Homework 5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>UID</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>TAXI_OUT</th>\n",
       "      <th>TAXI_IN</th>\n",
       "      <th>ACTUAL_ELAPSED_TIME</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>DISTANCE_GROUP</th>\n",
       "      <th>AIRLINE_ID</th>\n",
       "      <th>...</th>\n",
       "      <th>FL_NUM</th>\n",
       "      <th>UNIQUE_CARRIER</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>ORIGIN_CITY_NAME</th>\n",
       "      <th>ORIGIN_STATE_ABR</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DEST_CITY_NAME</th>\n",
       "      <th>DEST_STATE_ABR</th>\n",
       "      <th>FIRST_DEP_TIME</th>\n",
       "      <th>ARR_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1020</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>373</td>\n",
       "      <td>2,701</td>\n",
       "      <td>11</td>\n",
       "      <td>19930</td>\n",
       "      <td>...</td>\n",
       "      <td>879</td>\n",
       "      <td>AS</td>\n",
       "      <td>SEA</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>WA</td>\n",
       "      <td>LIH</td>\n",
       "      <td>Lihue, HI</td>\n",
       "      <td>HI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1220</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>183</td>\n",
       "      <td>1,171</td>\n",
       "      <td>5</td>\n",
       "      <td>19805</td>\n",
       "      <td>...</td>\n",
       "      <td>2491</td>\n",
       "      <td>AA</td>\n",
       "      <td>SAN</td>\n",
       "      <td>San Diego, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>DFW</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>945</td>\n",
       "      <td>48</td>\n",
       "      <td>6</td>\n",
       "      <td>355</td>\n",
       "      <td>2,552</td>\n",
       "      <td>11</td>\n",
       "      <td>19930</td>\n",
       "      <td>...</td>\n",
       "      <td>172</td>\n",
       "      <td>AS</td>\n",
       "      <td>ANC</td>\n",
       "      <td>Anchorage, AK</td>\n",
       "      <td>AK</td>\n",
       "      <td>PHX</td>\n",
       "      <td>Phoenix, AZ</td>\n",
       "      <td>AZ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1437</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>136</td>\n",
       "      <td>967</td>\n",
       "      <td>4</td>\n",
       "      <td>19977</td>\n",
       "      <td>...</td>\n",
       "      <td>2030</td>\n",
       "      <td>UA</td>\n",
       "      <td>SFO</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>CO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1735</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>151</td>\n",
       "      <td>984</td>\n",
       "      <td>4</td>\n",
       "      <td>20366</td>\n",
       "      <td>...</td>\n",
       "      <td>4220</td>\n",
       "      <td>EV</td>\n",
       "      <td>IAH</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>MKE</td>\n",
       "      <td>Milwaukee, WI</td>\n",
       "      <td>WI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  UID  DAY_OF_WEEK  CRS_DEP_TIME  TAXI_OUT  TAXI_IN  \\\n",
       "0      0    0            2          1020        11        8   \n",
       "1      1    1            6          1220        13        9   \n",
       "2      2    2            6           945        48        6   \n",
       "3      3    3            1          1437        17        8   \n",
       "4      5    5            7          1735        28        5   \n",
       "\n",
       "   ACTUAL_ELAPSED_TIME DISTANCE  DISTANCE_GROUP  AIRLINE_ID    ...    FL_NUM  \\\n",
       "0                  373    2,701              11       19930    ...       879   \n",
       "1                  183    1,171               5       19805    ...      2491   \n",
       "2                  355    2,552              11       19930    ...       172   \n",
       "3                  136      967               4       19977    ...      2030   \n",
       "4                  151      984               4       20366    ...      4220   \n",
       "\n",
       "   UNIQUE_CARRIER  ORIGIN   ORIGIN_CITY_NAME ORIGIN_STATE_ABR DEST  \\\n",
       "0              AS     SEA        Seattle, WA               WA  LIH   \n",
       "1              AA     SAN      San Diego, CA               CA  DFW   \n",
       "2              AS     ANC      Anchorage, AK               AK  PHX   \n",
       "3              UA     SFO  San Francisco, CA               CA  DEN   \n",
       "4              EV     IAH        Houston, TX               TX  MKE   \n",
       "\n",
       "          DEST_CITY_NAME DEST_STATE_ABR FIRST_DEP_TIME ARR_DELAY  \n",
       "0              Lihue, HI             HI            NaN       -11  \n",
       "1  Dallas/Fort Worth, TX             TX            NaN         1  \n",
       "2            Phoenix, AZ             AZ            NaN        18  \n",
       "3             Denver, CO             CO            NaN         0  \n",
       "4          Milwaukee, WI             WI            NaN       -17  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"training_set.csv\")\n",
    "\n",
    "# Removing outliers (Arrival Delay of more than 100 mins)\n",
    "df = data[data['ARR_DELAY'] < 100]\n",
    "df = df.reset_index()\n",
    "\n",
    "train = df.drop(['ARR_DELAY'], axis = 1)\n",
    "y = df['ARR_DELAY']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of outliers removed:  149\n"
     ]
    }
   ],
   "source": [
    "print \"No. of outliers removed: \", len(data) - len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleaning(data):\n",
    "    for i in range(len(data)):\n",
    "        #print i\n",
    "        data.loc[i, 'DISTANCE'] = data.loc[i, 'DISTANCE'].replace(',','')\n",
    "        data.loc[i, 'DISTANCE'] = int(data.loc[i, 'DISTANCE'])\n",
    "\n",
    "        date = data.loc[i, 'FL_DATE']\n",
    "        data.loc[i, 'FL_DATE'] = datetime.datetime.strptime(date, '%Y-%m-%d')\n",
    "\n",
    "        data.loc[i, 'DAY_OF_MONTH'] = data.loc[i, 'FL_DATE'].day\n",
    "        data.loc[i, 'MONTH'] = data.loc[i, 'FL_DATE'].month\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>UID</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>TAXI_OUT</th>\n",
       "      <th>TAXI_IN</th>\n",
       "      <th>ACTUAL_ELAPSED_TIME</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>DISTANCE_GROUP</th>\n",
       "      <th>AIRLINE_ID</th>\n",
       "      <th>...</th>\n",
       "      <th>UNIQUE_CARRIER</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>ORIGIN_CITY_NAME</th>\n",
       "      <th>ORIGIN_STATE_ABR</th>\n",
       "      <th>DEST</th>\n",
       "      <th>DEST_CITY_NAME</th>\n",
       "      <th>DEST_STATE_ABR</th>\n",
       "      <th>FIRST_DEP_TIME</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1020</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>373</td>\n",
       "      <td>2701</td>\n",
       "      <td>11</td>\n",
       "      <td>19930</td>\n",
       "      <td>...</td>\n",
       "      <td>AS</td>\n",
       "      <td>SEA</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>WA</td>\n",
       "      <td>LIH</td>\n",
       "      <td>Lihue, HI</td>\n",
       "      <td>HI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1220</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>183</td>\n",
       "      <td>1171</td>\n",
       "      <td>5</td>\n",
       "      <td>19805</td>\n",
       "      <td>...</td>\n",
       "      <td>AA</td>\n",
       "      <td>SAN</td>\n",
       "      <td>San Diego, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>DFW</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>945</td>\n",
       "      <td>48</td>\n",
       "      <td>6</td>\n",
       "      <td>355</td>\n",
       "      <td>2552</td>\n",
       "      <td>11</td>\n",
       "      <td>19930</td>\n",
       "      <td>...</td>\n",
       "      <td>AS</td>\n",
       "      <td>ANC</td>\n",
       "      <td>Anchorage, AK</td>\n",
       "      <td>AK</td>\n",
       "      <td>PHX</td>\n",
       "      <td>Phoenix, AZ</td>\n",
       "      <td>AZ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1437</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>136</td>\n",
       "      <td>967</td>\n",
       "      <td>4</td>\n",
       "      <td>19977</td>\n",
       "      <td>...</td>\n",
       "      <td>UA</td>\n",
       "      <td>SFO</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>CO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1735</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>151</td>\n",
       "      <td>984</td>\n",
       "      <td>4</td>\n",
       "      <td>20366</td>\n",
       "      <td>...</td>\n",
       "      <td>EV</td>\n",
       "      <td>IAH</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>MKE</td>\n",
       "      <td>Milwaukee, WI</td>\n",
       "      <td>WI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  UID  DAY_OF_WEEK  CRS_DEP_TIME  TAXI_OUT  TAXI_IN  \\\n",
       "0      0    0            2          1020        11        8   \n",
       "1      1    1            6          1220        13        9   \n",
       "2      2    2            6           945        48        6   \n",
       "3      3    3            1          1437        17        8   \n",
       "4      5    5            7          1735        28        5   \n",
       "\n",
       "   ACTUAL_ELAPSED_TIME DISTANCE  DISTANCE_GROUP  AIRLINE_ID  ...   \\\n",
       "0                  373     2701              11       19930  ...    \n",
       "1                  183     1171               5       19805  ...    \n",
       "2                  355     2552              11       19930  ...    \n",
       "3                  136      967               4       19977  ...    \n",
       "4                  151      984               4       20366  ...    \n",
       "\n",
       "  UNIQUE_CARRIER  ORIGIN   ORIGIN_CITY_NAME  ORIGIN_STATE_ABR DEST  \\\n",
       "0             AS     SEA        Seattle, WA                WA  LIH   \n",
       "1             AA     SAN      San Diego, CA                CA  DFW   \n",
       "2             AS     ANC      Anchorage, AK                AK  PHX   \n",
       "3             UA     SFO  San Francisco, CA                CA  DEN   \n",
       "4             EV     IAH        Houston, TX                TX  MKE   \n",
       "\n",
       "          DEST_CITY_NAME DEST_STATE_ABR FIRST_DEP_TIME DAY_OF_MONTH MONTH  \n",
       "0              Lihue, HI             HI            NaN          3.0   1.0  \n",
       "1  Dallas/Fort Worth, TX             TX            NaN         28.0   1.0  \n",
       "2            Phoenix, AZ             AZ            NaN         21.0   1.0  \n",
       "3             Denver, CO             CO            NaN         23.0   1.0  \n",
       "4          Milwaukee, WI             WI            NaN          1.0   1.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = cleaning(train)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>UID</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>TAXI_OUT</th>\n",
       "      <th>TAXI_IN</th>\n",
       "      <th>ACTUAL_ELAPSED_TIME</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>DISTANCE_GROUP</th>\n",
       "      <th>AIRLINE_ID</th>\n",
       "      <th>...</th>\n",
       "      <th>FIRST_DEP_TIME</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>ORIG_STATE</th>\n",
       "      <th>DEST_STATE</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>OUT_TAXI</th>\n",
       "      <th>IN_TAXI</th>\n",
       "      <th>ELAPSED_TIME</th>\n",
       "      <th>NEW_DIST_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>(600, 1200]</td>\n",
       "      <td>(0, 20]</td>\n",
       "      <td>(0, 10]</td>\n",
       "      <td>(360, 390]</td>\n",
       "      <td>(2700, 2800]</td>\n",
       "      <td>11</td>\n",
       "      <td>19930</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>(1200, 1800]</td>\n",
       "      <td>(0, 20]</td>\n",
       "      <td>(0, 10]</td>\n",
       "      <td>(180, 210]</td>\n",
       "      <td>(1100, 1200]</td>\n",
       "      <td>5</td>\n",
       "      <td>19805</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>(600, 1200]</td>\n",
       "      <td>(40, 60]</td>\n",
       "      <td>(0, 10]</td>\n",
       "      <td>(330, 360]</td>\n",
       "      <td>(2500, 2600]</td>\n",
       "      <td>11</td>\n",
       "      <td>19930</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>2550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>(1200, 1800]</td>\n",
       "      <td>(0, 20]</td>\n",
       "      <td>(0, 10]</td>\n",
       "      <td>(120, 150]</td>\n",
       "      <td>(900, 1000]</td>\n",
       "      <td>4</td>\n",
       "      <td>19977</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>(1200, 1800]</td>\n",
       "      <td>(20, 40]</td>\n",
       "      <td>(0, 10]</td>\n",
       "      <td>(150, 180]</td>\n",
       "      <td>(900, 1000]</td>\n",
       "      <td>4</td>\n",
       "      <td>20366</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  UID  DAY_OF_WEEK  CRS_DEP_TIME  TAXI_OUT  TAXI_IN  \\\n",
       "0      0    0            2   (600, 1200]   (0, 20]  (0, 10]   \n",
       "1      1    1            6  (1200, 1800]   (0, 20]  (0, 10]   \n",
       "2      2    2            6   (600, 1200]  (40, 60]  (0, 10]   \n",
       "3      3    3            1  (1200, 1800]   (0, 20]  (0, 10]   \n",
       "4      5    5            7  (1200, 1800]  (20, 40]  (0, 10]   \n",
       "\n",
       "  ACTUAL_ELAPSED_TIME      DISTANCE  DISTANCE_GROUP  AIRLINE_ID  \\\n",
       "0          (360, 390]  (2700, 2800]              11       19930   \n",
       "1          (180, 210]  (1100, 1200]               5       19805   \n",
       "2          (330, 360]  (2500, 2600]              11       19930   \n",
       "3          (120, 150]   (900, 1000]               4       19977   \n",
       "4          (150, 180]   (900, 1000]               4       20366   \n",
       "\n",
       "       ...       FIRST_DEP_TIME  DAY_OF_MONTH  MONTH  ORIG_STATE DEST_STATE  \\\n",
       "0      ...                  NaN           3.0    1.0          47        9.0   \n",
       "1      ...                  NaN          28.0    1.0           4       42.0   \n",
       "2      ...                  NaN          21.0    1.0           0        3.0   \n",
       "3      ...                  NaN          23.0    1.0           4        5.0   \n",
       "4      ...                  NaN           1.0    1.0          42       48.0   \n",
       "\n",
       "  DEP_TIME OUT_TAXI IN_TAXI ELAPSED_TIME NEW_DIST_GROUP  \n",
       "0    900.0     10.0     5.0        375.0         2750.0  \n",
       "1   1500.0     10.0     5.0        195.0         1150.0  \n",
       "2    900.0     50.0     5.0        345.0         2550.0  \n",
       "3   1500.0     10.0     5.0        135.0          950.0  \n",
       "4   1500.0     30.0     5.0        165.0          950.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pre_processing(data):\n",
    "    from sklearn.preprocessing import LabelEncoder  \n",
    "    \n",
    "    # Creating bins for continuous values in CRS_DEP_TIME feature\n",
    "    CRS_DEP_TIME__bins = [0, 600, 1200, 1800, 2400]\n",
    "    data['CRS_DEP_TIME'] = pd.cut(data['CRS_DEP_TIME'], CRS_DEP_TIME__bins)\n",
    "\n",
    "    TAXI_OUT__bins = [0, 20, 40, 60, 80, 150]\n",
    "    data['TAXI_OUT'] = pd.cut(data['TAXI_OUT'], TAXI_OUT__bins)\n",
    "    \n",
    "    TAXI_IN__bins = [0, 10, 20, 30, 40, 85] \n",
    "    data['TAXI_IN'] = pd.cut(data['TAXI_IN'], TAXI_IN__bins)\n",
    "    \n",
    "    ACTUAL_ELAPSED_TIME__bins = [0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360, 390, 650]\n",
    "    data['ACTUAL_ELAPSED_TIME'] = pd.cut(data['ACTUAL_ELAPSED_TIME'], ACTUAL_ELAPSED_TIME__bins)\n",
    "    \n",
    "    DISTANCE__bins = np.arange(0, 5100, 100)\n",
    "    data['DISTANCE'] = pd.cut(data['DISTANCE'], DISTANCE__bins)\n",
    "    \n",
    "    # Changing string to int values\n",
    "    le = LabelEncoder()\n",
    "    data['ORIG_STATE'] = le.fit_transform(data['ORIGIN_STATE_ABR'])\n",
    "\n",
    "    # Using Python's Dictionary as Hash-Map to to encode states as integers\n",
    "    # Using the same encoding for Destination State as well (for consistency)\n",
    "    states = {}\n",
    "\n",
    "    for i in range(len(data)):\n",
    "        key = data.loc[i, 'ORIGIN_STATE_ABR']\n",
    "        value = data.loc[i, 'ORIG_STATE']\n",
    "        states[key] = value\n",
    "\n",
    "    for i in range(len(data)):\n",
    "        val = data.loc[i, 'DEST_STATE_ABR']\n",
    "        data.loc[i, 'DEST_STATE'] = states[val]\n",
    "        data.loc[i, 'DEP_TIME'] = data.loc[i, 'CRS_DEP_TIME'].mid\n",
    "        data.loc[i, 'OUT_TAXI'] = data.loc[i, 'TAXI_OUT'].mid\n",
    "        data.loc[i, 'IN_TAXI'] = data.loc[i, 'TAXI_IN'].mid\n",
    "        data.loc[i, 'ELAPSED_TIME'] = data.loc[i, 'ACTUAL_ELAPSED_TIME'].mid\n",
    "        data.loc[i, 'NEW_DIST_GROUP'] = data.loc[i, 'DISTANCE'].mid\n",
    "\n",
    "    return data\n",
    "\n",
    "train = pre_processing(train)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing redundant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>DISTANCE_GROUP</th>\n",
       "      <th>AIRLINE_ID</th>\n",
       "      <th>ORIGIN_CITY_MARKET_ID</th>\n",
       "      <th>DEST_CITY_MARKET_ID</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>ORIG_STATE</th>\n",
       "      <th>DEST_STATE</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>OUT_TAXI</th>\n",
       "      <th>IN_TAXI</th>\n",
       "      <th>ELAPSED_TIME</th>\n",
       "      <th>NEW_DIST_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>19930</td>\n",
       "      <td>30559</td>\n",
       "      <td>32982</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>19805</td>\n",
       "      <td>33570</td>\n",
       "      <td>30194</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>19930</td>\n",
       "      <td>30299</td>\n",
       "      <td>30466</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>2550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>19977</td>\n",
       "      <td>32457</td>\n",
       "      <td>30325</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>20366</td>\n",
       "      <td>31453</td>\n",
       "      <td>33342</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  DAY_OF_WEEK  DISTANCE_GROUP  AIRLINE_ID  ORIGIN_CITY_MARKET_ID  \\\n",
       "0      0            2              11       19930                  30559   \n",
       "1      1            6               5       19805                  33570   \n",
       "2      2            6              11       19930                  30299   \n",
       "3      3            1               4       19977                  32457   \n",
       "4      5            7               4       20366                  31453   \n",
       "\n",
       "   DEST_CITY_MARKET_ID  DAY_OF_MONTH  MONTH  ORIG_STATE  DEST_STATE  DEP_TIME  \\\n",
       "0                32982           3.0    1.0          47         9.0     900.0   \n",
       "1                30194          28.0    1.0           4        42.0    1500.0   \n",
       "2                30466          21.0    1.0           0         3.0     900.0   \n",
       "3                30325          23.0    1.0           4         5.0    1500.0   \n",
       "4                33342           1.0    1.0          42        48.0    1500.0   \n",
       "\n",
       "   OUT_TAXI  IN_TAXI  ELAPSED_TIME  NEW_DIST_GROUP  \n",
       "0      10.0      5.0         375.0          2750.0  \n",
       "1      10.0      5.0         195.0          1150.0  \n",
       "2      50.0      5.0         345.0          2550.0  \n",
       "3      10.0      5.0         135.0           950.0  \n",
       "4      30.0      5.0         165.0           950.0  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the un-cleaned columns (ONLY FOR NOW !!!)\n",
    "redundant_cols = ['UID', 'FL_NUM','FIRST_DEP_TIME', 'ORIGIN', 'ORIGIN_CITY_NAME', 'DEST', 'DEST_STATE_ABR', \n",
    "                  'UNIQUE_CARRIER']\n",
    "new_train = train.drop(redundant_cols, axis=1)\n",
    "\n",
    "# These features have been used to create new features which made more sense\n",
    "treated_cols = ['FL_DATE', 'ORIGIN_STATE_ABR', 'DEST_CITY_NAME', 'CRS_DEP_TIME', 'TAXI_OUT',\n",
    "               'TAXI_IN', 'ACTUAL_ELAPSED_TIME', 'DISTANCE']\n",
    "new_train = new_train.drop(treated_cols, axis=1)\n",
    "\n",
    "new_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = new_train.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/harshitsrivastava/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:5: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  \"\"\"\n",
      "/Users/harshitsrivastava/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:8: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "RobustScaler()\n",
    "scaler_x = RobustScaler().fit(new_train)\n",
    "scaler_y = RobustScaler().fit(y.reshape(-1, 1))\n",
    "\n",
    "scaled_train = scaler_x.transform(new_train)\n",
    "scaled_y = scaler_y.transform(y.reshape(-1, 1))\n",
    "\n",
    "scaled_train = pd.DataFrame(scaled_train, columns=cols)\n",
    "scaled_y = pd.DataFrame(y, columns = [\"ARR_DELAY\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>DISTANCE_GROUP</th>\n",
       "      <th>AIRLINE_ID</th>\n",
       "      <th>ORIGIN_CITY_MARKET_ID</th>\n",
       "      <th>DEST_CITY_MARKET_ID</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>ORIG_STATE</th>\n",
       "      <th>DEST_STATE</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>OUT_TAXI</th>\n",
       "      <th>IN_TAXI</th>\n",
       "      <th>ELAPSED_TIME</th>\n",
       "      <th>NEW_DIST_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "      <td>4762.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000950</td>\n",
       "      <td>-0.017115</td>\n",
       "      <td>0.288324</td>\n",
       "      <td>0.187549</td>\n",
       "      <td>0.154707</td>\n",
       "      <td>0.144193</td>\n",
       "      <td>-0.002454</td>\n",
       "      <td>-0.078293</td>\n",
       "      <td>0.129196</td>\n",
       "      <td>0.129608</td>\n",
       "      <td>-0.250315</td>\n",
       "      <td>4.841453</td>\n",
       "      <td>2.201806</td>\n",
       "      <td>0.056431</td>\n",
       "      <td>0.280854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.576210</td>\n",
       "      <td>0.498444</td>\n",
       "      <td>0.811021</td>\n",
       "      <td>0.742116</td>\n",
       "      <td>0.665185</td>\n",
       "      <td>0.669533</td>\n",
       "      <td>0.555578</td>\n",
       "      <td>0.576178</td>\n",
       "      <td>0.580742</td>\n",
       "      <td>0.577707</td>\n",
       "      <td>0.839533</td>\n",
       "      <td>10.026800</td>\n",
       "      <td>6.207427</td>\n",
       "      <td>0.862863</td>\n",
       "      <td>0.877853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.994324</td>\n",
       "      <td>-0.750000</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>-0.801556</td>\n",
       "      <td>-0.716286</td>\n",
       "      <td>-0.717324</td>\n",
       "      <td>-0.937500</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.653846</td>\n",
       "      <td>-0.653846</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.029183</td>\n",
       "      <td>-0.418568</td>\n",
       "      <td>-0.418050</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.384615</td>\n",
       "      <td>-0.384615</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>-0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.970817</td>\n",
       "      <td>0.581432</td>\n",
       "      <td>0.581950</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.996351</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>2.657588</td>\n",
       "      <td>2.353216</td>\n",
       "      <td>2.275934</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.230769</td>\n",
       "      <td>1.230769</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>4.277778</td>\n",
       "      <td>6.142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index  DAY_OF_WEEK  DISTANCE_GROUP   AIRLINE_ID  \\\n",
       "count  4762.000000  4762.000000     4762.000000  4762.000000   \n",
       "mean      0.000950    -0.017115        0.288324     0.187549   \n",
       "std       0.576210     0.498444        0.811021     0.742116   \n",
       "min      -0.994324    -0.750000       -0.666667    -0.801556   \n",
       "25%      -0.500000    -0.500000       -0.333333    -0.029183   \n",
       "50%       0.000000     0.000000        0.000000     0.000000   \n",
       "75%       0.500000     0.500000        0.666667     0.970817   \n",
       "max       0.996351     0.750000        2.666667     2.657588   \n",
       "\n",
       "       ORIGIN_CITY_MARKET_ID  DEST_CITY_MARKET_ID  DAY_OF_MONTH        MONTH  \\\n",
       "count            4762.000000          4762.000000   4762.000000  4762.000000   \n",
       "mean                0.154707             0.144193     -0.002454    -0.078293   \n",
       "std                 0.665185             0.669533      0.555578     0.576178   \n",
       "min                -0.716286            -0.717324     -0.937500    -1.000000   \n",
       "25%                -0.418568            -0.418050     -0.500000    -0.500000   \n",
       "50%                 0.000000             0.000000      0.000000     0.000000   \n",
       "75%                 0.581432             0.581950      0.500000     0.500000   \n",
       "max                 2.353216             2.275934      0.937500     0.833333   \n",
       "\n",
       "        ORIG_STATE   DEST_STATE     DEP_TIME     OUT_TAXI      IN_TAXI  \\\n",
       "count  4762.000000  4762.000000  4762.000000  4762.000000  4762.000000   \n",
       "mean      0.129196     0.129608    -0.250315     4.841453     2.201806   \n",
       "std       0.580742     0.577707     0.839533    10.026800     6.207427   \n",
       "min      -0.653846    -0.653846    -2.000000     0.000000     0.000000   \n",
       "25%      -0.384615    -0.384615    -1.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.615385     0.615385     0.000000     0.000000     0.000000   \n",
       "max       1.230769     1.230769     1.000000   105.000000    57.500000   \n",
       "\n",
       "       ELAPSED_TIME  NEW_DIST_GROUP  \n",
       "count   4762.000000     4762.000000  \n",
       "mean       0.056431        0.280854  \n",
       "std        0.862863        0.877853  \n",
       "min       -1.000000       -0.857143  \n",
       "25%       -0.666667       -0.428571  \n",
       "50%        0.000000        0.000000  \n",
       "75%        0.333333        0.571429  \n",
       "max        4.277778        6.142857  "
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index \t\t<type 'numpy.float64'>\n",
      "DAY_OF_WEEK \t\t<type 'numpy.float64'>\n",
      "DISTANCE_GROUP \t\t<type 'numpy.float64'>\n",
      "AIRLINE_ID \t\t<type 'numpy.float64'>\n",
      "ORIGIN_CITY_MARKET_ID \t\t<type 'numpy.float64'>\n",
      "DEST_CITY_MARKET_ID \t\t<type 'numpy.float64'>\n",
      "DAY_OF_MONTH \t\t<type 'numpy.float64'>\n",
      "MONTH \t\t<type 'numpy.float64'>\n",
      "ORIG_STATE \t\t<type 'numpy.float64'>\n",
      "DEST_STATE \t\t<type 'numpy.float64'>\n",
      "DEP_TIME \t\t<type 'numpy.float64'>\n",
      "OUT_TAXI \t\t<type 'numpy.float64'>\n",
      "IN_TAXI \t\t<type 'numpy.float64'>\n",
      "ELAPSED_TIME \t\t<type 'numpy.float64'>\n",
      "NEW_DIST_GROUP \t\t<type 'numpy.float64'>\n"
     ]
    }
   ],
   "source": [
    "cols = []\n",
    "for col in scaled_train:\n",
    "    cols.append(col)\n",
    "    # Printing the data type of last element of every feature\n",
    "    print col, \"\\t\\t\", type(scaled_train[col][len(scaled_train)-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box-Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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SbNmyJatWrcp73vOefO5znxt0aQAjRVgGGELr1q3LpZdemqVLl6a1lp07d+b6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\nB2EZAAA6CMsAANBBWAYAgA7/PyZ1LwGxaP/1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1065f6190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0b240790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1ziSfT3IsyauH6SAAwB62traW6enph9Wmp6eztra2Sz2C7TtteG6t/UmS52xS/2I2WS2j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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a159f4150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iGQAAGpEMAACNSAYAgEYkAwBAI5IBAKARyQAA0IhkAABoRDIAADQiGQAAGpEMAADNnkUv\nAOB0e/G1v59HvveDRS9jR+w7cNuil7Btzzj7yfnc21+16GUA/BiRDJxxHvneD3Lvda9d9DK2bTab\nZXV1ddHL2LbdEPrA7mO7BQAANCIZAAAakQwAAI1IBgCARiQDAEAjkgEAoBHJAADQiGQAAGhEMgAA\nNCIZAAAakQwAAI1IBgCARiQDAEAjkgEAoBHJAADQiGQAAGhEMgAANCIZAAAakQwAAI1IBgCARiQD\nAEAjkgEAoBHJAADQ7Fn0AgBOt6dfdCAvOnRg0cvYGYcWvYDte/pFSfLaRS8D4MeIZOCM8+17rsu9\n1y1/lM1ms6yuri56Gdu278Bti14CwHFstwAAgEYkAwBAI5IBAKARyQAA0IhkAABoRDIAADQiGQAA\nGpEMAACNSAYAgEYkAwBAI5IBAKARyQAA0IhkAABoRDIAADQiGQAAGpEMAACNSAYAgEYkAwBAI5IB\nAKARyQAA0IhkAABoRDIAADQiGQAAGpEMAADNSSO5qn6rqh6sqs9vGHtmVd1eVV+afp83jVdVvbuq\nDlfVXVX10nkuHgAA5mErV5Lfl+SyNnYgyR1jjAuT3DGdJ8mrk1w4/VyT5D07s0wAADh9ThrJY4z/\nmuShNnx5kkPT8aEkV2wYf/9Y94kk51bV83ZqsQAAcDrseZz3WxljPJAkY4wHquq50/jeJPdtmHdk\nGnugP0BVXZP1q81ZWVnJbDZ7nEsBOHW74T3n6NGju+J1JLvj3wPYXR5vJD+W2mRsbDZxjHEwycEk\n2b9//1hdXd3hpQA8ho/elt3wnjObzXbF69gt/x7A7vJ4P93iG8e2UUy/H5zGjyS5YMO885Pc//iX\nBwAAp9/jjeRbk1w1HV+V5JYN42+aPuXikiSPHNuWAQAAy+Kk2y2q6oNJVpM8u6qOJHl7kuuS3FRV\nVyf5WpIrp+kfSfKaJIeTfDfJm+ewZgAAmKuTRvIY4w2PcdOlm8wdSd663UUBAMAi+cY9AABoRDIA\nADQiGQAAGpEMAACNSAYAgEYkAwBAI5IBAKARyQAA0IhkAABoRDIAADQiGQAAGpEMAACNSAYAgEYk\nAwBAI5IBAKDZs+gFACzCvgO3LXoJO+Ojy/86nnH2kxe9BIDjiGTgjHPvda9d9BJ2xL4Dt+2a1wLw\nRGO7BQAANCIZAAAakQwAAI1IBgCARiQDAEAjkgEAoBHJAADQiGQAAGhEMgAANCIZAAAakQwAAI1I\nBgCARiQDAEAjkgEAoBHJAADQiGQAAGhEMgAANCIZAAAakQwAAI1IBgCARiQDAEAjkgEAoBHJAADQ\n7Fn0AgB2o6o6Pc/zjvk/xxhj/k8C8ATjSjLAHIwx5v6ztrZ2Wp4H4EwkkgEAoBHJAADQiGQAAGhE\nMgAANCIZAAAakQwAAI1IBgCARiQDAEAjkgEAoBHJAADQiGQAAGhEMgAANCIZAAAakQwAAI1IBgCA\nRiQDAEAjkgEAoBHJAADQ1Bhj0WtIVf1xkj9a9DoAlsyzk3xz0YsAWDLPH2M852STnhCRDMCpq6pP\njzH2L3odALuR7RYAANCIZAAAaEQywPI6uOgFAOxW9iQDAEDjSjIAADQiGQAAGpEMsEVV9WhV3bnh\n58A0PquqTT+Krap+vaq+XlVP2jD2d6vqj6fH+GJV/f1pfKWqPlxVn5vGPzKN76uq77XnftN0271V\ndff088Wq+tWqeupjrOVFG+7/UFV9dTr+2PQcn5/mrVbVqKqrN9z3JdPYP57O37fh/ndW1X/bmf/K\nAE8Mexa9AIAl8r0xxsVbnTyF8d9Jcl+Sv55ktuHmG8cY/6CqnpvkC1V1a5J/neT2McavT/f/Kxvm\nf/kEz/2KMcY3q+onsv7HfAeTXNUnjTHuTnLx9NjvS/LhMcaHpvN9bfrdSV6X5Prp/PVJPtfm/JNj\n9wfYbVxJBpifVyT5fJL3JHnDZhPGGA8m+XKS5yd5XpIjG26761SebIxxNMlbklxRVc98nGs+5mtJ\nnjZd3a4klyX5z9t8TIClIZIBtu7stuXhdSeZ/4YkH0xyc5Kfq6on9wlV9VNJfirJ4SS/keT6qlqr\nqn9RVX9hw9QXtOf+a5s94RjjT5J8NcmFj+P1dR9KcmWSn0nyh0m+327/tQ3ruWEHng/gCcN2C4Ct\n2/J2i6p6SpLXJPmHY4xvV9Unk7wqyW3TlNdV1c9mPTx/cYzxUJL/MkXzZUleneSzVfWXp/kn2m5x\n3NNvcd7J3JTkxiR/Meux/zPtdtstgF3LlWSA+bgsyTOS3F1V9yb52fz4losbxxgXjzF+eoxx87HB\nMcZDY4zfGWP8QpJPZX0v85ZV1dOT7Evyv7a5/owx/neSHyT5m0nu2O7jASwTV5IB5uMNSf7eGOOD\nSVJV5yT5alX9uce6Q1W9MsknxhjfnWL3BVnfG7wl0x/u/fskvzfGeHhbq/8z/yrJc8cYj65vTQY4\nM4hkgK07u6ru3HD+0THGgen4tqr6wXT835NcmuQXj00cY3ynqv4gyd86weP/1ST/rqp+mPX/0/eb\nY4xPTZ888YL23L81xnj3dLw2/XHdk7K+//nfPL6Xd7wxxok+2u3Xqupfbjh/2Rjj/+7UcwMskq+l\nBgCAxp5kAABobLcA2IWq6kVJfrsNf3+M8dOLWA/AsrHdAgAAGtstAACgEckAANCIZAAAaEQyAAA0\nIhkAAJr/DyQQXKEWUGvmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15eb0690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of 'Arrival Delay'\n",
    "ax0 = plt.figure(figsize = (12, 8))\n",
    "ax0 = df.boxplot(column=['ARR_DELAY']) \n",
    "\n",
    "ax1 = plt.figure(figsize = (12, 8))\n",
    "ax1 = new_train.boxplot(column=['OUT_TAXI', 'IN_TAXI']) \n",
    "\n",
    "ax2 = plt.figure(figsize = (12, 8))\n",
    "ax2 = new_train.boxplot(column=['DEP_TIME', 'NEW_DIST_GROUP']) \n",
    "\n",
    "ax3 = plt.figure(figsize = (12, 8))\n",
    "ax3 = new_train.boxplot(column=['ELAPSED_TIME']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter-Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ORIG_STATE\n"
     ]
    },
    {
     "data": {
      "image/png": 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aOZP9A4mgMQNgKhnkNPatUS9SWUvG9WOH7ETgZErU+Ka9ZnWDVJ7nXlIlUmkH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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1065f6050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEP_TIME\n"
     ]
    },
    {
     "data": {
      "image/png": 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KepOqwf8TZvYWSY9Ien3A/gAAAJABcVdBR8amNLS5b5m7WVzQkO3ud0kaqHPXecvdS6tG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\nQjYAAACQsP8PoVrAc+LsXJQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1618c7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OUT_TAXI\n"
     ]
    },
    {
     "data": {
      "image/png": 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AqyPiLuDq9rG0ZbrdCfUO6WByHW9JUlWVObmSzPwoMNbhoecX3Yu2L++Q1svk+CiTf/Kx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\nEfE3tMZ4A5CZL9m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JkjYqsgbjksfGxvL48eNltyFJkqSai4g7M3Osn9eWNblSkiRJ2lYM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\nqQAGb0mSJKkA/z8W56/5c6L8rQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a174b1d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ELAPSED_TIME\n"
     ]
    },
    {
     "data": {
      "image/png": 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OCY+6LCPqqNTwtpwtD3PiAMoRdVYOQDzsLtJDpmfnVV/ZuP66vrLakJP6G0CrZRkpc5ua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\no1kOAPoPRTaAwjjxrhia5QCg/7AmG0AhnHhXHM1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a176d0ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['ORIG_STATE', 'DEP_TIME', 'OUT_TAXI', 'ELAPSED_TIME']\n",
    "for col in cols:\n",
    "    print col\n",
    "    feat = train[col]\n",
    "    try:\n",
    "        plt.figure(figsize = (12, 8))\n",
    "        plt.scatter(feat, y)\n",
    "        plt.ylabel(\"Arrival Delay\")\n",
    "        plt.xlabel(col)\n",
    "        plt.show()\n",
    "    except ValueError:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mutual Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Plotting function for Information Gain of each feature\n",
    "\n",
    "def plot_info(res, title):\n",
    "    \n",
    "    feat = res.keys()\n",
    "    imp = res.values()\n",
    "    # Plotting Feature_Names on X-Axis and Feature_importances on Y-Axis\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    plt.bar(range(len(feat)), imp)\n",
    "    plt.xticks(range(len(feat)), feat, rotation=40)\n",
    "    plt.xlabel('Feature Name')\n",
    "    plt.ylabel('Feature Importance')\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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57YdrJEmSJM1gkhbt/YFfA38J/BvwfWC/nkVJkiRJi90qW7Sr6uokdwB2qaoT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a154b4050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_regression\n",
    "\n",
    "discrete_feat = ['DAY_OF_WEEK', 'DISTANCE_GROUP', 'ORIGIN_CITY_MARKET_ID', 'DEST_CITY_MARKET_ID', 'ORIG_STATE',\n",
    "                'DAY_OF_MONTH', 'MONTH', 'DEST_STATE', 'DEP_TIME', 'OUT_TAXI', 'IN_TAXI', 'ELAPSED_TIME', \n",
    "                 'AIRLINE_ID', 'NEW_DIST_GROUP']\n",
    "dis_mutual_info = mutual_info_regression(scaled_train[discrete_feat], y, copy = True)\n",
    "\n",
    "dis_res = dict(zip(discrete_feat, dis_mutual_info))\n",
    "plot_info(dis_res, \"Mutual Information of Discrete Features (treated automatically)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZt\nSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmS\nJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSp\nAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZtSZIkqQGD\ntiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7Yk\nSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS\n1EDToB0Ru0bE1RGxICKOGOH+FSPi7Hr/xRExt+/+jSPiTxFxWMs6JUmSpInWLGhHxCzgE8BuwNbA\niyJi677JDgQWZebmwPHAcX33Hw98rVWNkiRJUistW7S3BxZk5jWZeTdwFrBX3zR7AafV2+cAO0ZE\nAETE3sA1wFUNa5QkSZKaaBm0NwCu6wxfX8eNOE1m3gvcBqwbEasCbwPe07A+SZIkqZmWQTtGGJcD\nTvMe4PjM/NOYTxBxcETMj4j5CxcuXMoyJUmSpIk3u+G8rwc26gxvCNw4yjTXR8RsYE3gFuAJwL4R\n8UFgLeC+iLgrM0/oPjgzTwJOAthuu+36Q7wkSZI0NC2D9iXAFhGxKXADsB/w4r5p5gH7Az8A9gUu\nzMwEntqbICLeDfypP2RLkiRJU1mzoJ2Z90bEocDXgVnAKZl5VUQcDczPzHnAycDpEbGA0pK9X6t6\nJEmSpMnUskWbzLwAuKBv3FGd23cBzx9nHu9uUpwkSZLUkL8MKUmSJDVg0JYkSZIaMGhLkiRJDRi0\nJUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJ\nkqQGDNqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKk\nBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM\n2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqS\nJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJ\nUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVID\nBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZt\nSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmS\nJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSp\nAYO2JEmS1IBBW5IkSWqgadCOiF0j4uqIWBARR4xw/4oRcXa9/+KImFvHbx8Rl9e/KyLiuS3rlCRJ\nkiZas6AdEbOATwC7AVsDL4qIrfsmOxBYlJmbA8cDx9Xx/wdsl5mPAXYFToyI2a1qlSRJkiZayxbt\n7YEFmXlNZt4NnAXs1TfNXsBp9fY5wI4REZn558y8t45fCciGdUqSJEkTrmXQ3gC4rjN8fR034jQ1\nWN8GrAsQEU+IiKuAnwCHdIK3JEmSNOW1DNoxwrj+lulRp8nMizPzUcDjgbdHxEp/9wQRB0fE/IiY\nv3DhwmUuWJIkSZooLYP29cBGneENgRtHm6b2wV4TuKU7QWb+DLgTeHT/E2TmSZm5XWZuN2fOnAks\nXZIkSVo24wbtiNgyIr4ZEf9Xh7eNiCMHmPclwBYRsWlErADsB8zrm2YesH+9vS9wYWZmfczs+nyb\nAFsB1w70iiRJkqQpYJAW7c8AbwfuAcjMKymheUy1T/WhwNeBnwFfzMyrIuLoiHhOnexkYN2IWAC8\nGehdAvApwBURcTnwn8BrM/PmwV+WJEmSNFyDXDJvlcz8UcQDulMPdGJiZl4AXNA37qjO7buA54/w\nuNOB0wd5DkmSJGkqGqRF++aI2Ix6kmJE7Av8rmlVkiRJ0jQ3SIv264CTgEdExA3Ar4GXNq1KkiRJ\nmubGDdqZeQ2wU0SsCiyXmXe0L0uSJEma3ga56sj7I2KtzLwzM++IiLUj4pjJKE6SJEmargbpo71b\nZt7aG8jMRcDu7UqSJEmSpr9BgvasiFixNxARKwMrjjG9JEmS9KA3yMmQZwDfjIjPUa488krgtKZV\nSZIkSdPcICdDfjAifgLsCATw3sz8evPK1MzcI84fdgn3u/bYPYZdgiRJUhODtGiTmV8Dvta4FkmS\nJGnGGOSqI/tExC8j4raIuD0i7oiI2yejOEmSJGm6GqRF+4PAnpn5s9bFSJIkSTPFIFcd+b0hW5Ik\nSVoyg7Roz4+Is4GvAH/tjczMc5tVJUmSJE1zgwTtNYA/Azt3xiVg0JYkSZJGMcjl/Q6YjEIkSZKk\nmWTcoB0RKwEHAo8CVuqNz8xXNqxLkiRJmtYG6TpyOvBzYBfgaOAlgCdHSpK0FPzRMOnBY5Crjmye\nme8E7szM04A9gG3aliVJkiRNb4ME7Xvq/1sj4tHAmsDcZhVJkiRJM8AgXUdOioi1gSOBecBqwDub\nViVJkiRNc4ME7W9m5iLgO8DDASJi06ZVSZIkSdPcIF1HvjzCuHMmuhBJkiRpJhm1RTsiHkG5pN+a\nEbFP56416FzmT5IkSdLfG6vryFbAs4G1gD074+8ADmpZlCRJkjTdjRq0M/O/IuKrwNsy8/2TWJMk\nSZI07Y3ZRzsz/wY8a5JqkSRJkmaMQa468v2IOAE4G7izNzIzL2tWlSRJkjTNDRK0n1z/H90Zl8Az\nJ74cSZIkaWYYN2hn5jMmoxBJkiRpJhn3OtoRsWZEfCQi5te/D0fEmpNRnCRJkjRdDfKDNadQLun3\ngvp3O/C5lkVJkiRJ090gfbQ3y8zndYbfExGXtypIkiRJmgkGadH+S0Q8pTcQEf8M/KVdSZIkSdL0\nN0iL9muA02q/7ABuAfZvWpUkSZI0zQ1y1ZHLgX+MiDXq8O3Nq5IkSZKmuUGuOrJuRHwcuAj4VkR8\nLCLWbV6ZJEmSNI0N0kf7LGAh8Dxg33r77JZFSZIkSdPdIH2018nM93aGj4mIvVsVJEmSJM0Eg7Ro\nfysi9ouI5erfC4DzWxcmSZIkTWeDBO1XA2cCd9e/s4A3R8QdEeGJkZIkSdIIBrnqyOqTUYgkSZI0\nkwzSR5uI2BaY250+M89tVJMkSZI07Y0btCPiFGBb4Crgvjo6AYO2JEmSNIpBWrSfmJlbN69EkiRJ\nmkEGORnyBxFh0JYkSZKWwCAt2qdRwvZNwF+BADIzt21amSRJkjSNDRK0TwFeBvyExX20JUmSJI1h\nkKD928yc17wSSZIkaQYZJGj/PCLOBM6jdB0BvLyfJEmSNJZBgvbKlIC9c2ecl/eTJEmSxjDIL0Me\nMBmFSJIkSTPJqEE7Iv6d0nI9osx8Q5OKJEmSpBlgrBbt+ZNWhSRJkjTDjBq0M/O0ySxEkiRJmkkG\n+WVISZIkSUvIoC1JkiQ1YNCWJEmSGhg3aEfElhHxzYj4vzq8bUQc2b40SZIkafoapEX7M8DbgXsA\nMvNKYL+WRUmSJEnT3SBBe5XM/FHfuHtbFCNJkiTNFIME7ZsjYjPqj9dExL7A75pWJUmSJE1z4/4E\nO/A64CTgERFxA/Br4CVNq5IkSZKmuTGDdkQsB2yXmTtFxKrAcpl5x+SUJkmSJE1fY3Ydycz7gEPr\n7TsN2ZIkSdJgBuk68o2IOAw4G7izNzIzb2lWlSRJmhLmHnH+sEu437XH7jHsEqQlMkjQfmX9/7rO\nuAQePvHlSJIkSTPDuEE7MzedjEIkSZKkmWTcoB0RLx9pfGb+x8SXI0mSJM0Mg3QdeXzn9krAjsBl\ngEFbkiRJGsUgXUde3x2OiDWB05tVJEmSJM0Ag/wyZL8/A1tMdCGSJEnSTDJIH+3zqD+/TgnmWwNf\nalmUJEmSNN0N0kf7Q53b9wK/yczrG9UjSZIkzQiDdB3ZPTO/Xf++l5nXR8RxzSuTJEmSprFBgvaz\nRhi320QXIkmSJM0ko3YdiYjXAK8FHh4RV3buWh34XuvCJEmSpOlsrD7aZwJfAz4AHNEZf0dm3tK0\nKkmSJGmaGzVoZ+ZtwG3AiwAi4iGUH6xZLSJWy8zfTk6JkiRJ0vQzbh/tiNgzIn4J/Br4NnAtpaVb\nkiRJ0igGORnyGOCJwC8yc1PKT7DbR1uSJEkawyBB+57M/COwXEQsl5nfAh4zyMwjYteIuDoiFkTE\nESPcv2JEnF3vvzgi5tbxz4qISyPiJ/X/M5fgNUmSJElDN8gP1twaEasB3wU+HxF/oPxwzZgiYhbw\nCcrlAa8HLomIeZn5085kBwKLMnPziNgPOA54IXAzsGdm3hgRjwa+DmywJC9MkvTgMPeI84ddwv2u\nPXaPYZcgaQoZpEV7L+DPwL8A/w38CthzgMdtDyzIzGsy827grDqv/nmfVm+fA+wYEZGZP87MG+v4\nq4CVImLFAZ5TkiRJmhLGbdHOzDsjYhNgi8w8LSJWAWYNMO8NgOs6w9cDTxhtmsy8NyJuA9altGj3\nPA/4cWb+dYDnlCRJkqaEQa46chCltfnEOmoD4CsDzDtGGJdLMk1EPIrSneTVo9R2cETMj4j5Cxcu\nHKAkSZIkaXIM0nXkdcA/A7cDZOYvgYcM8LjrgY06wxsCN442TUTMBtYEbqnDGwL/Cbw8M3810hNk\n5kmZuV1mbjdnzpwBSpIkSZImxyBB+6+1jzVwfyDub5keySXAFhGxaUSsAOwHzOubZh6wf729L3Bh\nZmZErAWcD7w9M72UoCRJkqadQYL2tyPiHcDKEfEs4EvAeeM9KDPvBQ6lXDHkZ8AXM/OqiDg6Ip5T\nJzsZWDciFgBvZvFPvR8KbA68MyIur3+DtKJLkiRJU8Igl/c7gnIZvp9Q+kpfAHx2kJln5gV1+u64\nozq37wKeP8LjjqH8UI4kSZI0LY0atCNi48z8bWbeB3ym/kmSJEkawFhdR+6/skhEfHkSapEkSZJm\njLGCdvfSew9vXYgkSZI0k4zVRztHuS1JkqQJMPeI84ddwv2uPXaPYZcw44wVtP8xIm6ntGyvXG9T\nhzMz12henSRJkjRNjRq0M3OQn1mXJEmSNIJBrqMtSZIkaQkZtCVJkqQGDNqSJElSAwZtSZIkqQGD\ntiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7Yk\nSZLUgEFbkiRJasCgLUmSJDUwe9gFSJKmlrlHnD/sEu537bF7DLsESVpqtmhLkiRJDdiiLT3I2Xop\nSVIbtmhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAU+GlCaYJxdKkiSwRVuSJElqwqAtSZIkNWDQ\nliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYk\nSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmS\nGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqYPawC5CkmWzuEecPu4QH\nuPbYPYZdgtTUVPrM+XmTLdqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhow\naEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhL\nkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5Ik\nSQ0YtCXP1WkhAAAgAElEQVRJkqQGDNqSJElSA02DdkTsGhFXR8SCiDhihPtXjIiz6/0XR8TcOn7d\niPhWRPwpIk5oWaMkSZLUQrOgHRGzgE8AuwFbAy+KiK37JjsQWJSZmwPHA8fV8XcB7wQOa1WfJEmS\n1FLLFu3tgQWZeU1m3g2cBezVN81ewGn19jnAjhERmXlnZv4vJXBLkiRJ007LoL0BcF1n+Po6bsRp\nMvNe4DZg3YY1SZIkSZOiZdCOEcblUkwz+hNEHBwR8yNi/sKFC5eoOEmSJKmllkH7emCjzvCGwI2j\nTRMRs4E1gVsGfYLMPCkzt8vM7ebMmbOM5UqSJEkTp2XQvgTYIiI2jYgVgP2AeX3TzAP2r7f3BS7M\nzIFbtCVJkqSpanarGWfmvRFxKPB1YBZwSmZeFRFHA/Mzcx5wMnB6RCygtGTv13t8RFwLrAGsEBF7\nAztn5k9b1StJkiRNpGZBGyAzLwAu6Bt3VOf2XcDzR3ns3Ja1SZIkSS35y5CSJElSAwZtSZIkqQGD\ntiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7Yk\nSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAZmD7sASZIkTQ9zjzh/2CXc79pj\n9xh2CeOyRVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7Yk\nSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS\n1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZtSZIkqQGDtiRJktSA\nQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFb\nkiRJamD2sAuYCeYecf6wS7jftcfuMewSJEmShC3akiRJUhMGbUmSJKkBg7YkSZLUgEFbkiRJasCg\nLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1J\nkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIk\nNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDXQ\nNGhHxK4RcXVELIiII0a4f8WIOLvef3FEzO3c9/Y6/uqI2KVlnZIkSdJEaxa0I2IW8AlgN2Br4EUR\nsXXfZAcCizJzc+B44Lj62K2B/YBHAbsCn6zzkyRJkqaFli3a2wMLMvOazLwbOAvYq2+avYDT6u1z\ngB0jIur4szLzr5n5a2BBnZ8kSZI0LbQM2hsA13WGr6/jRpwmM+8FbgPWHfCxkiRJ0pQVmdlmxhHP\nB3bJzFfV4ZcB22fm6zvTXFWnub4O/4rScn008IPMPKOOPxm4IDO/3PccBwMH18GtgKubvJjJsR5w\n87CLWELTrWbrbct627Letqy3velWs/W2Nd3q7bdJZs4Zb6LZDQu4HtioM7whcOMo01wfEbOBNYFb\nBnwsmXkScNIE1jw0ETE/M7cbdh1LYrrVbL1tWW9b1tuW9bY33Wq23ramW71Lq2XXkUuALSJi04hY\ngXJy47y+aeYB+9fb+wIXZmlinwfsV69KsimwBfCjhrVKkiRJE6pZi3Zm3hsRhwJfB2YBp2TmVRFx\nNDA/M+cBJwOnR8QCSkv2fvWxV0XEF4GfAvcCr8vMv7WqVZIkSZpoLbuOkJkXABf0jTuqc/su4Pmj\nPPZ9wPta1jfFTMcuMNOtZutty3rbst62rLe96Vaz9bY13epdKs1OhpQkSZIezPwJdkmSJKkBg7Yk\nSZLUgEF7hqi/qKlGIsLPyjharIMu98kTEY+MiA2HXYc0XdQro70kIjYedi2aOL3vson6TvNLbJqL\niF0jYqucop3tI2K/YdewLCLiaRGxUWbeZ+j7exGxfO92bx2ciI1TRLygzvO+ZZ3XsEXEoyJit2HX\nMZqIWC4i5gKnACsNt5qRRcQpEfH4YdexLCJih6nyGiJi5YhYvTM8oxtqImKliFg+ImZN8KwfC+wE\n7BkRa0zwvKeFiNgsIh47w3Y2lu8OLOt3v8FhGouI1wEnAKt1xk2ZDWbdqB0bEUcOu5Zl8GzgSxEx\nq4btKbN8hy0ing2cHBFnR8TxEbElLA7cyzDfk4DX1i/Hab2NioiVgB2BF0fE9sOuZyR1Z+aPwJ+A\nhVNpmUfEehExj3Li/iWd8dPqcxgRrwA+DazRGTfpryEi1oyIc4ETgR9FxL/0Gmqm0vs+kSJiG+Bb\nlNc8oVe5yMwfAGcDmwHPnqnLcDQRsRfwFeCdwMciYo8hl7TMIuJVwMfr99AxsOwNPg+qlWImiYhP\nAXsDT8nMSyNiPVj2kDMRImKdiDiqXvt8Z+BlEfHiYde1JCLiXRExB3gbcBXwH1CW73T7km8hIl5P\n2QjNA74AzAXetCwtt3W9uRC4OzOfXi//+bCJqHdY6mv4GvA94OVTrWtGROwfEU8CVgYCuGuqHEWI\niC2AC4HLMvOAOm6viFhlKmznBhURHwAOBvbIzG9GxBoRsfxkv4YaOL9K+X2KNwLHAutTGkMeOhMb\nEmrw+zzwCeAjwNoRcfwyzvOlEbFzZ9Q3gGdQLlX87GWZ93RSvwPeDzwHeCVlO/fiiFh7qIUtg4g4\nBjgI+BIwH3hSRPz3sh4JMWhPQzXMPBn4t8y8KSJ2AC6aKoclgbWBF0XEhpn5C+BQ4Jj6hT5dWqOe\nCjy2fhm+DVinfmFOiZ2ZYYqItwOvAZ6bmedk5lcoG6ebgZ0jYrOlmOc6lOB+V2YeWse9Gfj37iHu\n6SAiZkfEIb3hzPwl8GdgB0pL/ZpDK66qRwtWAR4H7EoJCTdk5l870yw3rM9qRGwO7APcAHy2jjse\nOARYYRg1LY2I2AnYC3hnZl4TEY8FzgeeNsl1zKW0PH4vM4/MzEWZeRpl2d4AvANm1ratdms7lPKa\nz8jM/wM+Ayy/tOt1PSp1L/DRiFglIlal/FbI9yit5k+JiEl9b4doNcoq8+vMXERpkLoXuG24ZS2Z\n3roQEf8DPCYzn5CZ38zMkzLzmcA6lJ4DS82gPY1ExMYR8RjKB/qLwOMi4v3AB4HDu4dWh1TfChEx\nOzN/BVwJ3AOQmd8A/g04KUp/5ynZKhwRK3cGfw+sCJCZN1NagJ4ZEQfVaadc/a11XvNylHVwVu9Q\naV1GXwAeRWndXpL5vgA4oM7zdxGxd0ScCOwCvDEz75iYV9BeRKxLaR3+ZEQcWMe9iLL+nFUne92Q\nygMgIp4MnEv5wbLTKO/nLsATIuLtEfGiiNiE8gUz6UcUIuKdwAcofcYvA94SERcB62Xmbpl562TX\ntKQi4iERsS2lRX4esH1EHE4Jeh/LzG9OZi3AjcCZQHZ3hGtDyDxKQ8Kqk1VTa7UxaiXgLcAWEfHC\netfjKdv1Jco+dcf0XODgzDyL8lm+GPgu8MXaOPB5yg717nWHasapjQjvjYhHZOYHKN2Pzq1370lZ\nritM0+/H3wIP7Q1E6fYH5SjF85ZlB8qgPQ1EsQ7wM8ohsE0pG+x1gX2Bd9Vf4exNv8QtihNQ40uB\n84AzIuIJlJNE/ql3f2Z+Cvh/wGkRsfJUazmJiA2AyyLiyIjYFfgFsE3v/vqF9DbgNRGx+1Srv7Xa\nqtxbNh8E/gIcCGxZ718uM38K/I7SEjnofA8H3gx8KzO/BFxEaYXaEtg7M6/vTDulg0A9snQFJaA+\nCfhwRJxOWU6vqL92+z3gHyLiDUOq8SXAvwMnZ+btmXkppaXzHuAPQK8l+UTgf4CzI2LdyfrijIjP\nUvu0Z+ZC4EPULi3AWzvTLR8RI/6q8LDV9fRS4FPA1pSdho0oO5NHZ+Y5nWk3b1zLDpRGmTmUPuIJ\nHBIRa3UmW0BZ5uu3rGWy1GX6OuCldZv0b8CrI+KLwDOBXrfGJZnf/wI3Zear6uhjgR9RurmdDJCZ\nfwS+TNnRnjZHXZZEZt4LbAwcHhFrZuYrgTUi4teU9ecVmXnXdPh+rLlqM0qjA/W9XRQRX6nDd9Ws\n8gfgB8BSH4k0aE8DWdwCnEoJMm8AVqV8YX4NeHjUM54j4r2UE9RWG2V2rZxJ2bhdDTyXshNwUETs\n3KstM98E3E3pZjClZOYNwJuAa4H3ArsBe0TETr2QkZkXUb6s3li/6KfjXvvSSmAW5Qt5K0p4mAM8\nJyLmdvr1/oES0MYV5TyDY4EzMvMygMw8HfhP4P8oreO9aS+g7OhMSbX1+hTKF81vMvNiynr+Ekqr\n/E/qpN+ihLCNJrsLSUS8hbIMD8zML0fE2hFxUA3bn6cEh/8BXpCZu1LOAdkzM//Y+oszSr/l8yjd\na+7LzN7RsEWU7dxNwL71c/dQyk7701vWtAz+Qtl5WQg8D3gIpVvUfGCtqH1Y6/vxX1HOTWi1Lfkl\nZef1GErXri9QQuChsbjf6QqUlvcbGtUwqTJzAWWdeXJEvDAzv05pmHoE8ObM/N2g84rSx/vLlNbx\n7/R2UDLzbuAwysnDH+o890+A92TmDyfsBU0BfUd7X0nJH/9ah59LWbe+l5l/i4jZk13f0qi56lfA\nunUHn8zcGXhERHywDv+lTv5XylHupWLQnuIionvo9reUlqcrgddTrhRwDqX174URcSqlBWWfzPzT\nJNS2UvcDmJkLMvNdmXkEJZDOAY4GzoyIz0bEzpm5a2Z+vHVtg4rOWeKZ+d+ZeQbly/7DlMPmrwC+\nFRGfiIhnAZ8DnpOZ90yHvfaJEBFR16fPUkLEiymHXz8EbAs8rR5SfAsliF85zvxWjogvUbou7Aoc\n0HdY7lOUQ7DPjojdI+IbwJWZedREv7aJUHdujwa+T9nR6C2zL1F2JM7uhZrM/DNlR+LIzJzsvoxz\ngKsy8/IoJ8Z9jXI+BZl5LuUozlMp/bXJzN9m5m3R8EoKtVVpdepVMDJzC+DqiOh1s6F+GZ5COUr2\nPkrI/nxmDrULTr961LF3hYJfABtSttcvBO6krNc7UrqgnUhpXd05M29ptS3JzBsp3YPuBN5Xg+BX\nam37ROmKeDZwSV03p6WIeEyUK7sAUMP1hZRuHDtl5hcoJ4K+LurVkQaY58MojS+HAC8H9qjzW70+\nxyLgXygnzB3Wee5bJuZVTQ1R+qUf29tBrEcD3gRsGxFvql37DgEOqzs29w6x3IF1djR3Bv4pIt5V\nh58JvLJ3xKw2CK1F6VGwdDLTvyn2RzlUGpQvwkuBver4tSgn0ryR0kJxZJ3ueZT+Yqd25jGrcX2b\nUwLVycDqnftm1/9vBd5Sb29CaSXeYTLqW4LXcD7lsOKTeuM69z8FuLje3r7W/+7O/csNez2ZrOXU\nN7xtXWb/CqxCaVU8g9JX8QJg3ZEe1zePjYGXdIZfXdfzDTvj1qXs1CwADp0q680Ir+X1lOA3l/Ll\ncyzlJNruNGcDF463bBvVN4cSUh9Xh79AOaP+Ssqh9e60q9T3dY8hLMet+2o+Hziub5oXAl8HdumM\nmxKfQ8oRgR8CB3XGfbguzw9RAtlydVv94/o+RJ2u+TpdP7efB95Uh59HPSeC+v0yWevkBL+uoFzz\neO/6enbuu/9twCeBNevweZRL0Y253lC6r32yb9z+dZu0K7B8Z/wzKTszU2rbNIHLeAfKyYBv54Hf\nkdtRcsfTO8vnzOm0DvXeM2CL+l3zsjr8NEpjz4+Bz3bXt6V6nmG/UP9GfPOXq/+Poxx+/Daln+dz\ngd0pJy49ldJf+3V12m36V57GNS5P6be0gNIH8JXAQzr3b0c5YWSV7muaSn+UlsX/phxePRCY27lv\nDcohw9nDrnMIyyUoe/kjvmeUcP0x4LV1eD/gmM79I65/lEO36/SN623o3ksJrMt17psLbNsZnjLr\nEKW/3qnAip1xj6IE7XcCD++b/pfAsye5xn+qX4RHdcatVdfrj/VNuxvlMl2T9iUJvADYv2/c8vX/\nFrX2g/ruX72zjg79C53FDQtHUU7cXAC8lhJkDwGeUL+0T6B02QH4p87jJ2xbDaw9xn2z6nfGfwLP\nq+NeBjyyM83Ql+cyvv4DKK3W3e/C2XUb/691eLXed9I489qK0g3y5X3j30oJ7k8e9uttvCxnURrT\net/fT6MckXlN33RvpFzecMrvZFAabzYb6bXW/7sA17C44e1A4MP90y3Vcw/7xfv3d2/6+sAVneH3\n1w34DpRDftcCp9T7Xkg5nL9BZ/qmYaR+4B5aP4hvpYT/fYB3Ufb+Hk05GWQryuHIoS/Tvvq3BFaq\nt4+qf9vUjeeJwH71vgAup1ynvPv4KRP2Gi2fDYHvAIeNM91zKf0eD+gbP1rIfjpwO6UP8OYsbmHq\nbeRWpLQWnTbK46dECOCBLTo/plxxoHv/kyjX630ztXW/jp/UHTbKYe7bqK1NddxLKK3W21Ja/55f\nxx/U28ZM1vIGNqA0ItxH6ev6jP7nrtuaK+nbQZlC68LqwDeBh1Naq19PCXWvrduVW4D/rNMeSOlO\nt3Hn8RMZsk+on5+xjiStRGn5vZgHhtFpt02j9Cs/uS7T0yhXE5lDOXLwRcoVanrTPhd4a9/j/+41\nU/rSr9YZ3hH4dd/nYhblO/mjdHaYZtIf5cd3vk85qnQeJWCvTNkx/jSwb2fa3YD3j7dsh/1HuYzp\nz6kt1iPc39vmvAa4rvs5nYjXNPQF4N8D3szeF+E84L/ruJWB/wLeQ9k7fycl1K5MaXVddxLrO5Vy\naGjFOvwq4Af19orA32qtJ9YV+zCmSMtTrfEASt/JR9Thh9YP34Z1+CJKGHwb8FLK4cONh1XvEJbP\nPwPXA68cY5pu0DyI2iWh/74RHrcS5bD/FfVL6tN0dhDrNOtRThgb9fmH/Qc8rG/4auDYvnF7UML2\nG4AVustmMr6EKCF2G0qIXaOOO79+fns7Ns+h7PScW9f7ueO9hxNU2yxg5c76cyb1PA7ggyNM/yrK\nNaiH/t731bU3pbX66Locl6/r74cooXc2i3fkV6MEww0a1DG7Lrsze8t1nOnXpLPzNR3/KI1Rl1K6\n5jyN0j3nUkoQ3IbSrfLsumweS9mJfOEA8/x5/X54D7BVHb8v5WjURp1pH0L5jtu61Wsc4rJ9FGXn\n9oC6/NanXCnpOEoDySso54UdQmlg+zHlnKWh1z7Ga3oBpYvUi8eYpvu99n4eeCR1mbeJQ18I/t3/\nZr6LcqWFh9Xh+cBJ9fYmwCUsPvQ47qGvCa5tHUor57kj3HcK8PFa+ztqrR8F3tGZZuhBm9I1YQE1\nOLP4EPVb6xf+V+sGZHtK0P4YncPUw65/EpbPAfXL6gbgo3XcciO9dkboWzrKdCtRWgg2qMOH1A31\nlpQW31/WjeD6ncc8dNjLYoxl9HJKeP1g78uFcsLsTZSrjXSnfTVwOPXoySTWuBPlREEov0a4qG47\njhjlPf8ci3cGWh8N2xI4HdiuDj+nLsteV7k/UrqbHTLs93qc1/HWuky3oTQkfBY4s963eX0Nb6zD\nKzasY1U6jTJ13P7Aq8Z4TDdQTPnD/aO8htcAJ/SNewXlXJHtKQ1QX6LsRF5GpwV2jHmuRNk5/hbl\nV4Avop6/Qwne3+xbdpP6uZ7EZbsLteGAxd2i1qN8//e6Cj6LchThDODxw655nNdzBGUH6kvAqzvj\nR/q+Wq5veMK+94e+IPxLKK0RvwI26YxbnRJ6eievPJnyy0vPn+TaHkM51PituvHpfSmvQAlib6K0\ngu48mXUt4Wv4HOVnh0/r3zBQAtFf6LSmAat2bj8YQvYelG4gvZb93wJvG+310zlKQedE2L5pNqe0\nhHT7Bx9AuV429QvxLkpIuYIavjrTTrkQQPnhgu9Qws0vKC0f+9Yv49uY5PMkRqnxyZQ+2I+tw+8D\nbumbZi6dE0wno17KDsCldA7dUoLi5fW+fSnd4l5LOWx9av86N+z3v9bxmbqt7p68uTbliEEvoDyR\ncmTv9Q3rWL1ug4+jhIkNKTsAP6T8ut1Ij+nuGG/YqraGr7m3Q/Yuate2vm31x4GzO8Nr8cDzhkba\nls2ltlZTGpQ+S+nmsyelEeD7lJbz+4Czhr0MJmEZH0Ht7lSHe2F7O0pDVe87YjkWHx2bcl1Fal0H\nU46izqnbxU/T2QntXx944I7UhDZmDn1hPJj/KK1hF1L69X2Yctm07oZjG0pXhl3q8Cv6vyAb1zeH\n0t/tBXX4I8BX+6Z5HKVPU+9wcHdjPvQPIOUQ36fqRvRA4KT+LyLK1TKeO0L9U+LLfRKWz7/x9yf1\n3cbiVtsR31NKv+vTqf2tO+N3Au6gc4Jk574zKWHg5yw+W/1IYKdhL4tRls/KwJzO8EsoR27eRAlU\nF1N+fe4Wyq/vrdf3+Mm4sshcHnhFnGMp17TtDX8dOK/e/mfKjs0Bk1Uj5UjGn4Efd8b1zpPYj9KS\nPZ/FRz5WBrYc9nvf9xpWr8vxm5QWz93pnHxI6dd6MbXbEyWkHdyolm0oR9y2pHQTOq5+pr5LbQgZ\n4TG9kLpC3aaP2Fd1qv5RujH0XsNrKdd87wW9XuPPXMoJ1Q/t206NuH5TjmCeX7dFH6rbs43qNu3g\nOs1GlO4p36WeGzXT/iiXBJ5Tb29K+T58Vm/ZUVr716acSPuQvsdOue/IWuuZwK6dcWtS8tVJjNDV\npW99eQXlqPzEnUMx7IXyYP6jHIrutRy+kHKR/Rf0TbMPZW96iyHV2D2ha426kf503zSnU34IYOjL\ndIT61+zcfgQlJH2cB55AeiSlL+W4fRxnyh+ln+HXuu8lD9yj3wu4lcV9FWf33b8/5dfS+oPlgZTD\n6idRzuDunfTY69d/QL3/IRP9mhoso+0p4elrlKMhve4AbwSOBx5JaZV9FOXErDupO2yTVF/vMqBH\nUY7KHNn5wjyPzglglHB9WX3PdpnEGl9PuWrSJvX5P9R3/2P/f3vnHSZJVb3h99vALkvOIqgEJSpR\nwipJwgKKwBJVUIIkQYIB+AFKFAQJSpIoICIZiYIgiGQEAQFBVJIEJSclh+/3x7m1U9s7uzO7TE/X\n7J73efqZ7uqq7lt3qm+de+453yFWCaowrRG19zo+Ua+1ZTRldaaM1WcQMm9T1fZZgUiea+vqHrHK\neCgRHz4zsXp0GpEIWJ8wH0lNapIwRP9AFCHqeJ/28lxFhCicDpxQtg0u57tfy77L0gt5OWIi94fa\n5y1F3IvvJpR6liHkFzeoXZczdrov2tC3Hy398Adi0lE5m/Yu187itX0/Q6iLzNnpdvdwTosRK+/1\nFepqIjYbMek/GVi2dn3VHUl7EyFHfWoLdLxj8jHmHzy83JSOah2oiaTCz9Ze94eXTON5Ple5kPeo\n3is3mEbK4NEV4lD9XZJY8v8RXdJFo6jJXE3uj/I/vJ8oYFFt25YodFTvq90JY3vqluMPIpKNpmrZ\nPpK4+c9bXp8I3N2yz9LE0vvMfXU+beqjUXRVOZ2jvL6T4qUn4jZ/TAkXIZLh+nXyQPGoEl6oWwjj\nekfCI7NBad8CZZ/Zyz4LV//jdo4jhDH0RcKLVN3o5iIK+nyzZd8bgR93+n/e0/m0vK5k3j7fsn1T\natJvfdnHwHK150sTk709iUT0VQlHzVcIg+Jcxg6jWI4IPRww4xyx4nsd4b1fgQjHObq8twbhYT2O\nkIJcmkji270XnzsvtfCIsm0QEWpwPSFIsHkZ49Zq92+lQ327DDH5/nrp522IyUyVQHoA4cXfrowp\nj9Aiddi0B10J4BvXtu1MET8or+ch7KkzKfep2nunE6tDff6/7njn5KNrECd0HvchDMFxkgza/WPv\n7ecTxuoDjKuB23EPVOuPZzz7rFxuUgf2Z/824VEG0WkIL8bmZdtxhLei8obWJ1bHUwu1KTe0HVr2\nGUJ4gHZhXA/3NcBFLdvOok3L6n3YT99h3IIuHyXyEdYvN6fDCM/i3C379cdEeEPCy14p6KxT/ldr\nlZvktUQ88Qqt7aL98dgfJyYl+9GVfFyFln2OCIf7fG3/tVr7uqmP2lg9mEjkPJRabH4bv/eqYkT8\nvPyvhxPG5xGU5FEiXOVXRG7PfrVjh5Vjxquz3aQHYdgOKobPNbXtizO2p3I+Il/iLGLVZILhUJSk\n6/I7vpMWTWUi6e80uian36NFD39yeRCKWs/WXn+KiE9vXbU8oowjIyfUt51+EKuLsxP3td3KtvMI\n50PrJHlpYlWyvlq/Mi2a/X3avk53UD7G/KOrm+B8hCfqSPpxmYbw2m3fOvhMYP/R1Kr7NeFBVD77\nBT1X/RpcbkqNVjdoQ/8sCdxee349ERpxVi+OHV+c40eJcITjyg2yUnOpV057iC5PsKipjDT1QRix\nR9ReV0lBoymVwgiv2rY9XW9tal+lhvAwETqwMOG9qZQwTiPi5O/r53bNQygQ7TKBfbYm4tmrmOy2\nKXN8iPOYkFRlFSs8K5HjcGi7xmrKKgkRnvQsMXH9P6JY2FcJx8yBhJ6xCA/k+rXjG5dU3ItzrlZq\nFiEcAFW1vtFlLNmNlhAtxs6j6E4je2+ihPZIIk79YmClbv6nN1AK+kyOD8Z2mpxCybkq98NXCHWO\nX7T0Z79Jk07iOW0HXFiez1fuac9SihS1nkfLOfXLpGEISduRNDMxg/oz8KjtB1r3cfmv235U0mWE\nZ/A//djM+YhkEEt61fYLE9rZ9sX90qpeUPr3QuDftr/Zw76y/b6kS2y/0z8tbAwzEkof2L5H0n6E\nV+6EagdJwwjP6CG2H622V9dnN6wJXGt7/3L8cElTETf9d8s+qwJPSnrE9umEkYWkQbY/6MPzm2Qk\nVXr1f7L9QyIhaElJH7X9b0IjnvL3IwC2f187XhPooz7H9m3AbeX/dQThuVkUeNX2/ZJ2JQzet/qr\nTQV4m4oAACAASURBVIW5gJttHwMg6bNEItII25eXtp8maVnC47SC7bf7uY3jpXZNTgW83d3/1fYH\nZfsLko4n4qD7dKyWJCL++k5Ju9q+RNLWhPf264SCy/LERO8FQiXjCdsnt5zL++N+enORtAfwKUmn\n275V0lHA9yUtCGxM/EZnAfaQtDFwju3LbT9ffcZ4xpShRJLcnoRhdiVwkKRtiXvye5IGEXkW/2rn\nOXYKSacBs0m62PZptreV9HtJdxCrJesTqx+bAb+R9HPb51THN2Ws7ob3CVWxyn76DvE7ub7aQdJc\nwLaSTrP9RM3e6pcxOw3tNiNpScJw+TthzL4l6UjbD43vmHITrY5v6w1c0qy2X7B9m6RZiSS4/xVD\n9I2evr/TxlLp35MJw+7antpl2+Wc3in79auB1N8Uo3cfwuv2LlGNDwDbN0o6E9hL0o7EeHAS4QV9\ntLvP64Y5iXAAJO1JqC8sCbwpaU3bb9r+t6QVaDH6GjZwz0VIWH1G0v1EHsLmwBqSrrD9YtnvPeA/\nkoYDb/f3gF1RXbe2/0/SVoQn+dPAypIesH2XpGNsv1ffvx+a9gywpaRbCZ1sEzGxM0saaXtvANs7\nSJqhH9rTa4oxd4Ckt4GZJB1bn0zVqY0j9xGxwX2O7Rcl7QscLOkftn8r6WhiBWlR27+XdBuRBDia\nKMpRP75Jv68ekfRLwhlwOKH/ju2riiNlHyIc5tyy768JOdyrJ/B5g2sTjSuJictzhKLE14n78f7E\nWHUBsTJwm+0/t+H0OoakmYgJyv22vyxp7trb6xFqOtfb/mPZdrWkRSuHYBPvj5I+QuRC/IYQaXi3\nbB9UJmh7ASdIWpWYmF0AnGL7iY40eFJd4fno1ZLGPMRMsVr6WoxYkl5vAsf0KEvUh+07lPAuHkFZ\n+iRmtScSHpLW2Kb60ss07WxbL9v/SWI5cGNiyfwoSpLm+Pqv9Zwm10d17kS87BlEKNJGdF9972Ai\nIe0Bxhb173GpkJjg3Egs6d5MhC8sR8SKXtDNddPI5cfStp3KNXRfOYeRxErJMeX3sCOhn71WP7dr\nJWqV6brrS2KCc2AZbw5hPMWG2tzOavl9A8KwOZmQE5y+tO9OIlGv3u5G/B4JScp7CW/nKkR4wpOE\nATt0PMe07TwYWwlhv9J3VZXPs4hVg+6Oa1z8bC/Pd29atNPL9k+Xvz8o49iCrWNId2MKoczyK0pi\nKuHNvpZQPfohcFTZ/hm6kuO376vzacKjdg/4FvDTlvdmpCtHZ8Eyfo+TJ9HU8ZrIUbmMyO84mxJf\nzbgKIq8Qif9f7Gh7O91hk+uDEutMxA3/vrb9l91d0OW96kbVViO29j1fIuSoHifiPa8oP7rDiSW2\nFWvH1C/g9YhEsI7FVhLZ9esAnyyvhxKThBNoqdLXzXnPTqwyDIjkoEnsn+lrzxcikqgeJcKXRhHh\nHPNV+xLG9hda+6qX19Fwwhs8mC4Zv9WoqZo08UHkJOxce70V4eFajTC6PkpMVL5L3OTPpyv5sF8M\nGmISfEVvr1VistBtEaF+7ttpWl6vRBTSaZyEZhnPXqBIftW2b0cULKl+J3XDurr2+2wMLL+j79CV\nQDqk9t4Z1GoYlN/xpS3HN9Io6uW5nwJ8pfZ6BkIl50q69PbPKuN7j1UZibCaDwgnwNcIJ9eyxKR5\n6dKf+9T2H5ATlF727V7AD2qvP0GssD9Ml4G6QRnzpu10e3s4l+G155XM6s2Esb02MUmeubw/K6EO\nVJcp7Mj/OUNH2oCkXxDL5DvZ3kzSLZJOJaSKFiY8yfX9q2XgDyQtDZwhaTXbz7WhbQJulHSQYyly\namKGfwEx+9ueiLt9FVhc0rO2/+GyBCfp24SXZ313NrZSxI9oP0lP2H5H0nXAtMBKpd1XQVcYSenf\npYjEvT1sv9y55rcPSd8F1pR0C3CV7Tsl/YpILlqMCPVYF5hW0suEN+ObjjhFwbjLzpLmIBRLrrX9\nbrVP6du3iBs/wPuS5iW8RGe3/WQnEUkfI2TD3i3n/G/bp5dr6BpiQnw2oTl8VK1f3B/hUpIGExJt\nQ0obLGlq22+Ob3/b79s+vv66je0bYfuN8b1v+/Wy30zE9XYQ4VXrtv2dQtL0hNHxNrEKgKSpbL9j\n+2RJyxOTnQ3K9T7m91FCTc6XtIHtR/qgOQsSq3TbSjrE9pu1tmwp6XpJR9velejTDesHt/uabDPP\nE/8DJA0hDKbriEJQm5Zwrh2Aecp4M0Fs3y5pM8IQe5bIRbmfiEF+gDDYf6zIGznXxQqbTBlBaIdT\n4tBnIxKSXwGOkXSX7d9Iutb2/zrYzglS4vVnkvRf27vYPloR0z8L8D8ioXMBYFAZP39HhBu5Nj52\n5v/c6RnK5PQgqoddwLjlg6ciBvPna9u6W+7akJidtbU8LhHS8h+6sv4PJLK7K8WILQjv3bvAYrXj\nfkYsB3eqvHRrKMuORBLYYnR5mOYgBuTTqEkSlfe+Wvp3rv5ob4f66CTCc/glQq7pR4SnbDgx47+M\nLrH+mYhZ/wQ9RITm6v30oNJCxEDuWa71LTrdFxNoZ7Wkuh4hhbYtISV2ILFMXy2pngz8sbtj+6GN\n6xPV75Yur79BTNC79aD252+SCNU6BVhxQtcOscq0PXAPNYWHJjyICcxZdHn0Ngb+WY13dGl/r0kU\nsBjG2Kt6owjljw9dSIyxi2qtQHhdv1/bNk21X7led+t0/7Xh/7EPtXAYapWGS/8vWXuv179Buirf\nzlSu2b8SDoPBZVzrNixocnoQHuzn6arwXC9qdCmwWtWv/TW+TWT7hxIqMWcR+T9PUKR5iUnEz8r1\nU61uTwcs3+l2j3UOnW7A5PIo//Drgetq23amxH0Rxu3LwKrl9ZCW4/cnjKAR/dTelYC/116fRomp\nrW2btfZ8XToYw1ZuMofSEh9btp1JTTKO8ArtC6xcXguYmzDK+6V/O9RHaxHLpfOU10sRE6hKLmsa\nonLj1RQDrnZst4YaEaLzKvDtXrbhy9SW4GngcjZjx4wfUPpoVmJl505KtUwipGaccr392M4DyvV9\nEmHULTGe/eoGYFuLRxETkduJVa1xtP672X9aupZyG3EtEOFAVwE/b9m+O6HmMXtt2zcYt5rlrmWs\nn6EP2rJHGfd/TFehpy/TTQgc8AUipniceP2B+mj5Lf6BUKKZtrweTKwsnTi+8ak3n09MWLcqr+eh\nAaFV/dinle77+sTqwKpEcvLcpb+bXihqUPmN3EmXY/CTRLL6TLXXpxD3/E+0HN+MHJBON2ByeRBL\nM1sRy71Vqe8/UfN4EJXSPqClwEEZULbr7xsREc/5+1r7fwuc3rJPx2+O5cc2Y7kRHtNN//26m5vh\ndLXn/aqZ2eG+2p+xizzcQSx9r07M9IcROrzr9OKz9ibKEp9UBrKqnHpvkyQb29+MHW97PkV/nfDK\nf7ab/fujCM1mhBG3A6HmMoTIhbiHWsng7s6DWDW7BPhuG9u3ebk5z9SyfQG6PJDj3OR7e8300/99\nXuANYNfatp0ocZzlOr+8PF+fSH5du7bviDLOf+jJTPmui4mVgSuA/cv26Utfn0pZlSNile+gVjCn\n0305Cec7PTF5XLBle3VOQ8p5XlD65XZKJcjWa2siv3c4kYf0pU73QRv7diF6KLhSrtvzCUfLLcB3\nau814vfZ3f+aSEw/jMhfGFJ+w7dSq5hNhOT+muJkatqjMkCSSaTIzLxKhG++VTQcdwcesL1GN/t/\nA7jV9sP93NRuKfHkb9r+tqRPEsvm+3e4WWOQtBOxXPQwEYv1GHFjP9T2M2WfOQiP35ds/61Tbe0E\nkj5S9UN5PZzwhs1B3GCeICTppiMy8Xchlmhf7+FzVySW/Xcqxx9MLLPuVN4fK065/lrS7MD/PIEY\n3v5E0krAY7afbNk+Jo5Z0o2EtNeeHWrjT4gCOMcRq0fvE5720wiP3h3Ab92NPJWkeYjQlyNtX9HG\nNu4P/Nf2kbVtZxHX2rHAle6SE6xfDysTMfD/bFfbekuJUb0EeNAhjXgicZNez/YrZZ+rieX2lwiD\n/M42tGNdwjBY0fZfJC1CqGSsY/s/ZUzbgPBirwBcYnvHvm5Hf1HO71QiZ+Sgbt4f7KhvMIK4nuYB\nPrB9Q/39D/H9HyVWK+YD3vJkZPhI+jKRA/Ej2xf2sO80hLNlattPl22NqWdQIWk+1yRmJX2JSFJ/\nnwjnqiSQFyNi+3cjVugblQMyhk5b+gP5QSRTXULIBl1BLEkOIxIvzqjtN6jsO6q+rdPtL+0Q8Bdg\nz063ZTz9Wy1Tb0uoZpxEeEUOpMSHErPcX9NS/ntyfxCesA+I+LS6QszMxNLadS37f4seZvzEZOZS\n4gZfeSmHEBrNZwD71q+d8rfuHd6SWNVpRLU/elDtoMubNjvwIrWqev3YxpWJBMzqeh5ByOLdRcQC\nf4oIj9qaWtngsu/ihNd14Ta1bW66vEbfpai0lDFtZcJ4+h6h4rNEvU/L8/2IkK2OxsISKz07luez\nlTH7OWJy0rrv1IRKzxzVufZhO+ahS8VkT+AcupR6biFChVYgJsYzEfeSuudxIHqy1yEKwZxX29Zd\njtL4qs/2Sf/TC7WSgfYov8lb6AqTHMQE8gZa+3h8fd7hc9qSiCn/KrVQECKM8VrgsNq2TxK5bbP0\nZxsn9jGIZKKRNKwI5s9JLMfsSSg3XEvEev6UKExzYMm6v4RIaLmm+gw3ZAbpuFqXJ27WjUHS4sSP\naGXbF9s+hYhdXJQwJN8AzpS0AGFIveIeqllOhrxGSDK9A5wm6WuSPmH7JcIbPbWkHaqdbZ9g+/FK\nOaEVSYsRXtQHCVmsd8px7xHX9ymEEs2WZbtbPJc/JJIwt3SHq/1JGlyKUMxPqHa8XBR2xsLhRRvs\nUPhZxvYl/d7YWAp9zbEiNtj2G7ZvIYys7Rye4CsIg2X2lmP/SYSV9OlKjoJpiJvdzsUj+CSwVVHC\n+AC40/Y2Dg/3LEQeBe5aJTiVcDys66JU0wmK4s5niSqLQx1VBHchJlb14mDTSzqYiCnf0faz5f/R\nl2P1xsBlpW+PJ5Ibfy3pJmIMnokwnu4kVvL2s/3T0r6BWOnxm8QkZ1dgNUmrV2+17uvK8otVh/r2\nPul/90KtZCChqHZ4MHCc7RskzUcobSw9nv3V2sfV64bxNnGPX4+oALpb2X4+EVZUrQhh+2HbFzmK\nOzXXnu20pT/QHsQAcSdwWTfvnQncWJ4vQsQ8v0ktbpKGeLKb/iCM6ovK86F0eR6/SMg1LUosVx9P\nzRtPA2fofdwvY8XAAjcRVc6WJ+LYzqUkjBIe76eIKnI9fe6Q0p/btXz+CLoUGKYhDOmbKZnqtX1P\nK9/fiP5n4lU7htae96vXkCiMczwtGrbEsuiNdK0stMa2tn0sKW04kJI0RWT+j/G+l20HEKsgs5bX\nw4kY2y0acB3sR5Tprm+rVmLWIIoTLULc2P9EGLbtbtPxtbHtY8Qk6ura+8MJ5adPtrZ5ID0ICcLd\nKInqRA7CS3Qla7cKAtTHtvmoKV7lY6x+GkaXUsimRA7HZsSk8Tvd7C/GXnVcnQ4meY/nnKZqeX0k\nsYI3stzjDqUozxDFi46hzepsfXp+nW7AQHwQISJPAbOV1/Wbzn3A6PJ8ZRoYLjIQHqWP/wQsVfUd\nXfKDl9G1TF0v6jBZ9y8Ru3gXY0tdrU5JYCUURV4i5PX2I8I/5u/F51bFlY4EDirP9yRivavl7Epi\nbEYidnSO+vHAhp3un27Oa1JUOz7fagD0Qzs/Xfp57W7eu5IWOcp2G13FONqVriz/qtDHruX1RYR3\n6RLCwL6MlmV5StGVTj/KuWxdnn+R8BbfQBQxmZlIOnyR8CZv1o4+JuTHfk1X4ZWpiInIseX10kQI\nyTjKPgNxTCNCb64kDKK5y7Zq7N6fyLepkngrB0rdEFyfmMxPUaGAvezbBYhwysMpxjKhtvEaofVe\n33dBWhRWiITC62lQ4SjCSXQNRZKPcO4cQZf05uGEHPFtxH1tGQbYJCwL1kwCtp+Q9DViMFjQseQ7\njSPB7DEibhbCu10t1cgNCRcZIDxDDNYbKorPPE1Xv4rQ+MZdyVdTQv+OJJaTfyRpS8cS+IuAJR1B\nrAKsRsRDbkQkuT4C4094Kcmwr0v6HrHsuIOkZ4lJzh+Jwe0LRIjUcY6Esd9Un0msPj4C9EXBjg9F\nKVAxK7H0eCmRIHQwYShu70g6G7N8Wo4Z5AgfmYowHm90hG20q41Dqmu2wvZfJZ0O7CvpfSJR7ylJ\nxxKrCi+27N+W5V5J0xGrcm8SRujSkg60fYekWYDRkja1vaGk+QlvN7YvLscPLs37wN0kbfYXJTls\nTkKZ4FVgHUnbEH15K5GT8g0iKe4sSR8HbnFX4l2fJYcpiq8MIiajO0nalIhp3xX4haTv2T6yhLfs\nI+lm23+pjh9oY1oJMboU+IPtH9WW8z8AsL2/otDP1cAa5bdXDz/7LiFBt5YbXDylE5TQvgsIIYDT\ny+8N2wcWIYP16Bqbv0ZcY9sT1ztlPBkErO5mhSB9jnAYvS7p/2z/XdJVwHGSPk+MMysR97VTgJNt\n/6dzzZ14UnXkQ6BQxFjP9qjatt8QUnO3dq5lkweSFiJkD2cmdGafIWa3HyM8qI1QtegvSmz1OcSM\n/l6iDyzpTCIhZD7bz5Z9p7L9zgQ+azoi3ON1QmPWJYZ5BOFhuwF411EB7xDgIdtntvP8PgwaGKod\nuwA32b6ntq0eN7krEcawIiFH9jawUbtviuW6qryQ79peo0w8fkVUAj2lqEGsQ/TxtbbPa/mMtlai\n7C2S9iGSp28kwpy+SeRzLEuEaLxj+zlJexGx2LvXjm2L+kKZAH6LKBh1KpHU/QzhxTsNOMT25ZIW\ntf1AX39/fyJpbaJWxO7l9VyEcTeVa9UzJf2DKAa1XW3bvsTYvkMTrqWmUYzn5W3vUl6LWH17t7y+\ngRi3XyXuB9vbvr9Mdg4BnrZ9bGdaP34UlYR3J6o7zku0+yVFHtw8wJrVpKs7R8WAoNMu9YH+IAbO\nw8rzq2ipCpmPD92/8wJHEasHY+l8MwDjFifh/H9IKEvMWF5vSiytXg78smybn4idnaM3fUL3xZV2\noRsdVsKDfg8Ni+lraePKNFS1o+W7rqNUZ2vZPqjl9SLUwlxoc8w4JVSGiPM8D1ihvN6CqCxahWmJ\nKMJ1AjH5bdTvD/g28AJdIX17EN681v6digh32bUf23Yl8Pny/GzgLSK04mhi0jyitu9ADBdZmNDJ\nXpvIoZmdcI78iqgq+wi16qDExG6hlv9Jo6r5NeVBVwGfb9OVKzG0ZZ+q1sQThNZ9db+oQnRm6/R5\ntLT3O3SFhc4AXEg41fYATirbd6LoqNMVetSoMae3j+ZmaQ4ctgXWkPQGMUPfEsbNnE4mDduP2f4u\n4Z3azvZWMMaDNlkvx5QM/QOI5KkDJX2KiMFei/DaLiRpD+AVwtj+WC/7xITh+bykhSTtTRhZf6x9\n98fKZ58N/ND2ZX13Zn1O41Q7KlrGgesJL3XljQLGDQ+w/aBL+EC7PcVlVe5EhWLMR4i4650lfZ1Y\nPVqCUMn4JWF4nwfsbfulBv7+HiFWfNYHsP0TIh54BgBJcys0vW8iVmiObneDFAwiJlmLluX7eYl+\nHUGsvGzu2upc6/XQdBQa2YcSOQZXEZP+PxCrCL8lcjoOBY5QKHYNtv2m7YcU6kCy/Y7t2zt2Eg2l\nePk3Ky8fAb4haTbb70oaWhtftiDCKZezvartV6pQLgBHmGEjUCjRHAmcpNDHnom4PlYjfpvDyurf\nOcDmZaWnChVt2pjTKzJG+0Ni25JGEsVSxsSuDrTBsunYflXSazAwZa4mkT8SccZzEXGzPyeSQVYh\nwju+SiTR3Usst947oQ/T2MWVTpc0I3FDfMD2ci27DyM8U6NtP1SOV0MHur8Dy0ia1mPHdf4NmF3S\n1LbPl3Sv7b9Xb5br6A0itKDPkbQkcKykowjv+ptE/Oml4+vHbgzrto0jko4mqq4dSkxA9iHia58j\nvNYb276qxIYuAmwCnGX7vaZcCy39dT2hjPMFSVsTk/NnbL9c3n+b0Kg+wfYZ3Rzf55Q+sqR7Ca/2\ndbZHlu8+mAhleXcg3zNsPyjpcmAtSc/b/p6ko2w/ra5CNDMSVWbfqV83U8g4/mH4B/A1SU+U3+Kv\nid/mRu4KGVmGyDm40/Zfy7ZGhHJ1h+1fKOR7tyBWNo4gQr0eLo8TiNyaV4nE/47le/QVGaPdxwzk\nATNpDtV1pKiyeDCRxDUC+C/hDdjT9mmKxC9VHufxXX/FqPoEMC2xbL0jYUgfBMzeshJzOJGs9Psy\nkWz0NS3p04SyyI+KR63+3pVESMzTtW1tNRKrz5c0A1F8YRbCy/oM8KrtjbtrQ+1/PgvwRdu/amMb\nFyfiNjdw0TyXtCjRj1cR15pt/6BdbfiwSFqFkB7cFnjOoZU+G5EU/H3gbtubl30rg2+4i55yf1/X\nijyCJ20fW481bcqkZWJQJD0uSSQfX1b6fhfgM8Aptu+o7bswkQNxgWtVRZPuKeNZPZF9e6Ia4mHE\nGHIk4QU+pxzyPeBA2+d083GNQmNX472dSLo/jMij+Ryh/HO1pLWAp2oTh0bfg3oiwxv6mIF8MSTN\noRhcchRSOY6IQ36XiGVblVJow/bl9bCO1utPk1ZcaQbb11Q3/yZd0woVh7Eog3Gl2jFK0txl335V\n7aixVPmeV20fbXtfQhHgaGApSV8shrhKO1X2/0DSUoSh2+6kuI8TqhtvlyXowY5EvMOIOOw/APOV\npesx1ENeGsDyhId6S+AQSR8vS+S/Ia6H5xQKF9C1hD6maEkHruu/AwuXFZYxCV0D0MhemQiFWZmI\ntT1BoRZxDLEaspGkRcvYsxsRD390Gtk9U8auO4E/STpO0uK2K3nSrYk6ABsRClEfJxLjt6yM7Ib9\nPsegrgI579fG8LWIao+r2F6XiO9/uLx3tUONaczY2N9t7kvSo50kDUDSjsD5Hk91S0mjCM3fKwkP\nUo/hDmWQugP4TxnI6u+dSRSOWEkRY3k4YcDvY/uosk+jvAhqqGpHvS2Epu0PgW2Ah23fXN6rvNWj\nCU/yV12TcSv7bEoYuZvWPfBtauvHCamwb9m+u9wIB5cwhosI7edBwNtuaOyspDmI38OxRKn41Yl8\nhmvLLrsRqzjfqYWPdAyFkstKtq/tceeGUkJyvksoQ9yiUKNZkVghu4rI/TiKUFY5hyi+M9T2g+X4\nAee970/KNfI1IhTEhMd3CSK86/vEeH6C7RfL/tVKTaPG6gpF7seJbokRr1Z0JC1NqENtMpB/Fz2R\nMdpJ0gw2AP5NeJTHwfY1ihjrrxBxe3f39IHFa7ohcKsigeb5aunc9jck3SdptO2Ly7L20bavgeYZ\n2YX1iKXTMYZ2PbTFJbmtTBymcj8lFLa05RkiFnsWYAtJ5xJxza+XfS4uS8PnSFqyFsawDnFzHdWb\nSVQfMCGd+mHAs7b/0Q/tmCTK//TZMin4wKHZPCOxrP44oVhwO3B7E4xsAIfc5oA1Jsr4szZwm4vW\nvO03FLJyUxMVai8mJjuHAI/avrAcKxc60/qBge13FBrSQ4gcg2OIcW9VIixnFWAuhf76G9W41sCx\nGkmzEh73n7S+V4zswbbvKg6UayTN0pTfal+ToSNJ0iFqoQODiGXlbgfL2vLZmcBRtns0sisciSRV\ncSVciiuVt1uLK1VGdmOK/6jhqh21dg4uT08mlsofIYy9UYTawpdr7TuISDR8q/YRDwHr9JORXRl9\n5xEG0sGSFpQ0g6STiXCbp/qjHb2lePrGUPuf/hdYVaHQM5pYEbicWKF50iVmv6lL6gMJ288Q1/dr\nJSSk2v4WEe4wB7FKdj+xMnZhbZ80sLtB0nBJd0g6TNLnABy1EK4mHCr7EzHv+xMrNOcBL/XXOPFh\nKKuzcxKTg+7ef7/ca84i1FImSyMb0tBOko5QYvGOkvRZwrD5IxHzOI40ZOW1Lc9vnNjvKsccI+ma\n8vr18tb7wPPVd9S/b2K/ox0oVDtulLSBpE/Qpdox3jbWDN6Ktk4YJM0haTixRA6hevFPQr/2QUJb\neAmiytlPire9iiuvxy4+3N/97lCTOZaQjPwFIeU41PbaxVPZCONUUfXuVEnz1bZV/XYsEad9HhGO\nc6ntw4Ftqj4u+zXimh6o1K6FGwilo4UVBVQAKCsizxN62Ni+rxyXNsaEmZUIEfkksQJ2OIDtJwmV\nqSFE2Ai2LyVCdvbpUFt7RNIXJH1F0mpl01VEIZrxXQsqK5J3lhet4/dkQf4IkqSfkbQxUXzgNeD/\niLi7bYDZYLzLgKod//mJvYHZPh54QtJh5TOuIrSnG1fBtHZTf5SII16C8FKuCXy0ZZ/6cVU59VkU\nOtBtNbAUSi4nlbadL2leh7zg/cQk6gZCZWQkoRH7Cl3JPpT2dXTlwA3XqZe0iO2HCQPuW2U5ukoc\nrUIfjyWq8d5RJj3YbpRHfqBTXQtlJeQ6IjlvVUmrAkhan5CI/FfLcY1YGWsaklaCMdfpFYTKzwGE\n1vqhkjaz/S8idGTBEl4xpj+bMgmuU9r4M0In/khJhxKreutCV4J/63Fl+wySPtIfq4+dIJMhk6Qf\nURSBWR/4dhUCUjy3KxPqCT+2fV4V01jer0v9nUdUIv3dJHy3CD3nhYADbFdGd6PisSUtbfuulm3z\nEomOxwA7276y6qOWvlqKkIraYWJCbCayfcOI0tlDge2B+YjY+Q2IZLenJV1GJBJu3M3xjUwIq/Vn\nI64HST8Dpre9tSL++leEN/U42/9TVyLY+kQVwsVctIWTD4ekLxAhCvd2F36lkPfbkDCqBhGVWL83\nKStuUxqS9iLGio1s/0sR+36k7c0U8pQ3EYb3b4hJ/GvAI7Zf6lije6A4HRYiii89r0i2ngbYG/gP\ncKXtP9b2r4/ZnyEcKpu6h1oQA5X0aCdJPyHpBGIZ8GyH0sPQYtTcY/tnRFb5XpI+1xIu8oGi47G1\nPwAAFCRJREFUKMHFwB6TYmSXzzEwkhgMG2dkK9iPkLbaUtIKtbf/Zfu3RB8dKWmJmpetGrA3JQzx\n0W00skXEu09nexPbL9u+y/buZfsFZdffAZXSwtD6ZzTRyIaxvJYdvR4UcatnEasX25U2vUJIyY0i\nEjinLkb2HMRqwfVpZPcpmxHeyTGxtPU3bf+biCMeQowpo23f2ERPa5OQdDqRS7Bm8VhDyLYOUii6\nnENIay5IlFSf3/adtl9qYhhO+a1eSky6jipG9iBCA/tvhELNK8AoRZgkMNaY/WXgFCIJfLI0siEN\n7SRpO5KmVmhZDyO0Q7eStFIxDOpJfdcSyUZnSZq+tky4BnHT27iKZZtUbL/thlYwLYNvXbXjIEnb\nS5qmaqfti4lY4nOqMAEYR7WjbdJ4pY0bEnrYs5XvrsIVtgCmlbQmoXixk6L4zHvj+7xkbIqhdhMw\nU5nIvCdpC0nfKSEkhxIrPwtKWpGQP/u37R1rxyeTiKSZAWxvAwwtxl+3k0OHKs0RwPK2n2pKuFET\nkTSrpN8SUojXAzNU7zmk+q4BTgVutr2j7TeBXWyfXNuvMWM1jFGhuZsI8fsZMFLScg4FqKoOxPNE\n7YfhwGhFEa/q+J2JYjureTKo/jghMnQkSdpIMcY2JGJ1q6IC2xOeuvVqN6j3a8csZ/tPtdcrAH/2\n2CoVkxW1MAABZxGe4X8QlSufI5YeL6/t/2nXkt0UCXOP9NeNXhFjeYrtBcvraWy/Xrw7Zzhk/GZz\ni35s0jOSPkZUQp0f2IVQzdmseMiQtF3ZPgvw3drvqlETx4GEIgntOkJi9MTind6CqP54eHeT19YQ\nqKaGRHUahdb4XoRi0sGEUfoSIftZVX+cgQh/OqmE6zS6cmgJ0XsCGGn7cknzADsQTpJf2364JTxk\neeC/jqJY9c+4tzUsaXIkPdpJ0l42IfRPn4Yxg+ZJhIbxGepK4BujaV8Z2eXmh+2bJ1cjWwNUtcMT\nVnJ5tjx/IT2sE49DcWEz4C1gRdtLVUZ2ef9kwmAZlUZ231CMnSuJaoMHK6o/3k4kaM8JYytC1L3X\nkkZLGtE0Y7AJSNqA0JH+qe0flT46kYht/5KkOcuubwLzECueuPmVQ/cGTq+cH7YfJ0IbpwfWkzSr\nbdfuYbfXjeyy7e4pwciGNLSTpC1I2rjcrE4jNIm/KGnZ2qB5MPAkUSp6rIG1YnIfhDTAVTvcg5KL\nC51q30CmTGS2I8KtxiDpKElftX2O7ftrE600sicBScuUOHeA84mwrFOAPQije2qi0umYWO36Cpyk\n04DFCUMxqSHp+8A+hHRrpf8/pEwaTyOKuawmaWaHmsv3iWT3xiLp62U1cSNguIocIYxxEF1HVGnd\nQCWPolNtbRJpaCdJHyPp50ThjGWJ39iFhJ7z2pI+BWOKPOxOyDlt3am2dgJJw0rM+pzAVoTE4YPA\ntZLmsn0RcB/wXBnQK4/1IY7KaU3yEm8LrCHpDeCPtreE1A/uC2yfCjxd3cwlXQ7MUHmxyz5pYE8i\nZSXpQCIXYqPilfwooXKxE7AAUXhplKRvwJjJ4/uSZpd0BXCD7f1zQjk2JU9jA2B12xe6qzLse+Xv\njYSDYRSwuqJi74O2H1cDtaQlDZF0PhEGOb+igNRmhFd+x2o/21cSlXtHAot1pLENJEuwJ0kfIul4\nYDbbK9U2/1PSxYQE3HqSzrT9nO0XJH3JUQlsiqAYyTcD/7G9btl8F3CXQr7wAuBzhGrHHOWYoa4p\nSjTppl6WR0cCX3JDk0wHONsQ18YbwP62fwLZxx8WharLzcDmwEbAVyTNDxxNqIl8lVhtGkUkrM1b\nO/ajhOd7bzdQh7+T1Maq14Fbbb8saTGiOuJKwN+IGOaHbJ+vkC0dVA8NbJoXuMSYnwP80/b3a289\nVyZg50t61F1qWGcDD9r+c3+3talkMmSS9BEKfeWzgN0cWspTVR7YYpCtTdy4HgV+4VoZXfVTqfAm\noNBYvRVY0iEHNby60Ui6j/D0P09k4i9I6PkOiIEqDcC+p/yuciLTB0iajghX+x+wtbuKiCwEXEYU\n/5kOmAn4ge23JC1p+57aZ8wDvOKQXEwKkqYm5EV/T8S3/5LI1xhJVEh8jUjwfRDYb6BcwwrlpF8Q\nCcmvqytxfYhDFWhTYD/gKy4VQWvHNi6RsxPk8maS9BG23yYq2H2mvH6n/K2KqlxFyJF9iriR1Y+d\nIoxsAIeU09cIjxrlZj5NefsxYIRDB3th2y8OpIF6oNw8BxJusCTlQEJRVfN3wJu2tyxG9vbAKiVu\neFVgOWApIilvnnLoX8rxQyAS39LI7pbBREGlrxPFrDYnJi+bEIb1HsC+wPJE7PsYGhYO18psRN2A\nKuG7klp9TyFDex4RW354a8jcQBq720ka2knSR5Qb0TOExq/KtirebjlJO9g+lxh026b1PBBwqnYk\nk0Aa2ZNGicceDDwE3CRpGkn7E5rk/4Ixai+7EyXBZyecBvVCRqkH3w2SPlK82e/aPoNIfjwCGGz7\nbNt/qoUHbkLIlY7lWGmyQWr7IWA6SQeU1y4x2gDzSdrA9gFEpcv8fXZDGtpJ0keUG9Evga2B9SVN\nW/NUbwcsVJbdXk4DMlU7kqQ/KDkE+xFyiT8HPk0UBVra9kjbj1b72v637dOB+VrDAJJxKcpJJxKJ\njReVsLgTgL8SMolTF6WWpSSdS0iVfssDRK615ijaCVhF0uYQq7UldvtQQmUE2//NJPDuyU5Jkomk\nbiS3Pi9G4mGEsf1LSbtKup7Ih9itMrzTgBxDqnYkSXuZntCn35IIA7mQyBM5t76TpINLoiO2/5fO\ngPHTjXLSnsRqwbVEWODPiYTIA8pYvwDwkO31bL/WUGWROSStLWlota3mKLqH0AP/maSfStqDyLO5\nw/Yxtf3To90NmQyZJBNBSczah0gGOqpsq5Id65WwPkl4L2YD3rN9Stk+xSQ99pZMdkuSvqUYSwu4\nFAmR9EVgHcIwOkPS14HPApcQUppnAC9WE91k/JQJyB2MrZxUvXcmMI/tlRTFtQ4hyqofUduncfcA\nScsQ2t7H2z6xh/0+TYQWPeyQYs0xuwfS0E6SiUDStMC6hHrIb2xf1vL+eLOsczDqmeyjJPlwFEPw\nz8CiwA+AP9m+qahDLA9cT8Rhf5tQxPg88LNWx0FHGj9A6IVy0n62L1YULXvb9u3lvcb1raSvEFKO\n+9g+bhKOzzG7B1JHO0l6gaTlgMdtPyvpUmAEsImk52zfXg2g9UG0xcPdOC9GE8kBO0k+HGV1bV3g\nCULhaEFJ2xESc08QxvVjhMzfjMBZtn8LaTT1FttPSKqUkxZ0UU4qSd2PUZQ5gBvr94QGGtl7Ezrq\n5wKLS5rB9qsTcx3k9dIzGQeZJD1Qblq3AZdI2oYo3nAeoZW6naT5ys1tUNlftXCSWSUNSyM7SZL+\noqgarUAUSdkOuKG83hFYEzgIGA4cXDOylUZT7+lBOen5sq1RhnUdSSsCiwBfAL5LxJQfAmE8t+bJ\ntOQjzV6UVpJekIZ2kkyAYiRfRuihzkhIZF1EJBYNBe4HdpQ0dRmchlSebUmrEp6CGTrU/CRJplBs\n30Yk5V1i+1TbOxI6zvcBswL/qzsAmmwUNpWelJOaiKRZyqrsEGBb268CbwOnAtNI2hfGGNuVTO2g\n2urslkRhnpyU9ZKM0U6S8SDpCGAq27soij3cCOwFPA4sRsQ/PkzEPR5h+8e1Y7cCtgA2tP1if7c9\nSZIEQNIvCI3nHbp5r3ExwwONYozeRVTXPMB2ZXQ3LgxHUQ7+DOBq4v41RllEUQdiOcK7fblDE3ys\n85D0Q+Le9/WBIlHYBNKjnSQtqKtK4c+BBSTtZPsFQrLvp8Bstn8FfJnQUH0K+Fvt+AOAtYFRaWQn\nSdJhtgGWl7RnfWPdS5lMOqUPRwKbN9zIHkLIqZ5oe6+agT1C0lSOOhB/IdRHtpG0GnTFYEs6DZgW\n2CSN7IkjPdpJUkPSxoSiyNm2r5f0eeBg4FjbF5UEmP2AdW3/vRwz1Pa75fnHKLJZeRNLkqQJKCpD\nrm374k63ZXKnoUb2/LYfkXQk8IbtH5aJ1zyEh/om4CDbr0uaEVgVuMWloqWk+YElKjm/ZOJIQztJ\nCpL2IzzRhwFP276jbP8K4Qn4ge3bSgzbFsDijsIOjRtYkyRJkqSEDr0OfA9YBdiBSIz9E1Eu/gUi\nIfKuVnm/khDpdBp9OFLeL0kARWnZlQmvz8u17Z+yfa6kOYE9JH3f9oGSXiEGr5Q3SpIkSRqFpOmI\nMJDXgV1Lgv7NwN3A0oQSzbsl6XEh4LXWz8h7W9+QMdpJEnwS+G2LkX0WcLKktWz/lIjF/klRIjmm\nLumXJEmSJE1A0ghCKWtm21uWe9UuRBz5i7avsf12MbKXJFZyX+looydj0qOdTLFImhv4iO0/E7P5\nKs56ELAi8BZRQW20pH8A3wFWsf129Rk540+SJEkahoEzgTWLt3oDYD1g82qHkk/0VWArYHfbV3Si\noVMCaWgnUxxFjmkEMch8WtJewJPAXpJOsv2OpDtt31D2PxeY1/ajwLUda3iSJEmSjAdJHwFeJeKq\nTy+JjX8AHrC9XMvuw4BngdG2HyrHp9xjG0hDO5niKAPJ65KuBqYDdra9l6T1gCskrWv7DRgj1Tc1\ncG/nWpwkSZIk40fS0cAnCAm+tyTtSEjUzgnMXttvEHA4oaV9ZhUCafuDNLLbQ6qOJFMUknYjltUu\ntP20pGWJ5bRHbB8t6SKihO5UgMpjE9tv5Ww/SZIkaRKShhFJj0OB7YH5iNXadQk1kQ8ISdrniDoQ\nvwSes71NRxo8BZKGdjJFUDKwzwTeBGYmBp0DbT8saW1gNHCd7fOKZuhiAJXurKTB9XLFSZIkSdJJ\nShjkHcB/bK/b8t6ZwDy2V5K0COHFXhXYx/ZRZZ+Upu0H0tBOJmvKQDQ1cCUhZbSGpKmAXwHX2j6l\nZGivA6xRtp3X8hlpZCdJkiSNQ9LHgVuBJW0/L2l4VblR0n3AfrYvlrQyMMz2NeW9NLL7iZQmSyZ3\nBpd461OAlyStYPsdwvD+hKQlyvsXAPcBq0iauRjoAKSRnSRJkjQR208AXwNuLq/fkjRNefsxInQE\n4Maaka00svuPTIZMJlsk7QQsKelfwBvAJcDOkuYlltHuAL4h6XrgeuA8wuv98vg+M0mSJEmahO0b\nJR0j6Rrbo2y/Xt56H3i+7OPa/hnK0I+kRzuZLCkZ2F8ndLCfBXYBRhKx2ScAW9hehwgZuYpIHHnJ\n9st1b3aSJEmSNB3bxwNPSDoMQNJVwGu2b+1sy5KM0U4mOyQtDhwCbFAVl5G0KHASYVSPICb1P+hc\nK5MkSZKk7yhOoruAhYADbFdGd8Zjd5D0aCeTIx8H3rL9tqShJZnxAeAwYGdCwH8+SfvWD0pPdpIk\nSTJQKSEhI4lS62lkN4SM0U4mR+4F5pa0lO27JQ2SNNT25ZJuAd4hvNtv1w/KuLUkSZJkIFNWcX8D\naWQ3hTS0k8mRZwhVkQ0lPWv7aboyr4cBz9r+R8dalyRJkiRtJo3sZpChI8lkR5HvO4/Qzz5Y0oKS\nZpB0MjAYeKqjDUySJEmSZIogkyGTyZYi47czsCzwKlF2dqvyXpZTT5IkSZKkraShnUz2SJoBmLaE\nkGSlxyRJkiRJ+oU0tJMpgsqDnckhSZIkSZL0F2loJ0mSJEmSJEkbyGTIJEmSJEmSJGkDaWgnSZIk\nSZIkSRtIQztJkiRJkiRJ2kAa2kmSJEmSJEnSBtLQTpIk6QCS3pf0l9pjnkn4jBkl7dj3rRvz+VtK\n+kDSYrVtf52UtiZJkkyJpKGdJEnSGd60vUTt8fgkfMaMwEQb2pIGT8TuTwH7TOx3JEmSJGloJ0mS\nNAZJgyUdLulOSfdJ2r5sn1bSdZLulnS/pPXKIYcC8xeP+OGSVpF0Re3zjpO0ZXn+uKR9Jd0MbCxp\nfkm/k3SXpJskLTSeZl0BLCppwW7ae4KkP0t6QNIBte2PSzpE0m3l/aUkXS3pEUk71PbbvXauB7R+\nfpIkyUBnSKcbkCRJMoUytaS/lOeP2R4NfBN41fYykoYBt0i6BngSGG37NUmzArdLugz4P+DTtpcA\nkLRKD9/5lu0Vyr7XATvY/qek5YCfA6t2c8wHwE+AvYEtWt7bx/ZLxUN+naTFbN9X3nvS9khJPwXO\nAD4PDAceAE6UNAr4FLAsIOAySSvZvrGHc0iSJBkwpKGdJEnSGd6sDOQao4DFJG1UXs9AGKNPAYdI\nWokwfOcC5piE7zwPwkMOfA64QFL13rAJHHc2sI+keVu2byJpO+JeMiewCFAZ2peVv/cD09r+L/Bf\nSW9JmrGc6yjgnrLftOVc09BOkmSyIQ3tJEmS5iBgZ9tXj7Uxwj9mA5a2/a6kxwnvcCvvMXZIYOs+\nr5e/g4BXujH0u8X2e5KOBPastWle4PvAMrZflnRGy/e9Xf5+UHtevR5CnOuPbZ/UmzYkSZIMRDJG\nO0mSpDlcDXxL0lAASQtImobwbD9XjOwvAJ8o+/8XmK52/L+ARSQNkzQDsFp3X2L7NeAxSRuX75Gk\nxXto2xnA6oTBDzA9Ybi/KmkOYO2JO1WuBrYu3nUkzSVp9on8jCRJkkaTHu0kSZLmcCowD3C3Iqbj\neWB94NfA5ZL+DPwFeAjA9ouSbpH0V+Aq27tLOp8I3/gnXWEZ3bEZcIKkHwBDgXOBe8e3s+13JB0D\nHF1e3yvpHiLm+lHglok5UdvXSFoYuK2Er/wP2Bx4bmI+J0mSpMnIdqfbkCRJkiRJkiSTHRk6kiRJ\nkiRJkiRtIA3tJEmSJEmSJGkDaWgnSZIkSZIkSRtIQztJkiRJkiRJ2kAa2kmSJEmSJEnSBtLQTpIk\nSZIkSZI2kIZ2kiRJkiRJkrSBNLSTJEmSJEmSpA38P+92nnQisKYsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16285750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dis_mutual_info = mutual_info_regression(scaled_train[discrete_feat], y, discrete_features = False, copy = True)\n",
    "\n",
    "dis_res = dict(zip(discrete_feat, dis_mutual_info))\n",
    "plot_info(dis_res, \"Mutual Information of Discrete Features (treated as continuous)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OshFtrPSjktwe2Kiqrptw2ZcCO4xc3x64fIY2lw5jsLcGrhxtUFXfTnI98Af4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\nyMjIyMhoArKhnZGRkZGRkZGRkdEE/D++ydmcc3owtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a154a9410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dis_mutual_info = mutual_info_regression(scaled_train[discrete_feat], y,   discrete_features = True, copy = True)\n",
    "\n",
    "dis_res = dict(zip(discrete_feat, dis_mutual_info))\n",
    "plot_info(dis_res, \"Mutual Information of Discrete Features (treated as discrete )\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  7,  8, 11, 12])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "kbest = SelectKBest(mutual_info_regression, k=5)\n",
    "kbest.fit(scaled_train, y)\n",
    "kbest.get_support(indices=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 index\n",
      "1 DAY_OF_WEEK\n",
      "2 DISTANCE_GROUP\n",
      "3 AIRLINE_ID\n",
      "4 ORIGIN_CITY_MARKET_ID\n",
      "5 DEST_CITY_MARKET_ID\n",
      "6 DAY_OF_MONTH\n",
      "7 MONTH\n",
      "8 ORIG_STATE\n",
      "9 DEST_STATE\n",
      "10 DEP_TIME\n",
      "11 OUT_TAXI\n",
      "12 IN_TAXI\n",
      "13 ELAPSED_TIME\n",
      "14 NEW_DIST_GROUP\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for col in scaled_train:\n",
    "    print i, col\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing features with low Information Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>DISTANCE_GROUP</th>\n",
       "      <th>AIRLINE_ID</th>\n",
       "      <th>ORIGIN_CITY_MARKET_ID</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>ORIG_STATE</th>\n",
       "      <th>DEST_STATE</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>OUT_TAXI</th>\n",
       "      <th>IN_TAXI</th>\n",
       "      <th>ELAPSED_TIME</th>\n",
       "      <th>NEW_DIST_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.50</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.243191</td>\n",
       "      <td>-0.464212</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.153846</td>\n",
       "      <td>-0.307692</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.097510</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.50</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.243191</td>\n",
       "      <td>-0.599066</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.653846</td>\n",
       "      <td>-0.538462</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>2.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.75</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.334630</td>\n",
       "      <td>0.520228</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.461538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.091440</td>\n",
       "      <td>-0.000519</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>1.192308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DAY_OF_WEEK  DISTANCE_GROUP  AIRLINE_ID  ORIGIN_CITY_MARKET_ID  MONTH  \\\n",
       "0        -0.50        2.666667    0.243191              -0.464212   -1.0   \n",
       "1         0.50        0.666667    0.000000               1.097510   -1.0   \n",
       "2         0.50        2.666667    0.243191              -0.599066   -1.0   \n",
       "3        -0.75        0.333333    0.334630               0.520228   -1.0   \n",
       "4         0.75        0.333333    1.091440              -0.000519   -1.0   \n",
       "\n",
       "   ORIG_STATE  DEST_STATE  DEP_TIME  OUT_TAXI  IN_TAXI  ELAPSED_TIME  \\\n",
       "0    1.153846   -0.307692      -1.0       0.0      0.0      2.666667   \n",
       "1   -0.500000    0.961538       0.0       0.0      0.0      0.666667   \n",
       "2   -0.653846   -0.538462      -1.0      40.0      0.0      2.333333   \n",
       "3   -0.500000   -0.461538       0.0       0.0      0.0      0.000000   \n",
       "4    0.961538    1.192308       0.0      20.0      0.0      0.333333   \n",
       "\n",
       "   NEW_DIST_GROUP  \n",
       "0        3.000000  \n",
       "1        0.714286  \n",
       "2        2.714286  \n",
       "3        0.428571  \n",
       "4        0.428571  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_feat = ['DAY_OF_MONTH', 'DEST_CITY_MARKET_ID', 'index']\n",
    "trimmed_train = scaled_train.drop(bad_feat, axis=1)\n",
    "trimmed_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Predicting from the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 494.848498446\n"
     ]
    }
   ],
   "source": [
    "#imp_feat = ['AIRLINE_ID', 'ORIG_STATE', 'DEP_TIME', 'OUT_TAXI', 'IN_TAXI', 'ELAPSED_TIME']\n",
    "#x = np.copy(trimmed_train[imp_feat])\n",
    "\n",
    "# 20% split to test set as it will be used in the very end\n",
    "x_train, x_test, y_train, y_test = np.array(train_test_split(new_train, y, train_size = 0.7))\n",
    "\n",
    "y_train = np.array(y_train)\n",
    "mean = y_train.mean()\n",
    "#inv_scaled_mean = scaler_y.inverse_transform(mean)\n",
    "preds = []\n",
    "\n",
    "for i in range(len(y_test)):\n",
    "    \n",
    "    preds.append(mean)\n",
    "    \n",
    "#print preds\n",
    "print \"Mean Squared Error:\", mse(y_test, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "390.56100688\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "x_train, x_test, y_train, y_test = np.array(train_test_split(new_train, y, train_size = 0.7))\n",
    "\n",
    "LR_model = LinearRegression()\n",
    "LR_model.fit(x_train, y_train)\n",
    "preds = LR_model.predict(x_test)\n",
    "\n",
    "MSE = mse(y_test, preds)\n",
    "print MSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Fold Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[433.63609166562748], [478.67560370888725], [444.54435129505487], [395.18368695270919], [414.18281423788363], [434.43597096483973], [471.64269072147476], [445.93363249830929], [487.52219983442387], [410.56828933202388]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "441.6325331211234"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "def Linear_Regression(train, y, deg, folds):\n",
    "    \n",
    "    MSE_stats = []\n",
    "\n",
    "    for j in range(folds):\n",
    "\n",
    "        MSE_list = []\n",
    "\n",
    "        kf = KFold(len(train), n_folds = folds, shuffle=True) \n",
    "\n",
    "        for tr, te in kf:\n",
    "            x_train = [train[ii] for ii in tr]\n",
    "            y_train = [y[ii] for ii in tr]\n",
    "            x_test = [train[ii] for ii in te]\n",
    "            y_test = np.array([y[ii] for ii in te])\n",
    "\n",
    "        LR_model = Pipeline([('poly', PolynomialFeatures(degree = deg)),('linear', LinearRegression())])\n",
    "\n",
    "        LR_model.fit(x_train, y_train)\n",
    "        preds = np.array(LR_model.predict(x_test))\n",
    "        #print preds\n",
    "        \n",
    "        #inv_scaled_pred = scaler_y.inverse_transform(preds.reshape(-1, 1))\n",
    "        #inv_scaled_y = scaler_y.inverse_transform(y_test.reshape(-1, 1))\n",
    "        #print preds, inv_scaled_pred\n",
    "\n",
    "        #MSE = mse(inv_scaled_y, inv_scaled_pred)\n",
    "        MSE = mse(y_test, preds)\n",
    "        MSE_list.append(MSE)\n",
    "\n",
    "        MSE_stats.append(MSE_list)\n",
    "    \n",
    "    return MSE_stats\n",
    "\n",
    "#imp_feat = ['AIRLINE_ID', 'ORIG_STATE', 'DEP_TIME', 'OUT_TAXI', 'IN_TAXI', 'ELAPSED_TIME',\n",
    "#            'ORIGIN_CITY_MARKET_ID', 'DISTANCE_GROUP']\n",
    "imp_feat = ['ORIG_STATE', 'DEP_TIME', 'OUT_TAXI', 'ELAPSED_TIME']\n",
    "\n",
    "train_set = np.copy(scaled_train[imp_feat])\n",
    "MSE_stats = Linear_Regression(train_set, scaled_y.values, 1, 10)\n",
    "print MSE_stats\n",
    "np.mean(MSE_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Degree - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[476.44295682060948], [421.29115772644445], [481.63790096034381], [454.79829590900357], [395.89825174739968], [485.18842303387368], [435.99572521962926], [432.81714244525244], [370.23017425121424], [414.27711875138942]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "436.85771468651603"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE_stats = Linear_Regression(train_set, y.values, 2, 10)\n",
    "print MSE_stats\n",
    "np.mean(MSE_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Degree - 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[464.61484317371941], [450.0723505735773], [411.16878711743391], [516.92441043047791], [335.97185407597425], [438.66279254260718], [406.03730640241486], [478.70621991076496], [450.7608018655186], [398.32039610682688]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "435.12397621993148"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE_stats = Linear_Regression(train_set, y.values, 3, 10)\n",
    "print MSE_stats\n",
    "np.mean(MSE_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Leaves:  1 Squared error:  888.923752061\n",
      "Leaves:  2 Squared error:  775.389405153\n",
      "Leaves:  3 Squared error:  698.228697166\n",
      "Leaves:  5 Squared error:  613.68335619\n",
      "Leaves:  7 Squared error:  565.201782058\n",
      "Leaves:  10 Squared error:  525.081085038\n",
      "Leaves:  15 Squared error:  496.153143439\n",
      "Leaves:  30 Squared error:  457.599896676\n",
      "Leaves:  50 Squared error:  440.535031926\n",
      "Leaves:  70 Squared error:  435.490161757\n",
      "Leaves:  100 Squared error:  443.041055254\n",
      "Leaves:  200 Squared error:  443.743308511\n",
      "Leaves:  500 Squared error:  448.197877018\n",
      "Leaves:  1000 Squared error:  477.926014652\n",
      "Leaves:  2000 Squared error:  483.163023421\n",
      "Leaves:  3000 Squared error:  483.163023421\n",
      "Leaves:  4000 Squared error:  483.163023421\n",
      "Leaves:  5000 Squared error:  483.163023421\n",
      "Leaves:  1000 Squared error:  477.926014652\n",
      "Leaves:  2000 Squared error:  483.163023421\n",
      "Leaves:  5000 Squared error:  483.163023421\n",
      "Leaves:  8000 Squared error:  483.163023421\n",
      "Leaves:  10000 Squared error:  483.163023421\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "x_train, x_test, y_train, y_test = np.array(train_test_split(scaled_train, y, train_size = 0.7))\n",
    "\n",
    "\n",
    "leaves = [1, 2, 3, 5, 7, 10, 15, 30, 50, 70, 100, 200, 500, 1000, 2000, 3000, 4000, 5000, 1000, 2000, 5000, 8000, 10000]\n",
    "\n",
    "train_set = np.copy(scaled_train)\n",
    "\n",
    "for i in leaves:\n",
    "    \n",
    "    DT_model = DecisionTreeRegressor(min_samples_leaf=i)\n",
    "    DT_model.fit(x_train, y_train)\n",
    "    preds = DT_model.predict(x_test)\n",
    "    cv_score = cross_val_score(DT_model, train_set, y, cv = 5, scoring='neg_mean_squared_error')\n",
    "    print \"Leaves: \", i, \"Squared error: \", -cv_score.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information Gain from Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TbEc7uXHl6AxJ9hsZPBz46nB7JXBMku2T7AvsB3yhY62SJEnSnOrWR7uq1ic5AbgAWAKc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\nZDKZTKYBZEc7k8lkMplMJpNpAP8PkZEX0l3KibkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0af90d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DT_model = DecisionTreeRegressor(min_samples_leaf=50)\n",
    "DT_model.fit(x_train, y_train)\n",
    "\n",
    "fi = DT_model.feature_importances_\n",
    "#x_train=train_df.drop('churndep', axis=1)\n",
    "feat_names = np.array( scaled_train.columns.values)\n",
    "\n",
    "# Plotting Feature_Names on X-Axis and Feature_importances on Y-Axis\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.bar(range(len(feat_names)), fi)\n",
    "plt.xticks(range(len(feat_names)), feat_names, rotation=50)\n",
    "plt.xlabel('Feature Name')\n",
    "plt.ylabel('Feature Importance')\n",
    "plt.title('Feature Importance Ranking')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "def kfold_random_forest(train, y, trees, folds, msl):\n",
    "    \n",
    "    MSE_stats = pd.DataFrame()\n",
    "    R2_stats = pd.DataFrame()\n",
    "    \n",
    "    for j in range(folds):\n",
    "\n",
    "        MSE_list = []\n",
    "\n",
    "        kf = KFold(len(train), n_folds = folds, shuffle=True) \n",
    "\n",
    "        for train, test in kf:\n",
    "            x_train = [train_set[ii] for ii in train]\n",
    "            y_train = [y[ii] for ii in train]\n",
    "            x_test = [train_set[ii] for ii in test]\n",
    "            y_test = np.array([y[ii] for ii in test])\n",
    "\n",
    "        for i in trees:\n",
    "            RF_model = RandomForestRegressor(n_estimators=i, min_samples_leaf=msl)\n",
    "            RF_model.fit(x_train, y_train)\n",
    "            preds = RF_model.predict(x_test)\n",
    "                    \n",
    "            # Scaling back the predictions before caclulating the Mean-Squared Error\n",
    "            #inv_scaled_pred = scaler_y.inverse_transform(preds.reshape(-1, 1))\n",
    "            #inv_scaled_y = scaler_y.inverse_transform(y_test.reshape(-1, 1))\n",
    "            \n",
    "            MSE = mse(y_test, preds)\n",
    "            #MSE = mse(inv_scaled_y, inv_scaled_pred)\n",
    "            MSE_list.append(MSE)\n",
    "\n",
    "        MSE_stats[j] = MSE_list\n",
    "    \n",
    "    return MSE_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Min_samples_leaf:  20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/harshitsrivastava/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:23: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trees: 5\n",
      "\tMSE: 432.766149866\n",
      "Trees: 10\n",
      "\tMSE: 432.768598645\n",
      "Trees: 30\n",
      "\tMSE: 428.508614735\n",
      "Trees: 50\n",
      "\tMSE: 429.225268849\n",
      "Trees: 80\n",
      "\tMSE: 428.258649732\n",
      "Trees: 100\n",
      "\tMSE: 427.063612915\n",
      "Trees: 200\n",
      "\tMSE: 427.213530414\n",
      "Trees: 500\n",
      "\tMSE: 427.10765416\n",
      "Trees: 1000\n",
      "\tMSE: 427.131247093\n",
      "\n",
      "Min_samples_leaf:  50\n",
      "Trees: 5\n",
      "\tMSE: 423.97849561\n",
      "Trees: 10\n",
      "\tMSE: 423.225280727\n",
      "Trees: 30\n",
      "\tMSE: 422.228419524\n",
      "Trees: 50\n",
      "\tMSE: 422.171802299\n",
      "Trees: 80\n",
      "\tMSE: 421.954123436\n",
      "Trees: 100\n",
      "\tMSE: 423.200102414\n",
      "Trees: 200\n",
      "\tMSE: 422.588432376\n",
      "Trees: 500\n",
      "\tMSE: 422.483332137\n",
      "Trees: 1000\n",
      "\tMSE: 422.224178497\n",
      "\n",
      "Min_samples_leaf:  100\n",
      "Trees: 5\n",
      "\tMSE: 447.440405256\n",
      "Trees: 10\n",
      "\tMSE: 445.502709376\n",
      "Trees: 30\n",
      "\tMSE: 444.414354504\n",
      "Trees: 50\n",
      "\tMSE: 444.663034093\n",
      "Trees: 80\n",
      "\tMSE: 444.845361764\n",
      "Trees: 100\n",
      "\tMSE: 444.905553708\n",
      "Trees: 200\n",
      "\tMSE: 444.66727621\n",
      "Trees: 500\n",
      "\tMSE: 444.676772544\n",
      "Trees: 1000\n",
      "\tMSE: 444.558600018\n",
      "\n",
      "Min_samples_leaf:  200\n",
      "Trees: 5\n",
      "\tMSE: 425.07567899\n",
      "Trees: 10\n",
      "\tMSE: 424.013027858\n",
      "Trees: 30\n",
      "\tMSE: 424.664724501\n",
      "Trees: 50\n",
      "\tMSE: 424.466028751\n",
      "Trees: 80\n",
      "\tMSE: 424.251952262\n",
      "Trees: 100\n",
      "\tMSE: 424.500669772\n",
      "Trees: 200\n",
      "\tMSE: 424.35152035\n",
      "Trees: 500\n",
      "\tMSE: 424.455323919\n",
      "Trees: 1000\n",
      "\tMSE: 424.462367269\n",
      "\n",
      "Min_samples_leaf:  300\n",
      "Trees: 5\n",
      "\tMSE: 422.068043102\n",
      "Trees: 10\n",
      "\tMSE: 423.029433709\n",
      "Trees: 30\n",
      "\tMSE: 422.384532162\n",
      "Trees: 50\n",
      "\tMSE: 422.734860234\n",
      "Trees: 80\n",
      "\tMSE: 422.362357175\n",
      "Trees: 100\n",
      "\tMSE: 422.076216105\n",
      "Trees: 200\n",
      "\tMSE: 422.238901536\n",
      "Trees: 500\n",
      "\tMSE: 422.267742069\n",
      "Trees: 1000\n",
      "\tMSE: 422.22713008\n",
      "\n",
      "Min_samples_leaf:  400\n",
      "Trees: 5\n",
      "\tMSE: 449.301486946\n",
      "Trees: 10\n",
      "\tMSE: 449.925498322\n",
      "Trees: 30\n",
      "\tMSE: 448.810773112\n",
      "Trees: 50\n",
      "\tMSE: 449.625620429\n",
      "Trees: 80\n",
      "\tMSE: 449.402629886\n",
      "Trees: 100\n",
      "\tMSE: 449.484497943\n",
      "Trees: 200\n",
      "\tMSE: 449.638031162\n",
      "Trees: 500\n",
      "\tMSE: 449.584966628\n",
      "Trees: 1000\n",
      "\tMSE: 449.544501901\n",
      "\n",
      "Min_samples_leaf:  500\n",
      "Trees: 5\n",
      "\tMSE: 532.386308584\n",
      "Trees: 10\n",
      "\tMSE: 532.118225728\n",
      "Trees: 30\n",
      "\tMSE: 531.917565086\n",
      "Trees: 50\n",
      "\tMSE: 532.103588917\n",
      "Trees: 80\n",
      "\tMSE: 532.142790798\n",
      "Trees: 100\n",
      "\tMSE: 532.083714702\n",
      "Trees: 200\n",
      "\tMSE: 532.070371717\n",
      "Trees: 500\n",
      "\tMSE: 532.141217949\n",
      "Trees: 1000\n",
      "\tMSE: 532.122612573\n",
      "\n",
      "Min_samples_leaf:  800\n",
      "Trees: 5\n",
      "\tMSE: 476.085220694\n",
      "Trees: 10\n",
      "\tMSE: 475.746611858\n",
      "Trees: 30\n",
      "\tMSE: 476.146549098\n",
      "Trees: 50\n",
      "\tMSE: 476.501463069\n",
      "Trees: 80\n",
      "\tMSE: 476.213802329\n",
      "Trees: 100\n",
      "\tMSE: 476.221029995\n",
      "Trees: 200\n",
      "\tMSE: 476.104989164\n",
      "Trees: 500\n",
      "\tMSE: 476.191736818\n",
      "Trees: 1000\n",
      "\tMSE: 476.119095273\n"
     ]
    }
   ],
   "source": [
    "#train_set = np.copy(scaled_train)\n",
    "\n",
    "trees = [5, 10, 30, 50, 80, 100, 200, 500, 1000]\n",
    "msl = [20, 50, 100, 200, 300, 400, 500, 800]\n",
    "\n",
    "for ms in msl:   \n",
    "    print \"\\nMin_samples_leaf: \", ms\n",
    "    MSE_stats = kfold_random_forest(train_set, scaled_y.values, trees, 5, ms)\n",
    "\n",
    "    kfold_mean_MSE = {}\n",
    "\n",
    "    for i in range(len(trees)):\n",
    "        print \"Trees:\", trees[i]\n",
    "\n",
    "        kfold_mean_MSE[i] = MSE_stats.loc[i].mean()\n",
    "        print \"\\tMSE:\", kfold_mean_MSE[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.cross_validation import KFold\n",
    "\n",
    "def kfold_SVM(train_set, y, kernels, folds):\n",
    "\n",
    "    MSE_stats = pd.DataFrame()\n",
    "    \n",
    "    for j in range(folds):\n",
    "\n",
    "        MSE_list = []\n",
    "\n",
    "        kf = KFold(len(train_set), n_folds = folds, shuffle=True) \n",
    "\n",
    "        for train, test in kf:\n",
    "            \n",
    "            x_train = [train_set[ii] for ii in train]\n",
    "            y_train = [y[ii] for ii in train]\n",
    "            x_test = [train_set[ii] for ii in test]\n",
    "            y_test = [y[ii] for ii in test]\n",
    "            \n",
    "        for i in kernels:\n",
    "            SVM_model = SVR(kernel = i, epsilon = 0.1, degree=2)\n",
    "            SVM_model.fit(x_train, y_train)\n",
    "            preds = SVM_model.predict(x_test)\n",
    "\n",
    "            #inv_scaled_pred = scaler_y.inverse_transform(preds.reshape(-1, 1))\n",
    "            #inv_scaled_y = scaler_y.inverse_transform(y_test)\n",
    "\n",
    "            MSE = mse(y_test, preds)\n",
    "            MSE_list.append(MSE)\n",
    "\n",
    "        MSE_stats[j] = MSE_list\n",
    "    \n",
    "    return MSE_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kernel: linear\n",
      "\tMSE: 426.388074362\n",
      "Kernel: rbf\n",
      "\tMSE: 434.25874652\n",
      "Kernel: sigmoid\n",
      "\tMSE: 3954.17331934\n"
     ]
    }
   ],
   "source": [
    "#imp_feat = ['AIRLINE_ID', 'ORIG_STATE', 'DEP_TIME', 'OUT_TAXI', 'IN_TAXI', 'ELAPSED_TIME',\n",
    "#           'ORIGIN_CITY_MARKET_ID', 'DISTANCE_GROUP']\n",
    "imp_feat = ['ORIG_STATE', 'DEP_TIME', 'OUT_TAXI', 'ELAPSED_TIME']\n",
    "\n",
    "train_set = np.copy(trimmed_train[imp_feat])\n",
    "\n",
    "#kernels = ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']\n",
    "kernels = ['linear', 'rbf', 'sigmoid']\n",
    "\n",
    "folds = 8\n",
    "MSE_stats = kfold_SVM(train_set, np.copy(y), kernels, folds)\n",
    "\n",
    "kfold_mean_MSE = {}\n",
    "\n",
    "for i in range(len(kernels)):\n",
    "    print \"Kernel:\", kernels[i]\n",
    "    \n",
    "    kfold_mean_MSE[i] = MSE_stats.loc[i].mean()\n",
    "    print \"\\tMSE:\", kfold_mean_MSE[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forests to Predict from Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = pd.read_csv(\"testing_set.csv\")\n",
    "#imp_feat = ['ORIG_STATE', 'DEP_TIME', 'OUT_TAXI', 'ELAPSED_TIME']\n",
    "ID_set = x_test['UID']\n",
    "#x_test = x_test[imp_feat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_pre_processing(data):\n",
    "    from sklearn.preprocessing import LabelEncoder  \n",
    "    \n",
    "    # Creating bins for continuous values in CRS_DEP_TIME feature\n",
    "    CRS_DEP_TIME__bins = [0, 600, 1200, 1800, 2400]\n",
    "    data['CRS_DEP_TIME'] = pd.cut(data['CRS_DEP_TIME'], CRS_DEP_TIME__bins)\n",
    "\n",
    "    TAXI_OUT__bins = [0, 20, 40, 60, 80, 150]\n",
    "    data['TAXI_OUT'] = pd.cut(data['TAXI_OUT'], TAXI_OUT__bins)\n",
    "    \n",
    "    TAXI_IN__bins = [0, 10, 20, 30, 40, 85] \n",
    "    data['TAXI_IN'] = pd.cut(data['TAXI_IN'], TAXI_IN__bins)\n",
    "    \n",
    "    ACTUAL_ELAPSED_TIME__bins = [0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360, 390, 650]\n",
    "    data['ACTUAL_ELAPSED_TIME'] = pd.cut(data['ACTUAL_ELAPSED_TIME'], ACTUAL_ELAPSED_TIME__bins)\n",
    "    \n",
    "    DISTANCE__bins = np.arange(0, 5100, 100)\n",
    "    data['DISTANCE'] = pd.cut(data['DISTANCE'], DISTANCE__bins)\n",
    "    \n",
    "    # Changing string to int values\n",
    "    le = LabelEncoder()\n",
    "    data['ORIG_STATE'] = le.fit_transform(data['ORIGIN_STATE_ABR'])\n",
    "\n",
    "    # Using Python's Dictionary as Hash-Map to to encode states as integers\n",
    "    # Using the same encoding for Destination State as well (for consistency)\n",
    "    states = {}\n",
    "\n",
    "    for i in range(len(data)):\n",
    "        key = data.loc[i, 'ORIGIN_STATE_ABR']\n",
    "        value = data.loc[i, 'ORIG_STATE']\n",
    "        states[key] = value\n",
    "        data.loc[i, 'DEP_TIME'] = data.loc[i, 'CRS_DEP_TIME'].mid\n",
    "        data.loc[i, 'OUT_TAXI'] = data.loc[i, 'TAXI_OUT'].mid\n",
    "        data.loc[i, 'IN_TAXI'] = data.loc[i, 'TAXI_IN'].mid\n",
    "        data.loc[i, 'ELAPSED_TIME'] = data.loc[i, 'ACTUAL_ELAPSED_TIME'].mid\n",
    "        data.loc[i, 'NEW_DIST_GROUP'] = data.loc[i, 'DISTANCE'].mid\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UID</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>TAXI_OUT</th>\n",
       "      <th>TAXI_IN</th>\n",
       "      <th>ACTUAL_ELAPSED_TIME</th>\n",
       "      <th>DISTANCE</th>\n",
       "      <th>DISTANCE_GROUP</th>\n",
       "      <th>AIRLINE_ID</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>...</th>\n",
       "      <th>DEST_STATE_ABR</th>\n",
       "      <th>FIRST_DEP_TIME</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>ORIG_STATE</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>OUT_TAXI</th>\n",
       "      <th>IN_TAXI</th>\n",
       "      <th>ELAPSED_TIME</th>\n",
       "      <th>NEW_DIST_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>(1800, 2400]</td>\n",
       "      <td>(0, 20]</td>\n",
       "      <td>(0, 10]</td>\n",
       "      <td>(60, 90]</td>\n",
       "      <td>(300, 400]</td>\n",
       "      <td>2</td>\n",
       "      <td>20304</td>\n",
       "      <td>2017-01-25 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>(1800, 2400]</td>\n",
       "      <td>(0, 20]</td>\n",
       "      <td>(0, 10]</td>\n",
       "      <td>(180, 210]</td>\n",
       "      <td>(1400, 1500]</td>\n",
       "      <td>6</td>\n",
       "      <td>19393</td>\n",
       "      <td>2017-01-10 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>(1200, 1800]</td>\n",
       "      <td>(0, 20]</td>\n",
       "      <td>(0, 10]</td>\n",
       "      <td>(120, 150]</td>\n",
       "      <td>(700, 800]</td>\n",
       "      <td>4</td>\n",
       "      <td>20366</td>\n",
       "      <td>2017-01-17 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>GA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>(600, 1200]</td>\n",
       "      <td>(0, 20]</td>\n",
       "      <td>(0, 10]</td>\n",
       "      <td>(90, 120]</td>\n",
       "      <td>(700, 800]</td>\n",
       "      <td>4</td>\n",
       "      <td>19790</td>\n",
       "      <td>2017-01-09 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>FL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14</td>\n",
       "      <td>900.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>(1200, 1800]</td>\n",
       "      <td>(0, 20]</td>\n",
       "      <td>(0, 10]</td>\n",
       "      <td>(270, 300]</td>\n",
       "      <td>(2400, 2500]</td>\n",
       "      <td>10</td>\n",
       "      <td>21171</td>\n",
       "      <td>2017-01-07 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>VA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>2450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UID  DAY_OF_WEEK  CRS_DEP_TIME TAXI_OUT  TAXI_IN ACTUAL_ELAPSED_TIME  \\\n",
       "0    8            3  (1800, 2400]  (0, 20]  (0, 10]            (60, 90]   \n",
       "1   15            2  (1800, 2400]  (0, 20]  (0, 10]          (180, 210]   \n",
       "2   29            2  (1200, 1800]  (0, 20]  (0, 10]          (120, 150]   \n",
       "3   33            1   (600, 1200]  (0, 20]  (0, 10]           (90, 120]   \n",
       "4   65            6  (1200, 1800]  (0, 20]  (0, 10]          (270, 300]   \n",
       "\n",
       "       DISTANCE  DISTANCE_GROUP  AIRLINE_ID              FL_DATE  \\\n",
       "0    (300, 400]               2       20304  2017-01-25 00:00:00   \n",
       "1  (1400, 1500]               6       19393  2017-01-10 00:00:00   \n",
       "2    (700, 800]               4       20366  2017-01-17 00:00:00   \n",
       "3    (700, 800]               4       19790  2017-01-09 00:00:00   \n",
       "4  (2400, 2500]              10       21171  2017-01-07 00:00:00   \n",
       "\n",
       "       ...        DEST_STATE_ABR  FIRST_DEP_TIME  DAY_OF_MONTH MONTH  \\\n",
       "0      ...                    CA             NaN          25.0   1.0   \n",
       "1      ...                    TX             NaN          10.0   1.0   \n",
       "2      ...                    GA             NaN          17.0   1.0   \n",
       "3      ...                    FL             NaN           9.0   1.0   \n",
       "4      ...                    VA             NaN           7.0   1.0   \n",
       "\n",
       "  ORIG_STATE DEP_TIME OUT_TAXI IN_TAXI ELAPSED_TIME NEW_DIST_GROUP  \n",
       "0          4   2100.0     10.0     5.0         75.0          350.0  \n",
       "1          4   2100.0     10.0     5.0        195.0         1450.0  \n",
       "2         27   1500.0     10.0     5.0        135.0          750.0  \n",
       "3         14    900.0     10.0     5.0        105.0          750.0  \n",
       "4          4   1500.0     10.0     5.0        285.0         2450.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = cleaning(x_test)\n",
    "x_test = test_pre_processing(x_test)\n",
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ORIG_STATE</th>\n",
       "      <th>DEP_TIME</th>\n",
       "      <th>OUT_TAXI</th>\n",
       "      <th>ELAPSED_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.521739</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.521739</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.086957</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.521739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ORIG_STATE  DEP_TIME  OUT_TAXI  ELAPSED_TIME\n",
       "0   -0.521739       0.5       0.0     -0.333333\n",
       "1   -0.521739       0.5       0.0      1.000000\n",
       "2    0.478261       0.0       0.0      0.333333\n",
       "3   -0.086957      -0.5       0.0      0.000000\n",
       "4   -0.521739       0.0       0.0      2.000000"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "imp_feat = ['ORIG_STATE', 'DEP_TIME', 'OUT_TAXI', 'ELAPSED_TIME']\n",
    "\n",
    "x_test = x_test[imp_feat]\n",
    "\n",
    "RobustScaler()\n",
    "scaler_test = RobustScaler().fit(x_test)\n",
    "\n",
    "scaled_test = scaler_test.transform(x_test)\n",
    "scaled_test = pd.DataFrame(scaled_test, columns=imp_feat)\n",
    "scaled_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/harshitsrivastava/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1428 Training MSE: 415.560088782\n",
      "[ -0.75320872  -3.55957879  -1.59914663  -7.89659178  -3.29587098\n",
      "  -4.09499173  -6.85257586  -0.75320872  -4.09499173  -8.35851158\n",
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      " -10.15086501  14.76301549   6.10236498  -5.58025075  -5.79650264\n",
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      "  -3.32952983  -3.91797233  -3.84851924  -2.9642164   12.9120381\n",
      "   8.86984805  -9.38492195  -7.40556108  -5.66704621   1.92454528\n",
      "   8.86984805  -5.66704621  -5.29321764  -5.29321764  -5.28725701\n",
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      "  -2.95165003  -4.47898382   3.09331837  -9.10189817   8.65011772\n",
      "  -6.33439799   2.75699744  -1.10006849  10.49603632  11.14952509\n",
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      "  -6.95258205   2.39741824  -5.16468052  -2.17534534  -7.16109196\n",
      "  -0.75320872  -4.87902048  -8.06669914  -6.57724357   4.17121651\n",
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      " -10.09922101  -4.76477784  -7.38655543  11.06004008  -2.86143708\n",
      "   8.65011772  -7.87961049  -2.40120689  -5.61001798  -4.43499646\n",
      "  -6.98872962  -2.99935241  -4.08707053  -6.49577341  -7.59798472\n",
      "  14.73282769  -0.74287052  -5.20368923   0.15440026 -10.25088262\n",
      "  -4.42788353  -7.09601181  -1.48345191  -3.13629651  14.76301549\n",
      "  30.19443413   8.6679005   -8.4339296   -4.95902367  -0.76078598\n",
      "  -2.49010748   1.93967618   2.60033172  -4.33820827  -4.08707053\n",
      "  -5.93848456  -3.91797233  -4.38257646  11.10390317   1.8016258\n",
      "  -4.20933012  -3.64188063  -3.19183136  -3.19183136  -4.87755635\n",
      "  30.19443413  -3.91797233  -3.22877472  -1.10006849  -3.29587098\n",
      "  -0.75320872  15.17001028  -6.33439799  -0.75320872]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "RF_model = RandomForestRegressor(n_estimators=80, min_samples_leaf=50)\n",
    "RF_model.fit(train_set, scaled_y.values)\n",
    "\n",
    "preds_train = RF_model.predict(train_set)\n",
    "print i, \"Training MSE:\", mse(scaled_y.values, preds_train)    \n",
    "\n",
    "preds_test = RF_model.predict(scaled_test)\n",
    "print preds_test\n",
    "preds_test = pd.DataFrame(preds_test)\n",
    "preds_test.to_csv(\"predicted labels.csv\")\n",
    "ID_set.to_csv(\"ID_SET.csv\")"
   ]
  }
 ],
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