
      "[21:23:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[251]\ttest-mae:1.60076\n",
      "[21:23:51] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[252]\ttest-mae:1.60032\n",
      "[21:23:52] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[253]\ttest-mae:1.6002\n",
      "[21:23:53] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[254]\ttest-mae:1.60015\n",
      "[21:23:55] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[255]\ttest-mae:1.59993\n",
      "[21:23:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 70 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[256]\ttest-mae:1.59993\n",
      "[21:23:58] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[257]\ttest-mae:1.59997\n",
      "[21:23:59] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[258]\ttest-mae:1.5998\n",
      "[21:24:00] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[259]\ttest-mae:1.5996\n",
      "[21:24:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[260]\ttest-mae:1.5997\n",
      "[21:24:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[261]\ttest-mae:1.59953\n",
      "[21:24:04] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[262]\ttest-mae:1.59956\n",
      "[21:24:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[263]\ttest-mae:1.59959\n",
      "[21:24:07] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[264]\ttest-mae:1.59955\n",
      "[21:24:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[265]\ttest-mae:1.59951\n",
      "[21:24:10] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 76 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[266]\ttest-mae:1.59934\n",
      "[21:24:11] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[267]\ttest-mae:1.59937\n",
      "[21:24:13] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[268]\ttest-mae:1.59928\n",
      "[21:24:14] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[269]\ttest-mae:1.59931\n",
      "[21:24:16] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[270]\ttest-mae:1.59922\n",
      "[21:24:17] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[271]\ttest-mae:1.59922\n",
      "[21:24:18] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[272]\ttest-mae:1.5993\n",
      "[21:24:20] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[273]\ttest-mae:1.59933\n",
      "[21:24:21] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[274]\ttest-mae:1.59927\n",
      "[21:24:23] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 66 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[275]\ttest-mae:1.59929\n",
      "[21:24:24] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[276]\ttest-mae:1.5994\n",
      "[21:24:25] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[277]\ttest-mae:1.59939\n",
      "[21:24:27] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[278]\ttest-mae:1.5994\n",
      "[21:24:28] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[279]\ttest-mae:1.59931\n",
      "[21:24:30] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[280]\ttest-mae:1.59935\n",
      "[21:24:31] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 64 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[281]\ttest-mae:1.59942\n",
      "[21:24:32] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[282]\ttest-mae:1.59942\n",
      "[21:24:34] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[283]\ttest-mae:1.59943\n",
      "[21:24:35] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[284]\ttest-mae:1.59957\n",
      "[21:24:36] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[285]\ttest-mae:1.59958\n",
      "[21:24:38] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[286]\ttest-mae:1.59962\n",
      "[21:24:39] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 70 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[287]\ttest-mae:1.59965\n",
      "[21:24:40] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[288]\ttest-mae:1.59964\n",
      "[21:24:42] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[289]\ttest-mae:1.59932\n",
      "[21:24:43] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[290]\ttest-mae:1.59936\n",
      "[21:24:45] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 70 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[291]\ttest-mae:1.59914\n",
      "[21:24:46] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[292]\ttest-mae:1.59914\n",
      "[21:24:47] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[293]\ttest-mae:1.59902\n",
      "[21:24:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[294]\ttest-mae:1.59883\n",
      "[21:24:50] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[295]\ttest-mae:1.59889\n",
      "[21:24:52] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[296]\ttest-mae:1.59892\n",
      "[21:24:53] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[297]\ttest-mae:1.59894\n",
      "[21:24:54] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[298]\ttest-mae:1.59898\n",
      "[21:24:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[299]\ttest-mae:1.5989\n",
      "[21:24:57] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[300]\ttest-mae:1.59876\n",
      "[21:24:58] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[301]\ttest-mae:1.59893\n",
      "[21:25:00] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 44 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[302]\ttest-mae:1.59895\n",
      "[21:25:01] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 76 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[303]\ttest-mae:1.59899\n",
      "[21:25:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[304]\ttest-mae:1.59897\n",
      "[21:25:04] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 78 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[305]\ttest-mae:1.59902\n",
      "[21:25:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[306]\ttest-mae:1.59881\n",
      "[21:25:07] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[307]\ttest-mae:1.59886\n",
      "[21:25:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[308]\ttest-mae:1.59884\n",
      "[21:25:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[309]\ttest-mae:1.59884\n",
      "[21:25:11] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[310]\ttest-mae:1.59885\n",
      "[21:25:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 76 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[311]\ttest-mae:1.59882\n",
      "[21:25:14] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[312]\ttest-mae:1.59882\n",
      "[21:25:15] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[313]\ttest-mae:1.59877\n",
      "[21:25:16] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[314]\ttest-mae:1.59845\n",
      "[21:25:18] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 80 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[315]\ttest-mae:1.59811\n",
      "[21:25:19] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[316]\ttest-mae:1.598\n",
      "[21:25:20] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[317]\ttest-mae:1.5981\n",
      "[21:25:22] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[318]\ttest-mae:1.5979\n",
      "[21:25:23] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[319]\ttest-mae:1.59775\n",
      "[21:25:25] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[320]\ttest-mae:1.59774\n",
      "[21:25:26] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[321]\ttest-mae:1.59739\n",
      "[21:25:27] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[322]\ttest-mae:1.59737\n",
      "[21:25:29] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[323]\ttest-mae:1.59732\n",
      "[21:25:30] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 66 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[324]\ttest-mae:1.59729\n",
      "[21:25:32] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[325]\ttest-mae:1.59696\n",
      "[21:25:33] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[326]\ttest-mae:1.59697\n",
      "[21:25:34] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[327]\ttest-mae:1.59696\n",
      "[21:25:36] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[328]\ttest-mae:1.59688\n",
      "[21:25:37] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[329]\ttest-mae:1.59694\n",
      "[21:25:39] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[330]\ttest-mae:1.5968\n",
      "[21:25:40] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 70 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[331]\ttest-mae:1.59678\n",
      "[21:25:41] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[332]\ttest-mae:1.59628\n",
      "[21:25:43] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 86 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[333]\ttest-mae:1.5962\n",
      "[21:25:44] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[334]\ttest-mae:1.5962\n",
      "[21:25:45] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[335]\ttest-mae:1.59611\n",
      "[21:25:47] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[336]\ttest-mae:1.59603\n",
      "[21:25:48] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[337]\ttest-mae:1.59592\n",
      "[21:25:50] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[338]\ttest-mae:1.59592\n",
      "[21:25:51] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[339]\ttest-mae:1.59599\n",
      "[21:25:52] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 66 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[340]\ttest-mae:1.59604\n",
      "[21:25:54] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[341]\ttest-mae:1.59605\n",
      "[21:25:55] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 86 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[342]\ttest-mae:1.59604\n",
      "[21:25:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[343]\ttest-mae:1.59611\n",
      "[21:25:58] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[344]\ttest-mae:1.59612\n",
      "[21:25:59] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[345]\ttest-mae:1.59602\n",
      "[21:26:01] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 68 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[346]\ttest-mae:1.59598\n",
      "[21:26:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[347]\ttest-mae:1.59588\n",
      "[21:26:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[348]\ttest-mae:1.59589\n",
      "[21:26:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[349]\ttest-mae:1.59597\n",
      "[21:26:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[350]\ttest-mae:1.59591\n",
      "[21:26:08] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[351]\ttest-mae:1.59593\n",
      "[21:26:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 50 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[352]\ttest-mae:1.59592\n",
      "[21:26:10] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[353]\ttest-mae:1.59588\n",
      "[21:26:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 72 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[354]\ttest-mae:1.59587\n",
      "[21:26:13] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[355]\ttest-mae:1.59593\n",
      "[21:26:14] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[356]\ttest-mae:1.59594\n",
      "[21:26:16] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[357]\ttest-mae:1.59579\n",
      "[21:26:17] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[358]\ttest-mae:1.59545\n",
      "[21:26:19] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[359]\ttest-mae:1.5953\n",
      "[21:26:20] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[360]\ttest-mae:1.59546\n",
      "[21:26:22] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[361]\ttest-mae:1.59557\n",
      "[21:26:23] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[362]\ttest-mae:1.59539\n",
      "[21:26:24] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[363]\ttest-mae:1.59549\n",
      "[21:26:26] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 90 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[364]\ttest-mae:1.59553\n",
      "[21:26:27] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[365]\ttest-mae:1.59549\n",
      "[21:26:28] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[366]\ttest-mae:1.59546\n",
      "[21:26:30] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[367]\ttest-mae:1.59567\n",
      "[21:26:31] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[368]\ttest-mae:1.59574\n",
      "[21:26:33] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[369]\ttest-mae:1.59546\n",
      "[21:26:34] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[370]\ttest-mae:1.59528\n",
      "[21:26:35] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[371]\ttest-mae:1.59496\n",
      "[21:26:37] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[372]\ttest-mae:1.59486\n",
      "[21:26:38] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 72 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[373]\ttest-mae:1.59483\n",
      "[21:26:40] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[374]\ttest-mae:1.59457\n",
      "[21:26:41] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 106 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[375]\ttest-mae:1.59452\n",
      "[21:26:42] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[376]\ttest-mae:1.59422\n",
      "[21:26:44] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 42 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[377]\ttest-mae:1.59423\n",
      "[21:26:45] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 112 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[378]\ttest-mae:1.59423\n",
      "[21:26:47] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[379]\ttest-mae:1.5943\n",
      "[21:26:48] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 84 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[380]\ttest-mae:1.59405\n",
      "[21:26:49] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 92 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[381]\ttest-mae:1.59405\n",
      "[21:26:51] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 94 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[382]\ttest-mae:1.59407\n",
      "[21:26:52] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 96 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[383]\ttest-mae:1.59374\n",
      "[21:26:54] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 88 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[384]\ttest-mae:1.59388\n",
      "[21:26:55] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[385]\ttest-mae:1.59372\n",
      "[21:26:56] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 108 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[386]\ttest-mae:1.59349\n",
      "[21:26:58] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 100 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[387]\ttest-mae:1.5934\n",
      "[21:26:59] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 86 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[388]\ttest-mae:1.59344\n",
      "[21:27:00] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 64 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[389]\ttest-mae:1.59344\n",
      "[21:27:02] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 110 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[390]\ttest-mae:1.59343\n",
      "[21:27:03] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 104 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[391]\ttest-mae:1.59344\n",
      "[21:27:05] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[392]\ttest-mae:1.5934\n",
      "[21:27:06] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[393]\ttest-mae:1.59354\n",
      "[21:27:07] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[394]\ttest-mae:1.59356\n",
      "[21:27:09] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 74 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[395]\ttest-mae:1.59351\n",
      "[21:27:10] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 98 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[396]\ttest-mae:1.59356\n",
      "[21:27:12] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 82 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[397]\ttest-mae:1.5935\n",
      "[21:27:13] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 102 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[398]\ttest-mae:1.59356\n",
      "[21:27:14] C:\\Users\\Administrator\\Desktop\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6\n",
      "[399]\ttest-mae:1.59321\n"
     ]
    }
   ],
   "source": [
    "#Fit data and optimise the model, generate predictions\n",
    "xgbm = XGBoost(X_train,X_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "XGBPredictions = xgbm.predict(xgb.DMatrix(X_test), ntree_limit = xgbm.best_ntree_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error using XGBoost for all possible attributes: 11.41\n"
     ]
    }
   ],
   "source": [
    "print('Mean Squared Error using XGBoost for all possible attributes: %.2f' % mean_squared_error(y_test, XGBPredictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apparently ensemble approache (i.e. XGBoost) improved the performance over baseline approaches (i.e.Ordinary least squares and Linear Regression) significantly."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}