Predict The Flight Ticket Price .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flight Ticket Price Prediction\n",
    "* Flight ticket prices can be something hard to guess, today we might see a price, check out the price of the same flight tomorrow, it will be a different story. We might have often heard travellers saying that flight ticket prices are so unpredictable. \n",
    "* Here you will be provided with prices of flight tickets for various airlines between the months of March and June of 2019 and between various cities.\n",
    "\n",
    "## Data Overview\n",
    "Here each data point corresponds to trip of flight from one city to another.\n",
    "* Airline: The name of the airline.\n",
    "\n",
    "* Date_of_Journey: The date of the journey\n",
    "\n",
    "* Source: The source from which the service begins.\n",
    "\n",
    "* Destination: The destination where the service ends.\n",
    "\n",
    "* Route: The route taken by the flight to reach the destination.\n",
    "\n",
    "* Dep_Time: The time when the journey starts from the source.\n",
    "\n",
    "* Arrival_Time: Time of arrival at the destination.\n",
    "\n",
    "* Duration: Total duration of the flight.\n",
    "\n",
    "* Total_Stops: Total stops between the source and destination.\n",
    "\n",
    "* Additional_Info: Additional information about the flight\n",
    "\n",
    "* Price(target): The price of the ticket\n",
    "\n",
    "#### Type of Machine Learning task : \n",
    "It is an regression problem where given a set of features we need to predict the price of ticket from one city to another.\n",
    "\n",
    "#### Performace Metric\n",
    "Since it is an regression problem we will use Root Mean Squared error (RMSE) and R-squared as regression metric.\n",
    "\n",
    "#### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import re\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from math import sqrt\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from mlxtend.regressor import StackingRegressor\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('Data_Train.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 10683 instances each is having 11 different features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10683.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9087.064121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4611.359167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8372.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79512.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price\n",
       "count  10683.000000\n",
       "mean    9087.064121\n",
       "std     4611.359167\n",
       "min     1759.000000\n",
       "25%     5277.000000\n",
       "50%     8372.000000\n",
       "75%    12373.000000\n",
       "max    79512.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The maximum ticket price is Rs.79512 and minimum is Rs.1759."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10683</td>\n",
       "      <td>10683</td>\n",
       "      <td>10683</td>\n",
       "      <td>10683</td>\n",
       "      <td>10682</td>\n",
       "      <td>10683</td>\n",
       "      <td>10683</td>\n",
       "      <td>10683</td>\n",
       "      <td>10682</td>\n",
       "      <td>10683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>128</td>\n",
       "      <td>222</td>\n",
       "      <td>1343</td>\n",
       "      <td>368</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>18/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>18:55</td>\n",
       "      <td>19:00</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3849</td>\n",
       "      <td>504</td>\n",
       "      <td>4537</td>\n",
       "      <td>4537</td>\n",
       "      <td>2376</td>\n",
       "      <td>233</td>\n",
       "      <td>423</td>\n",
       "      <td>550</td>\n",
       "      <td>5625</td>\n",
       "      <td>8345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Airline Date_of_Journey Source Destination            Route  \\\n",
       "count         10683           10683  10683       10683            10682   \n",
       "unique           12              44      5           6              128   \n",
       "top     Jet Airways      18/05/2019  Delhi      Cochin  DEL → BOM → COK   \n",
       "freq           3849             504   4537        4537             2376   \n",
       "\n",
       "       Dep_Time Arrival_Time Duration Total_Stops Additional_Info  \n",
       "count     10683        10683    10683       10682           10683  \n",
       "unique      222         1343      368           5              10  \n",
       "top       18:55        19:00   2h 50m      1 stop         No info  \n",
       "freq        233          423      550        5625            8345  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = ['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Banglore' 'Kolkata' 'Delhi' 'Chennai' 'Mumbai']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df['Source'].unique())\n",
    "df['Destination'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the flight are from 5 different cities to all other 6 different cities except the same city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is only single entry is missing in Route and Total_stops."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sorting the entire dataset on date of journey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values('Date_of_Journey', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting the Date_of_journey into date, month and year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = pd.DatetimeIndex(df['Date_of_Journey']).year\n",
    "df['month'] = pd.DatetimeIndex(df['Date_of_Journey']).month\n",
    "df['Day'] = pd.DatetimeIndex(df['Date_of_Journey']).day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No info', '1 Long layover', 'Business class', 'Red-eye flight',\n",
       "       'Change airports', '2 Long layover', 'No Info', '1 Short layover',\n",
       "       'In-flight meal not included', 'No check-in baggage included'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Additional_Info'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Price of ticket')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "plt.title('Price VS Additional Information')\n",
    "plt.scatter(df['Additional_Info'], df['Price'])\n",
    "plt.xticks(rotation = 90)\n",
    "plt.xlabel('Information')\n",
    "plt.ylabel('Price of ticket')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above scatter plot it is clear that the ticket prices for Business class is higher which is quite obivious."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count of flights')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10 , 10))\n",
    "plt.title('Count of flights month wise')\n",
    "sns.countplot(x = 'month', data = df)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Count of flights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are around 2500 flights scheduled in the month of may so at this time can be the peak month for the airlines as compared to april which seems like slack season for airlines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], <a list of 11 Text xticklabel objects>)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CV8z4LEmSpI7RyB66ZwIPAp+NiNGI+HREHAw8LTPvByi+PrU4vw/YPuP9O4rafPUdVep7iYh1EbEpIjY9+OCD+35lkiRJLaSRgW5/4MXAJzNzDfBLfjO8Wk21+W+5hPrexcxLM7M/M/tXrFgxf6slSZJKppGBbgewIzNvKY6voRLwflYMl1J8fWDG+UfNeP+RwM4F6kdWqUuSJHWUhgW6zPxXYHtErC5KrwR+CFwHTK9UXQtcWzy/DjinWO16MvCLYkh2BDg1Ig4vFkOcCowUrz0SEScXq1vPmfFZkiRJHWP/Bn/+nwFfjIgDgLuBt1IJkV+JiLcB9wFvKs79BvBqYBvwaHEumbkrIj4A3Fac9/7M3FU8fyfwOaAH+GbxkCRJ6ihRWSDaOfr7+3PTpk3NboYkSdKCIuL2zOxf6DzvFCFJklRyBjpJkqSSM9BJkiSVnIFOkiSp5Ax0kiRJJWegkyRJKjkDnSRJUskZ6CRJkkrOQCdJklRyjb71l6Q6GR4dY8PIVnaOT7Cyt4ehgdUMrulrdrMkSS3AQCeVwPDoGOs3bmZicgqAsfEJ1m/cDGCokyQ55CqVwYaRrbvD3LSJySk2jGxtUoskSa3EQCeVwM7xiZrqkqTOYqCTSmBlb09NdUlSZzHQSSUwNLCanu6uPWo93V0MDaxuUoskSa3ERRFSCUwvfHCVqySpGgOdVBKDa/oMcJKkqhxylSRJKjkDnSRJUskZ6CRJkkrOQCdJklRyBjpJkqSSM9BJkiSVnIFOkiSp5NyHTlW9+VPf53s/2bX7+JRnHcEX3/7SJrZIkiTNxR467WV2mAP43k928eZPfb9JLZIkSfMx0Gkvs8PcQnVJktRcBjpJkqSSM9BJkiSVnIFOeznlWUfUVJckSc1loNNevvj2l+4V3lzlKklS63LbElVleJMkqTzsoZMkSSo5A50kSVLJGegkSZJKzkAnSZJUcgY6SZKkkjPQSZIklZyBTpIkqeQMdJIkSSXnxsJSSQyPjrFhZCs7xydY2dvD0MBqBtf0NbtZkqQWYKCTSmB4dIyha+5gcioBGBufYOiaOwAMdZIkh1ylMnjf17fsDnPTJqeS9319S5NaJElqJQY6qQQeenSyprokqbMY6CRJkkrOQCeVQG9Pd011SVJnMdBJJXDh659H936xR617v+DC1z+vSS2SJLUSV7lKJTC9ktVtSyRJ1RjopJIYXNNngJMkVeWQqyRJUskZ6CRJkkrOQCdJklRyBjpJkqSSM9BJkiSVXEMDXUTcExGbI+IHEbGpqB0RETdExF3F18OLekTERyNiW0TcGREvnvE5a4vz74qItTPqJxSfv614b+zdCkmSpPa2HD10L8/MF2Vmf3F8HnBjZq4CbiyOAU4HVhWPdcAnoRIAgQuAk4ATgQumQ2BxzroZ7zut8ZcjSZLUWpox5HoGcHnx/HJgcEb9iqy4GeiNiKcDA8ANmbkrMx8CbgBOK147NDO/n5kJXDHjsyRJkjpGowNdAn8fEbdHxLqi9rTMvB+g+PrUot4HbJ/x3h1Fbb76jip1SZKkjtLoO0Wckpk7I+KpwA0R8eN5zq02/y2XUN/7gythch3A0UcfPX+LJUmSSqahPXSZubP4+gDwNSpz4H5WDJdSfH2gOH0HcNSMtx8J7FygfmSVerV2XJqZ/ZnZv2LFin29LEmSpJbSsEAXEQdHxCHTz4FTgX8GrgOmV6quBa4tnl8HnFOsdj0Z+EUxJDsCnBoRhxeLIU4FRorXHomIk4vVrefM+CxJkqSO0cgh16cBXyt2Etkf+FJmfisibgO+EhFvA+4D3lSc/w3g1cA24FHgrQCZuSsiPgDcVpz3/szcVTx/J/A5oAf4ZvGQJEnqKFFZINo5+vv7c9OmTc1uhiRJ0oIi4vYZW7/NyTtFSJIklZyBTpIkqeQMdJIkSSVnoJMkSSo5A50kSVLJGegkSZJKzkAnSZJUcgY6SZKkkjPQSZIklZyBTpIkqeQMdJIkSSVnoJMkSSo5A50kSVLJGegkSZJKbv9mN0DS4gyPjrFhZCs7xydY2dvD0MBqBtf0NbtZkqQWYKCTSmB4dIz1GzczMTkFwNj4BOs3bgYw1EmSHHKVymDDyNbdYW7axOQUG0a2NqlFkqRWYqCTSmDn+ERNdUlSZzHQSSWwsrenprokqbMY6KQSGBpYTU931x61nu4uhgZWN6lFkqRW4qIIqQSmFz64ylWSVM2CgS4iTsnM7y1Uk9RYg2v6DHCSpKoWM+T6sUXWJEmS1ARz9tBFxEuBlwErIuK9M146FOiq/i5JkiQtt/mGXA8Anlycc8iM+sPAGxvZKEmSJC3enIEuM78LfDciPpeZ90bEwZn5y2VsmyRJkhZhMXPoVkbED4EfAUTECyPiE41tliRJkhZrMYHufwIDwL8BZOYdwO82slGSJElavEVtLJyZ22eVpqqeKEmSpGW3mI2Ft0fEy4CMiAOA91AMv0qSJKn5FtND9w7g3UAfsAN4UXEsSZKkFrCYHronMvPNMwsRcSzFnDpJkiQ112J66L4eEYdOH0TEc4GvN65JkiRJqsViAt1fUgl1T46IE4BrgD9qbLMkSZK0WAsOuWbm9RHRDfw9lTtGDGbmXQ1vmSRJkhZlvnu5fgzIGaVDgbuBP4sIMvM9jW6cJEmSFjZfD92mWce3N7IhkiRJWpr57uV6OUBEHAz8KjOniuMu4EnL0zxJkiQtZDGLIm4EemYc9wD/X2OaI0mSpFotJtAdmJn/Pn1QPD+ocU2SJElSLRYT6H4ZES+ePii2LploXJMkSZJUi8XcKeJc4OqI2FkcPx34PxvXJEmSJNViMfvQ3RYRzwFWAwH8ODMnG94ySZIkLcp8+9C9IjNviogzZ720qtiHbmOD2yZJkqRFmK+H7veAm4DXVXktAQOdJElSC5hvH7oLiqfvz8yfznwtIo5taKskSZK0aItZ5frVKrVr6t0QSZIkLc18c+ieAzwPOGzWPLpDgQMb3TBJkiQtznxz6FYDrwV62XMe3SPA2xvZKEmSJC3efHPorgWujYiXZub3l7FNkiRJqsGCc+gMc5IkSa1tMYsiJEmS1MLmDHQR8efF11OWrzmSJEmq1Xw9dG8tvn5sORoiSZKkpZlvleuPIuIeYEVE3DmjHkBm5gsa2jJJkiQtypw9dJl5NnAysI3KtiXTj9dS/XZgVUVEV0SMRsTfFcfHRsQtEXFXRFwVEQcU9ScVx9uK14+Z8Rnri/rWiBiYUT+tqG2LiPNqunJJkqQ2Me+iiMz818x8IXA/cEjx2JmZ99bwPf4c+NGM4w8BH8nMVcBDwNuK+tuAhzLz2cBHivOIiOOAs6hscnwa8IkiJHYBHwdOB44Dzi7OlSRJ6igLrnKNiN8D7qISnj4B/EtE/O5iPjwijgReA3y6OA7gFfzm1mGXA4PF8zOKY4rXX1mcfwbw5cx8rLin7DbgxOKxLTPvzsxfA18uzpUkSeoo882hm/Zh4NTM3AoQEb8DXAmcsIj3/k/gv1Lp2QP4LWA8Mx8vjncAfcXzPmA7QGY+HhG/KM7vA26e8Zkz37N9Vv2kao2IiHXAOoCjjz56Ec2WJEkqj8XsQ9c9HeYAMvNfgO6F3hQRrwUeyMzbZ5arnJoLvFZrfe9i5qWZ2Z+Z/StWrJin1ZIkSeWzmB66TRFxGfD54vjNwO3znD/tFOD1EfFq4EDgUCo9dr0RsX/RS3cksLM4fwdwFLAjIvYHDgN2zahPm/meueqSJEkdYzE9dO8EtgDvobLA4YfAOxZ6U2auz8wjM/MYKosabsrMNwPfBt5YnLYWuLZ4fl1xTPH6TZmZRf2sYhXsscAq4FbgNmBVsWr2gOJ7XLeI65EkSWorC/bQZeZjVObRfbhO3/O/AV+OiIuAUeCyon4Z8PmI2EalZ+6s4vtviYivUAmSjwPvzswpgIj4U2AE6AI+k5lb6tRGSZKk0ohKJ1jn6O/vz02bNjW7GZIkSQuKiNszs3+h8xYz5CpJkqQWtuhAFxEHN7IhkiRJWprFbCz8soj4IcXdHiLihRHxiYa3TJIkSYuymB66jwADwL8BZOYdwKLuFCFJkqTGW9SQa2Zun1WaakBbJEmStASL2Vh4e0S8DMhiv7f3UAy/SpIkqfkW00P3DuDdVO6fugN4UXEsSZKkFrCYjYV/TuV2X5IkSWpBi1nlenlE9M44PjwiPtPYZkmSJGmxFjPk+oLMHJ8+yMyHgDWNa5IkSZJqsZhFEftFxOFFkCMijljk+yRJkhgeHWPDyFZ2jk+wsreHoYHVDK7pa3az2spigtnfAP87Iq4pjt8EfLBxTZIkSe1ieHSM9Rs3MzFZ2fFsbHyC9Rs3Axjq6mjBIdfMvAJ4A/Az4AHgzMz8fKMbJkmSym/DyNbdYW7axOQUG0a2NqlF7WnOHrqIODQzHy6GWP8V+NKM147IzF3L0UBJklReO8cnaqpraeYbcv0S8FrgdiBn1KM4fmYD2yVJktrAyt4exqqEt5W9PU1oTfuaM9Bl5msjIoDfy8z7lrFNagHnD2/mylu2M5VJVwRnn3QUFw0e3+xmSZJKZmhg9R5z6AB6ursYGljdxFa1n3nn0GVmAl9bpraoRZw/vJkv3HwfU1npmJ3K5As338f5w5ub3DJJUtkMrunj4jOPp6+3hwD6enu4+MzjXRBRZ4tZ5XpzRLwkM29reGvUEq68ZfucdXvpJEm1GlzTZ4BrsMUEupcD74iIe4BfUsyhy8wXNLJhap7pnrnF1iVJUnMtJtCd3vBWqKV0RVQNb10RTWiNJElayJxz6CLiwIg4FxgCTgPGMvPe6ceytVDL7uyTjqqpLkmSmmu+HrrLgUngf1HppTsO+PPlaJSaa3qenKtcJUkqh/kC3XGZeTxARFwG3Lo8TVIr6H/GEXz7xw+yc3yC3z7sQPqfcUSzmyRJkuYwX6CbnH6SmY+H86c6hvfdkySpXObbh+6FEfFw8XgEeMH084h4eLkaqOXnffckSSqX+e4U0bWcDVHr8L57kiSVy2K2LVGH8b57kqR68naSjTfvrb/UmYYGVtPTvWcHrffdkyQthbeTXB4GOu3F++5JkuplvttJqn4cclVV3ndPklQP3k5yedhDJ0mSVHIGOkmSpJIz0EmSpIbpm2OHhLnqWhoDnSRJahh3TlgeLoqQJEkNM73AbsPIVnaOT7Cyt4ehgdUuvKszA50kSWood05oPIdcJUmSSs5AJ0mSVHIGOkmSpJIz0EmSJJWcgU6SJKnkDHSSJEkl57YldTQ8OuY+O5IkadkZ6OpkeHSM9Rs3MzE5BcDY+ATrN24GKGWoe8EF3+Lhx6Z2Hx/6pC7ufN9pTWyRJEmai0OudbJhZOvuMDdtYnKKDSNbm9SipZsd5gAefmyKF1zwrSa1SJIkzcdAVydj4xM11VvZ7DC3UF2SJDWXga5OuiJqqkuSJNWLga5OpjJrqkuSJNWLga5O+np7aqq3skOf1FVTXZIkNZeBrk6GBlbT071n4Onp7mJoYHWTWrR0d77vtL3Cm6tcJUlqXW5bUifTW5O0yz50hjdJksrDQFdHg2v6ShvgJElSeTnkKkmSVHIGOkmSpJIz0EmSJJVcwwJdRBwYEbdGxB0RsSUi3lfUj42IWyLiroi4KiIOKOpPKo63Fa8fM+Oz1hf1rRExMKN+WlHbFhHnNepaJEmSWlkje+geA16RmS8EXgScFhEnAx8CPpKZq4CHgLcV578NeCgznw18pDiPiDgOOAt4HnAa8ImI6IqILuDjwOnAccDZxbmSJEkdpWGBLiv+vTjsLh4JvAK4pqhfDgwWz88ojilef2VERFH/cmY+lpk/BbYBJxaPbZl5d2b+Gvhyca4kSVJHaei2JUUv2u3As6n0pv0EGM/Mx4tTdgDT+3z0AdsBMvPxiPgF8FtF/eYZHzvzPdtn1U+aox3rgHUARx999L5dlCRJqsnw6Fjb7NPaqhq6KCIzpzLzRcCRVHrUnlvttOJrtbvY5xLq1dpxaWb2Z2b/ihUrFm64JEmqi+HRMdZv3MzY+AQJjI1PsH7jZoZHx5rdtLayLKtcM3Mc+A5wMtAbEdM9g0cCO4vnO4CjAIrXDwN2zazPes9cdUmS1CI2jGxlYnJqj9rE5BRFchtoAAAgAElEQVQbRrY2qUXtqZGrXFdERG/xvAf4A+BHwLeBNxanrQWuLZ5fVxxTvH5TZmZRP6tYBXsssAq4FbgNWFWsmj2AysKJ6xp1PZIkqXY7xydqqmtpGjmH7unA5cU8uv2Ar2Tm30XED4EvR8RFwChwWXH+ZcDnI2IblZ65swAyc0tEfAX4IfA48O7MnAKIiD8FRoAu4DOZuaWB1yNJkmq0sreHsSrhbWVvTxNa076i0gnWOfr7+3PTpk3NboYkSR1heg7dzGHXnu4uLj7zeBdGLEJE3J6Z/Qud19BVrpIkqbNNhzZXuTaWgU6SJDXU4Jo+A1yDGejqyH12JElSMxjo6mT2HIHpfXaAUoY6w6kkSeWxLPvQdYJ22mdneHSMc6/6wR6bQJ571Q/cBFKSpBZloKuTdtpn59yrflBTXZIkNZeBrk7m2k/HfXYkSVKjGejqZGhgNd1de95etrsrGBpY3aQWSZKkTmGgq6OpqZz3WJIkqREMdHVy4XVbeGJW7YmiXjZRY12SJDWXga5Oxicma6q3sjeffHRNdUmS1FwGOu3lylu211SXJEnNZaCrk8MP6q6p3sqmsvrcv7nqkiSpuQx0dfKaFzy9prokSVK9GOjq5Po776+pLkmSVC8Gujp56NHqix/mqkuSJNWLgU6SJKnkDHR10tNd/T/lXPVWds8lr6mpLkmSmqt8aaNFuRmvJElqFgNdnTw6Ofs+EfPXW9kx511fU12SJDWXgU6SJKnkDHSSJEklZ6CTJEkqOQOdJElSyRnotBe3LZEkqVz2b3YD1JoMb5IklYeBrk76ensYG5+oWpckqZMNj46xYWQrO8cnWNnbw9DAagbX9DW7WW3FIdc6GRpYTU931x61nu4uhgZWN6lFkiQ13/DoGENX38HY+AQJjI1PMHT1HQyPjjW7aW3FHro6mf6XRrv8C+T84c1cect2pjLpiuDsk47iosHjm90sSVLJXHjdFiafyD1qk08kF163pbS/I1uRgU57OX94M1+4+b7dx1OZu48NdZKkWoxPTNZU19I45Fonw6NjrN+4eY8u5fUbN5eyS/nKW7bXVJckSc1loKuTDSNbmZic2qM2MTnFhpGtTWrR0k1l1lSXJGkuhx/UXVNdS2Ogq5NqK1znq0uS1AkueN3ziFm1KOqqHwNdnXTF7L+u89clSeoEm+7dxezxnSzqqh8DXZ200zCl4VRavOHRMU655CaOPe96TrnkplLOm5UayXnZy8NAVydzbSBcxo2F2ymcSo3UTouhpEbxd8ryMNDVSTttLOwEVmlx2mkxlKRycx+6Ohlc08eme3ftsRnvG07oK+Wmib+a9QtqobrUqXbOsehprrqWh7eZUieyh65OhkfHuOrW7bu7kKcyuerW7aUcepmYfKKmutSpVs4xpWKuuhrPYfDW005TklqZga5O5ru1iaT21E5TLdqFw+Ctx/9PlodDrnXSTrc2Ofygbh56dO92O4dO2lO73cO5HTgM3nr8/2R5GOi0l2phbr56K3MujRptcE0558q2q5W9PVU3dHcYvLn8/6TxHHKtkwO6qu/RNlddjedcGqnzOLynTmUPXZ3Mnj+3UF2NN99cGv+lKO3p/OHNe6zSP/uko7ho8PhmN6tmDu+1pnb5+9XKDHR1Mtf+iO6b2DzOpZEW5/zhzXzh5vt2H09l7j4u4y9dh/daS7v9/WpVDrnWyX5zjKzOVVfjuaWEtDjemkmN5N+v5WGgq5Mn7V/9P+VcdTWec2mkxfHWTGok/34tD4dc6+RXc2y6O1e9lT3tkAP42SO/rlovE+fSSJI6hYGuTnrn2Lutt4R7t1ULc/PVW5lzaSRJncDxwDp5bI77nM5Vl6RW4a2Z1EhdUX0y+Vx1LY2Brk4enWNoda66JLUK55uqkZxDtzwMdJLU4QbX9HHxmcfT19tDUOmZu/jM452uoLqwB3h5OIeuTnp7uqvet7W3p3xz6CR1HuebqlGGBlazfuPmPTZ6twe4/uyhq5PXvvDpNdUlSeoE9gAvj4b10EXEUcAVwG8DTwCXZubfRsQRwFXAMcA9wB9m5kMREcDfAq8GHgX+Y2b+U/FZa4Hzi4++KDMvL+onAJ8DeoBvAH+e2ZxB+W//+MGa6pIkdQp7gBuvkT10jwN/kZnPBU4G3h0RxwHnATdm5irgxuIY4HRgVfFYB3wSoAiAFwAnAScCF0TE4cV7PlmcO/2+0xp4PfNqp9tMHdhVfeXRXHVJktRcDQt0mXn/dA9bZj4C/AjoA84ALi9OuxwYLJ6fAVyRFTcDvRHxdGAAuCEzd2XmQ8ANwGnFa4dm5veLXrkrZnzWsmun20z9aqp6J+dc9VY2PDrGKZfcxLHnXc8pl9zE8OhYs5skSVLdLcscuog4BlgD3AI8LTPvh0roA55anNYHzLyx246iNl99R5V6te+/LiI2RcSmBx9szBDoy5+zoqa6Gm94dIz1GzczNj5BAmPjE6zfuNlQJ0lqOw0PdBHxZOCrwLmZ+fB8p1ap5RLqexczL83M/szsX7GiMQHr+jvvr6muxtswsnWPVVUAE5NTbBjZ2qQWSVoO9sy3Hv9MGq+hgS4iuqmEuS9m5sai/LNiuJTi6wNFfQdw1Iy3HwnsXKB+ZJV6U1S77dd8dTVeO81rlLQ49sy3nuHRMYauvmOPP5Ohq+/wz6TOGhboilWrlwE/yswPz3jpOmBt8XwtcO2M+jlRcTLwi2JIdgQ4NSIOLxZDnAqMFK89EhEnF9/rnBmfJbXVvEZJi2PPfOu58LotTD6x5wDa5BPJhddtaVKL2lMje+hOAd4CvCIiflA8Xg1cArwqIu4CXlUcQ2XbkbuBbcCngHcBZOYu4APAbcXj/UUN4J3Ap4v3/AT4ZgOvRyXj7YykzmPPfOuptun+fHUtTcP2ocvMf6T6PDeAV1Y5P4F3z/FZnwE+U6W+CXj+PjRTbWx6z6MNI1vZOT7Byt4ehgZWuxeS1MZW9vYwViW82TOvduetv+qkK6Darh5u3dZcbmYpdRZvM9V69gt4osrvx/38/VhX3vqrTubaoq2EW7dJUmkNrunjDSf00RWVtNAVwRtO8B92zVQtzM1X19LYQydJYnh0rC2mJwyPjvHV28eYKu4COZXJV28fo/8ZR5TyetqBI1jLwx66Opnr76V/XyW1unba6sNVrq3HEazlYaCrk/3n+C85V12SWkU7hSBXuapTGTfqZPKJ2uqS1CraKQS5/6Q6lYFOkjpcO4Ug959UpzLQqa15/0BpYe0UggbX9HHxmcfT19tDAH29PVx85vEuiFDbc5Wr2tb0RO/puUHTE70Bf7hLM7TbJtzuP6lOZKBT25pvorc/7KU9GYKkcnPIVW2rnSZ6S5I0H3vo1La8p6O0eO2ysbDUqeyhU9tqp4neUiO108bCUqcy0KltudpNWpx22lhY6lQOuaqtOdFbWpjzTaXys4dOkjpcO20sLHUqA53amhsLSwsbGlhNd1fsUevuCuebSiXikKva1vDoGEPX3MHkVAKVid5D19wBuLGwtJdc4FhSS7OHTm3rfV/fsjvMTZucSt739S1NapHUmjaMbGXyiVn/rzyRLoqQSsRAp7b10KOTNdWlTuWiCKn8DHSS1OFcFCGVn4FObau3p7umutSp3IRbKj8DndrWha9/Ht37zVq5t19w4euf16QWSa3JTbil8nOVq9rW9C8j708pLcxNuKVys4dObe3cq36wx/0pz73qB81ukiRJdWegU9s65rzra6pLklRWDrlKkiQt0vnDm7nylu1MZdIVwdknHcVFg8c3u1kGOklS5c4qzjeV5nf+8Ga+cPN9u4+nMncfNzvUOeQqSR1ueHSM9Rs37zHfdP3Gzd77WJrlylu211RfTgY6SepwG0a2MjE5tUdtYnLKW39Js0xl9Zscz1VfTgY6ta2uiJrqUqfy1l/S4rTy7xUDndpWK/9LqtMNj45xyiU3cex513PKJTc5tNdk3vpLWpyzTzqqpvpyclGEpGU1PV9reohver4WUMpJ+O2wmGBoYPUefyZQ7lt/tcOfiVrT9MIHV7lK6njzzdcq2y/ddgmng2v62HTvrj1+Sb3hhHLeOaJd/kzUui4aPL4lAtxsDrlKWlZjc8zLmqveytplMcHw6BhX3bp993SEqUyuunV7KYfC2+XPRK2rVaeMGOgkaYnaZTHBhddtYfKJPeeWTj6RXHjdlia1aOna5c9EramVt/gx0EnSErXLYoLxicma6q2sXf5M1JpauQfYQCdpWbXysv9aDQ2spqe7a49amRcTtAP/TNRIrdwDbKBT22qn4NBOWnnZf60G1/Rx8ZnH09fbQwB9vT1cfObxTr5vIv9M1Eit3APsKle1rYMP2I+HH5uqWlfztPKyf7WHwTXlXKGr1tfKW/wY6NS2qoW5+epaPq267L9Ww6NjDF19x+4FBWPjEwxdfQfgFhnN5D50apTpv0et+PfLQCdJSzTf6tBW+AHfidyHTo3Wqj3Ajj1J0hK10+rQdtHKqxClRjLQSZLaRiuvQpQaySFXSVqiww/q5qFH9+6NO/yg7ia0RlBZbVjtriOtsApxKZwPqMWyh06Slui4px9SU12N10770LXyXQnUeuyhk7Tsjj3vemYuJQjgp5e8plnNWbKb736oproar5VXIdZqvvmAZbweNZaBTtKymh3mALKoly3UTd/MfrF1LY9WXYVYK+cDtqZXffg73PXAL3cfr3rqwdzw3t9vXoMKDrlKWlZzRR0jkOpleHSMUy65iWPPu55TLrmptEOUrXxXgk41O8wB3PXAL3nVh7/TnAbNYKCTJLWNdpp31k7zAdvF7DC3UH05GegkSW2jnfah8760qoVz6CRJbaPd5p21y3xANZ49dJKktnFgd/Vfa3PVpXbh33BJUtt47PEnaqpLteibY0HKXPXlZKCTtKz2i9rqarxW/iVVqyfmWC49V12qxdDA6r1+Vu0XtMRClYYFuoj4TEQ8EBH/PKN2RETcEBF3FV8PL+oRER+NiG0RcWdEvHjGe9YW598VEWtn1E+IiM3Fez4aEf46kErg/zrp6JrqrWyuHzpl+2E01y+jVvglJbWSTffu2usfB09kpd5sjeyh+xxw2qzaecCNmbkKuLE4BjgdWFU81gGfhEoABC4ATgJOBC6YDoHFOetmvG/295LUgi4aPJ4/Ovlouop/g3VF8EcnH81Fg8c3uWW1a5c99c675o6a6lKnuvKW7TXVl1PDAl1m/gMwO7KeAVxePL8cGJxRvyIrbgZ6I+LpwABwQ2buysyHgBuA04rXDs3M72dmAlfM+CxJLa7/GUfw24cdSAC/fdiB9D/jiGY3qaP9aqp6BJ2rLnWqVr47zHJvW/K0zLwfIDPvj4inFvU+YGa83VHU5qvvqFKvKiLWUenN4+ijyzesI7WT6Y1fp/cKm974FXB7BklaolZZFFFtykkuoV5VZl6amf2Z2b9ixYolNlFSPbTTxq9qPYc+qaumutQuljvQ/awYLqX4+kBR3wEcNeO8I4GdC9SPrFKX1OLG5tjgda66VIuuruq/1uaqS+1iuf+GXwdMr1RdC1w7o35Osdr1ZOAXxdDsCHBqRBxeLIY4FRgpXnskIk4uVreeM+OzJLWwrjkWpM9Vl2rx0KOTNdVb3fDoGKdcchPHnnc9p1xyUynvSdtOTnlW9fm+c9WXUyO3LbkS+D6wOiJ2RMTbgEuAV0XEXcCrimOAbwB3A9uATwHvAsjMXcAHgNuKx/uLGsA7gU8X7/kJ8M1GXYuk+mnlScW1ck89NdL0fNOx8QmS38w3NdQ1zxff/tK9wtspzzqCL779pU1q0W80bFFEZp49x0uvrHJuAu+e43M+A3ymSn0T8Px9aaOk5dfX21N1eNVNbKU9zTff1AVEzdMK4a0aJxVIWlbtdvN0qVH8f0W1MNBJWlbtshmv1Ggr5+i1nquuzmagkySpBQ0NrKane8/tVnq6u7wlm6pa7o2FJUnSIkzPk9swspWd4xOs7O1haGC18+dUlYFOkqQWNbimzwCnRXHIVZIkqeQMdJIkSSVnoJMkSSo5A50kSVLJGegkSZJKzkAnSZJUcgY6SZKkkjPQSZIklZyBTpIkqeQMdJIkSSVnoJMkSSo5A50kSVLJ7d/sBkiSpOqGR8fYMLKVneMTrOztYWhgNYNr+prdLLUgA50kSS1oeHSM9Rs3MzE5BcDY+ATrN24GMNRpLw65SpLUgjaMbN0d5qZNTE6xYWRrk1qkVmagkySpBe0cn6iprs5moJMkqQWt7O2pqa7OZqCTJKkFDQ2spqe7a49aT3cXQwOrm9QitTIXRUiS1IKmFz6UfZXrgV3Br6ayal31Y6CTJKlFDa7pK12Am+3HH3w1z/kf39gj1B3YFfz4g69uYqvaj4FOkiQ1lOGt8ZxDJ0mSVHIGOkmSpJJzyFWSpBblrb+0WAY6SZJakLf+Ui0ccpUkqQV56y/VwkAnSR3u8IO6a6preXjrL9XCQCdJHe6hRydrqmt5eOsv1cJAJ0lSC/LWX6qFiyIkSWpB7XLrLy0PA50kSS2qHW79peXhkKskSVLJGegkqcPdc8lraqpLaj0OuUqSDG9SydlDJ2lZ2RukRvLvlzqVPXSSlp2/XNVI/v1SJ7KHTpIkqeQMdJIkSSVnoJMkSSo5A50kSVLJGegkSZJKzkCntuX2BZKkTuG2JXVyzyWv4Zjzrq9aL5u+3h7Gxieq1sumjP/9JUmqlYGujtolPAwNrGb9xs1MTE7trvV0dzE0sLqJrZJaz6qnHsxdD/yyal2SlpNDrtrL4Jo+Lj7zePp6ewgqPXMXn3k8g2v6mt00qaXc8N7f3yu8rXrqwdzw3t9vToMkdazIzGa3YVn19/fnpk2bmt0MSZKkBUXE7ZnZv9B59tBJkiSVnIFOkiSp5Ax0kiRJJVf6QBcRp0XE1ojYFhHnNbs9kiRJy63UgS4iuoCPA6cDxwFnR8RxzW2VJEnS8ip1oANOBLZl5t2Z+Wvgy8AZTW6TJEnSsip7oOsDts843lHUJEmSOkbZA11Uqe21sV5ErIuITRGx6cEHH1yGZkmSJC2fsge6HcBRM46PBHbOPikzL83M/szsX7FixbI1TpIkaTmUPdDdBqyKiGMj4gDgLOC6JrdJkiRpWe3f7Absi8x8PCL+FBgBuoDPZOaWJjdLkiRpWZU60AFk5jeAbzS7HZIkSc1S9iFXSZKkjmegkyRJKjkDnSRJUskZ6CRJkkrOQCdJklRyBjpJkqSSM9BJkiSVnIFOkiSp5CJzr3vZt7WIeBC4t8Hf5inAzxv8PZZLu1xLu1wHeC2tql2upV2uA7yWVtQu1wHLdy3PyMwFb0TfcYFuOUTEpszsb3Y76qFdrqVdrgO8llbVLtfSLtcBXksrapfrgNa7FodcJUmSSs5AJ0mSVHIGusa4tNkNqKN2uZZ2uQ7wWlpVu1xLu1wHeC2tqF2uA1rsWpxDJ0mSVHL20EmSJJWcgU6SJKnkDHSSJEklZ6Crg4hYERF/HRHfiIibph/NbletIuJNEXFI8fz8iNgYES9udrv0GxFxeES8oNnt2BfFNZwYEb87/Wh2m9QeIqIrIjY0ux36jXb6vRIRr42Ils1NLduwkvki8CPgWOB9wD3Abc1s0BL935n5SET8H8AAcDnwySa3ackiojsi3hMR1xSPP4uI7ma3q1YR8Z2IODQijgDuAD4bER9udruWIiL+GPgHYITK/ysjwIXNbNNSRcSHFlMrg4h4fkT8YUScM/1odpuWIjOngBMiIprdln1RBNMvNLsdddJOv1fOAu6KiL+KiOc2uzGzGejq47cy8zJgMjO/m5n/CTi52Y1agqni62uAT2bmtcABTWzPvvokcALwieLxYsr5g+SwzHwYOBP4bGaeAPxBk9u0VH8OvAS4NzNfDqwBHmxuk5bsVVVqpy97K/ZRRFwAfKx4vBz4K+D1TW3UvhkFro2It0TEmdOPZjeqFkUwXRERZf75O61tfq9k5h9R+Zn1Eyr/sP5+RKyb7oFstv2b3YA2MVl8vT8iXgPsBI5sYnuWaiwi/l8qYeFDEfEkyh36X5KZL5xxfFNE3NG01izd/hHxdOAPgf/R7Mbso19l5q8igoh4Umb+OCJWN7tRtYiIdwLvAp4ZEXfOeOkQ4HvNadU+eSPwQmA0M98aEU8DPt3kNu2LI4B/A14xo5bAxuY0Z8nuAb4XEdcBv5wuZmbZeufb6vdKZj4cEV8FeoBzgf8ADEXERzPzY81sm4GuPi6KiMOAv6Dyr9xDgf/c3CYtyR8CpwF/nZnjRYgYanKb9sVURDwrM38CEBHP5Df/WiyT91MZmvzHzLytuI67mtympdoREb3AMHBDRDxE5R9AZfIl4JvAxcB5M+qPZOau5jRpn0xk5hMR8XhEHAo8ADyz2Y1aqsx8a7PbUCc7i8d+VP6xUFZt83slIl4H/CfgWcDngRMz84GIOIjKtKumBjo3FtZuEfHXVIb0tjS7LfUQEa8EPgvcDQTwDOCtmfntpjZMAETE7wGHAd/KzF83uz1LUcwLWpWZn42IpwCHZOZPm92uWkTEJ4D/TmV+0F8A/w78oGzBKCL+a2b+VUR8jEqP3B4y8z1NaNY+i4iDM/OXC5/ZeooFBHdm5vOb3ZZ6iIgrgE9n5j9Uee2VmXljE5r1mzYY6Jau3X6AFBPW30ql5/azwJWZ+YvmtmrfFN37q6kEuh9n5mNNblLNIuKvgIuACeBbVIbHzs3M0kyajohDi6GKI6q9XsaerWLuWT+wOjN/JyJWAldn5ilNbtqiFYsHjszM7cXxMcChmXnnfO9rRRHxusz8ekSsrfZ6Zl6+3G3aFxHxUuAy4MmZeXREvBD4k8x8V5ObVpOI+CKwPjPva3Zb6qXoyd49wtkqP78MdPug3X6ATCvmNL0VOJvKnKBPlalXq/if7WmZeVdx/CYq8x0ARjLzZ01r3BJExA8y80UR8R+AQSrD+d+eNT+wpUXE32XmayPip1T+8TNzFWJmZumG+CLiB1QmSP9TZq4pandmZqm2lYmI24uFNm0pIg4EXpeZVze7LbWIiFuozG+8bsbfr38uW29XVLbweglwK3vOBSzdwpuIWAd8gMo/rqfDU8v8/HIO3T7IzK8XX0sZ3KqJiC7gOcXj51S2yXhvRPxJZp7V1MYt3l8D/5vfzDP7Sypzng4CXga8o0ntWqrprVZeTaXXdFfZdmXIzNcWX49tdlvq6NeZmRGRUBkaa3aDlujmiPj/27vzMMvK+trj39WNCMEGEVBQZIygMsggKrRxAJEYDIoD0IQ4QJCoN6DEqNEkijfGx0S4IjFelOFiNBAGZQyzTILSCiINRBwYBG3iFGMLAtKs+8e7T/ep6lNVXYP17n1qfZ6nnqq9TxdZprvq/PY7/N7dbHex1dJAze+xV1IeSvcBrgM6VdAB2L5v1M96F9cAH1M7wAz6K2A72z+rHWSQFHTTIOkCBky19nTtCaTpbbYfcCXwD7YXNy99XNKd9ZJN2m7AEX3Xv+5Nf0v6ap1I03KBpO9QngrfIWkj4OHKmaZE0kLK+qwHJR1CaSXzyY5Ox5zZ7N57sqTDKYulP1c501S8HDhC0r2UERRRRh06NdIIoNKk+mBKi4zFwEJgS9sPVQ02NfdJ2gNw077kSMrC+06xfY2kzSlrTa9oNhDMr51rin4AtPbfUqZcp6FZ1A2lP9jGQG9N0yLgHtsfqBJsiiQdCpwx6JefpPW6sp5O0hLbO/Rdb2/7tubrzk1ZQDldAfiV7eXNSNAC2w/UzjVZTZuP5wE7UnaJnQy8zvZLx/3GlpK0N2UkSJTp/MsrR5q05s12Fbbvne0s0yHpfuCHlF6T5zbNbO/u6qhws8nmeEq7DwGXAUfZ/nnVYJPUPOy8DXiK7a0lPQv4v7b3qhxt0iTtTFlffiOwYj12W9bLp6CbAZKutf2Sie51QVM4PAtYq3dv0I6eNmt6ze0zuuCR9Azg4q6NPDRPtEcDm9l+W/MLcVvbF1aONmmSbra9i6S/A35k++TevdrZ5jpJT2Xkz32nRk0lHU9ZY7qE0lrmPGBJW9Y3zVXNWtMXADf2rQUc8dDdFZIWA1+l/Bt7vHe/LcuuMuU6MzaStJXtuwAkbQlsVDnTpDW7XI+iNEW+hXLaxdcY2aCzC/6JMk35l5Su8VCm9j7RvNY1pwI3Udb/AdxPWQ/UuYIOWCbpr4FDgJc0a506dRybpGWMvdTiEcq0zAdrtzBYXZL2A44Fnk7pQbc5ZWpvu5q5Jsv2UZLeRZlCXkT5WV9X0gHAf9j+ddWAk9QsrTgc2IKROyoPrZVpih6x/WhvLaCkNRhnqVLLPWb76NohxpKCbma8G7ha0l3N9RaMXMPVFb1jmb5u++WSnk0HF7Ta/oKkn1FaffTelG4D/s72xfWSTdnWtg+UtAjA9m/UtV0RKx1IWeN0mO0HJG1Gx4ps22M2eW0K1O0p5zt3ZWr/f1Me3q6wvbOkXkHUOS5TTl+hnArzBEpD20WUo/82rJltCs6jbOa4gm5uhui5RtIHgLWbJQrvAC6onGmqrmp2ul7AyCnXtC0ZJk2/s2c3l13td/YN27s1Q+QvtP1Ir2VG7WxzmaQbgL2A65vpyq0pu11fUDnatKk05l1k+521s0yFxmgs3OwKP7F2vtUh6Zu2n98sVdjZ5dSIxV3+99VsItimubwTWMP2bypGmrRh+d2r0lz4MPrWmlKa83au+GjaLo2WtiVDaFdWDo0/TxK2P1830qQNw7FM/Q2fPzXo9bYsYJ2ED1EaCj+zadK5EHhL1UTTIGknyijdAcDdwDl1E02N+hoLU6bF16RsjFrYlWKu8UtJTwKuBb4o6SfAY5UzTZmklwGnUc5CFfBM4M2U/31dcqGkP7L9H7WDTIftxym7v7u4A3yEtm+wyQjdDJD0r5Sz3W5h5dC4O1g4rKAOH8ukIWz4LGkDyvB+TOwAABviSURBVLSYKFPireyDNBZJ21COllpEOTj934H32B64w7ILhqix8DqUljjzgD+h/Nx/oS3TSJMl6SbgYNt3NtfbUEa0O9E8uW+NpoB1KFN7v2VlO5l1K8ZbbZLOtH2ApCUMPkmpUz8nAM00/tuB3obHq4ETbf+2Wqg+KehmgKT/BJ7bxSHkfpI+QlmzcYM7enbgsGp26G7OyMXRnRlxkPQ45d/WYba/39y7qy1TFVPRm5bs27m7DvC1rr1RSfq47fdNdK8rBhXVXSy0u07SJraXDktbHABJJ1E2cfUGBf4UWG77z+qlWilTrjPjNkofuqW1g0zTPZQRlE81T4nXAdfaPq9qqknS8DV8/jhlM8HtrNwqb7o1hfR6ygjdVZIuAc5g5PFfXTQsjYX3BkYXb68acK8rvinpZEqfQyg7qm+qmGdKJF05ulfboHtt1RRz84GTbb+idp4ZsptHHrn4lWbtaStkhG4GSLoK2InSmbx/50unCoceSRtT1je9B1h/vF19baTha/h8J7BjFzfajNaMYr2W8nexJ+VJ98u2L6sabIq63FhY0tspOw63Br7f99ICygacQ6oEm6Zmg9o7KWtNRXnw+ZeuLB1ROXt2Hcpu3Zex8sFnXUofzedUijYlks4H/tQdaUw/Hkk3A2+0/YPmeivg7Lb00UxBNwP6CogRbF8z21mmoxlOfi7wX5TRua9S1gd1coH0sDR8lnQx5ZdIp/poTUTSU4A3Agfa7lqvw16/yaW2H26u1waeZvueqsFWk6T1gPWBjwHv73tpWRfXz0l6DbCp7U8314sp/UANvNf22TXzrS5JRwHvovQF7N+U9ivgc7b/uUqwKZJ0JmX97+WUo+WATm5OQ9JelA1Qd1EK7c2Bt9q+qmqwRgq6WEHSlym/RO4ArqFMt941/ne1V7O2cd9RDZ//o4NPuOdQjsu6khYeNzNXSfomsEdv5KdplXG97d3qJpucpg3O/U2bopdRjmX7vO1f1k02OZKuBw6yfV9zfQtlFPhJwKldmarskfQXtk+onWO6hmlzGqwYAd6WUtC1qkVZ1tBNg8buGN+p3Ug9tvcHkPQcYB/Keqf5tjetm2zKhqXh8/nNR7TLGv3TeC7d8NesGWiKzgGeL+n3KWfrnk85OuuPqqaavDV7xVzjq81I4y+aqf6uOVHSkbR0R+XqaNbQ7d3V6fsxtLZFWQq6aeja2rKJSHo18AeUXyDrU9ZwXFc11DTYvkTl3NNON3zu6pPsHPBTSfvZPh9WTPl1qp1M43Hbj0l6HfBJ2ydI+taE39U+6/df2P5ffZedO4qRcrrFE5rPUHZUfgZoxY7K1WF7uaSNJK3ZlTWM4xmrRRmQgi5a51WUBcTH2+5cQ+ExtPZpaiLj9HHqjQB3qg1D87R+6RDtePtzSiPef6b8ndwHvKlupCn5rcqxcm8C/ri516nzdRs3Sjrc9oidxpKOoGxY65pW76ichHuA65vNEf1r6I6rlmjqnk+LW5SloAtgxZvttu7oEUyDtP1pajUc1Xx+ddUUM6R5Wn9I0nrDsOOt2en2ouaUBdleVjvTFL2VUpx+1OXYsi1ZuTO8S94NnCvpYODm5t6uwBMpO6u7ZrmkrUftqOzima4/bj7mUXZQd1mrW5RlU0SsMEzby2GoGj6vA/zG5YzNbShTyBd3aS1NzzDseJN0iO0vSDp60OsdHXkYGpL2BLZrLm+3/ZWaeaaq7Tsq56K2tyjLCN00Ddk00sPAEkmdfbMdpdVPU5NwLfAHktan7HT9JqXR8J9UTTU1FzUfXdZbYN/p0YZhPJoJoCngOlnE9bN9ZbMGuJU7KleXpI2A91KK7LV697vYqgj4cO0A40lBN01DNo00DG+2/TYE7mj6UbXuaWoSZPshSYcBJ9j+x44uWh+KDR62T2w+H1M7yzQtk7SQsm6u06PYQ6yza4D7fJFydvOrKVP7bwZ+WjXRFNm+RtLTgF5rosW2f1IzU78UdDNjKEa2huHNdpQP1w4wQyRpd8qI3GHNvU797A7jaFCzpul4yhSyga8B7+5Q78ZbgU8Am1DecE+3fUvdSNEzBGuAezawfbKko5pm+9dI6lTT/R5JBwD/RGkhI+AESX/VlqbVnXpTaLFOj2wN45stdO+kjnG8C/hryhFZtzeFRNfW0QzVBo/GvwGfBvZvrg8CTgdeWC3RJNg+Hjhe5fD0g4BTm2OnTgfOsP3dqgGj1TsqJ6G31neppH0pGyS62tv0g5Tdxz+BFdPJVwCtKOiyKSKQtInLQcqbD3rd9r2znWk6hq3h8zBrpvwO7uLuakk32n7hqHtft/2iWpmmS9LOwCmUs4Pn184zl0k6CzjSdqfXADf9Ta8DngmcQDmT9phe/8YukbTE9g591/OAb/ffqykjdNMwLCNbvV8Yowu33pst5aDrzhjChs9XMfjfVxcXFSNpJ8q/qwOAu4Ev1U00ZVdJej9wBuXv50DgouaMWrpyHqqkJwB/SBml24ty7F/X1wcOg0FrgG37NRUzTZrtC5sv/wd4ec0sM+ASSZdSRrGh/MxfXDHPCBmhm4ZhG9mCwW+2w3CeYJdJ2rXvci3g9cBjtt9bKdKkNe1WDgIWAT+nrNl6j+2BPztdIOnu5sveL1H1vWzbW81ypEmRtDfl72NfShuGM4BzbT847jfGrJD00v5L4MXAItvbjfEtrSTpNOCo3tnAzW79Y20fWjfZ1DQnqryY8ndyre0vV460Qgq634GuTSMN45vtsJN0je2XTvwn20HS45Rpl8Nsf7+5d1fbi55BJO0G3Gf7geb6zZQi+x7gwx0ambuKsg7wnK5knmuG4QFb0rds7zzRvS5omm4vtf1wc7028DTb91QN1siU6wzp+DTSdyhvtn/c92b77rqRoqc3hdeYR2llsHGlOFP1espDw1WSLqGMBmn8b2mtE4FXAEh6CfAx4C8oDUc/C7yhXrTVZ7vr019DaYwHbHX472uepPVt/zes+H3W1drjLGCPvuvlzb3dBv/x2dXV/6e2whD94A3Tm+0wuokyrSfgMcoDw2HjfkfLNNMSX25OvXgt5Zimp0n6DGX37mVVA07O/L4RrQOBz9o+BzhHUtp+xHQN2wP2scANks6m/B47APho3UhTtobtR3sXth+VtGbNQP0y5ToNwzSNBCuOmHotpUDdEziN7r3ZRkc0T+pvBA7s0gYPSbcBO9l+TNJ3gLfZvrb3mu3t6yaMLpO0P+UBew+g94B9ku0tqwabBknPpbynCLjS9h2VI01J02v2hN4OXUmvoexE3qtusiIF3TQM4w9eT1ffbIdNs+HmQds/k/QiymLc79s+t3K0OUvSB4E/An4GbAbsYtuSfh84zfbCqgFjKOQBu30kbU05+eIZlNHG+4E39QZ0aktBNwPygxe/C5L+FngL5RfHGZR1W1dTGtd+2/a7qoWb45riehPgst6u0GYJxpNs31w1XAydPGC3i6QnUeqnZbWz9EtBN8PygxczRdIdlIX2vwf8ENi4OdN1DeCWTO1FRMye5hzXfwCebvtVzVTy7rZPrhwNKDvmYgbZ/oXtE1PMxQx42PajTf+mH9h+CMD2Y8Cj439rREQ7SNpcUm9n+NqSutr8/f8BlwJPb66/SzmasRVS0MUKkj6+Ovdi1jxZ0uskvR5Yt/m6d71e7XARERORdDjlrNMTm1ubAl1dA7yh7TOBx2HFw/XyupFWSkEX/fYecO9Vs54ieq4B/phyoP21zdf91xERbfdOYCHwKwDb3wOeWjXR1D0oaQOa02GatbT/UzfSSulDF0h6O/AOYCtJt/a9tAC4vk6qsP3W2hkiIqbpkaZfGwDNGuCuLt4/Gjgf2FrS9cBGtKiReDZFBJLWA9andLx/f99Ly3IkUERETJWkfwR+CbyJcqLKO4A7bH+warApagrSbSk99e60/dvKkVZIQRcjSHox8Czbp0raEFhg++6Jvi8iImI0SfMoJ9u8klIEXUrp15riY4aloIsVJH0IeD6wre1tJD0dOCuNUiMiItota+ii3/7AzsDNALZ/3OHt5UND0huBS2wvk/Q3wC7A36eBbUS0laQljLNWzvaOsxhnTkhBF/0ebY4w6u3gWad2oADgb22f1UyH7wN8AvgM5cSIiIg2enXtAL8LknYEtqCvfrL9pWqB+qSgi35nSjqR0v/scMq6h5MqZ4qVfY72BT5j+zxJH66YJyJiXLbv7X0taWPgBZQRu2/YfqBasGmQdAqwI3A7TS86yv+mVhR0WUMXI0jam7J4FeBS21fUzBMg6ULgR5SzXHcFfgMstv28qsEiIiYg6c+AvwO+QtkU8VLgI7ZPqRpsCiTdYfu5tXOMJQVdIGkZK9c6aNTLDwM/AD5o+8pZDRYASPo94A+BJba/J2kTYAfbl1WOFhExLkl3AnvY/nlzvQFwg+1t6yabPEknA8favqN2lkEy5RrYHnPjg6T5wPbAF5vPMfs+ApzadFjH9lJgad1IERGr5X5gWd/1MuC+Slmm6zTga5IeAB6hDIC4LRs8UtDFuGwvB74t6YTaWeaw7wCfbRpangqcbrs1x81ERIzjR8CNks6jzAS9Blgs6WgA28fVDDdJpwB/Cixh5Rq61siUa0RHSNoWeCuwiHIk2+dsX1U3VUTE2Jr+pmOyfcxsZZkuSV+xvWftHGNJQRfRAc3U96spBd0zgTOBFwMP2j6oZraIiLlA0r8ATwYuoEy5Au1pW5KCLqLlJB0H7AdcCZxse3Hfa3d2cXFxRAw3SZ+0/S5JFzCgwbDt/SrEmhZJpw64bduHznqYAVLQRbScpEOBM2w/NOC19bKeLiLaRtKutm+S9NJBr9u+ZrYzDbsUdBEdIGl94FnAWr17tq+tlygiYmKSjrJ9/ET3uqAZoRs02pgRuoiYWNOY8yhgU+AW4EXA19q8ODciAkDSzbZ3GXXvW7Z3rpVpqiS9vu9yLcr55z+2fWSlSCOkoItoueaQ692Ar9veSdKzgWNsH1g5WkTEQJIWAQdTNm9d1/fSAmC57VdUCTaDJM0DrmjLw3X60EW038O2H5aEpCfa/k7TwiQioq1uoDRA3xA4tu/+MuDWKolm3rOAzWqH6ElBF9F+90t6MnAucLmk/wZ+XDlTRMSYbN8L3AvsXjvLTOk7JlPN5weA91UN1SdTrhEd0uwYWw+4xPajtfNERAwy6ozwES9RWn2sO8uRhl4KuoiWk/QRyhqUG2w/WDtPRMRcIunZzVKXXQa9bvvm2c40SAq6iJZr+tC9mDJ1sYxS3F1r+7yqwSIiJiBp4Boz2z+c7SxTJemztt8madBRi27LpogUdBEdIWlj4ADgPcD6thdUjhQRMa5ml37PWsCWwJ22t6sUaWhlU0REy0k6CXgu8F+U0bk3AK0Y4o+IGI/tHfqvm2nLIyrFmZbmTO19gS3oq59sH1crU78UdBHttwEwH/gl8AvgZ7YfqxspImLybN8sabfaOaboAuBhYAnweOUsq0hBF9FytvcHkPQcYB/gKknzbW9aN1lExPgkHd13OQ/YBfhppTjTtantHWuHGEsKuoiWk/Rq4A+AlwDrA19hZOf1iIi26l/r+xhwEXBOpSzTdbGkV9q+rHaQQbIpIqLlJH0auBa4znYaCkdEVCBpf+ALlJHG39Kynnop6CJarFmEe+kwnHsYEXOHpPPHe932frOVZaZIugt4LbDELSyeMuUa0WK2l0t6SNJ6tv+ndp6IiNW0O3AfcDpwI2U0q+u+B9zWxmIOUtBFdMHDwBJJlwMrToqwfWS9SBER49oY2BtYBBxMWTt3uu3bq6aanqXA1ZIuBh7p3UzbkohYXRc1HxERnWB7OXAJcImkJ1IKu6slfcT2CXXTTdndzceazUerZA1dREREzLimkNuXUsxtAZwPnGL7RzVzTZekddp4rnYKuoiWknSm7QOao3NW+UFtcz+kiJjbJJ0GbA9cDJxh+7bKkaZN0u7AycCTbG8m6XnAEbbfUTkakIIuorUkbWJ7qaTNB71u+97ZzhQRsTokPc7KNb/9hUarWn1MhqQbKUcvnm975+bebba3r5usyBq6iJayvbT5PKJwk7SQssj4nTVyRURMxPa82hl+F2zfJ43YsLu8VpbRUtBFdICknShF3AGURblfqpsoImLOuU/SHoAlrQkcCfxn5UwrpKCLaClJ2wAHURYU/xz4d8oyiZdXDRYRMTf9OXA88AzgfuAyWjRTkjV0ES3VrEG5DjjM9vebe3fZ3qpusoiIuaU5tedI2/+ndpaxDOUcd8SQeD3wAHCVpM9J2ovh6LYeEdEpTV+919TOMZ6M0EW0nKR1KOcHLgL2BE4Dvmz7sqrBIiLmEEkfBdajLH/pP7Xn5mqh+qSgi+gQSU8B3ggcaHvP2nkiIuYKSVcNuO22/C5OQRcRERHRcVlDFxERETEGSS+U9G1Jv5b0NUnPqZ1pkBR0EREREWP7NPAeYAPgOOCTdeMMloIuouUkfXx17kVExO/EPNuX237E9lnARrUDDZLGwhHttzfwvlH3XjXgXkREzLwnS3rdWNe2W3FyTzZFRLSUpLcD7wC2An7Q99IC4Hrbh1QJFhExh0g6dZyXbfvQWQszjhR0ES0laT1gfeBjwPv7Xlpm+xd1UkVERBuloIvoAEkvBp5l+1RJGwILbN9dO1dERLRDCrqIlpP0IeD5wLa2t5H0dOAs2wsrR4uIiJbILteI9tsf2I/mqBnbP6aso4uIiABS0EV0waMuQ+mGFWe7RkTELJL0RkkLmq//RtKXJO1SO1dPCrqI9jtT0omUrfKHA1cCJ1XOFBEx1/yt7WXNmuZ9gNOAz1TOtELW0EV0gKS9gVc2l5favqJmnoiIuUbSt2zvLOljwBLb/9a7VzsbpKCLaC1Jy2imWQGNevlhSm+6D9q+claDRUTMQZIuBH4EvALYFfgNsNj286oGa6Sgi+ggSfOB7YEv2t6+dp6IiGEn6feAP6SMzn1P0ibADrYvqxwNSEEX0WmSjrB9Yu0cERHDTNI84NY2P0BnU0REh6WYi4j43bP9OPBtSZvVzjKWNWoHiIiIiOiATYDbJS2m6QsKYHu/epFWSkEXERERMbFjagcYT9bQRURERKwGSZtTztW+otkkMd/2stq5IGvoIiIiIibUNHY/G+itXX4GcG69RCOloIuIiIiY2DuBhcCvAGx/D3hq1UR9UtBFRERETOwR24/2LiStwcrm79WloIuIiIiY2DWSPgCs3RzHeBZwQeVMK2RTRERERMQEmubCh1HO1RZwKXCSW1JIpaCLiIiI6Lj0oYuIiIgYg6QzbR8gaQkD1szZ3rFCrFVkhC4iIiJiDJI2sb206UG3Ctv3znamQVLQRURERIxD0nzgUtuvqJ1lLNnlGhERETEO28uBhyStVzvLWLKGLiIiImJiDwNLJF0OPNi7afvIepFWSkEXERERMbGLmo9WSkEXERERMY5mDd3etg+pnWUsWUMXERERMY5mDd1GktasnWUsGaGLiIiImNg9wPWSzmfkGrrjqiXqk4IuIiIiYmI/bj7mAQsqZ1lF+tBFREREdFxG6CIiIiImIGkj4L3AdsBavfu296wWqk82RURERERM7IvAd4AtgWMoa+q+UTNQv0y5RkRERExA0k22d5V0q+0dm3vX2H5p7WyQKdeIiIiI1fHb5vNSSftSNkhsWjHPCCnoIiIiIib2981Zrn8JnACsC7y7bqSVMuUaERER0XHZFBERERExAUmnSXpy3/X6kk6pmalfCrqIiIiIie1o+5e9C9v/DexcMc8IKegiIiIiJjZP0vq9C0lPoUV7EVoTJCIiIqLFjgVukHQ2YOAA4KN1I62UTRERERERq0HSc4E9AQFX2r6jcqQVUtBFREREdFzW0EVERER0XAq6iIiIiI5LQRcRERGxGiRtLukVzddrS1pQO1NPCrqIiIiICUg6HDgbOLG5tSlwbr1EI6Wgi4iIiJjYO4GFwK8AbH8PeGrVRH1S0EVERERM7BHbj/YuJK1B6UfXCinoIiIiIiZ2jaQPAGtL2hs4C7igcqYV0ocuIiIiYgKS5gGHAa+kNBa+FDjJLSmkUtBFREREdFzOco2IiIgYg6QljLNWzvaOsxhnTBmhi4iIiBiDpM3He932vbOVZTwp6CIiIiJWg6SNgRdQRuy+YfuBypFWyC7XiIiIiAlI+jNgMfA64A3A1yUdWjfVShmhi4iIiJiApDuBPWz/vLneALjB9rZ1kxUZoYuIiIiY2P3Asr7rZcB9lbKsIiN0EREREROQ9HlgB+A8yhq611CmYL8LYPu4eunStiQiIiJidfyg+eg5r/m8oEKWVWSELiIiIqLjMkIXERERMQZJn7T9LkkXMKDBsO39KsRaRQq6iIiIiLH9a/P5E1VTTCAFXURERMQYbN/UfLmT7eP7X5N0FHDN7KdaVdqWREREREzszQPuvWW2Q4wlI3QRERERY5C0CDgY2FLS+X0vLQB+XifVqlLQRURERIztBmApsCFwbN/9ZcCtVRINkLYlERERER2XEbqIiIiIMUhaxoB2JYAA2153liMNlBG6iIiIiI7LCF1ERETEBCRtNui+7R/OdpZBMkIXERERMQFJS/ou1wK2BO60vV2lSCNkhC4iIiJiArZ36L+WtAtwRKU4q0hj4YiIiIhJsn0zsFvtHD0ZoYuIiIiYgKSj+y7nAbsAP60UZxUp6CIiIiImtqDv68eAi4BzKmVZRTZFRERERHRcRugiIiIixjDq/NZV2N5vtrKMJwVdRERExNh2B+4DTgdupJwQ0TqZco2IiIgYg6T5wN7AImBHytq5023fXjXYKGlbEhERETEG28ttX2L7zcCLgO8DV0v6i8rRRsiUa0RERMQ4JD0R2JcySrcF8CngSzUzjZYp14iIiIgxSDoN2B64GDjD9m2VIw2Ugi4iIiJiDJIeBx5sLvuLJgG2ve7sp1pVCrqIiIiIjsumiIiIiIiOS0EXERER0XEp6CIiIiI6LgVdREQfSftLsqRnN9dPl3T2GH92C0m3NV8/X9KnZjNrRERPNkVERPSRdCawCXCl7Q+P8+fWADYFLrS9/SzFi4gYKCN0ERENSU8CFgKHAQc19/pH4d4i6SxJFwCXjfrel0m6sPn6w5JOkXS1pLskHdn35w6RtFjSLZJObI4VioiYlhR0ERErvRa4xPZ3gV9I2mXAn9kdeLPtPSf4bz0b2Ad4AfAhSU+Q9BzgQGCh7Z2A5cCfzFz8iJirUtBFRKy0CDij+fqM5nq0y23/YjX+WxfZfsT2z4CfAE8D9gJ2Bb4h6Zbmeqvpx46IuS5nuUZEAJI2APYEtpdkYD6lK/y/jPqjD47+3jE80vf1csrvWwGn2f7racaNiBghI3QREcUbgM/b3tz2FrafCdxN2fgwU64E3iDpqQCSniJp8xn870fEHJWCLiKiWAR8edS9c4APzNT/Adt3AH8DXCbpVuByyo7aiIhpSduSiIiIiI7LCF1EREREx6Wgi4iIiOi4FHQRERERHZeCLiIiIqLjUtBFREREdFwKuoiIiIiOS0EXERER0XH/H+ZXVGn+nlywAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "plt.title('Price VS Airlines')\n",
    "plt.scatter(df['Airline'], df['Price'])\n",
    "plt.xticks(rotation = 90)\n",
    "plt.xlabel('Airline')\n",
    "plt.ylabel('Price of ticket')\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ticket rate for Jet Airways Business airline is high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]),\n",
       " <a list of 11 Text xticklabel objects>)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "plt.title('Count of flights with different Airlines')\n",
    "sns.countplot(x = 'Airline', data = df)\n",
    "plt.xlabel('Airline')\n",
    "plt.ylabel('Count of flights')\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are more number of flights of Jet Airways. \n",
    "* Jet Airways Business, Vistara Premium economy, Trujet have actually almost negligible flights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting sparse classes of Airline into one category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Airline'].replace(['Trujet', 'Vistara Premium economy'], 'Another', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting the Total_Stops into numbers and dropping the rows with NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9039</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>09:45</td>\n",
       "      <td>09:25 07 May</td>\n",
       "      <td>23h 40m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No info</td>\n",
       "      <td>7480</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Airline Date_of_Journey Source Destination Route Dep_Time  \\\n",
       "9039  Air India       6/05/2019  Delhi      Cochin   NaN    09:45   \n",
       "\n",
       "      Arrival_Time Duration Total_Stops Additional_Info  Price  year  month  \\\n",
       "9039  09:25 07 May  23h 40m         NaN         No info   7480  2019      6   \n",
       "\n",
       "      Day  \n",
       "9039    5  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Total_Stops'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_into_stops(X):\n",
    "    if X == '4 stops':\n",
    "        return 4\n",
    "    elif X == '3 stops':\n",
    "        return 3\n",
    "    elif X == '2 stops':\n",
    "        return 2\n",
    "    elif X == '1 stop':\n",
    "        return 1\n",
    "    elif X == 'non stop':\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total_Stops'] = df['Total_Stops'].map(convert_into_stops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(0, inplace  = True)\n",
    "df['Total_Stops'] = df['Total_Stops'].apply(lambda x : int(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting the flight Dep_Time into proper time i.e. mid_night, morning, afternoon and evening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flight_dep_time(X):\n",
    "    '''\n",
    "    This function takes the flight Departure time \n",
    "    and convert into appropriate format.\n",
    "    '''\n",
    "    if int(X[:2]) >= 0 and int(X[:2]) < 6:\n",
    "        return 'mid_night'\n",
    "    elif int(X[:2]) >= 6 and int(X[:2]) < 12:\n",
    "        return 'morning'\n",
    "    elif int(X[:2]) >= 12 and int(X[:2]) < 18:\n",
    "        return 'afternoon'\n",
    "    elif int(X[:2]) >= 18 and int(X[:2]) < 24:\n",
    "        return 'evening'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['flight_time'] = df['Dep_Time'].apply(flight_dep_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count of flights')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "plt.title('Count of flights according to departure time')\n",
    "sns.countplot(x = 'flight_time', data = df)\n",
    "plt.xlabel('Flight Time')\n",
    "plt.ylabel('Count of flights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are more number of flights in the morning time as compared to other time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting the flight duration into seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_into_seconds(X):\n",
    "    '''\n",
    "    This function takes the total time of flight from\n",
    "    one city to another and converts it into the seconds.\n",
    "    '''\n",
    "    a = [int(s) for s in re.findall(r'-?\\d+\\.?\\d*', X)]\n",
    "    if len(a) == 2:\n",
    "        hr = a[0] * 3600\n",
    "        min = a[1] * 60\n",
    "    else:\n",
    "        hr = a[0] * 3600\n",
    "        min = 0   \n",
    "    total = hr + min\n",
    "    return total\n",
    "\n",
    "df['Duration(sec)'] = df['Duration'].map(convert_into_seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Price of ticket')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "plt.title('Price VS duration of flights')\n",
    "plt.scatter(df['Duration(sec)'], df['Price'])\n",
    "plt.xlabel('Duartion in seconds')\n",
    "plt.ylabel('Price of ticket')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Duration(sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.603897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065495</td>\n",
       "      <td>-0.024136</td>\n",
       "      <td>0.738204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.603897</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010705</td>\n",
       "      <td>-0.165448</td>\n",
       "      <td>0.506604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0.065495</td>\n",
       "      <td>0.010705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.264896</td>\n",
       "      <td>0.055147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>-0.024136</td>\n",
       "      <td>-0.165448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.264896</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration(sec)</th>\n",
       "      <td>0.738204</td>\n",
       "      <td>0.506604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.055147</td>\n",
       "      <td>-0.038422</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Total_Stops     Price  year     month       Day  Duration(sec)\n",
       "Total_Stops       1.000000  0.603897   NaN  0.065495 -0.024136       0.738204\n",
       "Price             0.603897  1.000000   NaN  0.010705 -0.165448       0.506604\n",
       "year                   NaN       NaN   NaN       NaN       NaN            NaN\n",
       "month             0.065495  0.010705   NaN  1.000000 -0.264896       0.055147\n",
       "Day              -0.024136 -0.165448   NaN -0.264896  1.000000      -0.038422\n",
       "Duration(sec)     0.738204  0.506604   NaN  0.055147 -0.038422       1.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to notice that the correlation for 'year' is shown NaN. This is because that feature is not vary at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping the duplicate rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 16)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10462, 16)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there were around 220 duplicate rows. So it is good idea to drop them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handling the Typos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No info', '1 Long layover', 'Business class', 'Red-eye flight',\n",
       "       'Change airports', '2 Long layover', 'No Info', '1 Short layover',\n",
       "       'In-flight meal not included', 'No check-in baggage included'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Additional_Info'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'No info' is same as 'No Info'. So replacing them with single common label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Additional_Info'].replace('No Info', 'No info', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28c40d7ae10>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that there are some points which are seeems like outliers but we cannot remove them because these can be flight ticket price also."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Before we move to the next module, let's save the new dataframe that we have cleaned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Files/cleaned_data.csv', index = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Encoding the dummy varibales (for categorical features)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns = ['Airline', 'Source', 'Destination', 'Additional_Info', 'flight_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Duration(sec)</th>\n",
       "      <th>Airline_Air Asia</th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_Another</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Source_Banglore</th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "      <th>Destination_Banglore</th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
       "      <th>Additional_Info_1 Long layover</th>\n",
       "      <th>Additional_Info_1 Short layover</th>\n",
       "      <th>Additional_Info_2 Long layover</th>\n",
       "      <th>Additional_Info_Business class</th>\n",
       "      <th>Additional_Info_Change airports</th>\n",
       "      <th>Additional_Info_In-flight meal not included</th>\n",
       "      <th>Additional_Info_No check-in baggage included</th>\n",
       "      <th>Additional_Info_No info</th>\n",
       "      <th>Additional_Info_Red-eye flight</th>\n",
       "      <th>flight_time_afternoon</th>\n",
       "      <th>flight_time_evening</th>\n",
       "      <th>flight_time_mid_night</th>\n",
       "      <th>flight_time_morning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>BLR → BOM → AMD → DEL</td>\n",
       "      <td>08:50</td>\n",
       "      <td>23:55 02 Mar</td>\n",
       "      <td>39h 5m</td>\n",
       "      <td>2</td>\n",
       "      <td>17135</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>140700</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>BLR → MAA → DEL</td>\n",
       "      <td>11:50</td>\n",
       "      <td>08:55 02 Mar</td>\n",
       "      <td>21h 5m</td>\n",
       "      <td>1</td>\n",
       "      <td>14594</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>75900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>14:05</td>\n",
       "      <td>07:40 02 Mar</td>\n",
       "      <td>17h 35m</td>\n",
       "      <td>1</td>\n",
       "      <td>22270</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>63300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>07:00</td>\n",
       "      <td>05:05 02 Mar</td>\n",
       "      <td>22h 5m</td>\n",
       "      <td>1</td>\n",
       "      <td>26890</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>79500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>18:25</td>\n",
       "      <td>21:20</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>0</td>\n",
       "      <td>12649</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date_of_Journey                  Route Dep_Time  Arrival_Time Duration  \\\n",
       "0      01/03/2019  BLR → BOM → AMD → DEL    08:50  23:55 02 Mar   39h 5m   \n",
       "1      01/03/2019        BLR → MAA → DEL    11:50  08:55 02 Mar   21h 5m   \n",
       "2      01/03/2019        BLR → BOM → DEL    14:05  07:40 02 Mar  17h 35m   \n",
       "3      01/03/2019        BLR → BOM → DEL    07:00  05:05 02 Mar   22h 5m   \n",
       "4      01/03/2019              BLR → DEL    18:25         21:20   2h 55m   \n",
       "\n",
       "   Total_Stops  Price  year  month  Day  Duration(sec)  Airline_Air Asia  \\\n",
       "0            2  17135  2019      1    3         140700                 0   \n",
       "1            1  14594  2019      1    3          75900                 0   \n",
       "2            1  22270  2019      1    3          63300                 0   \n",
       "3            1  26890  2019      1    3          79500                 0   \n",
       "4            0  12649  2019      1    3          10500                 0   \n",
       "\n",
       "   Airline_Air India  Airline_Another  Airline_GoAir  Airline_IndiGo  \\\n",
       "0                  1                0              0               0   \n",
       "1                  1                0              0               0   \n",
       "2                  0                0              0               0   \n",
       "3                  0                0              0               0   \n",
       "4                  0                0              0               1   \n",
       "\n",
       "   Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0                    0                             0   \n",
       "1                    0                             0   \n",
       "2                    1                             0   \n",
       "3                    1                             0   \n",
       "4                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          0                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Vistara  Source_Banglore  Source_Chennai  \\\n",
       "0                 0                0                1               0   \n",
       "1                 0                0                1               0   \n",
       "2                 0                0                1               0   \n",
       "3                 0                0                1               0   \n",
       "4                 0                0                1               0   \n",
       "\n",
       "   Source_Delhi  Source_Kolkata  Source_Mumbai  Destination_Banglore  \\\n",
       "0             0               0              0                     0   \n",
       "1             0               0              0                     0   \n",
       "2             0               0              0                     0   \n",
       "3             0               0              0                     0   \n",
       "4             0               0              0                     0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   0                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  Additional_Info_1 Long layover  \\\n",
       "0                    0                      1                               0   \n",
       "1                    0                      1                               1   \n",
       "2                    0                      1                               1   \n",
       "3                    0                      1                               0   \n",
       "4                    0                      1                               0   \n",
       "\n",
       "   Additional_Info_1 Short layover  Additional_Info_2 Long layover  \\\n",
       "0                                0                               0   \n",
       "1                                0                               0   \n",
       "2                                0                               0   \n",
       "3                                0                               0   \n",
       "4                                0                               0   \n",
       "\n",
       "   Additional_Info_Business class  Additional_Info_Change airports  \\\n",
       "0                               0                                0   \n",
       "1                               0                                0   \n",
       "2                               0                                0   \n",
       "3                               0                                0   \n",
       "4                               0                                0   \n",
       "\n",
       "   Additional_Info_In-flight meal not included  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "\n",
       "   Additional_Info_No check-in baggage included  Additional_Info_No info  \\\n",
       "0                                             0                        1   \n",
       "1                                             0                        0   \n",
       "2                                             0                        0   \n",
       "3                                             0                        1   \n",
       "4                                             0                        1   \n",
       "\n",
       "   Additional_Info_Red-eye flight  flight_time_afternoon  flight_time_evening  \\\n",
       "0                               0                      0                    0   \n",
       "1                               0                      0                    0   \n",
       "2                               0                      1                    0   \n",
       "3                               0                      0                    0   \n",
       "4                               0                      0                    1   \n",
       "\n",
       "   flight_time_mid_night  flight_time_morning  \n",
       "0                      0                    1  \n",
       "1                      0                    1  \n",
       "2                      0                    0  \n",
       "3                      0                    1  \n",
       "4                      0                    0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 50)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing the unused features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Date_of_Journey', 'Route', 'Dep_Time', 'Arrival_Time', 'Duration'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving the final dataframe to directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Files/final_data.csv', index = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preparing the data for ML models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Price']\n",
    "X = df.drop('Price', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = StandardScaler()\n",
    "X = s.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3668201641342015e-18\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9874208829065749"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X.mean())\n",
    "X.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of training input is (8369, 40)\n",
      "The size of training output is (8369,)\n",
      "**************************************************\n",
      "The size of testing input is (2093, 40)\n",
      "The size of testing output is (2093,)\n"
     ]
    }
   ],
   "source": [
    "print(\"The size of training input is\", X_train.shape)\n",
    "print(\"The size of training output is\", y_train.shape)\n",
    "print(50 *'*')\n",
    "print(\"The size of testing input is\", X_test.shape)\n",
    "print(\"The size of testing output is\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Machine Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Baseline Model\n",
    "* In baseline model we predict the flight ticket price as average of all the prices.\n",
    "* We will use this baseline model to perform hypothesis testing for other complex models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = np.ones(X_train.shape[0]) * y_train.mean()\n",
    "y_test_pred = np.ones(X_test.shape[0]) * y_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Baseline Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  4611.237763347602\n",
      "R-squared:  0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Baseline Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Baseline Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  4678.52596469354\n",
      "R-squared:  -0.0015766606282525686\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Baseline Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. KNN Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "          weights='uniform'),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_range = list(range(1, 30))\n",
    "params = dict(n_neighbors = k_range)\n",
    "knn_regressor = GridSearchCV(KNeighborsRegressor(), params, cv = 10, scoring = 'neg_mean_squared_error')\n",
    "knn_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "          metric_params=None, n_jobs=1, n_neighbors=4, p=2,\n",
      "          weights='uniform')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 4}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(knn_regressor.best_estimator_)\n",
    "knn_regressor.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred =knn_regressor.predict(X_train) ##Predict train result\n",
    "y_test_pred =knn_regressor.predict(X_test) ##Predict test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for KNN Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  1683.771336856514\n",
      "R-squared:  0.8666689826775966\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for KNN Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for KNN Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  2260.8748694909455\n",
      "R-squared:  0.7661059571420581\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for KNN Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_absolute_error', verbose=0)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params ={'alpha' :[0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]}\n",
    "ridge_regressor =GridSearchCV(Ridge(), params ,cv =5,scoring = 'neg_mean_absolute_error', n_jobs =-1)\n",
    "ridge_regressor.fit(X_train ,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge(alpha=100, copy_X=True, fit_intercept=True, max_iter=None,\n",
      "   normalize=False, random_state=None, solver='auto', tol=0.001)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'alpha': 100}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(ridge_regressor.best_estimator_)\n",
    "ridge_regressor.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred =ridge_regressor.predict(X_train) ##Predict train result\n",
    "y_test_pred =ridge_regressor.predict(X_test) ##Predict test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Ridge Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  2616.7656937454317\n",
      "R-squared:  0.6779712521029337\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Ridge Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Ridge Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  2509.7961358064663\n",
      "R-squared:  0.7117674657140494\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Ridge Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Lasso Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=15, error_score='raise',\n",
       "       estimator=Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_absolute_error', verbose=0)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params ={'alpha' :[0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]}\n",
    "lasso_regressor =GridSearchCV(Lasso(), params ,cv =15,scoring = 'neg_mean_absolute_error', n_jobs =-1)\n",
    "lasso_regressor.fit(X_train ,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso(alpha=10, copy_X=True, fit_intercept=True, max_iter=1000,\n",
      "   normalize=False, positive=False, precompute=False, random_state=None,\n",
      "   selection='cyclic', tol=0.0001, warm_start=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'alpha': 10}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(lasso_regressor.best_estimator_)\n",
    "lasso_regressor.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred =lasso_regressor.predict(X_train) ##Predict train result\n",
    "y_test_pred =lasso_regressor.predict(X_test) ##Predict test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Lasso Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  2616.8003535958283\n",
      "R-squared:  0.6779627213107648\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Lasso Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Lasso Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  2507.6045418836943\n",
      "R-squared:  0.7122706244058481\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Lasso Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best'),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'max_depth': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth  =list(range(3,30))\n",
    "param_grid =dict(max_depth =depth)\n",
    "tree =GridSearchCV(DecisionTreeRegressor(),param_grid,cv =10)\n",
    "tree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor(criterion='mse', max_depth=11, max_features=None,\n",
      "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
      "           min_impurity_split=None, min_samples_leaf=1,\n",
      "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "           presort=False, random_state=None, splitter='best')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'max_depth': 11}"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(tree.best_estimator_)\n",
    "tree.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred =tree.predict(X_train) ##Predict train result\n",
    "y_test_pred =tree.predict(X_test) ##Predict test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Decision Tree Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  1280.071358614762\n",
      "R-squared:  0.9229392326459311\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Decision Tree Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Decision Tree Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  1762.8885408368005\n",
      "R-squared:  0.8577946887562623\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Decision Tree Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=20, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': [100, 200, 300, 400, 500], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_absolute_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_params = {'n_estimators': [100, 200, 300, 400, 500], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4]}\n",
    "random_regressor = RandomizedSearchCV(RandomForestRegressor(), tuned_params, n_iter = 20, scoring = 'neg_mean_absolute_error', cv = 5, n_jobs = -1)\n",
    "random_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "           max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "           min_samples_leaf=1, min_samples_split=5,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 500, 'min_samples_split': 5, 'min_samples_leaf': 1}"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(random_regressor.best_estimator_)\n",
    "random_regressor.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = random_regressor.predict(X_train)\n",
    "y_test_pred = random_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Random Forest Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  1007.6890972948593\n",
      "R-squared:  0.9522450837312224\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Random Forest Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Random Forest Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  1574.255094365628\n",
      "R-squared:  0.8865991467121829\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Random Forest Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.XGBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1),\n",
       "          fit_params=None, iid=True, n_iter=20, n_jobs=-1,\n",
       "          param_distributions={'max_depth': [1, 2, 3, 4, 5], 'learning_rate': [0.01, 0.05, 0.1], 'n_estimators': [100, 200, 300, 400, 500], 'reg_lambda': [0.001, 0.1, 1.0, 10.0, 100.0]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_absolute_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_params = {'max_depth': [1, 2, 3, 4, 5], 'learning_rate': [0.01, 0.05, 0.1], 'n_estimators': [100, 200, 300, 400, 500], 'reg_lambda': [0.001, 0.1, 1.0, 10.0, 100.0]}\n",
    "model = RandomizedSearchCV(XGBRegressor(), tuned_params, n_iter=20, scoring = 'neg_mean_absolute_error', cv=5, n_jobs=-1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "       colsample_bytree=1, gamma=0, learning_rate=0.05, max_delta_step=0,\n",
      "       max_depth=5, min_child_weight=1, missing=None, n_estimators=400,\n",
      "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
      "       reg_alpha=0, reg_lambda=0.001, scale_pos_weight=1, seed=None,\n",
      "       silent=True, subsample=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'reg_lambda': 0.001,\n",
       " 'n_estimators': 400,\n",
       " 'max_depth': 5,\n",
       " 'learning_rate': 0.05}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(model.best_estimator_)\n",
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for XGBoost Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  1362.0142754970261\n",
      "R-squared:  0.9140329190845858\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for XGBoost Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for XGBoost Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  1494.7948390855377\n",
      "R-squared:  0.8916234839601243\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for XGBoost Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Stackinig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Akhsay\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "StackingRegressor(meta_regressor=DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best'),\n",
       "         refit=True,\n",
       "         regressors=[Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001), Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False), KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "          weights='uniform')],\n",
       "         store_train_meta_features=False, use_features_in_secondary=False,\n",
       "         verbose=0)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initializing models\n",
    "ridge = Ridge()\n",
    "lasso = Lasso()\n",
    "tree = DecisionTreeRegressor()\n",
    "knn = KNeighborsRegressor()\n",
    "\n",
    "stack = StackingRegressor(regressors = [ridge, lasso, knn], meta_regressor = tree)\n",
    "stack.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
      "   normalize=False, random_state=None, solver='auto', tol=0.001), Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
      "   normalize=False, positive=False, precompute=False, random_state=None,\n",
      "   selection='cyclic', tol=0.0001, warm_start=False), KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "          metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
      "          weights='uniform')]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(stack.regr_)\n",
    "stack.meta_regr_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = stack.predict(X_train)\n",
    "y_test_pred = stack.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Stacking Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  408.2981059739625\n",
      "R-squared:  0.9921599343544731\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Stacking Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Stacking Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  2794.852569180308\n",
      "R-squared:  0.6425758323093218\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Stacking Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving winnig model to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['winning_model_XBBoost_regressor.pkl']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "win_model = XGBRegressor(reg_lambda = 0.001, n_estimators = 400, max_depth = 5, learning_rate = 0.05)\n",
    "win_model.fit(X_train, y_train)\n",
    "joblib.dump(win_model, 'winning_model_XBBoost_regressor.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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