Baseline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Establishing a Baseline for the Problem\n",
    "## Using linear regression and its variants\n",
    "### With various feature combinations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the required datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>102.00</td>\n",
       "      <td>321.00</td>\n",
       "      <td>3.147059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>83.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>3.614458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2002</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>189.20</td>\n",
       "      <td>510.84</td>\n",
       "      <td>2.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2003</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>52.00</td>\n",
       "      <td>90.17</td>\n",
       "      <td>1.734038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2004</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>52.94</td>\n",
       "      <td>72.57</td>\n",
       "      <td>1.370797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    State_Name ind_district  Crop_Year       Season  Crop  \\\n",
       "0  Andaman and Nicobar Islands     NICOBARS       2000  Kharif       Rice   \n",
       "1  Andaman and Nicobar Islands     NICOBARS       2001  Kharif       Rice   \n",
       "2  Andaman and Nicobar Islands     NICOBARS       2002  Kharif       Rice   \n",
       "3  Andaman and Nicobar Islands     NICOBARS       2003  Kharif       Rice   \n",
       "4  Andaman and Nicobar Islands     NICOBARS       2004  Kharif       Rice   \n",
       "\n",
       "     Area  Production     Value  \n",
       "0  102.00      321.00  3.147059  \n",
       "1   83.00      300.00  3.614458  \n",
       "2  189.20      510.84  2.700000  \n",
       "3   52.00       90.17  1.734038  \n",
       "4   52.94       72.57  1.370797  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rice = pd.read_csv(\"/Users/macbook/Documents/BTP/Notebook/Rice.csv\")\n",
    "rice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>State_Name</th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4210</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>AMBALA</td>\n",
       "      <td>1997</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>182000.0</td>\n",
       "      <td>2.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4211</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>AMBALA</td>\n",
       "      <td>1998</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>71365.0</td>\n",
       "      <td>186000.0</td>\n",
       "      <td>2.606320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>AMBALA</td>\n",
       "      <td>1999</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>72185.0</td>\n",
       "      <td>206000.0</td>\n",
       "      <td>2.853778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4213</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>AMBALA</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>71840.0</td>\n",
       "      <td>217000.0</td>\n",
       "      <td>3.020601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4214</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>AMBALA</td>\n",
       "      <td>2001</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>74881.0</td>\n",
       "      <td>233000.0</td>\n",
       "      <td>3.111604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State_Name ind_district  Crop_Year       Season  Crop     Area  \\\n",
       "4210    Haryana       AMBALA       1997  Kharif       Rice  65000.0   \n",
       "4211    Haryana       AMBALA       1998  Kharif       Rice  71365.0   \n",
       "4212    Haryana       AMBALA       1999  Kharif       Rice  72185.0   \n",
       "4213    Haryana       AMBALA       2000  Kharif       Rice  71840.0   \n",
       "4214    Haryana       AMBALA       2001  Kharif       Rice  74881.0   \n",
       "\n",
       "      Production     Value  \n",
       "4210    182000.0  2.800000  \n",
       "4211    186000.0  2.606320  \n",
       "4212    206000.0  2.853778  \n",
       "4213    217000.0  3.020601  \n",
       "4214    233000.0  3.111604  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rice_haryana = rice[rice[\"State_Name\"]==\"Haryana\"]\n",
    "rice_haryana.head()"
   ]
  },
  {
   "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>State</th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Adilabad</td>\n",
       "      <td>1994</td>\n",
       "      <td>1199.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Adilabad</td>\n",
       "      <td>1995</td>\n",
       "      <td>1255.561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Adilabad</td>\n",
       "      <td>1996</td>\n",
       "      <td>1081.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Adilabad</td>\n",
       "      <td>1997</td>\n",
       "      <td>905.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Adilabad</td>\n",
       "      <td>1998</td>\n",
       "      <td>1128.950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State ind_district  Year     Value\n",
       "0  Andhra Pradesh     Adilabad  1994  1199.447\n",
       "1  Andhra Pradesh     Adilabad  1995  1255.561\n",
       "2  Andhra Pradesh     Adilabad  1996  1081.171\n",
       "3  Andhra Pradesh     Adilabad  1997   905.718\n",
       "4  Andhra Pradesh     Adilabad  1998  1128.950"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall = pd.read_csv(\"/Users/macbook/Documents/BTP/Notebook/rainfall.csv\")\n",
    "rainfall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        State ind_district  Year    Value\n",
      "1179  Haryana       Ambala  1994  620.808\n",
      "1180  Haryana       Ambala  1995  832.320\n",
      "1181  Haryana       Ambala  1996  784.208\n",
      "1182  Haryana       Ambala  1997  784.650\n",
      "1183  Haryana       Ambala  1998  649.086\n",
      "              Year       Value\n",
      "count   171.000000  171.000000\n",
      "mean   1998.000000  531.810170\n",
      "std       2.589572  165.230096\n",
      "min    1994.000000  166.299000\n",
      "25%    1996.000000  420.334500\n",
      "50%    1998.000000  531.747000\n",
      "75%    2000.000000  646.928500\n",
      "max    2002.000000  895.146000\n"
     ]
    }
   ],
   "source": [
    "rain_haryana = rainfall[rainfall[\"State\"]==\"Haryana\"]\n",
    "print(rain_haryana.head())\n",
    "print(rain_haryana.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Y</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMBALA</td>\n",
       "      <td>1997</td>\n",
       "      <td>182000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>784.650</td>\n",
       "      <td>784.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMBALA</td>\n",
       "      <td>1998</td>\n",
       "      <td>186000.0</td>\n",
       "      <td>182000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>649.086</td>\n",
       "      <td>784.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMBALA</td>\n",
       "      <td>1999</td>\n",
       "      <td>206000.0</td>\n",
       "      <td>186000.0</td>\n",
       "      <td>182000.0</td>\n",
       "      <td>396.134</td>\n",
       "      <td>649.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMBALA</td>\n",
       "      <td>2000</td>\n",
       "      <td>217000.0</td>\n",
       "      <td>206000.0</td>\n",
       "      <td>186000.0</td>\n",
       "      <td>593.737</td>\n",
       "      <td>396.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMBALA</td>\n",
       "      <td>2001</td>\n",
       "      <td>233000.0</td>\n",
       "      <td>217000.0</td>\n",
       "      <td>206000.0</td>\n",
       "      <td>469.118</td>\n",
       "      <td>593.737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ind_district  Crop_Year         Y        X1        X2       X3       X4\n",
       "0       AMBALA       1997  182000.0       NaN       NaN  784.650  784.208\n",
       "1       AMBALA       1998  186000.0  182000.0       NaN  649.086  784.650\n",
       "2       AMBALA       1999  206000.0  186000.0  182000.0  396.134  649.086\n",
       "3       AMBALA       2000  217000.0  206000.0  186000.0  593.737  396.134\n",
       "4       AMBALA       2001  233000.0  217000.0  206000.0  469.118  593.737"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_hr = pd.read_csv(\"/Users/macbook/Documents/BTP/Notebook/haryana.csv\")\n",
    "X_hr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning and preparing the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Y</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMBALA</td>\n",
       "      <td>1999</td>\n",
       "      <td>206000.0</td>\n",
       "      <td>186000.0</td>\n",
       "      <td>182000.0</td>\n",
       "      <td>396.134</td>\n",
       "      <td>649.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMBALA</td>\n",
       "      <td>2000</td>\n",
       "      <td>217000.0</td>\n",
       "      <td>206000.0</td>\n",
       "      <td>186000.0</td>\n",
       "      <td>593.737</td>\n",
       "      <td>396.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMBALA</td>\n",
       "      <td>2001</td>\n",
       "      <td>233000.0</td>\n",
       "      <td>217000.0</td>\n",
       "      <td>206000.0</td>\n",
       "      <td>469.118</td>\n",
       "      <td>593.737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AMBALA</td>\n",
       "      <td>2002</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>233000.0</td>\n",
       "      <td>217000.0</td>\n",
       "      <td>476.752</td>\n",
       "      <td>469.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AMBALA</td>\n",
       "      <td>2005</td>\n",
       "      <td>254000.0</td>\n",
       "      <td>233000.0</td>\n",
       "      <td>227000.0</td>\n",
       "      <td>1058.400</td>\n",
       "      <td>1202.900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ind_district  Crop_Year         Y        X1        X2        X3        X4\n",
       "2       AMBALA       1999  206000.0  186000.0  182000.0   396.134   649.086\n",
       "3       AMBALA       2000  217000.0  206000.0  186000.0   593.737   396.134\n",
       "4       AMBALA       2001  233000.0  217000.0  206000.0   469.118   593.737\n",
       "5       AMBALA       2002  183000.0  233000.0  217000.0   476.752   469.118\n",
       "8       AMBALA       2005  254000.0  233000.0  227000.0  1058.400  1202.900"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_finite = X_hr[np.isfinite(X_hr[\"X1\"])]\n",
    "X_finite = X_finite[np.isfinite(X_finite[\"X2\"])]\n",
    "X_finite = X_finite[np.isfinite(X_finite[\"X3\"])]\n",
    "X_finite = X_finite[np.isfinite(X_finite[\"X4\"])]\n",
    "X_finite = X_finite[np.isfinite(X_finite[\"Y\"])]\n",
    "X_finite.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Y</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>127.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2004.204724</td>\n",
       "      <td>182771.653543</td>\n",
       "      <td>181007.874016</td>\n",
       "      <td>174322.834646</td>\n",
       "      <td>489.894449</td>\n",
       "      <td>502.577819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.733883</td>\n",
       "      <td>147264.161147</td>\n",
       "      <td>147414.085609</td>\n",
       "      <td>142965.856835</td>\n",
       "      <td>211.255550</td>\n",
       "      <td>190.321342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1999.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>126.300000</td>\n",
       "      <td>126.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>49000.000000</td>\n",
       "      <td>52500.000000</td>\n",
       "      <td>55500.000000</td>\n",
       "      <td>342.916000</td>\n",
       "      <td>362.211500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>166000.000000</td>\n",
       "      <td>164000.000000</td>\n",
       "      <td>160000.000000</td>\n",
       "      <td>458.600000</td>\n",
       "      <td>486.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>251500.000000</td>\n",
       "      <td>240500.000000</td>\n",
       "      <td>227000.000000</td>\n",
       "      <td>573.491500</td>\n",
       "      <td>605.301500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>610000.000000</td>\n",
       "      <td>610000.000000</td>\n",
       "      <td>610000.000000</td>\n",
       "      <td>1433.900000</td>\n",
       "      <td>1227.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Crop_Year              Y             X1             X2           X3  \\\n",
       "count   127.000000     127.000000     127.000000     127.000000   127.000000   \n",
       "mean   2004.204724  182771.653543  181007.874016  174322.834646   489.894449   \n",
       "std       3.733883  147264.161147  147414.085609  142965.856835   211.255550   \n",
       "min    1999.000000    3000.000000    2000.000000    2000.000000   126.300000   \n",
       "25%    2001.000000   49000.000000   52500.000000   55500.000000   342.916000   \n",
       "50%    2005.000000  166000.000000  164000.000000  160000.000000   458.600000   \n",
       "75%    2007.000000  251500.000000  240500.000000  227000.000000   573.491500   \n",
       "max    2010.000000  610000.000000  610000.000000  610000.000000  1433.900000   \n",
       "\n",
       "                X4  \n",
       "count   127.000000  \n",
       "mean    502.577819  \n",
       "std     190.321342  \n",
       "min     126.300000  \n",
       "25%     362.211500  \n",
       "50%     486.380000  \n",
       "75%     605.301500  \n",
       "max    1227.900000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xn = X_finite\n",
    "Xn.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = Xn[\"Y\"]\n",
    "X = Xn[[\"X1\", \"X2\", \"X3\", \"X4\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of the Rice Production Values')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z5AjaEV8nVNWPZ1m/l9AGcH8myTtpn40/pyUUxzH+6RA2xftou3v2Ao6tqlE9okfQTg3w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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114267c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 5))\n",
    "plt.hist(y, bins=30)\n",
    "plt.xlabel('Production Value',fontsize=15)\n",
    "plt.ylabel('Occurences',fontsize=15)\n",
    "plt.title('Distribution of the Rice Production Values',fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WXz1DX0fMl2uppjBzKz2vrUrhT0REpMFG0zmuvm8T8WiYjk6/Pu5V6zdxxdkn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\nTmGb2SVhGvLknCnrv5rZG8b4WdYCdcC8sH9nZh8ys6+b2R786vzZui8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11478e588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xplot = Xn[[\"X1\", \"X2\", \"X3\", \"X4\",\"Y\"]]\n",
    "\n",
    "var_name = \"X1\"\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=var_name, y='Y', data=Xplot, scatter_kws={'alpha':0.6, 's':20})\n",
    "plt.xlabel(var_name + \" (Crop Produce of Last Year)\", fontsize=15)\n",
    "plt.ylabel('Y', fontsize=15)\n",
    "plt.title(\"Distribution of y variable with feature \"+var_name, fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rz1/BquPGFvrA9/jdsbmN2miYxtoomWyO2za1cdaKo2hQ+Oun8CciIiIjymRz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\nY/FUrjpjLkceMYm6yv3BpplpuTURkRI02n3+zgS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/1cq8KdU8uX4P965s4oUt+3L7PGb6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\neyUvrcHMHjazdcHP+iDdzOyOoNxeMrOT895zdZB/nZldnZd+ipm9HLz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F+CM+QLwQX4N5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\nIlJCFPyJiIiIlJD/Cz00ojahWJncAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147cdac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_name = \"X2\"\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=var_name, y='Y', data=Xplot, scatter_kws={'alpha':0.6, 's':20})\n",
    "plt.xlabel(var_name + \" (Crop Produce of Last to Last Year)\", fontsize=15)\n",
    "plt.ylabel('Y', fontsize=15)\n",
    "plt.title(\"Distribution of y variable with feature \"+var_name, fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PMHFlusF1mn0+LeKcc2b2K4RB2bfMLCoPkiV8bH4OeA/hY7eSuwnX0UY/x7f/fJ3tjbTj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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a6e7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_name = \"X3\"\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=var_name, y='Y', data=Xplot, scatter_kws={'alpha':0.6, 's':20})\n",
    "plt.xlabel(var_name + \" (Rainfall of Present Year)\", fontsize=15)\n",
    "plt.ylabel('Y', fontsize=15)\n",
    "plt.title(\"Distribution of y variable with feature \"+var_name, fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n3AOd3G8jnHM3Oud2Oed2bdu2rduHI3VoztfW0/AqIiL9r9Nn9FcD/8HM3ggM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\nmVmJOPgzMzMzK5H/B1guqtbZACV9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115bd55f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_name = \"X4\"\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=var_name, y='Y', data=Xplot, scatter_kws={'alpha':0.6, 's':20})\n",
    "plt.xlabel(var_name + \" (Rainfall of Last Year)\", fontsize=15)\n",
    "plt.ylabel('Y', fontsize=15)\n",
    "plt.title(\"Distribution of y variable with feature \"+var_name, fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Z-Score Normalization\n",
    "cols = list(X.columns)\n",
    "\n",
    "for col in cols:\n",
    "    col_zscore = col + '_zscore'\n",
    "    X[col_zscore] = (X[col] - X[col].mean())/X[col].std(ddof=0)\n",
    "X = X[[\"X1_zscore\", \"X2_zscore\", \"X3_zscore\", \"X4_zscore\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1_zscore</th>\n",
       "      <th>X2_zscore</th>\n",
       "      <th>X3_zscore</th>\n",
       "      <th>X4_zscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.033999</td>\n",
       "      <td>0.053912</td>\n",
       "      <td>-0.445583</td>\n",
       "      <td>0.772842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.170208</td>\n",
       "      <td>0.082001</td>\n",
       "      <td>0.493496</td>\n",
       "      <td>-0.561500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.245124</td>\n",
       "      <td>0.222449</td>\n",
       "      <td>-0.098737</td>\n",
       "      <td>0.480872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.354091</td>\n",
       "      <td>0.299695</td>\n",
       "      <td>-0.062458</td>\n",
       "      <td>-0.176503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.354091</td>\n",
       "      <td>0.369919</td>\n",
       "      <td>2.701738</td>\n",
       "      <td>3.694256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X1_zscore  X2_zscore  X3_zscore  X4_zscore\n",
       "2   0.033999   0.053912  -0.445583   0.772842\n",
       "3   0.170208   0.082001   0.493496  -0.561500\n",
       "4   0.245124   0.222449  -0.098737   0.480872\n",
       "5   0.354091   0.299695  -0.062458  -0.176503\n",
       "8   0.354091   0.369919   2.701738   3.694256"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Randomly splitting the dataset into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First baseline using Linear Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/scipy/linalg/basic.py:1018: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  warnings.warn(mesg, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alg = LinearRegression()\n",
    "alg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficients for the linear regression model learnt are\n",
      "\n",
      "[ 73701.89  70350.8    2056.26    -55.16]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "coef = alg.coef_\n",
    "coef = coef.round(decimals=2)\n",
    "np.set_printoptions(suppress=True) #gem \n",
    "print(\"The coefficients for the linear regression model learnt are\\n\")\n",
    "print(coef)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23298.87377886106\n"
     ]
    }
   ],
   "source": [
    "y_predict = alg.predict(X_test)\n",
    "rmse = sqrt(mean_squared_error(y_predict, y_test))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets calculate the average RMSE (Cross Validation, 5-Fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LinearRegression()\n",
    "scores = cross_val_score(clf, X, y, cv=5, scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,5):\n",
    "    scores[i] = sqrt(-1*scores[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 16563.8011325   28901.49081778  43028.30003751  14194.87872337\n",
      "  18528.23535214]\n",
      "\n",
      "\n",
      "Avg RMSE is  24243.3412127\n"
     ]
    }
   ],
   "source": [
    "print(scores)\n",
    "avg_rmse = scores.mean()\n",
    "print(\"\\n\\nAvg RMSE is \",scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# print(type(y_test))\n",
    "# print(type(y_predict))\n",
    "yt = y_test.as_matrix()\n",
    "print(type(yt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_predicted</th>\n",
       "      <th>y_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>196.711538</td>\n",
       "      <td>200.038462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>111.520955</td>\n",
       "      <td>118.830124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.500000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>149.450000</td>\n",
       "      <td>153.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>196.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>228.875000</td>\n",
       "      <td>251.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>433.800000</td>\n",
       "      <td>460.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       y_predicted      y_test\n",
       "count    26.000000   26.000000\n",
       "mean    196.711538  200.038462\n",
       "std     111.520955  118.830124\n",
       "min      19.500000    8.000000\n",
       "25%     149.450000  153.250000\n",
       "50%     183.000000  196.000000\n",
       "75%     228.875000  251.750000\n",
       "max     433.800000  460.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.DataFrame()\n",
    "p[\"y_predicted\"] = y_predict/1000\n",
    "p[\"y_test\"] = yt/1000\n",
    "\n",
    "p[\"y_predicted\"] = p[\"y_predicted\"].round(decimals=1)\n",
    "# p[\"y_test\"] = p[\"y_test\"].round(decimals=1)\n",
    "p.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Good enough results for the Haryana State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    y_predicted  y_test\n",
      "0         190.8   200.0\n",
      "1         217.4   222.0\n",
      "2         430.2   460.0\n",
      "3          34.2     8.0\n",
      "4         173.2   189.0\n",
      "5         360.3   391.0\n",
      "6         167.3   151.0\n",
      "7         165.5   175.0\n",
      "8         152.6   160.0\n",
      "9         118.1    94.0\n",
      "10         29.7    36.0\n",
      "11        277.8   284.0\n",
      "12        230.6   267.0\n",
      "13        105.9    63.0\n",
      "14        229.2   205.0\n",
      "15        166.3   161.0\n",
      "16        433.8   383.0\n",
      "17        193.4   204.0\n",
      "18        227.9   221.0\n",
      "19        215.6   233.0\n",
      "20        380.8   402.0\n",
      "21        175.2   192.0\n",
      "22        148.4   178.0\n",
      "23        216.8   258.0\n",
      "24         19.5    14.0\n",
      "25         54.0    50.0\n"
     ]
    }
   ],
   "source": [
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Root Mean Square Error\n",
    "It has the same unit as the data values.\n",
    "With range of the test data set being [8,460], rmse as 23.3 is a decent one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.29887377886106"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constructing dataset for Whole India"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing columns for the features, using rice production and rainfall datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain1 = rainfall\n",
    "rain2 = pd.read_csv(\"/Users/macbook/Documents/BTP/Notebook/rainfall_distt_2004-10_nax.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Crop_Year           Area    Production         Value\n",
      "count  13169.000000   13169.000000  1.314700e+04  13169.000000\n",
      "mean    2005.762397   50640.056200  1.057203e+05      1.942265\n",
      "std        5.063566   71019.932027  1.759126e+05      2.144512\n",
      "min     1997.000000       1.000000  0.000000e+00      0.000000\n",
      "25%     2001.000000    3200.000000  4.966500e+03      1.258584\n",
      "50%     2006.000000   19000.000000  2.944800e+04      1.894216\n",
      "75%     2010.000000   73275.890000  1.319115e+05      2.494297\n",
      "max     2015.000000  687000.000000  1.710000e+06    223.727273\n",
      "              Year        Value\n",
      "count  4878.000000  4878.000000\n",
      "mean   1998.000000  1157.397511\n",
      "std       2.582254   717.308841\n",
      "min    1994.000000    55.502000\n",
      "25%    1996.000000   743.227500\n",
      "50%    1998.000000   971.803500\n",
      "75%    2000.000000  1301.227500\n",
      "max    2002.000000  9357.259000\n",
      "              Year        Value\n",
      "count  3006.000000  3006.000000\n",
      "mean   2007.007651  1242.812309\n",
      "std       2.031272   872.137946\n",
      "min    2004.000000     7.500000\n",
      "25%    2005.000000   698.025000\n",
      "50%    2007.000000   994.700000\n",
      "75%    2009.000000  1484.925000\n",
      "max    2010.000000  9935.000000\n"
     ]
    }
   ],
   "source": [
    "print(rice.describe())\n",
    "print(rain1.describe())\n",
    "print(rain2.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>Value</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>102.00</td>\n",
       "      <td>321.00</td>\n",
       "      <td>3.147059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>83.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>3.614458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2002</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>189.20</td>\n",
       "      <td>510.84</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2003</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>52.00</td>\n",
       "      <td>90.17</td>\n",
       "      <td>1.734038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2004</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>52.94</td>\n",
       "      <td>72.57</td>\n",
       "      <td>1.370797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    State_Name ind_district  Crop_Year       Season  Crop  \\\n",
       "0  Andaman and Nicobar Islands     NICOBARS       2000  Kharif       Rice   \n",
       "1  Andaman and Nicobar Islands     NICOBARS       2001  Kharif       Rice   \n",
       "2  Andaman and Nicobar Islands     NICOBARS       2002  Kharif       Rice   \n",
       "3  Andaman and Nicobar Islands     NICOBARS       2003  Kharif       Rice   \n",
       "4  Andaman and Nicobar Islands     NICOBARS       2004  Kharif       Rice   \n",
       "\n",
       "     Area  Production     Value  X1  X2  X3  X4  \n",
       "0  102.00      321.00  3.147059 NaN NaN NaN NaN  \n",
       "1   83.00      300.00  3.614458 NaN NaN NaN NaN  \n",
       "2  189.20      510.84  2.700000 NaN NaN NaN NaN  \n",
       "3   52.00       90.17  1.734038 NaN NaN NaN NaN  \n",
       "4   52.94       72.57  1.370797 NaN NaN NaN NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.empty((rice.shape[0],1))*np.NAN\n",
    "rice = rice.assign(X1 = a)\n",
    "rice = rice.assign(X2 = a)\n",
    "rice = rice.assign(X3 = a)\n",
    "rice = rice.assign(X4 = a)\n",
    "rice.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Constructing the features X1 and X2, the production for the last 2 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = rice.shape[0]\n",
    "for row in range(0,l):\n",
    "    if row-1<0 or rice.iloc[row,1] != rice.iloc[row-1,1]:\n",
    "        continue\n",
    "    else:\n",
    "        rice.iloc[row,8] = rice.iloc[row-1,6]\n",
    "        if row-2<0 or rice.iloc[row,1] != rice.iloc[row-2,1]:\n",
    "            continue\n",
    "        else:\n",
    "            rice.iloc[row,9] = rice.iloc[row-2,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>Value</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>102.00</td>\n",
       "      <td>321.00</td>\n",
       "      <td>3.147059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2001</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>83.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>3.614458</td>\n",
       "      <td>321.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2002</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>189.20</td>\n",
       "      <td>510.84</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>321.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2003</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>52.00</td>\n",
       "      <td>90.17</td>\n",
       "      <td>1.734038</td>\n",
       "      <td>510.84</td>\n",
       "      <td>300.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>NICOBARS</td>\n",
       "      <td>2004</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>52.94</td>\n",
       "      <td>72.57</td>\n",
       "      <td>1.370797</td>\n",
       "      <td>90.17</td>\n",
       "      <td>510.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    State_Name ind_district  Crop_Year       Season  Crop  \\\n",
       "0  Andaman and Nicobar Islands     NICOBARS       2000  Kharif       Rice   \n",
       "1  Andaman and Nicobar Islands     NICOBARS       2001  Kharif       Rice   \n",
       "2  Andaman and Nicobar Islands     NICOBARS       2002  Kharif       Rice   \n",
       "3  Andaman and Nicobar Islands     NICOBARS       2003  Kharif       Rice   \n",
       "4  Andaman and Nicobar Islands     NICOBARS       2004  Kharif       Rice   \n",
       "\n",
       "     Area  Production     Value      X1      X2  X3  X4  \n",
       "0  102.00      321.00  3.147059     NaN     NaN NaN NaN  \n",
       "1   83.00      300.00  3.614458  321.00     NaN NaN NaN  \n",
       "2  189.20      510.84  2.700000  300.00  321.00 NaN NaN  \n",
       "3   52.00       90.17  1.734038  510.84  300.00 NaN NaN  \n",
       "4   52.94       72.57  1.370797   90.17  510.84 NaN NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(s):\n",
    "    x = s.strip()\n",
    "    return x.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>Value</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>nicobars</td>\n",
       "      <td>2000</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>102.00</td>\n",
       "      <td>321.00</td>\n",
       "      <td>3.147059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>nicobars</td>\n",
       "      <td>2001</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>83.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>3.614458</td>\n",
       "      <td>321.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>nicobars</td>\n",
       "      <td>2002</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>189.20</td>\n",
       "      <td>510.84</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>321.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>nicobars</td>\n",
       "      <td>2003</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>52.00</td>\n",
       "      <td>90.17</td>\n",
       "      <td>1.734038</td>\n",
       "      <td>510.84</td>\n",
       "      <td>300.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>nicobars</td>\n",
       "      <td>2004</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>52.94</td>\n",
       "      <td>72.57</td>\n",
       "      <td>1.370797</td>\n",
       "      <td>90.17</td>\n",
       "      <td>510.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    State_Name ind_district  Crop_Year  Season  Crop    Area  \\\n",
       "0  Andaman and Nicobar Islands     nicobars       2000  kharif  Rice  102.00   \n",
       "1  Andaman and Nicobar Islands     nicobars       2001  kharif  Rice   83.00   \n",
       "2  Andaman and Nicobar Islands     nicobars       2002  kharif  Rice  189.20   \n",
       "3  Andaman and Nicobar Islands     nicobars       2003  kharif  Rice   52.00   \n",
       "4  Andaman and Nicobar Islands     nicobars       2004  kharif  Rice   52.94   \n",
       "\n",
       "   Production     Value      X1      X2  X3  X4  \n",
       "0      321.00  3.147059     NaN     NaN NaN NaN  \n",
       "1      300.00  3.614458  321.00     NaN NaN NaN  \n",
       "2      510.84  2.700000  300.00  321.00 NaN NaN  \n",
       "3       90.17  1.734038  510.84  300.00 NaN NaN  \n",
       "4       72.57  1.370797   90.17  510.84 NaN NaN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rice['ind_district'] = rice['ind_district'].apply(func)\n",
    "rice['Season'] = rice['Season'].apply(func)\n",
    "rain1['ind_district'] = rain1['ind_district'].apply(func)\n",
    "rain2['ind_district'] = rain2['ind_district'].apply(func)\n",
    "rice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>adilabad</td>\n",
       "      <td>1994</td>\n",
       "      <td>1199.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>adilabad</td>\n",
       "      <td>1995</td>\n",
       "      <td>1255.561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>adilabad</td>\n",
       "      <td>1996</td>\n",
       "      <td>1081.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>adilabad</td>\n",
       "      <td>1997</td>\n",
       "      <td>905.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>adilabad</td>\n",
       "      <td>1998</td>\n",
       "      <td>1128.950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State ind_district  Year     Value\n",
       "0  Andhra Pradesh     adilabad  1994  1199.447\n",
       "1  Andhra Pradesh     adilabad  1995  1255.561\n",
       "2  Andhra Pradesh     adilabad  1996  1081.171\n",
       "3  Andhra Pradesh     adilabad  1997   905.718\n",
       "4  Andhra Pradesh     adilabad  1998  1128.950"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can reduce the time by searching only one variable for some cases atleast\n",
    "rice = rice[rice['Season'] == 'kharif']\n",
    "l = rice.shape[0]\n",
    "\n",
    "for row in range(0,l):\n",
    "    \n",
    "    dt = rice.iloc[row,1]\n",
    "    yr = rice.iloc[row,2]\n",
    "    \n",
    "    if yr<=2002:\n",
    "        \n",
    "        # rainfall for the same year\n",
    "        r = rain1.loc[(rain1.ind_district == dt) & (rain1.Year == yr)]\n",
    "        if r.shape[0] == 1:\n",
    "            rice.iloc[row,10] = r.iloc[0,3]\n",
    "            \n",
    "        # rainfall for the previous year\n",
    "        r = rain1.loc[(rain1.ind_district == dt) & (rain1.Year == yr-1)]\n",
    "        if r.shape[0] == 1:\n",
    "            rice.iloc[row,11] = r.iloc[0,3]\n",
    "            \n",
    "    if yr>2004:\n",
    "        \n",
    "        # rainfall for the same year\n",
    "        r = rain2.loc[(rain2.ind_district == dt) & (rain2.Year == yr)]\n",
    "        if r.shape[0] == 1:\n",
    "            rice.iloc[row,10] = r.iloc[0,3]\n",
    "            \n",
    "        # rainfall for the previous year\n",
    "        r = rain2.loc[(rain2.ind_district == dt) & (rain2.Year == yr-1)]\n",
    "        if r.shape[0] == 1:\n",
    "            rice.iloc[row,11] = r.iloc[0,3]\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>Value</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5463.000000</td>\n",
       "      <td>5463.000000</td>\n",
       "      <td>5.449000e+03</td>\n",
       "      <td>5463.000000</td>\n",
       "      <td>5.090000e+03</td>\n",
       "      <td>4.809000e+03</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2968.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2005.394106</td>\n",
       "      <td>64076.532900</td>\n",
       "      <td>1.375553e+05</td>\n",
       "      <td>1.946222</td>\n",
       "      <td>1.047449e+05</td>\n",
       "      <td>1.309790e+05</td>\n",
       "      <td>1030.554382</td>\n",
       "      <td>1054.704046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.949146</td>\n",
       "      <td>76112.336892</td>\n",
       "      <td>1.905742e+05</td>\n",
       "      <td>3.164509</td>\n",
       "      <td>1.821700e+05</td>\n",
       "      <td>1.918992e+05</td>\n",
       "      <td>623.505868</td>\n",
       "      <td>622.106761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>76.944000</td>\n",
       "      <td>118.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>7982.000000</td>\n",
       "      <td>1.006300e+04</td>\n",
       "      <td>1.212146</td>\n",
       "      <td>3.197750e+03</td>\n",
       "      <td>5.691000e+03</td>\n",
       "      <td>653.662000</td>\n",
       "      <td>677.295250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>32583.000000</td>\n",
       "      <td>5.785400e+04</td>\n",
       "      <td>1.852538</td>\n",
       "      <td>2.525150e+04</td>\n",
       "      <td>4.530000e+04</td>\n",
       "      <td>843.453000</td>\n",
       "      <td>883.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>101065.500000</td>\n",
       "      <td>2.059410e+05</td>\n",
       "      <td>2.483993</td>\n",
       "      <td>1.268458e+05</td>\n",
       "      <td>1.957000e+05</td>\n",
       "      <td>1190.236000</td>\n",
       "      <td>1197.745250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>545965.000000</td>\n",
       "      <td>1.710000e+06</td>\n",
       "      <td>223.727273</td>\n",
       "      <td>1.710000e+06</td>\n",
       "      <td>1.710000e+06</td>\n",
       "      <td>4999.200000</td>\n",
       "      <td>5243.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Crop_Year           Area    Production        Value            X1  \\\n",
       "count  5463.000000    5463.000000  5.449000e+03  5463.000000  5.090000e+03   \n",
       "mean   2005.394106   64076.532900  1.375553e+05     1.946222  1.047449e+05   \n",
       "std       4.949146   76112.336892  1.905742e+05     3.164509  1.821700e+05   \n",
       "min    1997.000000       1.000000  0.000000e+00     0.000000  0.000000e+00   \n",
       "25%    2001.000000    7982.000000  1.006300e+04     1.212146  3.197750e+03   \n",
       "50%    2005.000000   32583.000000  5.785400e+04     1.852538  2.525150e+04   \n",
       "75%    2010.000000  101065.500000  2.059410e+05     2.483993  1.268458e+05   \n",
       "max    2015.000000  545965.000000  1.710000e+06   223.727273  1.710000e+06   \n",
       "\n",
       "                 X2           X3           X4  \n",
       "count  4.809000e+03  2783.000000  2968.000000  \n",
       "mean   1.309790e+05  1030.554382  1054.704046  \n",
       "std    1.918992e+05   623.505868   622.106761  \n",
       "min    0.000000e+00    76.944000   118.200000  \n",
       "25%    5.691000e+03   653.662000   677.295250  \n",
       "50%    4.530000e+04   843.453000   883.950000  \n",
       "75%    1.957000e+05  1190.236000  1197.745250  \n",
       "max    1.710000e+06  4999.200000  5243.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X1 = prod-1\n",
    "# X2 = prod-2\n",
    "# X3 = rain\n",
    "# X4 = rain-1\n",
    "rice.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>Value</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>anantapur</td>\n",
       "      <td>1998</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>38300.0</td>\n",
       "      <td>96800.0</td>\n",
       "      <td>2.527415</td>\n",
       "      <td>37300.0</td>\n",
       "      <td>75400.0</td>\n",
       "      <td>881.473</td>\n",
       "      <td>797.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>anantapur</td>\n",
       "      <td>1999</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>37991.0</td>\n",
       "      <td>105082.0</td>\n",
       "      <td>2.765971</td>\n",
       "      <td>63900.0</td>\n",
       "      <td>96800.0</td>\n",
       "      <td>643.720</td>\n",
       "      <td>881.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>anantapur</td>\n",
       "      <td>2000</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>39905.0</td>\n",
       "      <td>117680.0</td>\n",
       "      <td>2.949004</td>\n",
       "      <td>45669.0</td>\n",
       "      <td>105082.0</td>\n",
       "      <td>767.351</td>\n",
       "      <td>643.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>anantapur</td>\n",
       "      <td>2001</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>32878.0</td>\n",
       "      <td>95609.0</td>\n",
       "      <td>2.907993</td>\n",
       "      <td>57236.0</td>\n",
       "      <td>117680.0</td>\n",
       "      <td>579.338</td>\n",
       "      <td>767.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>anantapur</td>\n",
       "      <td>2002</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>29066.0</td>\n",
       "      <td>66329.0</td>\n",
       "      <td>2.282013</td>\n",
       "      <td>108906.0</td>\n",
       "      <td>95609.0</td>\n",
       "      <td>540.070</td>\n",
       "      <td>579.338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State_Name ind_district  Crop_Year  Season  Crop     Area  Production  \\\n",
       "9   Andhra Pradesh    anantapur       1998  kharif  Rice  38300.0     96800.0   \n",
       "11  Andhra Pradesh    anantapur       1999  kharif  Rice  37991.0    105082.0   \n",
       "13  Andhra Pradesh    anantapur       2000  kharif  Rice  39905.0    117680.0   \n",
       "15  Andhra Pradesh    anantapur       2001  kharif  Rice  32878.0     95609.0   \n",
       "17  Andhra Pradesh    anantapur       2002  kharif  Rice  29066.0     66329.0   \n",
       "\n",
       "       Value        X1        X2       X3       X4  \n",
       "9   2.527415   37300.0   75400.0  881.473  797.051  \n",
       "11  2.765971   63900.0   96800.0  643.720  881.473  \n",
       "13  2.949004   45669.0  105082.0  767.351  643.720  \n",
       "15  2.907993   57236.0  117680.0  579.338  767.351  \n",
       "17  2.282013  108906.0   95609.0  540.070  579.338  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ricex = rice[np.isfinite(rice[\"Production\"])]\n",
    "ricex = ricex[np.isfinite(ricex[\"X1\"])]\n",
    "ricex = ricex[np.isfinite(ricex[\"X2\"])]\n",
    "ricex = ricex[np.isfinite(ricex[\"X3\"])]\n",
    "ricex = ricex[np.isfinite(ricex[\"X4\"])]\n",
    "ricex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>Value</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2210.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>2.210000e+03</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>2.210000e+03</td>\n",
       "      <td>2.210000e+03</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2003.684163</td>\n",
       "      <td>68763.716290</td>\n",
       "      <td>1.468667e+05</td>\n",
       "      <td>1.882002</td>\n",
       "      <td>1.142051e+05</td>\n",
       "      <td>1.357907e+05</td>\n",
       "      <td>1036.427027</td>\n",
       "      <td>1047.268537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.732909</td>\n",
       "      <td>80679.762759</td>\n",
       "      <td>2.050435e+05</td>\n",
       "      <td>0.971570</td>\n",
       "      <td>1.969355e+05</td>\n",
       "      <td>2.075148e+05</td>\n",
       "      <td>642.219167</td>\n",
       "      <td>633.995676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>76.944000</td>\n",
       "      <td>118.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>8160.500000</td>\n",
       "      <td>9.660250e+03</td>\n",
       "      <td>1.141382</td>\n",
       "      <td>3.300000e+03</td>\n",
       "      <td>3.957000e+03</td>\n",
       "      <td>642.850000</td>\n",
       "      <td>658.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>38615.000000</td>\n",
       "      <td>6.310500e+04</td>\n",
       "      <td>1.829612</td>\n",
       "      <td>3.100800e+04</td>\n",
       "      <td>4.085550e+04</td>\n",
       "      <td>840.850000</td>\n",
       "      <td>864.002500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>109404.500000</td>\n",
       "      <td>2.181170e+05</td>\n",
       "      <td>2.500961</td>\n",
       "      <td>1.430950e+05</td>\n",
       "      <td>2.015152e+05</td>\n",
       "      <td>1206.128500</td>\n",
       "      <td>1198.715750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>545965.000000</td>\n",
       "      <td>1.637000e+06</td>\n",
       "      <td>9.886125</td>\n",
       "      <td>1.710000e+06</td>\n",
       "      <td>1.710000e+06</td>\n",
       "      <td>4999.200000</td>\n",
       "      <td>5243.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Crop_Year           Area    Production        Value            X1  \\\n",
       "count  2210.000000    2210.000000  2.210000e+03  2210.000000  2.210000e+03   \n",
       "mean   2003.684163   68763.716290  1.468667e+05     1.882002  1.142051e+05   \n",
       "std       3.732909   80679.762759  2.050435e+05     0.971570  1.969355e+05   \n",
       "min    1998.000000       1.000000  0.000000e+00     0.000000  1.000000e+00   \n",
       "25%    2000.000000    8160.500000  9.660250e+03     1.141382  3.300000e+03   \n",
       "50%    2002.000000   38615.000000  6.310500e+04     1.829612  3.100800e+04   \n",
       "75%    2007.000000  109404.500000  2.181170e+05     2.500961  1.430950e+05   \n",
       "max    2010.000000  545965.000000  1.637000e+06     9.886125  1.710000e+06   \n",
       "\n",
       "                 X2           X3           X4  \n",
       "count  2.210000e+03  2210.000000  2210.000000  \n",
       "mean   1.357907e+05  1036.427027  1047.268537  \n",
       "std    2.075148e+05   642.219167   633.995676  \n",
       "min    1.000000e+00    76.944000   118.200000  \n",
       "25%    3.957000e+03   642.850000   658.925000  \n",
       "50%    4.085550e+04   840.850000   864.002500  \n",
       "75%    2.015152e+05  1206.128500  1198.715750  \n",
       "max    1.710000e+06  4999.200000  5243.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = ricex[[\"X1\",\"X2\",\"X3\",\"X4\"]]\n",
    "y = ricex[[\"Production\"]]\n",
    "ricex.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of the Rice Production Values')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a37550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 10))\n",
    "plt.hist(y, bins=250)\n",
    "plt.xlabel('Production Value',fontsize=25)\n",
    "plt.ylabel('Occurences',fontsize=25)\n",
    "plt.title('Distribution of the Rice Production Values',fontsize=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+ab5qQ+L193shMe/frZ4hcaArTE+blORrZq1xlYqIiDShSoJgvcv6zSTnHCOJ\nLMOJNLl8FSFxKM719zUmJEZCAQa7Iqq2MoP0TIuIiNSokiDYimX9nHPsTWUZHsuQzVdemq9USDy0\nvxASF05rVnJHOMhAV5iuiKLLTJvRZ9zMvg28GdjlnDvK3/ZvwFuANLAReK9zbtjMVgBPAk/5d7/P\nOfcX/n1OAK4GOoE7gI8455yZzQNuAVYAm4BznXMx89qnvwi8EYgD73HOPeTv62LgH/1j/LNz7ppG\nPX4REWkvlQTBVivrt9fvbs7kph8S33XKcl43zZDYFfHWSFS1ldlj1cximvbBzF4FjALXFoXF1wN3\nO+eyZvZ5AOfcpX5Y/EnhdhP28wDw18D9eGHxS865n5nZvwJDzrnPmdnfA4P+vt4I/BVeWDwZ+KJz\n7mQ/XK4FVgEOWAec4JyLTfY4Vq1a5dauXTvt50NERFrfI1tjXLlmI9mcGw+CpSavNPts6LFUllg8\nTTpbeUjcOrRv4kq9WxJVbaV+zGydc25Vrfef0avVOfdrPwQWb/vvov/eB7x9sn2Y2aFAn3PuPv//\n1wJnAz8DzgJW+ze9BlgDXOpvv9Z5yfg+Mxvw97Ma+Llzbsjf18+BM4Gban2MIiIyt1Ra37lZy/ol\n0jmG4mlSmcrrN28dinP9/Vu468mddW1JNDO6o0EGOiNEQlpIu1k021X7Prxu5IIXmdnDwAjwj865\n/wEWA9uKbrPN3wZwsHNuh//v54CD/X8vBraWuE+57Qcwsw8CHwRYtmxZdY9KRERa3mQtg80aBCeT\nzOSIxdMk0s0REns7Qgyo2kpTapor28w+AWSBG/xNO4Blzrnd/hjFH5rZkZXuzx/DWLc+dufc14Gv\ng9cNXa/9iohI82uXdRIBUtkcsbEM8XTlpfm2xeJcf98WfjExJJ68jNe9/OCaA17AjL7OMP2qttLU\nmiIsmtl78Ca+nO53FeOcSwEp/9/rzGwj8BJgO7Ck6O5L/G0AO83sUOfcDr+beZe/fTuwtMR9trOv\n27qwfU3dHpiIiLS8dlgnESCdzTMcTzOamv2QGAwY/Z1hVVtpEbN+lZvZmcDHgFc75+JF2xfgTVbJ\nmdmLgcOBZ5xzQ2Y2Yman4E1wuQj4sn+324GLgc/5f/+oaPtfmtnNeBNc9viB8k7gs2ZW+PXw9cDH\nG/l4RUSktbTyOomwr37z3mSm4vtsjyW4/v7N/PyJfSHxkL4O3nXKMl4/jZAYCgS8kNiphbRbyUwv\nnXMTXkvefDPbBnwSL5xFgZ/7F05hiZxXAZ82swyQB/6iMBEF+DD7ls75mf8HvJB4q5m9H9gMnOtv\nvwNvJvQGvKVz3gvgB8/LgQcstAUpAAAgAElEQVT923266BgiIiItuU4iePWbY/E0e6uo31wuJF54\n8jLOOLL2kBgOBujvCtOraistaUaXzmkXWjpHRKR5NWKJmkqXx2kGubxjT5X1mycLia8/8mDCNYbE\nSCjAQFdzLhVUjWZf9mgqLbV0joiISCM1aiJKpcvjzKZa6jc3KiRGw0EG26TaSjtNbqpV67+KIiIi\nNH4iSrMuj1NL/eZSIfHgvigXnrycM6YREjsj3hqJnZH2WEi7XSY3TdfceaQiItLWWn0iSrVqqd+8\nfTjB9fcdGBLfdfLyabUkdkdD9He2X0m+uXZNlTN3HqmIiLS1VpqIMt0xcIX7V1q/eftwghvu28J/\nP/FcXVsSezpCbV1tpZWuqUZSWBQRkbbQEw1xyeqVXLlmI3uT2fGJKM3WAjSdMXDV1m9+djjB9RNC\n4sLeKO86ZRlnHHlITSHRzOiJhhjoCtccMltFq1xTjabZ0DXQbGgRkebVzDNXR1NZPnrrem8MnN9S\nlcjkphwDV2395meHE9xw/xbufLx+ITHgl+Trn4Ml+Zr5mqqEZkOLiIgUadaJKFD9GLhq6zc3IiQG\nA0ZfR5i+OVySr5mvqZkwdx+5iIjIDKt0DFy19ZvLhcQLT17GmUfVFhIL1VZ6O0IqyTfHKSyKiIjM\nkKnGwFVbv3nHHm/iyp1P7BxfNqcQEs848pCaJp6EgwH6OsP0dajaingUFkVERGZQqQW+M7k8sXia\n0eTshsSBrjA9KsknEygsioiIzLDCGLhsLs8Lo6mK6zc/tyfJ9fdv5s7H9w+J7zxpGW84qraQGA0H\nGegM0z2Hx+TJ5HRliIiIzLBC/eY9iUzFIfGG+7fwX48/Nx4SF/REufCUZZxZY0tiRzjIYFf7VFuR\nxlFYFBGRpjOdpUoascxJvfaZLwqJldRvLhcSLzi59pbEroi3RmK7VVuRxlFYFBGRpjKdRasn3vd9\np61g8WDXtELedM6noNr6zY0IiT3REP1dYaIhhUSpjhblroEW5RYRaYxaF60udd/tsThPPreXlx/S\nSzQSrCnkTed8oPr6zfUOiWZGdzTY1iX5ZGpalFtERNpGtYtWl7tvNpfnmRfGCAB9XRECBleu2Vhx\nyKvH+exNZhiOZyqq3/zcSJIb79/Cz367LyTO74lwwUnLeOMrDq0pJPZ2hBiYg9VWpP4UFkVEpGlU\numj1VPd1DjK5PKFggGgoQDgYqDjkTfd8xlJZhsbSVYXE//rtc2SLQuKFJy/jDUdVHxIDZvR1humf\nw9VWpP4UFkVEpGlMtWh1pfdNZfLkHaxc0EU4GKgqdNZ6PvF0llg8U1H95udGktzktyRm69CSGAwY\n/Z1h+jrCqrYidacxizXQmEURkcaqx2zobbE437lnE9mcGw951Y5ZrOR8kpkcQ2NpkjWGxIP8kPim\nGkKiSvJJJTRmUURE2k5h0erp3HfpvC5esWSgLkvelDqfauo31zskhoMB+rvC9KraiswAhUUREWlb\n0wmd5aSzXmm+sQrqN+8smrhSr5A40BWmtyNc07mL1EJhUURE2kYjFuQuqKZ+886RJDc+sIWfPVaf\nkKiSfDKbdNWJiEhbqMfi2aVkc3mGE5mK6jfvGkly4wNbueOxHXUJiSrJJ81AYVFERFreaCrLVb/a\n6C2e3e0tcVPLuorFcnnHcDzNSK0hsTvCO09axpuPrj4kqiSfNBOFRRERaXnTWTx7omrqNz+/N8WN\n92/hjt/uIJMrDolLedMrDiVaZdhTST5pRgqLIiLS8qazmHeBc/tC4lT1m3eNJLnpga37hcR53REu\nqCEkqiSfNDuFRRERaXnTWczbOcdIMsue+NT1m8uFxHeetJQ31xASeztC9HeGCasknzQxhUUREWkL\nxywd5Ipzj61qNnSl9ZtLdTfXGhIDRSFRdZulFSgsiohI26h0XcXCEjsVhcQHtnDHY9MPicGA0dcR\npk91m6XFKCyKiMicEU9nGRpLk87WFhLPP3Epbzm6upCoknzS6hQWRUSk7VVav/n5vSluemALPy0K\niYNdYd550rKqQ2I4GKCvM0xfh0rySWtTWBQRkbaVzOSIxdMk0rWHxDcffWhV6x0WSvL1qG6ztAmF\nRRGRNtLIcnfNcLxKpbI5huOZKes3lwuJ55+4lLccs6iqkBgJBRjoaq7nQaQedEWLiLSJRpW7a5bj\nVaLS+s1lQ6Lf3VxNSOwIBxnoCtMV0VeqtCdd2SIibaAR5e4mO9b2oThfvnsDPR2hhh+vEtlcnlg8\nw2hq8tJ8z+9NcfODW/nJo89OOyR2Rry6zSrJJ+1OYVFEpA3Us9zdZAqtiaOJLE/v2ssJywbpioRK\nHm8muqgrrd/8wmiKmx4oERJr6G7ujnprJCokylwx42HRzL4NvBnY5Zw7yt82D7gFWAFsAs51zsXM\nGxn8ReCNQBx4j3PuIf8+FwP/6O/2n51z1/jbTwCuBjqBO4CPOOdcLccQEWkV9Sh3N5mJrYl9HWGe\n2T3KI9v38MeHzSedy+93vEZ3UVdav/mF0RQ3P7CVH08zJKokn8xls3HFXw2cOWHb3wN3OecOB+7y\n/w/wBuBw/88Hga/CeLj8JHAycBLwSTMrfAp9FfhA0f3OrOUYIiKtpFDuLpHJsXMkSSKTq7jcXcFo\nKsvWoTijEyaFrN8S46O3rudTP36CR7YNk0znCAcDHL14gGwuz/ZYYr/jFXeJL+zroDMc5Mo1Gw/Y\nby2c81oSt8bixOLpskFx92iKr9y9gQu/eT/ff3g7mZxjsCvMh179Ym74s5N5x6qlFQVFryRfmCWD\nnSzs7VBQlDlpxlsWnXO/NrMVEzafBaz2/30NsAa41N9+rfP6Fu4zswEzO9S/7c+dc0MAZvZz4Ewz\nWwP0Oefu87dfC5wN/KzaYzjndtTzcc+kZp2dKCK1qfQ9XUu5u4JyLYHFwa+vI8zGF0Z5aMswf3TY\nQXRGghy9pJ9Lz3wZiwc7x4/XiC7xSus37x5NcdODW/nJozvGF94e7Apznt+S2FlhS6JK8ons0yxJ\n4uCicPYccLD/78XA1qLbbfO3TbZ9W4nttRxjv7BoZh/Ea3lk2bJlVTy0mdWMsxNFpHbVvqcrLXdX\nbLLJMcXBb/doinzeMZzI8Ounn+fwg3v5hze+lCMO6d1vf/XuEq+kfvPuUW/iyo+LQuJApxcS33ps\ndSGxrzNMv0ryiYxrlrA4zh9fWH4Ayiwdwzn3deDrAKtWrWro+dWqHrMh1Sop0jxmaobzZC2BheA3\nkkjzxI4RIqEA83uiHHFIDwEzVi7sPWB/hS7xK9dsZG/SC4rVdolDZfWb6xUSgwGjvzNMX0dYJflE\nJmiWNLCz0PXrdzPv8rdvB5YW3W6Jv207+7qUC9vX+NuXlLh9LcdoOdPt+rl34wtctWYjATOi4YBa\nJUVm2UzNcJ6sJbAQ/P79v58mns4RCRovP7SPg/s72TmSLHsu0+kSr6R+89BYmpse2LJfSOz3Q+JZ\nVYTEUCBAf5dK8olMplkGYtwOXOz/+2LgR0XbLzLPKcAevyv5TuD1ZjboT2x5PXCn/7MRMzvFn+V8\n0YR9VXOMllP8gQ9U1fVz74YX+Oub1/P0zr1sfH6UVDpXtwHpIlKb6bynqzHV5Jhjlg7y3lNXAJBz\njqd3jfJsLD7lufREQyyd11VxUEykc2wfTvDcnmTZoDg0luaqNRu44Jv3872HtpPO5unvDPPBV72Y\nGz9wMuefuLSioBgOBljQG2XpvE76O8MKiiKTmI2lc27CaxWcb2bb8GY1fw641czeD2wGzvVvfgfe\nkjYb8Ja1eS+Ac27IzC4HHvRv9+nCZBfgw+xbOudn/h+qPUYrqrbrp9DFEwkFuOpXGwkY9ETDZPN5\nNr4wxsoFPXVvwWgn6rKXRqtXd24lilsCI6EA6Wye0VR2fHbzzeu2ctSiXjY+HyeTzfPEc3v58vn1\n6Q6vpH7z0Fiamx/cwu2PTK8lUSX5RKpnky1iKqWtWrXKrV27drZPo6xKQkzxoPl0Ls9oIsOu0TSh\ngBEKBhhNZXjJwb189V0n6EO1BE0kkpk0k7+YlLq253VHuez2x1nY10EmlyeVzbMnnuYz57yCpfO6\naj5WKpsjNpYZbzktZWgszS0PbuX2R54lNTEkHrOIzkhlITEaDjKoknwyR5nZOufcqlrvr3dNG5pq\nNuTEQfMjiTS/e24vhy/sZvPuBMlMhjw0rAWj1c1kWTURqG2Gcy3KXduXn3XUfmMaM7k8HZFgzd3h\n6Wye4Xh60mEuZUPiqiWcdeziikNiZ8RbSLvS24vIgfTNNgdNHDTf1xlh+bxOgoEAKxd044APr17J\nKSvnz+6JNqnC8xfpCDCWyhINBRoy6UBkppWbUJPO5uvSHV6o37w3mSl7m1Ihsa8jxLmrlnLOcZWH\nRJXkE6kffbPNQaVmPs7riXL5WUeRzuY1Bm8Kg90RRlNZHt8xQsAg72D5vK66TzqQuWe2x8FONit6\n6byummc35/KOWDzN3knqN5cLieeduJSzK2xJVEk+kcZQImgzlXzZlBs0f3BfxwyfbetyOMCBM/9/\nGvsr09MM42CnmlBTbXd4zq/fPDJJ/ebJWhLPPm5RRWMMrajaSljVVkTqTmGxjVTzZTOdNdDmuthY\nmt5omFcetoBUNk80FGBoLK1uaKlZM42DrcdnQz7vGPGrrjQyJKraisjM0Ddbmyj+sol0BBiOZ/jS\nXRv44juPm7SFUeGmeoWuukwuT090+qXMRGZq8e1K1frZ4JxjJJFlOJEmly8dEmNxPySuf5ZkjSFR\n1VZEZpaSQpsofNkkXI51O2LknSOdzXPPhud5/ZGHzvbptZWZXPtO5oZ611Iup1FjIp1z7E1lGR7L\nkM2XXky7HiExHAzQ16lqKyIzTd9ubaLwpfLo9j1EQwGCzsgHHTc/uJXTDlugIFNn6saXeuqJhnjv\naSu4as1GYmNpOiLBuv8C0qgxkXv97uZy9Ztj8TS3PriVH00zJA50hemJKiSKzAZ9w7WJnmiI805c\nwj/84LcE8AZ8H714gGzeaSxdg6gbX+pl/ZYY37lnEwEz8s7xvtNW1HVySyPGRI6lvPrN5ULicDzN\nrWu38cOHt9ccEqPhIAOdYbr1PhOZVXoHtpHTDlvA0Uv6CZrR3xUhk8uTyOQ0lk6kiRUHuYO6o8TT\nWb59zyaOWjJQt19G6jkmMpHOMRRPk8qULs1XLiS+Y9USzj52cUXBTwtpizQXhcU20hMN8ZHTD+fK\nNRsZGksfMJZuttdwE5EDzcTklnqMiUxmcgyNpUlWERJ7O0K844QlnHNcZSFRC2mLNCclhjZTbixd\nM6zhJiIHmonJLdOZlDVV/ebphkQtpC3S/KzcavpS3qpVq9zatWtn+zQqNprK8tFb13vjlfwvo0Qm\np1rGIk3ika0xrlyzkWzOjQe5RvwyV653odT2qeo3j4fE9dtJZmoLiVpIW2RmmNk659yqWu+vpDAH\nNNsabiKyv5maXV9qUtbEXoc//+MXsfSgbkaTpUPinniGW9ZurTkkaiFtkdajpDAHzNQabjIzNPa0\nPc3G7PriyTUdXUH2xNNc8Yvf8/E3vIyuCZNL9sQz3LpuKz94eF9I7Il6E1fOOW7x+LnH0zn2JDL0\nd4b320cwYPR1eCFRC2mLtBZ908wBWkS6fWjsqdRTbCxNNpsnEAkxlsoSDATG6zkXgl7ZkHjCEs45\nfvF+nyNP7hjhxge2kM87AgHjwpOXcdSiAfo7w/R2hBQSRVqU0sIcoUWkW18z1Q+W1pfPO8wgncsz\nksjQEQ6SzOTGS+lVExLBa1G88YEtdISCdISDpLI5vrtuG398+AJ6O8Kz8RBFpE70DTOHaBHp1qax\np81nsiEBzTpcYGL95gtOXsYN929hLJ0lGDDOOmYRN9y/ueKQWLAnkSGfd3RFQ0RDXlm+nSNJhuMZ\nhUWRFtc8n2AiMimNPW0ukw0JaMbhAuXqN7/0kD4+/oaXsS0W564nd/HJ258g4a+l2B0N8vbjl/C2\n45fQ0zH518XC3iidkSAB88rz6foUaR9ar0CkRqOpLFuH4mWXFqm3wtjTRCbHzpEkiUxOY09nSfGQ\ngIV9HXSGg1y5ZiOjqeykP5ste5MZtsUSvLA3tV9QBK9F8Mb7N/M3tzzCd9dtI5HJ0R0N8p7TlnPT\nn53CxaetmDQodoSDHNLfweEH9/KR0w/X9SnShvQuFqnBbLUcVTP2tFm7QdvBZEMCgKYZLlC4BkrV\nb96TyHDbum18/6Ht+7UkvuOEJfzpcVO3JHZFQgx07V9tRWOjRdqT3skiVZrtiSaVjD1txm7QdjLV\nkIDZHi4QT2cZGkuTzlYeEivtbu6OeiExGipdkk9jo0Xaj97RIlVq9okmsx1m54KplqOq51JV1bQQ\nT1a/eSSR4bvrtvGDh7cTT1cXElWST2Ru0zeHSJWafaJJs4fZdjFZl2u9umMrbSFOZnJsiyXYOZI8\nYDHs6YZEleQTEX1ziFSp2Rc5b/Yw26pKtfBN1uU63e7YSlqI09k8sXiatZuGDlgMe1F/54EhMRLk\nbScs4W3HL550OZtAUUgMKSSKzHllP8nMLOScm73peyJNrJkH8jd7mG1FszEGdLIW4mgoQCyeZjSZ\nPWAx7L3JDJ+943fsHk3vG5NYYUgslOTrU91mESky2bfHI2b2F865/5mxsxFpIc08kL+Zw2yxVpix\nXa6F7/KzjiKdzTfs3Eu1EAcCkHOObbEEzjlg32LYATOeem4vm3aPkc17P6s0JIYCAfo6Q/R1qG6z\niBxosk+4bcAaM7sG+L/Oud0zdE4iUgfNHGahdWZsl2rh2x6L83fffYRIMNCwcy9uIR5JZHDAeauW\nkPeDYEHQjK2xBDv2xMj5PwsGjHNPWML5Jy2dNCSGg16llb6OEGYKiSJSmhV+Oy35Q7O3A1cAncCl\nzrlvz9SJNbNVq1a5tWvXzvZpiLSs0VSWj9663mut81vNEplcU87YnniuQ6Mp1m6OsWr5APN6Ohp6\n7vm8Y/twgm2xOL0d+09c2ZvM8L112/neQ9sY88ckBs1YNNDBR04/jOOXzyu733AwwEBXmJ6oQqLI\nXGBm65xzq2q9/6SfbM6528zsDuAy4Ktm9h688Jgucds7aj0JaU6t0EU417Xqa9RKM7aLW/i2x+L8\n4YU4mWyOx3fs5chDjXk90f0W5K7H6+GcY08iw55EhlzecXBfx/jPSoXErkiQtx6ziD85YiGLBzv3\nC5XFIqEAA12tda2IyOyb8hPDORcHPmZmI8CngVeWuhlQ+tNJWlKrdBHOZa38GrXajO1jlg5y+VlH\n8XfffYQTlg/w+LN7cc7x+I4RjlnSTyhobBuKc/lPnxh/Pd532goWD3ZVFRydc4wks+yJZw4oyzea\nzHLbQ9u8kJjaFxLfdvxi3nb8Evo6y3c3d4SDDHSF6YooJIpI9ab85DCzFwNfBs4Avgn8GyVaFqV9\naFHn5tfqr1ErzthOZ/NEggEO6ungyEXG48+OkEjnGElmuWT1Sr59z6bx12N7LM5f3byelx/SSzQS\nrCjIjyQzDI9VHhL/9PjFvH2KkNgZ8RbS7izT0igiUonJls6JAB8HLgWeBv7YOXfvTJ2YzJ5W6iKc\nq9rhNWrWGdvluvaLW0MP6olyzNJ+9iaz/Ps7jiGdzY+/HtlcnmdeGCMA9HVFCBiTBvly9ZtLhcTO\nsB8ST1hC/yQhsTvqrZFYXLdZRKRWk306PwEsAP4R+KJz7sD6UdKWWq2LcDbN1pjBdnmNmm3G9mRd\n+6VaQ//u9S/h4L4ORlPZ8dfDOcjk8oSCAaKhAOFgoGSQL1e/eTohsScaon+Sus0iIrWY7FN6PfAR\n59z2mToZaQ6t2EU4G6oZM1jvUKnXqP4q6dov1xpa/HqkMnnyDlYu6CIcDBwQ5BPpHEPxNKkJ9ZtH\nk1m+99A2bqsyJJoZPdEQA10qyScijTHp0jkzdhJmRwC3FG16MfD/gAHgA8Dz/vZ/KMy6NrOPA+8H\ncsBfO+fu9LefCXwRb8LNN51zn/O3vwi4GTgIWAe82zmXNrMocC1wArAbOM85t2my850rS+e06kzb\nmVDN0i+NnIii12if6T4XW4fiXHb74ywsmnm8cyTJp956JEvndVV1Dtticb5zzyayOTce5I84pI9Y\nPE0iXb+Q2NsRYkAl+URkCg1dOmemOOeeAo4FMLMgsB34AfBe4D+cc/9efHszezlwPnAksAj4hZm9\nxP/xlcDr8BYVf9DMbnfOPQF83t/XzWb2Nbyg+VX/75hz7jAzO9+/3XkNfcAtotm6CJtJpWMGGz0R\nRa+Rpx6BvB5d+4XXY+m8Ll6xZIDYWJruaIh0Ns+zw4n9bjuayvL9h7Zx27rtjKa8yqqVhMSAGX2d\nYfpVkk9EZkgz/jp6OrDRObd5ktucBdzsnEs55/4AbABO8v9scM4945xL47UknmXeqrOvAW7z738N\ncHbRvq7x/30bcLpplVqZQnGwAMoGi/FQGdkXKrM5N74mn0xfcSBf2NdBZzjIlWs2jgewShW6khOZ\nHDtHkiQyuWl17Uf8MYvD8fT4dVI432vv3cQF37ifq+/ZzGgqS2c4yIUnL+PGD5zM+1/5opJBMRgw\n5nVHWDavi3ndEQVFEZkxzdgkcT5wU9H//9LMLgLWAn/rnIsBi4H7im6zzd8GsHXC9pPxup6HnXPZ\nErdfXLiPcy5rZnv8279QfFJm9kHggwDLli2bzuOTNlDpmMF2mYjSzOo5M7weM7SzuTyxeIbRVJbi\nYT7lWhLPOW4R7zhhKf1dpVsSQ4EA/Z1h+jpVbUVEZkdThUV/uZ634i3ZA1438eV4i35fDnwBeN9s\nnJtz7uvA18Ebszgb5yDNpZJgoYkojVE8PrHegbzWrv1sLs9wIsPe5IEh8QcPbee767ZVFRLDwQD9\nXWF6VZJPRGZZs31jvQF4yDm3E6DwN4CZfQP4if/f7cDSovst8bdRZvtuYMDMQn7rYvHtC/vaZmYh\noN+/vciUKgkW1bRWacLK1EqNT5zNQJ7LO4bjaUbqFBJVkk9Emk2zfRq9k6IuaDM71Dm3w//vOcBv\n/X/fDtxoZlfgTXA5HHgAMOBwf+bzdrwu7Qucc87Mfgm8HW8c48XAj4r2dTFwr//zu10zTBGXtlJJ\nqGzl8n0zZbIJQzO9wHc+v69+c77oI2MsleX7D2/ntnXb2Jv0QmJHOMA5xy3m3ElCYjQcZKAzTLdC\noog0mab5VDKzbrxZzH9etPlfzexYvG7oTYWfOeceN7Nb8RYOzwKXFBYNN7O/BO7EWzrn2865x/19\nXQrcbGb/DDwMfMvf/i3gOjPbAAzhBUyRhituRQRaunzfTJlsfOLSeV0z8lw5ty8k5vLTD4kqySci\nza5pvoWcc2N4E0uKt717ktt/BvhMie13AHeU2P4M3mzpiduTwDtqOGWRmk1sRXz78YtbvnxfPUzV\nDT+bE4acc4wks+yJ71+/eSyV5QcPe93NxSHx7GMXc+6qJQx0lT63roi3kLZK8olIs5s730IiTaJU\nV+rND27FmNuzpivphp+tCUMjyQx74pn96jfXGhJVkk9EWo3CYgvTZIjWVK4r9bwTl3DbQ9vn5Kzp\nahYvr8fyNtWcV2wsfUBI/OH67Xx37TZGCiExFODs48qHxEJJvv7OMJFQMy5vKyJS3tz4JmpDmgzR\nusp1pZ522AJOO2zBnPwFoNq1EhtduWYslSUWT5POTh0Szzp2EeeduHQ8JMbTOfYkMvT7k1VUkk9E\nWt3c+TZqI40uIddoc71FdKqu1Ln4nBQH6EgwwHA8g/nbZ1IinWMoniaV2Ve/ebKQeO6JSxksakl8\ncscINz6whXzeEQ0H+evXHMaL5ndPedy5/p4QkeamT6UWVM+KFTNNLaKemexKbRaTBaJCgP7sHb9j\n4/OjALx4QTcbd+2dkesjmckxNJYmWRQS42l/TGIFIdG7fY6bHtxCX4dXtzmRyfG1Xz/DFef2Tfr6\nVvueULAUkZmmT5oW1Kol5Fq9RbTeGt2V2kwqCUQrF/bSHQ1y3NIB+rsiZHL5hl8fqWyO2Fhmv9rN\n8XSWHz78LLeu3bpfSHyr3908MSSCV5IvYDkigcB4d3Qlv8RV+57QL1siMhs0iKYFFVphEpkcO0eS\nJDK5lpgMMd4iGtnXIprNOWJj6Vk+M2mk4kC0sK+DznCQK9dsHK9qUhAbS2MY83s7CAcDDb0+0tk8\nO0eSbI8lxoNiPJ3lxvu3cME37uebv/kDI8ksHaEA561awg0fOJm/ePXKA4JiOBhgfm+UpfM6WXZQ\nN6FQYL/9TfVLXDXviUqfRxGRemvudCFltWI3Zqu2iMr0TBw2EQkGeH4kxfZYgiMO6R2/3UxcH5lc\nnlg8zWhy8pbEaNHElVItiaVK8pUbiwqwdShe8n1azWNu5eEnItLa9AnTwlqtG3O21siT2VUciBLp\nHI9u30M2l+fz//UkHzn98PFu1EZeH9lcnlg8w2hqX/3mciHxrcd4IXFeicA2VUm+ib/Ebdi5l4/e\nur5st3E1j7lUsDQgnsoymsrqfSQiDWMqg1y9VatWubVr1872abSsuTJAf648zko8sjXGl+7awCPb\nhgkFjaMXD9AZCZLI5A4Yn1fP5y2XdwzH04wkpxcSaynJN5rK8tFb13vjEf1wV+rxFm5byWN+ZGuM\nK9dsJJtzjKWyOBy90bDGL4rIpMxsnXNuVa33n9vfYDKjir8Ql87rmu3TaShNRNjfMUsH+dgZR/Cp\nHz/BosFOwv6ag6W6USttMZ8sYOXzjuFEhpFEhvw0QmK3v5B2LSX5quk2rvQxF1out8cSfP6/nqS3\nIzweROfyZDERaSx9qsiMmEvhSbO+S1s8r4uezhCZXJ5wMFDVmMSJwbDc9ZTPO0aSGYbj+0JiIp3j\nh+u3c8uDlYVEM6M76rUkTqfaSqPGYPZEQ3RFghi238QYjV8UkUbRp4o0XCuHp1q6ROfyRITRVJbt\nsQQ454XDCiaATPWcTB5NHOAAACAASURBVAyG7zttBd++Z9N+19NXfrmBy956FNlcnlx+X0j80frt\n3LJ2G3sSGWDqkNjb4bUkhutQbaWRYzA1WUxEZlJ7f3NJU2jV8FRra+hc/SJfvyXGv/xs/0W1P/HG\nl+33nFU7i3/nSJIv/Pxp+jpCHNQdHf9FIwAc1B3FOUcoEGA0mWPTC2Mc2t9RdUgMmNHXGaavI1T3\nknyNWrVAk8VEZCbpk0UarhXD03RaQ+fiF/loKsuX797A5qG4N1PYwZahOF+86/d86Z3H1zQmcf2W\nGF/4+dM88ewInZEgRx7ax7ye6HgX855EmmAgQDyVJRDwAuHND27llge3jofESCjAW485lPNPXHZA\nSAwGjL6OMH2dYYIBq+8TUqRRqxa04vJZItKa9OkiDdeK4Wm6raFz7Ys8NpYmkckRMK+aCXhrGibS\n+Yqes4nd/YWw3tsRoiMcxDnH4ztGOGZJP8EAvO3YJdyydut4l/Oh/R382TVrGa4gJIYCAfo7w/R2\nhAg0MCTOhFZbPktEWpM+ZZpcuyy/0mrhqR6toe30RT7VdTjYHaEzHCTvIJvPg4M8js5IYMrnrFR3\n/7zuKLmc46DuKEcu6uPxZ0eIp7Ls2pvivX+0gmXzurnwpGX88qld/PcTu7jvmSHAC4lvOfpQzj9x\nKQf1RPc7TjgYoL8rTG80hFlrh0QRkZnUHt9kbardZhAXh6dmD8Gt2BraKJVchz3REH99+mF89o79\nxyx+5PTDJ33OynX3X37WUeNhvb8zzEsO7mEkmeVjZ7yUrbEx/vLGh9gWS5D1WxYnC4mlqq2IiEjl\ntCh3DWZiUe5qFvRtNa0Ugps91DZatdfhZLOhS9k6FOey2x9nYV/H+LadI0k+9dYj2bEnzld+uZF0\nNk8wYLz9hMU8tm2E6+7bTCbnfW4FDA7u6+DzbzuaJYOd++07Gg4y2BUeX15GRGSu0qLcbapVZxBP\npdWW0al3V3Krhc9qr8OeaGi/es9TKVnCziCdyzO/p4OPnfFSdo4k+d8NL/DpHz9JLO6NSQwYLJvX\nxcoFPYyls/tNUKml2oqIiJTX/N9Wc1QrziCuRLuG4Eq0UotqQeE6LMw8zuXzdb0OC939X7zr9zy3\nJ4nheNPRh5JIe5NlfvLos9zy4NbxkBgOGvN7oqyc301/V4RkJkcwYPT79ZprrbYiIiLltfe3cwtr\n1zFz7RqCp9JqLaoFPdEQr3vZQv71zqfJ5x2BgHHpGS+p6zlnsnnSWW/W9POjKUbu38K3frOJ3WNp\n9voVV8JB4y1HL+L8k5bywmiKG+7fwu6xFMGA8YFXvoiXHNw7rWorIiJSXvN+S0nLzSCuRLuG4Km0\naovqaCrLz5/cxUkrBsdbFv/7yV2c+YpF0z7vbC7PtuEE/3HX7wkFjOFEmkwuz5M7RykMpQ4HjTcf\nvYh3nrSU+f7Elfk9Uf7hjS/zZkv3RMg7r9u6HcJiqw1TEJG5QZ9GTa6dll8paMcQPJVWbVEthNyD\nuvfNMN45kmR7LEFXJFjT65fLO4bjaUaSWZ4dTpDJ5tkZz/D8aHo8JAYMFvZG+cSbXs6Ri/rG7xvw\nS/Itm9fFb7fv4VM/eaKluvUn04rDFERkbmj9X8WlJfVEQyytYLZsuyi0qCYyOXaOJElkci3Roloc\ncgFGEmmGxlJ89qdPcNntj/PRW9fzyNZYRfvK5x1DY2m2DsXZk8iQSGe5+3e7eHjrMBt27WtN7AwH\nOXHFICsX9vCi+d2AV21lsCvC0nldHNQTJZnNj3frL+zroDMc5Mo1GxlNZRvyPDRa8TCFdng8ItJe\nmvubSlqOutHKa0SLaqOf7+JhA9tjcTbtjpPJ5uiMhjlmcT8dfqiZbOxlPu8YSWbGy/QlMzl+/OgO\nbn5gy/jEFTMY7AqTyzuWDHYSCga48ORl9HWES1ZbiY2lSaVzRENBMrl8y3Trl9OqwxREZG7Qp5DU\njbrRplaPYQWFgLgtFuc792yq6fmuJmQes3SQy886ir/77iO8YlEfT+0aJRwM8PiOEU598UFlQ41z\njpFkluF4mlzekfJD4k1FIbEwJvGsYxcRDgaIBAOkc3kO6omwaKCzbLWVbbE4Tzy3d7y84MoFXXRE\nQk3frV9Oqw5TEJG5QWFR6qJVZ/u2mkIgT6VzPPHcXl5+SC+LBruqer6LQz3AeScu4bTDFkx6v3Q2\nTyTole6zXWNeOT/nGI5nDgg1zjn2prIMj2XI5vPjIfHmB7cyNJYGvJD4plccyjtPWsaC3n3jIcPB\nAANdYXo7wmXPZTSV5Tv3bOLlh/Sy8YUxb1LMc6N86fzpX2uz1TI+Vyd+iUhr0CeR1MVc6Eab7S72\n4kAeDQUJGGx8YYyFfR0VP9/F+0i4HI9u38M//OC3HL2kn4+cfjjHLB0cf5yRUIB0Ns9gd2S85SuT\ny3Pkoj4e3T5MOptnLJXlr04/7IAyjplc+ZD4xlccygUTQmI0HGTAXytxKoVrbdFgFwv7Okhl8+xJ\nZFgy2DWt53e2W8bn4sQvEWkN+jSSumj3brTZDhKwfyDP5PKEAgEvlGXzpHOVLZZd2EekI8C6HTGi\noQABIGjGlWs28t7TVvCdezYxNJpi81CC5Qd1Ma87wiWrV463fGXzjsUDHeTyEA4F+M49mwgYLB7s\nIp31QuJPHtvBTQ9MHRJrqbYy8VpL5/JEw4FpXWvN0jLeSqsfzPYvTyIyczQbWuqiVWf7VqJZZqoW\nh6Rw0Bunl3d4M4srfL4L+yhMNsGBmXnVUNI5rlqzkVDA2LEnRSRo40vbfOmuDaxc2MsV5x7LpWcc\nwUBnlOUHdbOwN4pzji/dvYGdI0m+99A2LvzWA1z5y40MjaUJB42zjlnE9e8/mY+cfvh4UOyKhFg0\n0Mmh/Z1Vl+VrxLU2HsQj+1rGszlHzA+7sr/1W2J89Nb1Vc+IF5HW1Prf5NI02rUbrVm62CeOa+uI\nhPjS+ceyZLCr4ue7sI8v3bXh/7P37nFy1fX9//NzrnPbmZ29ZrPZJLBJIBcIYABFbREqIral/VYt\ntL9vrdqvv0dFsUX71Wr9quWrX7WWtli0N7GXbwVRq6hFFEUsgmICJJAbSTa3vWWzl9m5z5zb5/vH\nmZnsbmY3u5tNMiTn+XhAsufMuQ6c89r35fXGcjw8VXJ5dzO26yHxfQylBNvz0IRgIm+x28vgepKn\nD4xy08YuIqaG6/n1jgXLRRUKA6kc7/znbUwWTzSu3LKpi9uv6aEjHpp2/EREx9RObyTfUv+3dr5H\nxpeSRonCBgQEnD2C/7MDlpSFptEWk8o62+mvMyUkcmWHwYkCCEF3Mjyva5kpkoAFR7829yT5m9uv\n5OkDozy4tR/HkxRtl3df38tf/XAfe46lSRccv6lFUzBVBUt4PLi1ny2rWyupb5djky7pks3BsTyW\n4wH1RaIQgpip0RzR0dWlS2YsZco2aDCZP43yy1NAQMDZI/g/O+AkzpYYW0wd4LmoHTwTQmL70RSf\n+t4eDo7mAehtj/HhWy6d17VURdLp3IuYqXHTxi6uW9M+TXgKBIpQCOt+c4uU4HiSTcsTFG2XPcMZ\njmdKHB7PcyxTrhlpq4rf3fw7M0RiU0ijOayjLaFIPFOcr5HxpSaIwgYEXHgIWX3aB8ybLVu2yG3b\ntp3r0zgjnC0xlis73PXQdj+VVXnhFG13zlTWYraZuf3pCIGlEtG5ssOdDzzH7uEMpqqCgLLjsbEr\nzt/cfuVJ+6533NO9F/XOafdQmvseP0BHPES+7PBcfwrb9ti8shkhBIWyw/quOP/28yPYFdsdAXTE\nTT793y5nVavfjawIQTzsm2mryskeiQEvf3b0p/xmJ1fWfnkKPFUDAhoXIcSzUsoti90++NU5oMbZ\nrEVaTCrrdNJfSyGClyrtmcpblCwPRYhaxM0WHkXLPelaZjvvhd6LuYRuzbvR9th9LItE0tUc4aKW\nKHuPZ5ks2KQKFhN5m58dnAD8iSs9yQhr2mMUbAdDU1AVQSKsEw/p06atBJx/BFHYgIALi4bKDQkh\nDgshXhRCbBdCbKssaxFCPCaE2F/5M1lZLoQQ9wohDgghXhBCXDVlP2+rfH6/EOJtU5a/orL/A5Vt\nxVzHeLmSKzv0TxQW3K17NjtCZ84cnk8qazHbQON0M1dJRg1ChoInJY7r4XgenvRtZKZey1znvZB7\nMVfn6tRjLG8Os7Y9youDGfaPZEHA9WvbOTSW59CYP89ZUwSdcZPrLm7lsu4EQoCmCla3RelJRmiO\nGIFQvEC40Oa7BwRcyDSUWKzwOinlFVPCpR8CfiSlXAv8qPIzwBuBtZV/3gV8EXzhB3wMuBa4BvjY\nFPH3ReB/TNnu5lMcoyFYiPg7HUuLxYqxhZ4jLM7+ZLGWKY1mixIzNd5341pWtkTIWw75ssOqlgh3\nTjG3nnrehqqQLljYjkfZ9mrRw/nci1MJ5eoxwrrKsXSRg2N5XNdjOF1i11CGb24fIlXwReKvbe7i\n3955DR/7tQ0gYCJv4SH5wOvX0d0cDkRiQEBAwHnKy+FXwluB6yt//xfgCeCDleX/Kv2iy58LIZqF\nEF2Vzz4mpZwAEEI8BtwshHgCiEspf15Z/q/AbwDfm+MY55yFpE9PN4282EaOxaZ4F5PKWsw2Mwvy\nM0V/woihnbvflTb3JLn/96+Zsxs6GTXIlR2e709RKLtIKYmYGgOpAj0tEXo7mnj/6y8BKemeJcJz\nqnR1c0THQ3IsXWT3cIaC5VKwXDJlFwBNEbzxsmX8zjUr6aw0rqxIRvjLt7RgOS4tMTOILAUEBASc\n5zTaU14CPxBCSODvpZT/AHRKKYcr648BnZW/dwP9U7YdqCyba/lAneXMcYwaQoh34UcwWbly5aIu\nbiGMZErsHc7ypZ/2kYya8xJ/S2FpsVAxthQCdaFiY6HbTBXBg6mCP5mkJcxHH955TgvzY6bGJV3x\nOT/jeC75soMiBEIoCAH/9OQhkPDlnx0+pUCfrXO1OaKTKdlM5m1+44rlfO77+xjNWlTb3YSA9pjJ\nR960nsu6EwAYmkJzJKhPCwgICLjQaLQ09GuklFfhp5jvEEL80tSVlSjiGW3fnu0YUsp/kFJukVJu\naW9vP5OnwENbj/Kme5/kzgee42cHJxieLAKnTp+eThoZTqSSgXnXIjVainc2NvckufvWTYQNjatX\nJ1nbGT/ntYv1mJrOT+UtTFUjGTVobwqxLB4ipKvkSvOvwayXrn77dauZLNgMpYp8/dl+7v7uHvpT\nRSR+d/PKlgivuriVtZ0xettjhHSVZYkQK5ILq09bbO3sy50L9boDAgLOXxoqRCClHKz8eVwI8U38\nmsMRIUSXlHK4kmY+Xvn4INAzZfMVlWWDnEgpV5c/UVm+os7nmeMYZ52RTIlPfW8vJdtBEQqOK3m+\nP0VXcxigJv7qdbeejh/gYlPJLyfPNcvxMFSFRNg/t0YzE575HbzjutWEDP/3OQF4SDwJqipQYJpA\nn+s6qtHiwVQBIQSeJ/natn6+8oujjOV8Ua8pglde3ELRdtFVP4L5jlevZk2HLxZP91ouFGuVC/W6\nAwICzm/O/RuyghAiCihSymzl7zcBfw58G3gb8OnKnw9XNvk28B4hxIP4zSzpitj7PvCpKU0tNwF/\nKqWcEEJkhBCvBJ4Bfg/4/JR91TvGWWfvcIZ82cFQFQRgagplx+PwaJ7O5hB3XN/LgZHsrC+kxdT0\n1Usl/82P9vPBm9efcrLIy2nyRSML23rfwT8+eYjfuGI56aLFkXE/utzbHuPOG9Zw/9OH530dRcsl\nXbSREh55cYh/f+aESFQVwRs3+TWJyxIhCpaL7XisbI3QGjOX7Frue6KPu2/dhOV4563VSjAGLyAg\n4HylkZ5gncA3K242GvAVKeWjQoitwENCiHcCR4C3Vj7/CHALcAAoAG8HqIjCu4Gtlc/9ebXZBXg3\n8M9AGL+x5XuV5Z+e5RhnnXhYx/MkJc9FCIGUEkXAH76ul9es9dPfNTPmWV5IC63pm1nrWLRcXhhI\n84nv7CIW0k4ZHVkqz7UzPTmm0YTt1Out9x3sGJgkV3YwNY3/edM6Nq9soTmiYzkeb79uNV9++vCc\n11GyXVIFi3TB5ns7j/GVZ44ymisDJ0Tib17ZTUhXSUR0YiGNFcnIaTf+1KudHUwV+MDXdmCoynkb\ncQvG4AUEBJyvNMwTTEp5ENhcZ/k4cGOd5RK4Y5Z93Q/cX2f5NmDTfI9xLljeHCZsqOTLLgKJlBA1\nVa5cmSRmavRPFJb8hTQ14qarCi8MTqKpCt3NYSzXm1d05HQNq89W+m4pzYRPR9zWSzlXvwNDVXhh\nMI2U0J8q4HqSv3hsP3fe0MuTB8anbdOdjJx0/LLjksrbTBasuiLx5o3L+N1rV5IqWNz/1CEUBKah\n8N7XraGjKbTo+1GlXvf5kYkiV69Okggb523ErZEj1wEBAQGnQ6M1uFzwWI7HpV1xOuImibBOR9zk\n0mVNWI4HLK6J5VQF91ObIAZTRRxXsrk7gaYqZ6Vh5WybZi+FmXDVz/LPvrWTP/y/z/LzvrF5b1vv\neu9/+jBvv2517TuwHQ9FgK4qxEwdpOSvf3gAXRHTtpkqFC3H43imxKHRPA/84ij//Uu/4G9+tJ/R\nXBlVEdxy2TL+9R1X8/6b1tEc0fnaswO0RA1WtESIGtqS3fOZTTWZksOqlvC0WtFGbII6XRbrAxoQ\nEBDQ6ARPsQYjGTVQ/VQ8mipwPb9HtSoGF5pKnW/ErtYEMVHgs99/iZDhNzWcjejIyy19N5Ip8ZeP\n7cP1PI5OFLFdj/c+uJ17b7uCV/W2nXL7wVSRXMkhEdKBE9e7IhmpNKIU+fh3dnJwNI+mKDiuhxDg\nun6keeo2qbyFqSn+OL6cxaO7/Eji8eyJSOIbNnbyu9eupCsRRlUE8ZBOumijCuELUZb+nk+N4Bqa\nwkcf3nlBRNyCMXgBAQHnI8GTrAGRSCzHJV92QQgOjObYOTDJphXNpPIWvR1N83ohVSNYmiIwNRXX\nmzulXPX9u/PGNWe1rm9qtNRQFSYLNqKyvJHIlR2eOjDKvzx9hL3HshQsh0RYJ2bq5Eo2X3iij8tW\nNM95r7YfTXHv4/vZN5Ll4Fi+EsEV2K6H7frTWbqTYd53w1re++B2ciUbT4LjeRQsl2ePprhiRTMh\nQ0VKyeBEgfFcmSf2jU4TiQJobzLpaQnzq5d30ZOMkgjrNIU0FEWga8q8Uqank2qfWprQSLWiZ5ql\nmiEeEBAQ0CgIKc+obeF5yZYtW+S2bdvOyL77Jwrc+cBz7B7OAhJVUTA1hVUtEeJhDceFkKHwvhvX\nnrKmr3+iwB9/9XmG02UkEoFgWcLkr3/7SnpaInNue6abTWayoz/Fpx7ZS99oDoCL26N85Jb1DdEE\nkSs7PLV/lH9/5gi7h7OoisBxJfmyjaIotEYNPAm97VE++ZuX1b23ubLDYKrIZx7dQ1NI95uIBicp\nWr5VTVtUZ7zgsKo1QkvU4I7reylYLp//0QH2jmQwNIWVyQhHUwVsx2NFMoIrPQpll4HJUq1MQVUE\nbTGD3rYYyahB2XFxpeTe266kqRLJrLKjP8V9T/ThuLIm4Kbe76WuIz3b/00FBAQEBPgIIZ6dMkZ5\nwQRP7AbD0BSOTPg2Kbqq4kmPouXSN5ojpKvomoInJZ98ZA/3//41c750q/syVEFY1yjZDkcnivPq\ndp1vdGSpBEBvR5PfyNPTTCJiYM+zseZMs/1ois8/foAdA5N+uA4I6Sp5zwEEjutRtF3WdcYIGWpd\nD8yq6MqVHPaNZLlqZZLWmMm1F7XyX/tGWb+siYNjBQxVcCxdorPJrF37x35tA5/4zm6WJ/0Ucmci\nxOGxPOP5MmNZi1JFJALctKGTN25axkPb+mltMjE1hbCuMDRZYmiyxCXLpovFuVKmZ8IGJoi4BQQE\nBLw8CZ7cDYbleLRGdVJ5C8vxa9XCukLJ8WiOGBiaiuN6HBzNMzhRmHNcnOV4rGqNcCxdomj7I+NW\ntYRqUajTZSkjT6m8hUDQVunG1VXlnNctVgWTIgSGpqAKwWi2TFhXMTSF1W0R+kbzuK7H/uN5PviG\ndSd5YFYtbsK6SiKkc3AszwuDk7xmTTuFsoOqCJpCOhKJqankyw5SQsly2T2UZlVrlGjIXy6AvtE8\nh8byWK7/HQqgOxmmLWbwtutW09Fk8p0XhlAEZIo2LwymcVyPzzy6p240ejYB93KrI10MQaQzICAg\nYH4ET8gGw9AUxnIWqgCvMniwZHtETb/WDKhFuBBitt0Afs1fS9Sgs8lEVRRcz8P25JLUAp5O5Kne\nS7oRbUeqgqklaqAIgaIIoqZKyXZxPUnJcrmqp5nORBjX83hk5zEEI8RCWu2efOEJX2y2Rn2D683d\nCZ49mmIwVSRsKPS2x3A8j5LtMpErA4KfHRwDBH/7+AFURbBlVTP/8fwQ/aniNKHfGTe5qC2GKiS2\nK+luDrO6Lcr7blzLvT/yo6GaKrhqZZKwoS4oMtiI38dSEkxaCQgICJg/gXVOgzFZsLFdDw9/QLUE\nDFWwujVK2fEo2g5lx6O3PUZ3Mjznvqqd07YnyZUdbE9Oayw4nRm2i50JXbWc+fi3d3HXQ9vZ0Z+q\nicfbtvQwkbcYTBUawnakKpgs12NjV5yy4yGEoDsZpjPuR0AH0yXKtks8bFC0/JT01HsiAE/KmtVR\nyFDZvKKZj//aBu69/Sre/Iputh1JMZm3KTsSU1P86CKSsK4ynC5x7+N99I3msSp2Om/ctIx3vXY1\nluuxa2iSXxyaIFOy+dT39rCjP0VvRxO/96pV9LbFeM2adlpj5oLtas5nG5izbdUUEBAQ8HLn5f/k\nP88olG0sx0MTYEtfLJYcyS+va2X3cI6i5RI2VO68cc28Xtyz1aWdbmRlMZGnetHITz6yhyZT90fa\nTRTpSYaJmhrvuG71OY/0TLUpcjzJxq44t27u4uEdw5i6QqbkIKVk13CGzSsShA0FwfR7EjJU3nHd\nav7xyUOMZsq17+6Srji5ssPXnh1AEQJNU1CQSCRNIX9KyxP7RilXIomKgJs2LOP/e+VKmiMGn/3+\nXl7Rk2TnUIamkE625KIponY/HU9ycDyPqQu6k9FFRQbPVxuYCyHFHhAQELCUBE/GBiNi6ggpKU8p\nK5TAtiOT/PVtVy5qtu7MurSlaF5YzOi8mS9pXVU4OJrnsuUJhtNlDFVwPFtmcyLE/U8fZtMpbGjm\nw+nWpc0UTKm8xcM7hkmEDTYuj7NrKEPRcsmUHD5w0zqAk+6JlL4dEoDt+pG6XNlhcKLAwdE8Yd2v\nSfQk5MsuEtcvQajQGjX4+K9tZGN3nFhII1dy0BWFiKmhKBDWNYqWX+t4cDTPlT1JljeHQEp2H8sC\ngpChLioyeD42pZzvKfaAgICApeb8egucB+iqwKnjZpQp+hHHU1nezIeliqwsNPI08yWdLvgp0bCh\nVtKuGkXbQVUUinb985lL/M3WhTy12WRFnfF4p2KmYKpeQ2vMZHNPgmzJ4RO/vhFdVUhGjWn3BPxZ\n3k0hHU3x7XI+/M2dbF7RzK1XLEdKiet5aIogW57eeNQc1vCAroTJ15/vZ3nzGi5ujxExHFRV4Hoe\nihCUbAchBEXbT6M2R/yu5+XJCAjBHa/rZcPyREPMwG4E4dloM8IDAgICGp3g6dhg9E8Uqed8eTQ1\nP8ub+bCUkZWFRJ5mvqRB0tseAwECX/QowhdBAiiUHXJlZ16p83qzlu+vdCFHohqDqTx3PridDV1x\nTF1ZdENDPaHx65d38elH99Y9r+osbz2ksG0ohamqKMJDSnjgF0cxNZVjmdK0SOL169q55bJl/NvP\nj9AaNehMhCnZLn//5CHu6UpMO4dlcZMjE0VWtoRQhKC3PYblemiqQqZo4UnJqtboORNC9b6z3o6m\ncy4ez9cUe0BAQMCZIDDlXgRn0pT7R7tHeOe/nrzv3pYI//wH1y5JZBFObcg8laWODE3dX9/xLPc9\n0cd4rszRiSKrWsKoil+912Tq0wTGXQ9t98VfReAWbZd73noFwEnrJvIWCn50zXY9ftY3juN5XHtR\nK0JQ27be9cwnemloCpbj1UbZ1TuvmKmRKzvc9dB2pOfXNqqKwHJc2mMh9h/P1WoSAdpjBnfcsIbO\nJpN/+dkR9h3LEjY0Ni6P0xozGcmU+ODNlxKp+DkC085l6v2cyJU5UrmfLTHznHT7Vq996r05li4R\nNVUEIuhCDggICDhLBKbc5xnZsl13eTikLWlNVW9HE+9//SUgJd0tkdOeLb0QpkYjZ84QnizYtSkn\nVYFx3xN9vP/1l8yaOgdOWjdZsGtdyNVxebqqoAhwKz6G9dLcC4le3nF9Ly1Rc86UfjUKeM9j+8iX\nbGwPXE8ykskAfuPKa9e285o1rVy1KsmyeIiPf2cXrVGDiKn5DTRDGTb3JMiXHT7z6J45hdbmniR3\n37qJD3xtB1evTpIIG0tiqL0YZpY7GKpC32iOK3uaaWsKnbPzCggICAhYGIF1ToMR1tW6y1+/oeO0\nXqhTbXKq9jWffXQvf/nDffQdz866zZmwGJlp2RMzNXpaInTGQ0QMP+o005IHKWupc2Ba6nxqWr26\nztQV3l2xfkkXLDwJ7VGDrYdT/OLQOHuOZRlMFeZ9vbOtM6bMWLZdj7FsCTjhZVl2XFqjJp3xEAXb\nn+9cjSZee1ELH33Telzp8eT+Mf7uJ33sGkrjeRAPG2zsiiOEoGS7TObtWqf0qb4Ly/EwVIVE2Jh2\nD+drm7NUzPxeJgv+L0KJyLk9r4CAgICAhRH8Ot9grO1sQsBJdYvPHJpgR39qUVG9qRExiSRfdlmW\nCJ2yE/pMWIxMPReA3756BdetaT+lOXd3S+SkWsF3XLe6li6u17CwuSfJZSuaSeUtDoxk+ZNvvAAS\nDF3h0vZYreO6eq0Fy51X9NJ2PbxKdHIyb/Hmq7r5+//q48i4P6axtz3G3uE08ZDOV545yndeGGZ8\niiBqj5l0J0N8RzDbmAAAIABJREFU6I2Xcu/j+2mJGERNnYLl8NWtA0j8iGhLzGTzigSZksNdv7KW\nv/1x3zQRPdt30SjdvjPrOwX+zG/b9aO8QRdyQEBAwMuDQCw2GOli/TS0JsSiUnYzbXLGsiX6RnOs\nrNQ+ninRUa/uL1d2+PzjB1Aqk2d2DWf48Dd3cvmKxLRRdG++qpsHt/af1Kk6NWU9MFHg/qcPT0sJ\n12tYqKaCdw+lkRJUReB4Ek1VcFzJU/tH+cbzgzUhnSn6TTbNEb3SKCJq6XGJZDCVp2+0UJu68v6v\n7cD1JMfSRdZ2NtGTjJAp2/zRV3cwkilhuydkf0fMZMPyOE1hnXTBwnJcFARRU5/2Xdx2dQ9ff26w\ndv0fuGkdvR1N8/ouqve9OmbwXHf7zmwkqdZUnuvzCggICAiYP8FTusHYN5yp2w1dsl00TV1wVG9m\ndLCaApws2LQ1mXMKwMVajMxW9/fU/lGe708hBGSLtj/rWvVnLt/3RB9vf9VqvvwzXwAKxElRx+o5\nAdz9n7vr+kTWawDKlR2+uq0fgGzJF+NP7h9lbWcTX3nmKM1Ro9Yxvf94Fk1REMKPEL75qm4++vBO\nypbLaK7M0fE8YVNDVxVKlsPOoUwtEvzCQIqC5dI3mqdou7Xjh3QFx/XwkCQjOh7+JJfVbbFpAjBT\ntLBdj809Sa5b036S8D3Vd1GvI7x7EVZBS81sNarn+rwCAgICAuZH8KRuMIazpbrLUwWb5pi54JTd\nzOig7Xpc3B7Fk5KRTOmUAnChL/fZDL/vvnUT//BkH5mijev54mo0a9ES1UlEDEazZb7wkz5aKsKt\nYDl8/blBrlvTfvK9WGB6PJW3cD2/mQQEnvRnKR8ezXEsrbFlVRJdVegbLRDSVV6xMontemTKDg9s\n7UdXBH2jedIlG8eVqIpHW0RnaNLff1UslhzYOZSpHdfUFJIRA1NTakJwKF0iEdG54/peOuOhmgAc\nTBVq3csffXjnrM0rs30X9e77/U8fbsjmkfPR6DsgICDgfCZ4YjcYTSG9/gpBXVGXKzsMpoqzdjXX\niw5+5Jb1C/K6W8jLfTYht3c4w6GxAqpQkMLDrYwy9CSVjmWJUvn81O0Wkh43NIX+icJJ15SMGv7+\nFUFHk8mxTAlDE4Qqs5t3DKa5qqe51jEdNTUOjeXY3p/G8yQeoPqZcySQLjkVn0if6Vba/izvtqhO\n0ZWoit/kUrBdooaGIuDtU0YZLrR7ebbvIhhhFxAQEBBwpgjeIg3GZd3xusv/7E3rT4o0bT+a4v98\nby99ozmkhBXJMB9646W8qrdt2udmi0gtVkTM5UM4m5DTVcVPLysQ0TVsx8NyPTwpyZYc7ri+l/uf\nPjyv+sh6Avj16zv46MM7T2qcAV9IvePVq/mf33iRfNkfiycE5Eo2YUPFLruM5/yO6d62KMezJZ49\nMjmtHMCV060DZgrE2r1ekUBXBQeO55DA8WwZz5M0RwxesSpJ2FD58tOHuWzKKMN63csLFXqN0tQS\nEBAQEHD+EYjFBqNozSZDxLSfqs0iRyYKqIogXbDYM2zzngee577br+SVMwTjUqX+qnVxZcvFA959\nfe80cVpXyF3awT/99CAgKdse0pMoqkLC0FnfFedzb9lMZzxE2FDnXR9ZjcgdGc/TFjP59KN7Cesq\nRenywmCaD39zJ6tboygKNXPv993Qy7d3DPN8/yRSSlShULQ9XCn51cuXsSIZ4R+fPMSLgykkvjiU\nnOhMn+2bqXJpZ4ze9hjPHJrArKSzJ/IWL41kec3atlrUdKYQXAqhF4ywCwgICAg4UwRvkgZDV+tb\nX840607lLYq2i+14TBZ9WxYpwbJc7n38ABe1x2pTPZZKMFTr4kqWU+sIvvPB7Xz+tiumidOZRtsf\nfXgnTSGday9uY+vhcXJll6iusLajiQ/efAmd8dBJ21WFUjWtXL3menOfLdejaLtc1Brl2eEUpqYg\nPcnB0Ty6Jnjt2nZs1+O/Dozzp7es5yPffJG+0TyelLieRBHwF99/iXXL4hQtl6Lty8JTicP2mEF7\nk8mtV3Txk31jtDeFsD05LZ0dNTUOjOYolGcXgksl9ILmkYCAgICAM0HwNmk0Zhm/+JH/eJGS5fLm\nLSsBPxqlq4JsyfbTqvj1fznLZedgmj/45600hXRChjLNluZ0SOUtypZL32gBTRGEdJ1c2ea+J/rY\nNCWtCicimdXZyJGoRsTQuHH9Mo6OF7jjdb28YnXLrDV5U8VgtmwjEMRM7aS5z3pIYSxX4tBYniZT\nw5MSx5Y1qxvFEgylCrQ2hShZLkhJ1NSImSoFy0VTFZCSkuNxaDSHqipoigApcWaZhCmAsK7gSfj9\n61bx1qtX8cqL27jviT5KlltLZ2sVL8He9hjuKRqKlkronenmkaUe/RgQEBAQ0PgET/sGIzvLdBRF\nEXzm+/t47boOOuMhYqbG7167ku1HJ8mWHdyKYNRUyJcdXhrJ0hozkUg++cgePn/7VYuONE6dh+zh\nj84L6fqUEXpi1vq6et3YzVG9rlCcerxqZy867BicRBGCX17bjuV63PdEn18/KGHbUAqJxHE9xnJl\nyrZHtmTTFFLJlV1cz+PZo5MkwhqW698LV0Km5CClRFcVIrpGpmyTrdUzStw6QlEVcFFbhJ6WCFFT\nx5OSH+4d5ZbLu6eNT0wVLO5/+nBNHH74lkvn1VDU6F3CZ2L0Y0BAQEBA49O4b6YLFE2pn4bWFIHn\nSY6M52tp2+vWtHNFTzN9ozkGJksIAbbr19oJ4adXFaHw0rEs733gOTShLDjSOFMgvGFDB3uPZcmV\nbXRVobctiqkrs9bXLSbFWu3sLUqXHQOTZIu+gD6WLrKiJcpYrkzectk7kiWkqSAEYUOjJWpwzeoW\n/vVnh8mVPb+TudpyjUAVMDBZ5DVr2ilbNnuO5QHJuH1iuooqwJ2Rfw5pClFTxXY8EiGdZYkItutR\ndjxyJYunDozyjecG52UQ/nJlNkukRrTmCQgICAhYWoKnfIORK9Wf4DKes4lHdFa1RmvLDoxkyVkO\nI9ky4EcWdVVgu5KyA2PZMooisByPg6N5PzIo/eja/b9/zaI8E//rwDif/a3LuP+pwyhCYOrKrJY+\nVbG00BRrMmogkbwwmMZQFRQ/U0zfaB5XSvYfz9MaNRjPWcRCGhFDZXN3gpLjsWc4S3PEQK1cd6pg\noyqAlAgBCoJ82SFd8ogaCvkZDUVTI4p+ql2ppPsdwobKkVQRV0pGczaW62I5HmM/LLO6LUpr1Jzm\nK3k6NFq6N7DmCQgICLhwCZ7yDcZs4/48oDnsN0zACSHXEjFojZnYjksqb1eMp33h6HgSRfopVVNT\nCOkajusLx8GJAt0tkTkFST2BkMpbRE2Nz71l86xp7dnSlfMRFVWR9BtXLOeFgTSKkMRMDYGg5Hjs\nPZZjZTLE0YkiridJF2w0RVCy/e7sou0ihCBVsLEcDwm+v6EimMxbNIf9Gcxlx6XseCjCDzxOzTq3\nRHSWN4d59/W9fHVrPy8OpklGDa5Y0cxk0ea5IxNEDIWC7SEl7BnOMpIpsaErzrJEmIFUgQ98bYc/\nnWYR6dpGTPcG1jwBAQEBFy71c54B54yRdLHuch3IlV0GJwrACSGXiBgo+DOPNU2hKaSjK4KwoaAq\ngqip1QylvUqnricle0eyvO+B5/ngN17gfQ88z47+1EnHnCoQAAZTefYcy3Lfj/v46MM7mciX60YU\nP//4AaQHLVGDsO7b4eTKDrmyQ/9EgdwsdZnbj6a466HtfPzbu3h4+zCrWyNs7Epw/boOrl6dpLc9\nytqOKCNZi6LtYWoCISBTdNg1nOH2q3sYmCyiq775dvW6J4s2x7NlbFeSKdk8f3iCibyF41EzB6+i\nCljVGuWTv7mJWy5fzh+9fh2r26K86uJWJHBkPI9EULAlAjA0FYCJvM0vDk/w433HOTiaJx7S6IiH\npl3/fJgazV3M9meKajlB0XYZyZQo2m5gzRMQEBBwgRA86RuMwiwtuDa+2EP4Cqgq5I5niliO54+T\n88BxZW1iSbZoowiBUASZoo3lWkggoiv83RMHSBWdWmTtU4/s5d7br5wWLZxab5jKW+w5lqO3LYKu\nCoSkbs3a47tHeP5oClNTUVXBxq44Zcfj4ecH+NHe4whE3WhZvZR3ruzgSo/xvIWmCt79y72+x6Pj\nIgSoiooiPKIhjYtbY7Q1maxqCTOcLmM5Xi2lrKsCq/JDyZFYolLLOAUVUFW4rDtBIqLT29HE9qMp\nPv/4fo5OFBiYLIIHmiZQFYGUEscFx3NP+DB64HoSKeW8JtHUo5HTvYE1T0BAQMCFSfC0bzCWx2dP\n67VGDbqTYcCP9Lz9utXc+eB2VFXQFg/RHNIYyZZJRnQOjuWJGgphQ6O7OcRLIzmaQjqmrtAVD/Hi\nYIbOhImhqjiex97hNH/81e2EdXWamKsKhN1DaT76rZ3+CLyKZl3dGpkmYn7WN8bHv7uLXMkmrzgk\nIwa/ODwBwIsDkxi6wuXdzbVo2T1vvQLwBVLBcinbHiFNxXE9Ioafcv/gzZcSMVQGUgW+/PRhXM+f\n+CI9ia4pxMM6ngea5ntUxsM6LVGDXNklW7YpWF5NKFaZKRQB4hGdK3ua6UyEGcmUGEwV+cJP+tAU\nwerWCAfH8mRLDi1Rg4sqP3uAkJxI/Qu4ZlWSZ49OMlmwaWsyF5yubfR0b6N3bAcEBAQELD3BU7/R\nmKMwQBWCvuPZWkRuRTLChq44ibCOqSmkizYDqSIDqSKeK5ES1rRHMDSVI+PFSk1diHzZ4YXBNK4n\nQfUjln5aV6EjHjqp0zVmarTFTA6O5XEqwksC+0ZyvDAwSU9LhFzZ4dPf20u+5CAUBdv1GM2W8SRE\nTRXHlRiayq6hDK/qbSVbcqZ1EU8WrVoTTq3L2lBr4vju/9yNpgi6kxF0VWHPcMa3qcnb6CrsGspy\n93d3kyvbjKRLKEJQmHUazslkK7WiVXGGlBwdy3F4vOhHDqVEepKJvMVkwcar+GFqip/K1hVBLKTh\nScHF7VG8U/gqzsaFOIml0Zp5AgICAgKmEzyZG4yhVGnWdW0JY5qIS0YNTF1BCF+8bT00Tqbsoivg\nAumSy0/2jyPw1z9zaJyrViZpjRlEDBXXkxQtB8vziBgqbTET8FOfkwWb3UNpNixPVMy1i3iexDeh\n8ZHAl548xC9f0sHgRIGBVBFFEaiKXy9ZsFxUAW0xk5FsiXTJpjmkky5YgOShrQPEQhp6SGHnUBqv\nMk3Fclx2DmX43FsuB2D3UJrRTImRTBlXSlThR/tUVRA1VQ4eL2C7HruGMsiKqOtsDjFZdKif1D+B\nmPLn9v5JrljZzPtuXIuuKRwcK6AIvy7Rdl0cFwwpEYqobRczVSQKQngIIfCk5CO3rJ+Xr+JsXEjp\n3kZs5gkICAgImM75+xZ6mdIc1Wdd57p+TWI19Ts1CrV3OE2q6CAAe0ZArSqYbFey7UiKV69p5aNv\nWs8jO49RtDx0FcqOPzZPUxUGUwW/keXxA5iGyh3X9xIPqSiKwHMlquLX50moGXIj/GaT5rBBpmTj\nen4DSDykIyW0RkxGsyXKjosrJbdd3cNXtw4QMTQGJgqkC7a/P8X3hpRScs8P9qGqAlNX2TmUqZiO\n+/Y/+bLDpu44yUgIjwJ5y0V6fvezI2FgiugWMKtorC5vimh0J8L89pYeejuaePj5ATzp1yDaroOq\nVCZFV2pGXQmqAqqqoioC1xP8r19dzw3rly2Jr+KFkO4NvBsDAgICXh4ET+QGo7c9Puu6/SM5Lu9J\nTKtf29yT5LYtPfzxQ9vnFEVQEU1Scvs1q7h50zJuvmx5LXq1c2CS+57oYyxXZt9IjvXLYixPRqb5\nBl7cFmHfSL7qcU3MUElEdJJRg2QUettjHJkoEA/r2K6H43qsX97EkfEijuvRFNb5X7+6gRvWdwLw\n9ecGyRQt9o9mkZW0rj+X2Y90vjSS9cfzhXQ/ZQ6oiu+XiPBtckYzJcqW7Y/Zm3G9EcMf6Sfxu5xj\npoqhqeTKjn9+3on7UrYlL41k+fA3d3JRWxTHc30xKBQQAqfi1C2ERFNUytL/ecuqJIamki7aXLly\n9qk0L0fOdHq4kZt5AgICAgJO0BDWOUKIHiHEj4UQu4UQu4QQ76ss/7gQYlAIsb3yzy1TtvlTIcQB\nIcRLQog3TFl+c2XZASHEh6Ysv0gI8Uxl+VeFEEZluVn5+UBl/eqzd+Unc2gsN+u6suvxG1d0T3uR\njmRKfPEnfWiqwKh6xdRBVP6lCOhs8sVmzNToaYlwYCTL/U8fRgEsx2NFMkx30jf/jhgajiuxHI/P\nvnkzm3v8tHQirLFheYI7b1xTi4J9+JZL2dgV56LWKJtXNPNnb1pPLKTT2x7l4o5oTShWz//NV3Vz\ndKLAWLZM2ZX+P7aH60lcVwK+R2S2ZONJapG+qKHhepJ9wxmeOTRBqjhdKAp8cWg7Lhe1RDA0gaZA\n2ZX0tkeJhXSu621hQ1cTzSEVVQHH9WiJmsRCGkfGC/RPlNi0PF47pgTaohpCKFiuhxC+GDU0FSGY\nc4rNy5GpNkZ3PbS9rrXS6TLTmqnRmnkCAgICAnwa5dd3B3i/lPI5IUQT8KwQ4rHKur+SUn5u6oeF\nEBuA24CNwHLgh0KIdZXV9wGvBwaArUKIb0spdwOfqezrQSHE3wHvBL5Y+TMlpVwjhLit8rnfPqNX\nOwe7h9Kzr5SSb20fZFVrhM09SbYfTfGXj+2j73iOguUS0hVsz0VWvAOnRhpl5V8Xd0RZu+xE9HJq\nKrA1apIuWmw9nGJ5IkQ8bEx7gfe0RPi/f/BK3+tRCLqT4WnCtbejiffcsIZM0ebSrjid8RA3X7ac\npw6M8tDWAR7ePsR3Xxzm9Zd28Nje4xTKDv3jeQDCuj+mpehIHFciDInj+ZNblCka2HIlYzl/PJ8i\n6kdSmyMqK5MxDk/kKbour7yohX0jOSxXomsqt129gge3DuB5EqEoLG82sF3f7kZKP33uSklL1OQN\nm6KMZ8tIJKauoiuiJmAPjhVIF+1Zp9i8XDlb6eELsZknICAg4OVIQzyVpZTDwHDl71khxB6ge45N\nbgUelFKWgUNCiAPANZV1B6SUBwGEEA8Ct1b2dwPwO5XP/AvwcXyxeGvl7wBfB/5WCCFkNS96lklG\nZq9ZNDSFUMV25u5bN/GFn/QRD2lETA1VEWRKDqoAoQjWd8U5PJYnbzkIBIamsLYjygffuN6vMcR/\nWc9MBSbCBitbwmRKDkXbO+kFHjM1Luk6OVW+/WiK//O9vfSN+pHRi9uj/PGvrKMlYtQaWSKGRqZo\n8dkf7GNtR5SDo36toZ/h9WozmSUwWXQBv9vYmaWpuZ4FDkCh7LF7OI0rfcPuzpjJVauSZEsOn/j1\njXz60b1cszqJqiiUbZdtR1NoSnUMoI3neoQMlVTBxpUQCfmiBnxvSc/zzdDvvW0dK5KR864J5Wym\nhy+kZp6AgICAlysN92SupIGvBJ4BXg28Rwjxe8A2/OhjCl9I/nzKZgOcEJf9M5ZfC7QCk1JKp87n\nu6vbSCkdIUS68vmxJb2weZKIhGZd55tBQ7bosHc4g+tKWqMmG7vi7BrOEPUknfEoUVMjHtZpi5nc\nekUXbbEQtuvhSul7FU7pPO3taDrJ1681ZnL3rZtmHec3k+rUliMTBX8coYQDx7O894Hn6W2P0Tea\n4xUrk0QMDVVRcD3JwdE8uuZ3TduexPVORAn9iTS+SJxNKM5FuWLvU41I7j6WJRHR+cBNl6CrSu2+\nAURNjYtaI5Rtz6+RFIJE2GBtZxQp4b03rGFtZ1PtHlwIwmapvR5PVfu4lM08gQ1PQEBAwNLTUE9T\nIUQM+AbwR1LKjBDii8Dd+DribuAvgXeco3N7F/AugJUrV56x4xRL1qzrXA+ePTqB58H9Pz1EyfGI\nWQ4tMZPNKxJkSg6fe8tmopWIYTJqcGAk6089sVx2H8uyYVnTtMaVe956Rd1UYGd8dtE6k8GJAuO5\nMo7rEtZUEJAve8RMhdaowaFx2HY0xSt6mhGCWppXdSVS+snyaUFCAYqiEBKS0hRfxyqq8LuRT9XQ\ngwBTVTB1lXe+xrdkyZWdk4RQS8zkD3/pYj79vZdoiRnkyzYvHcthux73PLaPD9y0rmbncq67lM+G\nGFrK9PDZtMY5m8cKRGlAQMCFRMM85YQQOr5Q/Hcp5X8ASClHpqz/R+C7lR8HgZ4pm6+oLGOW5eNA\nsxBCq0QXp36+uq8BIYQGJCqfn4aU8h+AfwDYsmXLGUtR52cJpQnAcV10TefKlQlChspwukiu5Ex7\noVuOR9SkZpT9hZ/0oSsCT1MQSPrG8nTEQ9NSi6eTCtx+NMX//s897BrO4LiSTNEhHtKQ+E0fJdvF\nsiXpos2PXxolYqrcfvUKHnp2kNFCGSFAE77dTRVPQnmOkGJEF0ihYGoKmaJdswoyK2P9qvWaEV3D\ncV00RdDTEqZ/okAyanDH9b3c84N99I8XaApp3HXTOno7mkhEddJFi+eOTPr7EOB6XsPYuZxNMbQU\n6eGzaY1zNo8VeEMGBARcaDSEWBRCCOBLwB4p5T1TlndV6hkBfhPYWfn7t4GvCCHuwW9wWQv8Al8j\nrBVCXIQvAm8DfkdKKYUQPwbeDDwIvA14eMq+3gb8rLL+8XNVrwjQEq5fs9gSVlizLEFXIoTleNiO\nR1jXeM/rerE9Sa7ocP+MFHNL1GQiZ3EsU8J2PdIFh7AhKTtexVPxRGpxoRGzXNlhcKLAX/1wHwOT\nRdqiJhOFMpYjmSzatfTu7mMZipaDqSm1mdXbjqRpj5lkig54klnGYU9DAXRNwVAFG7oTJEyFH+wZ\nq5ljK4CHn3o2FIGD39kN8JarlvPpR/fW7s0lnTF2VdL4QoFnDo7T29HE269bzXu+8jyelOiqQlNI\n58h4kd52ZVq9Xr2o0mIjTfPd7lx4Ep5uFHW+tY9LEaU7U3WWM88t8IYMCAi4EGmUp9urgf8OvCiE\n2F5Z9mHgdiHEFfjZxsPA/w8gpdwlhHgI2I3fSX2HlNIFEEK8B/g+oAL3Syl3Vfb3QeBBIcT/Bp7H\nF6dU/vy3SpPMBL7APGeM5sp1l48XPbTxHPtHsr53oJRoquCTjzhETY3dwxnWL4vRnYzWXmAfuvlS\nDo3nkR6UbAdPeqSLHgdHc7UI2+mkFnNFh73HMiiKX+cXNlSyJZvuRJh3vPYivvTTg4xlLTxPYmgC\ntTJq5qVjWQxdQRVQnmdNoocf5VM0lZeGs+QtB1MVNIX8Okjb9fjDX76Y77xwjIFUAdeTdMZN/uhX\n1vGtHUO1l/tIusDf/eQgrVETU4XxvMXnfrCfp/rG+a2rVnBRW5T+VAFTU9BVlVzJNwuviup6USUp\nWVSkaT4RqqpYqTc7u9E9CedT+7hUUbozMVO73rm1RM3AGzIgIOCCoyGeblLKn3Ji8tpUHpljm08C\nn6yz/JF621U6pK+ps7wEvGUh53smyZadWdeNZPx6RkMVKIqfcj2aKnDFimakJzlwPE9HPFx7gf3s\n4BiO49X2qSuCqCHoTxVJhDXuf/owYUM95ct5anQFqEVW4iGdvrEckwWLsKGiIFBVQUuTwbUXt/Ll\npw7XRhHansdorkTM1CnaDiFdJ2aqlAuzX+9MHA8yJZemkD9uT9cUCrZHe0ynZLt898VjhHQFQ1dw\nXMlEwcbxvNrLfTxX5oUBP10+WfDvpaYKbFcyWbD58+/urs18lpVUuAe847rVpPIW+TpRpXse24fl\neiQjBq1Rc96RpvlEqKaKlXTR5sBoriJiT8zObmRPwlPVPi5llG6pbXhmO7e7b9205KI0ICAgoNFp\nCLEYcILe1giPnfpjtEYNRrNlSpbL8/0pcmUHrwwj6RItMQOQ/GDXCIamYrgeihB4nsT2JGFD0BIL\noQhO+XKeGV1581XdNfHluB7rOmK8MJAmX3JQFMHF7VHed+NaJgs2/akCLVGDdMHGcj0sR6KGfP/E\n0Zy9qPsjAU9KHE+iVOoTh9NFXCnZPZwBKVFVhURIx0Py4C8GCBkK6aLFrqEMqurXInr4xt+KoiKA\n45kyqhCsXxZn/2gW25FcsqyJWzZ28sC2flzXH4dYtF3WdjQBULRcXhxI40m/q3rj8jitMXNekaZT\npU2nihUjpLBrOIMi/LnYtuOx+1iWz9/W+KnPuWoflzp1vJQ2PLOdm+V4gTdkQEDABUfwhGswFFU9\n5WcsV/qNHa4/t9h2FSKGSt5y2Tk0yaXL4tx+zUoe3j7EhuVN/PzgBK6USCkxNYGhqbUI1Vwv53rR\nla9uHUAiGUwVODCaw3I8VFXwoZsvZW1HDIQgpGuMZ0u4niSsq3TGQ+TLNsWyQ7Y4/0jibNiOR3NY\nJ1XwBaeq+JGlkuVieX66etQpoyuCgu3w+6/u5d9+fpSS7RI2VC7tjNE36o8tdFyPDcvjDKdLaKpC\nZyJEZyLEYKrIn7zhEr445frTRYu9x7J0NplEDI0XBtM1+x+Q7BrKsLknMa9I06nSplPFSr7soAgI\naRqbe5rRVIV0waI7GTnte3k2mK328UykjusdazE1kXOdW09L5IKwUAoICAioEjzlGozsHNY50z5X\ndokaCrbrR9sKlsv6ZTGOThTxJHxr+yAjmTLjeYuQrpAp+oKjZEt623V0Vam9AA1NqXUKzxb5cVwP\nKcHxJL91VTcffXgnJcsFIQjrCl/66UGaQjqHxwvYroeUEteTDJdspPStbhbCbLY4ugK2K1EVQdRU\ncVxZE8pNYb023UXgj+PrTxVpbwrxiV/fyMe+vYt4SCMeNuhKhDg8XqAlZqIgcD3JJZ2R2n1pCmtE\ndHVWw/JU3sZxPa5amUQAu4YzFC2XbMnhAzetO6WAOFXadKpYMTWlYkAuiZoadsU0/OWe+jw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w1WeVm1vWXIEmkXIoRbVzocEqqiLlfi1pYE15w5h1e3teH7iogwtiJCc2cKDSJtIyHhtR1tnHJk\nPe2JNPVj4sydVM3mpi5q4hEyvs+M+gq+9sFFBSOLcyOatzR10Z3K4AmEPY+U+oDg+8pr29uIRTw8\nICTSO4cwXxlMZnxiEa9X+SyWwgXga0+vJ+IJO9sSREPCjtYeJtbEe9se6nl7g7EcXjEm1cRNSTQM\nwzBMWSw3KqOl51nsb3nm/hTFwSYbTR0Lh0ikMogIM+rinDy7noXTx7KpsYuQJ2R8JRoWNjd309rl\n3M3VsTAtXSnnXq6t4NbzF3D3k2vpTvpURD2uXTyvJMUlGnZu/IyvpH0fVRfQo7p3rWwRYWxllKZg\neb8jCriz862AhQI2su7peDiEr0pFJEx3Mk3I80ik0r1tD9WcPVsOzzAMwzgU2C9KmTG1tmK4hzAg\nYmEPVPGDoJO3TK0hHPZI+8q0uko+c+EC7nlyHd3JDOLBq9taCXvC+KoojZ1J2rrTJDMZbnjX/F73\n8j0fOPGArHJZK1synSGZ9kmkMkRCHnMmVFEZjbCxsRNFWThtXO+KN1nrYSnRu/l5GrMWyYzv44kL\nCBFxx4difuJgJb82DMMwjL6wX5Qy4+XNLcM9hAGRSvv4QHUsREcizQubmpk7qZpbz1/Qq/zd/YET\naO5M0tWT5pZHXmFHSzcI1FVHqa+K8Ol3L+CoKTW9bR7ICie5VrZ5k2qYWBOnpTPFJ86dz7zJrs1n\n1u3mgWVbSAfJvvOthwe6okque3pyTYxNTd3MqIuT9vWQrDRiy+EZhmEYhwJTFsuMnW2lz1k8lIQ9\nN+8vGxGTn8A7FIIwQtpX6qqizK6vIhbxmDNxTG+drDL27Po9bG3uRlA8hFnjK6mOR5hWV1nyePKD\nOvKtbGMroiRSPuPHxHuVtnOOnbLf8n4HS9Yiua252yltkRDTDlG6GFsOzzAMwzgU2K9KmSFeeaa+\nTPtKTTyE5wkd3Wl8hYqIEPGE2eOr2d6WoK07jR/ke9zc3EUy7fPMut2cc+yU3nYKpalZu6uzN01N\nKRQK6pgzcUxJVrahWI85u+TgUASZ9IclvzYMwzCGmvLUTA5jTp83friHUJBoyEXHzqiroqYiwtGT\nqlk4vZZ5k8cwa0I1p84Zz5hAUamKhYh4HuGQ8MCyLTS0JXoTYuemqTnlyHrePrueY6bU9Kap6Y9i\nCbKBQVtirr8E3qWMp5RrBwtLfm0YhmEMJfbrUmZkdGArrQw1yQxs2NVJZSzMwuk1zKivpiuZZmdr\ngvZECnBRzOlGn0xG8cUtv9fY6dZHjoY8QiHhw6fO6jNNTV909KRZtb2VnpRPfVUM2DeoYzCsbAea\nisaCTIYmz6NhGIZRPtg3e5lxqFdaKZVYSECgJ+3zZmMXU8dVUhkNUxULc/N5R1MZDRENe3zq4ZcJ\nBalpupJpNjV28dZpY6mripLM+Hz3mY1cfuos7ntmI+2JNIJbUaU/skpcTzLDqp3toM46me9uPhg3\n80BS0RzuQSZDmefRMAzDKA/MDV1m1FeWn5IhQCjk4YkgQDLl05P29y67V1tBbVWUZNrnH087EvGE\nps4k25q7SGeU1Q3tPLuhkUSwZN+02kq+dMnxvH/RdBRlybKt3PDgClZuaS7Yf64SN7W2kgWTq1m1\ns53tLd0H5W7Op9dKGN1rJexvPef8NaHzx3MgLu2RRjm44A3DMIyhxyyLZYSqCwwpF7JLASrQncwQ\nDXvEws7C2NqdIhbxuOqMOfsFeFx+6ixqK6P8x2Ov0xDtIeJ5+CjLN7dwzNQxRMMe25q6ePCFrYyJ\nR6iMhmntTnLX42u4633H7Zd8O9/VO622ChCuOnMOx0wdO2iuz4FaCYu5v0e71c1c8IZhGIcHZlks\nA1SVJ1bt5IJ7/si3/7hxuIfTi+IUxuqoRzQk7tgTPnnuUXz6/AV86ZLjmTNxzH7Wpfue2QhAOOSx\ncNo4upIZdrX10NKdZFtzN9fc/xKf++UqVm5tIZHM0NjRw8otraza3sZND63cz8KYq8QBdCXTxKOh\nQVUUoX8rYX/X5gaZHA5Wt0Lvy+HkgjcMwzhcsL//w4hTEhv4ym/WsmpH23APpyinHzURVWjpSrK6\noZ2fr9jOb97YxVVnzCEeDtHRnaYmHgH2WpcQIRQSwp4QDgnjKsKIJ7T3pOlIZnjH7Do2NHbw0tYW\nwiKIQEU0RE08vM88wWzwxIdPncV3g3mOQ5lPcLBS0RwOVjfL82gYhnF4YN/qw0AxJfHts+vY2NBG\nQ1f5WJ8qI0Iq7TO2MspLW5qJhkNMG1dBMuNz+2OvUxEJsWZXOxsaO1g4bRwV0VDvPMarzpjDXY+v\nIZn2qYiGOLK+inV7OgC3rvXCaeNYtrGJlEJVPMyxU2qoqYjS0JaguTNZ0L09rbZyyKNuByMX4+ES\n+GJ5Hg3DMEY/5oY+hKgqj7/m3M1X/PDFXkXx7bPrWHLFO1jyT6cQiZbPWyLAjPoqMqpsa+4mnVGO\nmzaWcMgjEvLYsLuTaCjE22bUAsLyzc20J1K91qXjjqjlrvcdxzFTazhu+lgmjo3jq+KrW0u6Ihri\nrdNqWBCcr6uO9SpV0bBX0L09UhSSg3FpjzQsz6NhGMboxr7dDwGqyuOrGri7gCXx+rPn844j6/fW\nzQzHCAszriJMNOzxLxccQyrt84Wlq4lHQwC0drkI4XGVEcIhj9Pmjmdbczc3n7eAoybvXeJvUk2c\nm86Zz71Prae7M8mMukoEobEzSTgk3HjOUQDc+9R6GtoSva7MZNof8W5cs7oZhmEYowH79RpC+nI3\nX3fWfE6ZU7/fNfMmVbOtvelQDrM4ImxtTvDZR1/jpnPmc83iub3z00CZVV9FS1eKcZURkhmf6oow\n02or9msmX2kC9lOg8pWqjp70qHDjDsXygoZhGIZxKLFfsQAROQ+4GwgB31bV/xxoW1kl8e4n1/La\n9r1K4smz6rju7HmcOqf4kn7dGX+g3Q46HYkU46vj+wSdfOmS41nb0M5Lm5v55YrtLN/cjKpy5IRq\nPnPhAgC2NHURDXsk036v8pevNOVGDTd3JomG93W/V8fCXH7qLO59aj17OnpQhUtPOmLv2HKuS6b9\n/foreD85K43A/grraKPYyiqlrrhiK7MYhmEYYMoiACISAu4Fzga2AstE5FFVXXUg7agqS1/byT2/\nXceqHCVx0axarj9rPn8xt/91n194s+XABj+EpHzoSqVJZ5S075JTP7t+D19Yurp3pZnKsEckEuLN\nxk5e2dLCvU+tp6mjh01N3cysr6SuKlo0v2A2D2FTR5JNTV3MrKugrjrGVWfMQRXue2YjHYkU63Z1\n4Inwbw3t/Pj5zVx84jSeeGNX73X1lWEau9J99peb87C9J4UgVMfCozL/IRTP8Vhq7sfRniPSMAzD\nKJ3yiaYYXk4G1qnqBlVNAg8AF5V6sary61d3cOF//ZGP/Wh5r6K4aFYtP/rIyfz0Y6eWpCgCDNeU\nxUIrUguQTPus2NqCAKmMz52Pr+lNh5PxlfZkhupomJAIX35yHaiyo7WHaEjY2Zog4knB/ILZPIQR\nT9jZliAaEna09hD2hHueXMc9v11L2BN2tvWQyijJjE9lNMTGxk7uWLoaUWVnW4KwBxsbuwn30V9u\nzsPaqiibm7rY1NRFXVV0VOY/LJbjsaEtUVLux8MhR6RhGIZROmZZdEwDtuQcbwXenltBRK4ArgCY\nMWMGAL7vs3RVA1/97bp93M2LZtZy7eJ5nDZ/wlCPe1DwBMIhAYRk2rnBw+KSavsKqbTP+0+azp6O\nHnxfqa6I0NqdQgRUoTuVwRMhrT4ZBUWpiITpTqUJeR7dqf0DU7J5COPhEL4G9ZNB/aSzWlbFImR8\nH89zqqwgoJDOKBkFX5VIKISSIuJ5pH2/YH+5OQ87etJ44trrSftUxUZe4Ex/FMvxuKmxs6SgocMh\nR6RhGIZROvbNXyKq+k3gmwCLFi3Sx17Zwb2/21dJPHHGOK47ax6nz584XMMsSnbpvixhIVDsnMI3\nviqGr8qejh7GxMKMiUdQcYrgsZNrOHXuBDp70niekExnqK2MsqM14dr2YM74Ktbs6iAkTqlLpJxS\nlvH9goEp2TyEGd/HE1dfgvoV0RCKkgmUP99PO8UUhUCxDQl4IqQyGWf19H1CRfrLzXkYC3v46tam\niYW9ERs40xfFcjzOrK8qKWjocMkRaRiGYZSGuaEd24Ajco6nB2UFWbergyt/vLxXUXzbzHF8/8Mn\n8fCVf3HQiuLG/7ygz/NHTaji7bPG7uM2jnqwYPIYjp48hqqo13vOEwh7MKEqTDgkVAUJs2Mh8Dxh\nTDzM9HFxairC9KSdVfDKM+Ywb/IYulIZupMZ5oyv5oZz5lMdCzOpJs7N584nmVF60j41FWHmTqzi\nqEk1VMXD3HzufBBh8tgYyYwyuSZGyteC+QWzeQhTvquXzCiTx8ZI+8o1i+dy7eJ5pH1l6rg4sYgX\nKHYZZtVXcct5R6EiTK6JkfZhZn0F6T76y8152BSk75lZV0ljZ3JU5j8sluNxUk28pNyPh1OOSMMw\nDKN/RFX7rzXKEZEwsAZYjFMSlwF/p6qvFaofmzJPp3zoK5w4Yxz/vHgeZx41+JbEWbf8zz7HHzl1\nJtefe3TvD/aG3R0s39zE1LGVzJlY3RsJ3NmT5o0dbaQzSjgkHFFXSSTkkcr47OnoYXx1jFTapyuV\noTLqUt109jgX5cz6KibVxOnoSbOtqQvErcSSryQ0tCV661fFwvulvMmNUi414rZQ/dxzLZ3JfcZj\n0dD9Y9HQhmEYBoCIvKiqiwZ8vSmLDhE5H/gKLnXOfap6e7G6tTOP1p8tfZp3HT3pkI3PMAzDMAxj\nIByssmjmggBVfQx4rJS6cyZUm6JoGIZhGMZhgc1ZNAzDMAzDMIpiyqJhGIZhGIZRFFMWDcMwDMMw\njKKYsmgYhmEYhmEUxZRFwzAMwzAMoyimLBqGYRiGYRhFMWXRMAzDMAzDKIopi4ZhGIZhGEZRTFk0\nDMMwDMMwimLKomEYhmEYhlEUUxYNwzAMwzCMopiyaBiGYRiGYRTFlEXDMAzDMAyjKKYsGoZhGIZh\nGEURVR3uMYw4RGQ3sOkQdDUe2HMI+hnpmJxKw+RUGian0jA5lYbJqXRMVqUxEDnNVNUJA+3QlMUy\nRkReUNVFwz2OcsfkVBomp9IwOZWGyak0TE6lY7IqjeGQk7mhDcMwDMMwjKKYsmgYhmEYhmEUxZTF\n8uabwz2AEYLJqTRMTqVhcioNk1NpmJxKx2RVGodcTjZn0TAMwzAMwyiKWRYNwzAMwzCMopiyWIaI\nyHkislpE1onILcM9nqFCRI4Qkd+JyCoReU1Erg3KbxORbSKyItjOz7nmU4FcVovIuTnlBWUmIrNF\n5PmgfImIRIPyWHC8Ljg/69Dd+YEjIhtF5JVAHi8EZXUi8oSIrA1ea4NyEZF7gnt7WUROzGnnQ0H9\ntSLyoZzytwXtrwuulb76KEdE5KicZ2aFiLSJyHX2PDlE5D4R2SUir+aUDdsz1Fcfw0kROd0pIm8E\n43xERMYF5bNEpDvn2fpGzjWDJo9iMh9OishpWD9rxfoYTorIaUmOjDaKyIqgvHyfJ1W1rYw2IASs\nB44EosBK4JjhHtcQ3esU4MRgfwywBjgGuA24qUD9YwJ5xIDZgZxCfckMeBC4NNj/BvDxYP9K4BvB\n/qXAkuGWRz+y2giMzyv7AnBLsH8LcEewfz7wv4AA7wCeD8rrgA3Ba22wXxuc+3NQV4Jr391XH+W+\nBc/ETmCmPU+993s6cCLwajk8Q8X6GO6tiJzOAcLB/h059zArt15eO4Mij75kXoZyGrbPWrE+ylFO\neee/CPxruT9PZlksP04G1qnqBlVNAg8AFw3zmIYEVd2hqsuD/XbgdWBaH5dcBDygqj2q+iawDiev\ngjIL/nm9C/hpcP33gb/Oaev7wf5PgcXZf2ojiNx7yL+3H6jjOWCciEwBzgWeUNUmVW0GngDOC87V\nqOpz6r5NfkBhOeX2Ue4sBtaral/J8w+r50lVfw805RUP5zNUrI9hpZCcVPVxVU0Hh88B0/tqY5Dl\nUVDmB3WTg0CR56kYh+KzVqyPYaUvOQXjvgS4v682yuF5MmWx/JgGbMk53krfCtSoIHAlnAA8HxRd\nHe7FkZAAAA5cSURBVJjO75O9rs9isilWXg+05HzJ58qy95rgfGtQv1xR4HEReVFErgjKJqnqjmB/\nJzAp2D9QOU0L9vPL++qj3LmUfb+A7XkqzHA+QyP1u+5ynMUmy2wReUlEnhaR04KywZTHSJPTcH3W\nRpqcAE4DGlR1bU5ZWT5Ppiwaw46IVAM/A65T1Tbg68Ac4HhgB85Mf7jzTlU9EXg3cJWInJ57Mvi3\nOaSpDQ5FH4NBMLfpPcBDQZE9TyVgz1D/iMitQBr4cVC0A5ihqicANwA/EZGaUtsb6fIogH3WDowP\nsO+f2rJ9nkxZLD+2AUfkHE8PykYlIhLBKYo/VtWHAVS1QVUzquoD32KvK6GYbIqVN+JM7+G88n3a\nCs6PDeqXJaq6LXjdBTyCk0lD1m0XvO4Kqh+onLaxr1stV07F+ihn3g0sV9UGsOepH4bzGRpR33Ui\nchlwIfDB4EeZwOXZGOy/iJsnN5/BlceIkdMwf9ZGjJygd+zvBZZky8r5eTJlsfxYBswLIsGiOHfa\no8M8piEhmK/xHeB1Vf1STnnuvKW/AbJRZI8ClwbRcLOBebhJvwVlFnyh/w64OLj+Q8AvctrKRoFd\nDPw2+wNQbohIlYiMye7jJtu/yr73kH9v/xBEw70DaA3cFEuBc0SkNnAPnQMsDc61icg7gvfkHygs\np9w+ypl9/q3b89Qnw/kMFeuj7BCR84BPAu9R1a6c8gkiEgr2j8Q9QxsGWR4FZT6U9ztQhvmzVqyP\ncuUs4A1V7XUvl/XzpMMcKWRbwain83GRweuBW4d7PEN4n+/EmcxfBlYE2/nAD4FXgvJHgSk519wa\nyGU1QTRYXzLDRdn9GTfZ+SEgFpTHg+N1wfkjh1sefcjpSFyU30rgtez94ebpPAmsBX4D1AXlAtwb\nyOIVYFFOW5cH97wO+HBO+SLcF/t64KvsTdhfsI9y3YAqnJVhbE6ZPU9ujPfj3Fwp3DyljwznM9RX\nH2Uop3W4eV7Z76lsNO7fBp/JFcBy4K+GQh7FZF6GchrWz1qxPspNTkH594CP5dUt2+fJVnAxDMMw\nDMMwimJuaMMwDMMwDKMopiwahmEYhmEYRTFl0TAMwzAMwyiKKYuGYRiGYRhGUUxZNAzDMAzDMIpi\nyqJhjHBE5LcisjIngW22/G9FREXk7OA4KiJ3isgfRKRbRA4oFYKIvFdE1mXzgOWUHy8iS0Rkp4gk\nRWS7iPxYRE46+Ls7OERkYyADDcb2hoh8JsjpNlR9Vgf9XTZUfQwFQT7PB0Sksa/xi8j3ROSFIR7L\nfBG5TUTG9VPvZyKyXkTiBc4tFZHXh/K9zuvvNyLyiUPRl2EcakxZNIyRz1XAAuCabIG4JRS/Ajyo\nqk8ExZXAR4Eu4JkD6UBEPODfgDtVNZNT/l5crrN64HpcotkbcasqPD7A+xlsfgKcApwNPAB8FviP\nYR1RefJx4K+AK3Dy+p9hHMt83PvUp7IIXAtMBD6VWygiF+OSDX9cVZNDMsL9uQP4ZPDZM4xRRbj/\nKoZhlDOq+rqIfBG4TUQeUNXtwOdwCtv1OfVaRKROVVVErgbedQDdLMat+fqTbIGITAW+j0s6e5nu\nm7T1fhG5sFhjIlKhqt0H0P/BsENVnwv2nxaR6cDHROQTWiDR7CEeWzlxNLBaVX823AMpFVXdKiK3\nAbeLyA9VdZ24VY6+DPxAVZ8a6jHkPC+/ARK4FYS+NdT9GsahxCyLhjE6+DzQBHxFRBbirIyfDRTH\nXgopRyXyIeBxVW3PKfsoEAVuLNSuqv4qux+4g78YuIC3Am055y4RkVdEpEdEtojI7bkudRG5LHCL\nnpTjQl8jIn8zwHt5EbfSy/igfRWRG0TkKyKyG7faQbbvq0VkbTC2dSJyfX5jgbt/TTCu3+OUrvw6\nWQU9t+w2EdmTVzZTRO4XkT0i0iUiL4vI3+Wcj4vIFwI59QTTD87v74ZFZLyIfD9wMXeJyFMisijn\n/EbcChwnZN32/bXZR19TROQ+EdmQ8179e747WEQ+Fcg0ISINIvJrEZksImcAvwyqvRmMZ2MfXd6N\nW7Hjv4Ljz+Ks6Dfl9Xd80EeHiLQGcp6Qc75GRL6e815uEJG7cy2Fgfw1eC6+Grx/y6D3s/Uwbik2\nwxhVmGXRMEYBqtolItcCPwdOAlYB9wxiF+/C/Sjn8pfAC6q6p0D9QvwdbimrKwm+e0TkHGAJ8APg\nE8BCnOJbD3ws7/olwNdwLuSPAg+JyNtUdeUB3sssIIlTrrN8Avg98PcEf6JF5B9xCsiXcGunngl8\nUURiqvqfQZ0Tg3E9gnOJvgV48ADHQ9DWROBZ3DSBm3DLy70FOCKn2k+Bk3EK0XrgEuBREVmkqiv6\naP7nwNyg3T3B/f5ORE5Q1XW4dXz/HbfE2ocHMv4cxuNkewPQjHMp3wZMAP4puNd/AD4N3Ix7Jupx\nz1gVbpmzm4C7gPfilkrrKdaZqqZF5OPAH0XkM8B1wFWqujtbR0QWAH8A/oR7DmPA7bj37Z1BtTFA\nBufS3oN7Tm4NXi/K6/ZW3BJr/y+v/BngShGpUtXOPmRkGCOL4V430TbbbBu8DXgBt972mf3Uu5rA\nGFJCm1ODNi/IK38DuL/ENjbifvTjeeXPAb/LK/sk7kd7enB8WdD/p3PqeEH/D5TQ7xdxymklcCHQ\nCvw0p44Cy/Ou84BtwHfzyr8WXB8Pjh/EKeaSU+fWoM3L8vq4Oq+t24A9Ocf/H+gkZz3dvPqLg3b+\nMq/898BDfcjgvPzrcErZbuC/c8q+h1P++3svS6qXUz+MU9ASQDQo+yrwsz6uuTAY86wD6OdbwTV/\nyn0/gnMP4SzG4ZyyYwEfWNzHuBcHdSYFZfGgj2eLXHN0cP60Usdtm20jYTM3tGGMEgK34gm4H6sz\nBrHpycFrIQvigbgrn1TVRPZAXFT1ibgf8lyW4JS1U/LKH+ntVNUHfoGzsvXHDUAKp4j9EqdcXZVX\n57G84+k4JbnQ2GqAtwbHJwOPqmquHB4uYUyFeBfwa1XdUeT8WcBO4E8iEs5uOAvXoiLXZMe4S1Wf\nzhaos3r9ir1WtUFDHNeJyCoR6cbJ/sc4a96MoNoK4HwR+ZyInCx5EfYD5M7g9Yt57wc42T0cjC8r\nt9W4PzC57vjLA9d+ZzDu3wACzMtrr1jwT/YzMrnIecMYkZiyaBijAHHRyl/HuTE/h4vKPHKQms+m\nJcl3BW5j749/KTTkHY8HIgXKs8d1eeW7ChxPKaHfH+Fc8wuBGlX9K1Ut1meWKUXK88c2uci4BkI9\nTnkpxvigv1Tedhv7uqrzmVJkTA3sL+PB4DqcC/kRnPv2ZPYq59ln6T6cG/oS4HmgIZjXeDBKYzLv\nFej9UzIO+Ff2l91UAtmJyAeA7wBPAxcDbwcuzRt3lvznIkv2M7JfKh/DGMnYnEXDGB18DGdVPBFn\nMfl73JzFohHJB0B2bl9+GpOngFvFRVg30T/51p49uB/siXnlk/L6zTIRaMw77ku5ytKgqv3lBcwf\nW7bd/sa2s0Cd/GNwSkR+vr/avONG+lZ+m3AK+l/3UacQO4qMaRL7y3gweB/OzX9rtkBEjsmtEFiG\nvwx8WUSOAD6Im0O4FfjGYA5GVTMi0oZzn/+wQJWsIv0+4GlVzU1Blf8e9TZbpDz7GRkKuRrGsGGW\nRcMY4QSBEbcD/6WqL6tqDy4a+gIRyZ+YPxA24qw1s/PKv4NT9u4qMq4L+mpUXb7GF3E/0rlcgpsn\n9mxeeW/0c2BJvQiX43Eo2ApsLzK2NvZGTC8D3iMiklPnvUXaW5A9CMa/OK/Ok8C5IjKJwjyJsyx2\nqOoL+Vsf9/I8MFFETs/pvxK4APhjH9cNlAr2t0J/sFhlVd2iLmBoHZBVKrPWwcGy0D0JHFtIbqq6\neSDjLsKs4HXNQYzVMMoOsywaxsjnLqAbFyELgKo+JiK/wKXSeVyDvIEi8m5ccMPxwfHFwSXLVHVT\nocZVNSEiLwJvA76bU75d3Cof94vLXXgfzvI1Dee+O53+3ZyfBZaKyHdxCbPfiouG/paqbs2r+1ER\nSQKv4qKh5+Jy2g06quqLy9/33yLSCDyBi/7+OC7QJjv38g6cMvagiHwHF738kQJNPgJcJSIvARuC\n8dfk1fkyLu3KH0Tkdlw09AKgSlW/EIxhKfCEiNyBiyKuwb2XcVX9FAVQ1aUi8gywRERuwVkwb8Ip\nR3cWuqYEanOenVweC8Z5jYg8j4vY/iDuvepFRP4bZ317DhcwdCZuXuDNQZXVwes/icgDQJeqvsLA\n+QzwnIg8irMwNuHmpZ4LfF1VnwnGfaeIfBJ4CXgPBz6ncxFufujagxirYZQfwx1hY5tttg18wylk\nCnygwLmZuKCOz+eUbQzq52+X9dPPTcC6IudOwEUFN+Asjdtx8wRPzOv3riLXvx9nqUviLHC3s2/U\n6mXBGE/GRbomgLXA35Ygn6L95tTZL1I559w/4yxeSZySd32BOu8L6iRwlrqT8mUKVOMSmDfhXNf/\ngptbuievrZm4IJpmXAqdlcClOedjwXXZMe0Efk1epHqBMU7ApSdqxv2xeBo4Ka/O9yg9GrrQM6Q4\ny1o17k9FU7B9m73RzW/JeU//FJzvAl4GPpLXz43AJiANbCxhXLOCPi4scv5YnNKelcFaXHT7lOB8\nBJceahfOerwEpywqcFZQJxsN/dEifSzFKZ/D/t1gm22DuYnqgHOvGoZxmBC4RjcD71TVZYe478tw\nyscYVe04lH0bRqmIyHjc/NBTtP85soYxorA5i4Zh9Iu66OFv4xJPG4axP1fj0kOZomiMOkxZNAyj\nVD4PvD5IOfEMY7TRiMvpaRijDnNDG4ZhGIZhGEUxy6JhGIZhGIZRFFMWDcMwDMMwjKKYsmgYhmEY\nhmEUxZRFwzAMwzAMoyimLBqGYRiGYRhFMWXRMAzDMAzDKMr/AVEY654YJxZzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115bd5400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xplot = ricex[[\"X1\", \"X2\", \"X3\", \"X4\",\"Production\"]]\n",
    "\n",
    "var_name = \"X1\"\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=var_name, y='Production', data=Xplot, scatter_kws={'alpha':0.6, 's':20})\n",
    "plt.xlabel(var_name + \" (Crop Produce of Last Year)\", fontsize=15)\n",
    "plt.ylabel('Y', fontsize=15)\n",
    "plt.title(\"Distribution of y variable with feature \"+var_name, fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WYi9D1wN/C2CM+YuIPATsAaLAbYlZyiLyd8CTgBtYZ4z5i7OLLwAPiMi/Ai8D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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113fb0400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xplot = ricex[[\"X1\", \"X2\", \"X3\", \"X4\",\"Production\"]]\n",
    "\n",
    "var_name = \"X2\"\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=var_name, y='Production', data=Xplot, scatter_kws={'alpha':0.6, 's':20})\n",
    "plt.xlabel(var_name + \" (Crop Produce of Last to Last Year)\", fontsize=15)\n",
    "plt.ylabel('Y', fontsize=15)\n",
    "plt.title(\"Distribution of y variable with feature \"+var_name, fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X9jG84OxVeK0I3+PQNCxjeZ0exht49Rn/H7t+4EX/n45f4n3evmDePJgv4v1z+0bGnkH/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\nWIbgmeMZSl5Am2UhpcTzA4puQFvCZqLocmSswI6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oWJ6jYyWECHscO36AJQWSGuHSCCAR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\nUuCGcGquz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t+LhnswdIHQNOq+jxFohEg2D+ZY15edp9iUm5HCZxtJvIvrhyosXUqKNY+C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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117909c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xplot = ricex[[\"X1\", \"X2\", \"X3\", \"X4\",\"Production\"]]\n",
    "\n",
    "var_name = \"X3\"\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=var_name, y='Production', data=Xplot, scatter_kws={'alpha':0.6, 's':20})\n",
    "plt.xlabel(var_name + \" (Rainfall of Present Year)\", fontsize=15)\n",
    "plt.ylabel('Y', fontsize=15)\n",
    "plt.title(\"Distribution of y variable with feature \"+var_name, fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ketKdWibEmmUt3Hj+Ws1vKCIiMo3obi0TRvMbioiITC/qShYRERERQIGhiIiI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XMxljCYrXrc8b39fORXrtGRwwgdTdU8ldGf6IuURiH7rqMFPnb3/uaYtOds6OWJ0SJeKPn0vYeU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\nVneRxVcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1179154a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xplot = ricex[[\"X1\", \"X2\", \"X3\", \"X4\",\"Production\"]]\n",
    "\n",
    "var_name = \"X4\"\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=var_name, y='Production', data=Xplot, scatter_kws={'alpha':0.6, 's':20})\n",
    "plt.xlabel(var_name + \" (Rainfall of Last Year)\", fontsize=15)\n",
    "plt.ylabel('Y', fontsize=15)\n",
    "plt.title(\"Distribution of y variable with feature \"+var_name, fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Z-Score Normalization\n",
    "\n",
    "cols = list(X.columns)\n",
    "for col in cols:\n",
    "    col_zscore = col + '_zscore'\n",
    "    X[col_zscore] = (X[col] - X[col].mean())/X[col].std(ddof=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X[[\"X1_zscore\", \"X2_zscore\", \"X3_zscore\", \"X4_zscore\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1_zscore</th>\n",
       "      <th>X2_zscore</th>\n",
       "      <th>X3_zscore</th>\n",
       "      <th>X4_zscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.390598</td>\n",
       "      <td>-0.291085</td>\n",
       "      <td>-0.241334</td>\n",
       "      <td>-0.394757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.255498</td>\n",
       "      <td>-0.187936</td>\n",
       "      <td>-0.611623</td>\n",
       "      <td>-0.261568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.348092</td>\n",
       "      <td>-0.148017</td>\n",
       "      <td>-0.419073</td>\n",
       "      <td>-0.636660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.289344</td>\n",
       "      <td>-0.087294</td>\n",
       "      <td>-0.711895</td>\n",
       "      <td>-0.441613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.026914</td>\n",
       "      <td>-0.193677</td>\n",
       "      <td>-0.773053</td>\n",
       "      <td>-0.738233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X1_zscore  X2_zscore  X3_zscore  X4_zscore\n",
       "9   -0.390598  -0.291085  -0.241334  -0.394757\n",
       "11  -0.255498  -0.187936  -0.611623  -0.261568\n",
       "13  -0.348092  -0.148017  -0.419073  -0.636660\n",
       "15  -0.289344  -0.087294  -0.711895  -0.441613\n",
       "17  -0.026914  -0.193677  -0.773053  -0.738233"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alg = LinearRegression()\n",
    "alg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef = alg.coef_\n",
    "intercept = alg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([[  38573.18,  158184.25,   18750.67,  -13781.87]])\n",
      "array([ 145906.44863478])\n"
     ]
    }
   ],
   "source": [
    "coef = coef.round(decimals=2)\n",
    "pp = pprint.PrettyPrinter()\n",
    "pp.pprint(coef)\n",
    "pp.pprint(intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = alg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp = y_predict\n",
    "yt = y_test.as_matrix()\n",
    "type(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70628.2264795402\n"
     ]
    }
   ],
   "source": [
    "rmse = sqrt(mean_squared_error(y_predict, y_test))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets calculate the average RMSE (Cross Validation, 5-Fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LinearRegression()\n",
    "scores = cross_val_score(clf, X, y, cv=5, scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,5):\n",
    "    scores[i] = sqrt(-1*scores[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 90696.68166565  61035.55459524  51367.37486568  95344.81806082\n",
      "  74890.37880977]\n",
      "\n",
      "\n",
      "Avg RMSE is  74666.9615994\n"
     ]
    }
   ],
   "source": [
    "print(scores)\n",
    "avg_rmse = scores.mean()\n",
    "print(\"\\n\\nAvg RMSE is \",scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "yt = yt/1000\n",
    "yp = yp/1000\n",
    "yt = yt.round(decimals=1)\n",
    "yp = yp.round(decimals=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "yo = np.concatenate((yp,yt),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>138.243213</td>\n",
       "      <td>143.045928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>191.785703</td>\n",
       "      <td>213.756353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.325000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>62.950000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>174.400000</td>\n",
       "      <td>191.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1255.300000</td>\n",
       "      <td>1362.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Predicted       Actual\n",
       "count   442.000000   442.000000\n",
       "mean    138.243213   143.045928\n",
       "std     191.785703   213.756353\n",
       "min       2.600000     0.000000\n",
       "25%      27.325000     6.200000\n",
       "50%      62.950000    59.000000\n",
       "75%     174.400000   191.175000\n",
       "max    1255.300000  1362.000000"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.DataFrame(data=yo,columns=['Predicted','Actual'])\n",
    "p.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result\n",
    "### This time the predictions are not as good as we got for just one state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.1</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>127.2</td>\n",
       "      <td>123.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83.7</td>\n",
       "      <td>112.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.6</td>\n",
       "      <td>23.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.9</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.8</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64.3</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66.3</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>157.0</td>\n",
       "      <td>214.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>33.6</td>\n",
       "      <td>76.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>281.2</td>\n",
       "      <td>413.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>166.1</td>\n",
       "      <td>215.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>178.2</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>232.5</td>\n",
       "      <td>262.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55.9</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>43.6</td>\n",
       "      <td>38.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>37.2</td>\n",
       "      <td>81.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>696.6</td>\n",
       "      <td>804.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>151.6</td>\n",
       "      <td>125.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>319.8</td>\n",
       "      <td>358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>262.7</td>\n",
       "      <td>307.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>354.4</td>\n",
       "      <td>391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>112.4</td>\n",
       "      <td>94.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63.0</td>\n",
       "      <td>76.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>177.2</td>\n",
       "      <td>125.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>38.8</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>112.4</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>34.2</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>74.9</td>\n",
       "      <td>65.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>56.2</td>\n",
       "      <td>283.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>89.0</td>\n",
       "      <td>93.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>166.1</td>\n",
       "      <td>105.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>229.5</td>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>36.4</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>205.3</td>\n",
       "      <td>171.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>50.4</td>\n",
       "      <td>152.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>13.9</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>139.3</td>\n",
       "      <td>67.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>28.2</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>336.9</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>14.5</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>165.5</td>\n",
       "      <td>188.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>105.2</td>\n",
       "      <td>163.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>46.9</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>13.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>87.3</td>\n",
       "      <td>65.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>40.6</td>\n",
       "      <td>15.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>193.5</td>\n",
       "      <td>204.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>183.7</td>\n",
       "      <td>119.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>69.2</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>18.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>84.2</td>\n",
       "      <td>109.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>41.6</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>193.7</td>\n",
       "      <td>211.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>33.0</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>194.3</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>181.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predicted  Actual\n",
       "0         28.1   107.0\n",
       "1        100.0    90.0\n",
       "2        127.2   123.3\n",
       "3         83.7   112.5\n",
       "4         46.6    23.2\n",
       "5         17.9     0.8\n",
       "6         12.8     0.3\n",
       "7         64.3    59.0\n",
       "8         66.3     3.2\n",
       "9        157.0   214.1\n",
       "10        33.6    76.2\n",
       "11       281.2   413.1\n",
       "12       166.1   215.6\n",
       "13       178.2     2.2\n",
       "14       232.5   262.9\n",
       "15        55.9    25.4\n",
       "16        43.6    38.8\n",
       "17        37.2    81.7\n",
       "18       696.6   804.9\n",
       "19       151.6   125.2\n",
       "20       319.8   358.0\n",
       "21       262.7   307.1\n",
       "22       354.4   391.0\n",
       "23       112.4    94.4\n",
       "24        63.0    76.3\n",
       "25       177.2   125.9\n",
       "26        38.8    39.1\n",
       "27       112.4    64.9\n",
       "28        34.2    27.4\n",
       "29        74.9    65.1\n",
       "..         ...     ...\n",
       "412       56.2   283.7\n",
       "413       89.0    93.3\n",
       "414      166.1   105.8\n",
       "415      229.5   392.0\n",
       "416       36.4    25.4\n",
       "417      205.3   171.6\n",
       "418       50.4   152.3\n",
       "419       13.9     0.9\n",
       "420       19.5     0.0\n",
       "421      139.3    67.5\n",
       "422       28.2     8.0\n",
       "423      336.9   293.0\n",
       "424       14.5     0.8\n",
       "425      165.5   188.7\n",
       "426      105.2   163.8\n",
       "427       46.9    27.1\n",
       "428       13.8     0.0\n",
       "429       87.3    65.3\n",
       "430       40.6    15.3\n",
       "431      193.5   204.3\n",
       "432      183.7   119.2\n",
       "433       69.2     6.1\n",
       "434       15.0     1.2\n",
       "435       18.8     0.0\n",
       "436       84.2   109.3\n",
       "437       41.6    10.7\n",
       "438      193.7   211.6\n",
       "439       33.0    11.3\n",
       "440      194.3   213.0\n",
       "441      181.0   264.0\n",
       "\n",
       "[442 rows x 2 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The range of the values is (0,1362) and rmse is 70.6\n",
    "We can say the results are decent but not good enough as we got with just one state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.6282264795402"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding a new feature\n",
    "### Quantity of Phosphorous in soil (low, medium, high)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This feaure is missing for some states in India, so little less number of samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the dataset\n",
    "ricep = pd.read_csv(\"/Users/macbook/Documents/BTP/Notebook/rice with soil(P).csv\")\n",
    "\n",
    "# Removing the rows with missing value of phosphorus\n",
    "ricep = ricep[np.isfinite(ricep[\"phosphorus\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>phosphorus</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>ANANTAPUR</td>\n",
       "      <td>1997</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>35600.0</td>\n",
       "      <td>75400.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>ANANTAPUR</td>\n",
       "      <td>1998</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>38300.0</td>\n",
       "      <td>96800.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>ANANTAPUR</td>\n",
       "      <td>1999</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>37991.0</td>\n",
       "      <td>105082.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>ANANTAPUR</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>39905.0</td>\n",
       "      <td>117680.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>ANANTAPUR</td>\n",
       "      <td>2001</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>32878.0</td>\n",
       "      <td>95609.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State_Name ind_district  Crop_Year  Season  Crop     Area  Production  \\\n",
       "13  Andhra Pradesh    ANANTAPUR       1997  Kharif  Rice  35600.0     75400.0   \n",
       "14  Andhra Pradesh    ANANTAPUR       1998  Kharif  Rice  38300.0     96800.0   \n",
       "15  Andhra Pradesh    ANANTAPUR       1999  Kharif  Rice  37991.0    105082.0   \n",
       "16  Andhra Pradesh    ANANTAPUR       2000  Kharif  Rice  39905.0    117680.0   \n",
       "17  Andhra Pradesh    ANANTAPUR       2001  Kharif  Rice  32878.0     95609.0   \n",
       "\n",
       "    phosphorus  X1  X2  X3  X4  \n",
       "13        -1.0 NaN NaN NaN NaN  \n",
       "14        -1.0 NaN NaN NaN NaN  \n",
       "15        -1.0 NaN NaN NaN NaN  \n",
       "16        -1.0 NaN NaN NaN NaN  \n",
       "17        -1.0 NaN NaN NaN NaN  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding collumns for the other 4 features\n",
    "a = np.empty((ricep.shape[0],1))*np.NAN\n",
    "ricep = ricep.assign(X1 = a)\n",
    "ricep = ricep.assign(X2 = a)\n",
    "ricep = ricep.assign(X3 = a)\n",
    "ricep = ricep.assign(X4 = a)\n",
    "ricep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constructing features X1 and X2\n",
    "l = ricep.shape[0]\n",
    "for row in range(0,l):\n",
    "    if row-1<0 or ricep.iloc[row,1] != ricep.iloc[row-1,1]:\n",
    "        continue\n",
    "    else:\n",
    "        ricep.iloc[row,8] = ricep.iloc[row-1,6]\n",
    "        if row-2<0 or ricep.iloc[row,1] != ricep.iloc[row-2,1]:\n",
    "            continue\n",
    "        else:\n",
    "            ricep.iloc[row,9] = ricep.iloc[row-2,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the strings in the dataset uniform, with other datasets\n",
    "ricep['ind_district'] = ricep['ind_district'].apply(func)\n",
    "ricep['Season'] = ricep['Season'].apply(func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constructing features X3 and X4\n",
    "l = ricep.shape[0]\n",
    "\n",
    "for row in range(0,l):\n",
    "    \n",
    "    dt = ricep.iloc[row,1]\n",
    "    yr = ricep.iloc[row,2]\n",
    "    \n",
    "    if yr<=2002:\n",
    "        \n",
    "        # rainfall for the same year\n",
    "        r = rain1.loc[(rain1.ind_district == dt) & (rain1.Year == yr)]\n",
    "        if r.shape[0] == 1:\n",
    "            ricep.iloc[row,10] = r.iloc[0,3]\n",
    "            \n",
    "        # rainfall for the previous year\n",
    "        r = rain1.loc[(rain1.ind_district == dt) & (rain1.Year == yr-1)]\n",
    "        if r.shape[0] == 1:\n",
    "            ricep.iloc[row,11] = r.iloc[0,3]\n",
    "            \n",
    "    if yr>2004:\n",
    "        \n",
    "        # rainfall for the same year\n",
    "        r = rain2.loc[(rain2.ind_district == dt) & (rain2.Year == yr)]\n",
    "        if r.shape[0] == 1:\n",
    "            ricep.iloc[row,10] = r.iloc[0,3]\n",
    "            \n",
    "        # rainfall for the previous year\n",
    "        r = rain2.loc[(rain2.ind_district == dt) & (rain2.Year == yr-1)]\n",
    "        if r.shape[0] == 1:\n",
    "            ricep.iloc[row,11] = r.iloc[0,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State_Name</th>\n",
       "      <th>ind_district</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>phosphorus</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>anantapur</td>\n",
       "      <td>1999</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>37991.0</td>\n",
       "      <td>105082.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>96800.0</td>\n",
       "      <td>75400.0</td>\n",
       "      <td>643.720</td>\n",
       "      <td>881.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>anantapur</td>\n",
       "      <td>2000</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>39905.0</td>\n",
       "      <td>117680.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>105082.0</td>\n",
       "      <td>96800.0</td>\n",
       "      <td>767.351</td>\n",
       "      <td>643.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>anantapur</td>\n",
       "      <td>2001</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>32878.0</td>\n",
       "      <td>95609.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>117680.0</td>\n",
       "      <td>105082.0</td>\n",
       "      <td>579.338</td>\n",
       "      <td>767.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>anantapur</td>\n",
       "      <td>2002</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>29066.0</td>\n",
       "      <td>66329.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>95609.0</td>\n",
       "      <td>117680.0</td>\n",
       "      <td>540.070</td>\n",
       "      <td>579.338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>anantapur</td>\n",
       "      <td>2005</td>\n",
       "      <td>kharif</td>\n",
       "      <td>Rice</td>\n",
       "      <td>25008.0</td>\n",
       "      <td>69972.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>85051.0</td>\n",
       "      <td>44891.0</td>\n",
       "      <td>819.700</td>\n",
       "      <td>564.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State_Name ind_district  Crop_Year  Season  Crop     Area  Production  \\\n",
       "15  Andhra Pradesh    anantapur       1999  kharif  Rice  37991.0    105082.0   \n",
       "16  Andhra Pradesh    anantapur       2000  kharif  Rice  39905.0    117680.0   \n",
       "17  Andhra Pradesh    anantapur       2001  kharif  Rice  32878.0     95609.0   \n",
       "18  Andhra Pradesh    anantapur       2002  kharif  Rice  29066.0     66329.0   \n",
       "21  Andhra Pradesh    anantapur       2005  kharif  Rice  25008.0     69972.0   \n",
       "\n",
       "    phosphorus        X1        X2       X3       X4  \n",
       "15        -1.0   96800.0   75400.0  643.720  881.473  \n",
       "16        -1.0  105082.0   96800.0  767.351  643.720  \n",
       "17        -1.0  117680.0  105082.0  579.338  767.351  \n",
       "18        -1.0   95609.0  117680.0  540.070  579.338  \n",
       "21        -1.0   85051.0   44891.0  819.700  564.500  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing rows with any missing values\n",
    "ricep = ricep[np.isfinite(ricep[\"Production\"])]\n",
    "ricep = ricep[np.isfinite(ricep[\"X1\"])]\n",
    "ricep = ricep[np.isfinite(ricep[\"X2\"])]\n",
    "ricep = ricep[np.isfinite(ricep[\"X3\"])]\n",
    "ricep = ricep[np.isfinite(ricep[\"X4\"])]\n",
    "ricep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "      <th>phosphorus</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1931.000000</td>\n",
       "      <td>1931.000000</td>\n",
       "      <td>1.931000e+03</td>\n",
       "      <td>1931.000000</td>\n",
       "      <td>1.931000e+03</td>\n",
       "      <td>1.931000e+03</td>\n",
       "      <td>1931.000000</td>\n",
       "      <td>1931.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2004.201968</td>\n",
       "      <td>69936.771103</td>\n",
       "      <td>1.556052e+05</td>\n",
       "      <td>0.557224</td>\n",
       "      <td>1.588625e+05</td>\n",
       "      <td>1.567246e+05</td>\n",
       "      <td>861.667570</td>\n",
       "      <td>871.507723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.631699</td>\n",
       "      <td>81619.498004</td>\n",
       "      <td>2.151250e+05</td>\n",
       "      <td>0.665363</td>\n",
       "      <td>2.176855e+05</td>\n",
       "      <td>2.170873e+05</td>\n",
       "      <td>476.198957</td>\n",
       "      <td>458.603987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>76.944000</td>\n",
       "      <td>108.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>6392.500000</td>\n",
       "      <td>6.326000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.545500e+03</td>\n",
       "      <td>6.987500e+03</td>\n",
       "      <td>592.700000</td>\n",
       "      <td>607.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>41040.000000</td>\n",
       "      <td>7.190000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.574800e+04</td>\n",
       "      <td>7.204800e+04</td>\n",
       "      <td>765.714000</td>\n",
       "      <td>773.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>111052.000000</td>\n",
       "      <td>2.333050e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.372640e+05</td>\n",
       "      <td>2.285185e+05</td>\n",
       "      <td>1023.702000</td>\n",
       "      <td>1045.679500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>545965.000000</td>\n",
       "      <td>1.637000e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.710000e+06</td>\n",
       "      <td>1.710000e+06</td>\n",
       "      <td>4755.700000</td>\n",
       "      <td>4076.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Crop_Year           Area    Production   phosphorus            X1  \\\n",
       "count  1931.000000    1931.000000  1.931000e+03  1931.000000  1.931000e+03   \n",
       "mean   2004.201968   69936.771103  1.556052e+05     0.557224  1.588625e+05   \n",
       "std       3.631699   81619.498004  2.151250e+05     0.665363  2.176855e+05   \n",
       "min    1999.000000       1.000000  0.000000e+00     0.000000  1.000000e+00   \n",
       "25%    2001.000000    6392.500000  6.326000e+03     0.000000  7.545500e+03   \n",
       "50%    2005.000000   41040.000000  7.190000e+04     0.000000  7.574800e+04   \n",
       "75%    2007.000000  111052.000000  2.333050e+05     1.000000  2.372640e+05   \n",
       "max    2010.000000  545965.000000  1.637000e+06     2.000000  1.710000e+06   \n",
       "\n",
       "                 X2           X3           X4  \n",
       "count  1.931000e+03  1931.000000  1931.000000  \n",
       "mean   1.567246e+05   861.667570   871.507723  \n",
       "std    2.170873e+05   476.198957   458.603987  \n",
       "min    1.000000e+00    76.944000   108.800000  \n",
       "25%    6.987500e+03   592.700000   607.830000  \n",
       "50%    7.204800e+04   765.714000   773.600000  \n",
       "75%    2.285185e+05  1023.702000  1045.679500  \n",
       "max    1.710000e+06  4755.700000  4076.200000  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ricep['phosphorus'] = ricep['phosphorus'] + 1\n",
    "ricep.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ricep[[\"X1\",\"X2\",\"X3\",\"X4\",\"phosphorus\"]]\n",
    "y = ricep[[\"Production\"]]\n",
    "ricep.to_csv(\"ricep.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/iyZ92Wr4L/rGaVgM+KsjKXATNcbUXJifNubPxrnPRM+nse7Df/H+oTWZziQ0lx72fHLO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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1178fdc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xplot = ricep[[\"X1\", \"X2\", \"X3\", \"X4\", \"phosphorus\", \"Production\"]]\n",
    "\n",
    "var_name = \"phosphorus\"\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=var_name, y='Production', data=Xplot, scatter_kws={'alpha':0.6, 's':20})\n",
    "plt.xlabel(var_name + \" (in Soil)\", fontsize=15)\n",
    "plt.ylabel('Y', fontsize=15)\n",
    "plt.title(\"Distribution of y variable with feature \"+var_name, fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Z-Score Normalization\n",
    "\n",
    "cols = list(X.columns)\n",
    "for col in cols:\n",
    "    col_zscore = col + '_zscore'\n",
    "    X[col_zscore] = (X[col] - X[col].mean())/X[col].std(ddof=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1_zscore</th>\n",
       "      <th>X2_zscore</th>\n",
       "      <th>X3_zscore</th>\n",
       "      <th>X4_zscore</th>\n",
       "      <th>phosphorus_zscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.285176</td>\n",
       "      <td>-0.374714</td>\n",
       "      <td>-0.457800</td>\n",
       "      <td>0.021735</td>\n",
       "      <td>-0.837691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.247120</td>\n",
       "      <td>-0.276111</td>\n",
       "      <td>-0.198113</td>\n",
       "      <td>-0.496827</td>\n",
       "      <td>-0.837691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.189232</td>\n",
       "      <td>-0.237950</td>\n",
       "      <td>-0.593035</td>\n",
       "      <td>-0.227176</td>\n",
       "      <td>-0.837691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.290648</td>\n",
       "      <td>-0.179903</td>\n",
       "      <td>-0.675518</td>\n",
       "      <td>-0.637250</td>\n",
       "      <td>-0.837691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.339162</td>\n",
       "      <td>-0.515288</td>\n",
       "      <td>-0.088153</td>\n",
       "      <td>-0.669613</td>\n",
       "      <td>-0.837691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X1_zscore  X2_zscore  X3_zscore  X4_zscore  phosphorus_zscore\n",
       "15  -0.285176  -0.374714  -0.457800   0.021735          -0.837691\n",
       "16  -0.247120  -0.276111  -0.198113  -0.496827          -0.837691\n",
       "17  -0.189232  -0.237950  -0.593035  -0.227176          -0.837691\n",
       "18  -0.290648  -0.179903  -0.675518  -0.637250          -0.837691\n",
       "21  -0.339162  -0.515288  -0.088153  -0.669613          -0.837691"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X[[\"X1_zscore\", \"X2_zscore\", \"X3_zscore\", \"X4_zscore\", \"phosphorus_zscore\"]]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1_zscore</th>\n",
       "      <th>X2_zscore</th>\n",
       "      <th>X3_zscore</th>\n",
       "      <th>X4_zscore</th>\n",
       "      <th>phosphorus_zscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.931000e+03</td>\n",
       "      <td>1.931000e+03</td>\n",
       "      <td>1.931000e+03</td>\n",
       "      <td>1.931000e+03</td>\n",
       "      <td>1.931000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.471865e-17</td>\n",
       "      <td>2.943730e-17</td>\n",
       "      <td>1.582255e-16</td>\n",
       "      <td>2.023814e-16</td>\n",
       "      <td>5.887459e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000259e+00</td>\n",
       "      <td>1.000259e+00</td>\n",
       "      <td>1.000259e+00</td>\n",
       "      <td>1.000259e+00</td>\n",
       "      <td>1.000259e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-7.299643e-01</td>\n",
       "      <td>-7.221251e-01</td>\n",
       "      <td>-1.648317e+00</td>\n",
       "      <td>-1.663538e+00</td>\n",
       "      <td>-8.376908e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.952976e-01</td>\n",
       "      <td>-6.899339e-01</td>\n",
       "      <td>-5.649681e-01</td>\n",
       "      <td>-5.751063e-01</td>\n",
       "      <td>-8.376908e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-3.819089e-01</td>\n",
       "      <td>-3.901588e-01</td>\n",
       "      <td>-2.015511e-01</td>\n",
       "      <td>-2.135461e-01</td>\n",
       "      <td>-8.376908e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.602528e-01</td>\n",
       "      <td>3.308001e-01</td>\n",
       "      <td>3.403544e-01</td>\n",
       "      <td>3.798853e-01</td>\n",
       "      <td>6.656372e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.127436e+00</td>\n",
       "      <td>7.156926e+00</td>\n",
       "      <td>8.179441e+00</td>\n",
       "      <td>6.989739e+00</td>\n",
       "      <td>2.168965e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X1_zscore     X2_zscore     X3_zscore     X4_zscore  \\\n",
       "count  1.931000e+03  1.931000e+03  1.931000e+03  1.931000e+03   \n",
       "mean   1.471865e-17  2.943730e-17  1.582255e-16  2.023814e-16   \n",
       "std    1.000259e+00  1.000259e+00  1.000259e+00  1.000259e+00   \n",
       "min   -7.299643e-01 -7.221251e-01 -1.648317e+00 -1.663538e+00   \n",
       "25%   -6.952976e-01 -6.899339e-01 -5.649681e-01 -5.751063e-01   \n",
       "50%   -3.819089e-01 -3.901588e-01 -2.015511e-01 -2.135461e-01   \n",
       "75%    3.602528e-01  3.308001e-01  3.403544e-01  3.798853e-01   \n",
       "max    7.127436e+00  7.156926e+00  8.179441e+00  6.989739e+00   \n",
       "\n",
       "       phosphorus_zscore  \n",
       "count       1.931000e+03  \n",
       "mean        5.887459e-17  \n",
       "std         1.000259e+00  \n",
       "min        -8.376908e-01  \n",
       "25%        -8.376908e-01  \n",
       "50%        -8.376908e-01  \n",
       "75%         6.656372e-01  \n",
       "max         2.168965e+00  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alg = LinearRegression()\n",
    "alg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef = alg.coef_\n",
    "intercept = alg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([[ 132506.04,   80946.49,    7100.73,   -7645.37,    -628.54]])\n",
      "array([ 156958.37262622])\n"
     ]
    }
   ],
   "source": [
    "coef = coef.round(decimals=2)\n",
    "pp = pprint.PrettyPrinter()\n",
    "pp.pprint(coef)\n",
    "pp.pprint(intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = alg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp = y_predict\n",
    "yt = y_test.as_matrix()\n",
    "type(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54248.31068781849\n"
     ]
    }
   ],
   "source": [
    "rmse = sqrt(mean_squared_error(y_predict, y_test))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets calculate the average RMSE (Cross Validation, 5-Fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LinearRegression()\n",
    "scores = cross_val_score(clf, X, y, cv=5, scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,5):\n",
    "    scores[i] = sqrt(-1*scores[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 76204.91294612  27552.79356665  63041.13041068  55146.78083903\n",
      "  39417.41847221]\n",
      "\n",
      "\n",
      "Avg RMSE is  52272.6072469\n"
     ]
    }
   ],
   "source": [
    "print(scores)\n",
    "avg_rmse_phos = scores.mean()\n",
    "print(\"\\n\\nAvg RMSE is \",scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "yt = yt/1000\n",
    "yp = yp/1000\n",
    "yt = yt.round(decimals=1)\n",
    "yp = yp.round(decimals=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "yo = np.concatenate((yp,yt),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>387.000000</td>\n",
       "      <td>387.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>149.640052</td>\n",
       "      <td>142.889406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>201.643917</td>\n",
       "      <td>189.788422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.600000</td>\n",
       "      <td>9.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>72.600000</td>\n",
       "      <td>71.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>227.450000</td>\n",
       "      <td>221.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1630.500000</td>\n",
       "      <td>1637.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Predicted       Actual\n",
       "count   387.000000   387.000000\n",
       "mean    149.640052   142.889406\n",
       "std     201.643917   189.788422\n",
       "min      -5.600000     0.000000\n",
       "25%      14.600000     9.650000\n",
       "50%      72.600000    71.300000\n",
       "75%     227.450000   221.750000\n",
       "max    1630.500000  1637.000000"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pd.DataFrame(data=yo,columns=['Predicted','Actual'])\n",
    "p.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>171.7</td>\n",
       "      <td>190.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.0</td>\n",
       "      <td>56.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>263.1</td>\n",
       "      <td>223.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.9</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81.3</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>90.4</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>190.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>211.5</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>53.8</td>\n",
       "      <td>67.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>172.9</td>\n",
       "      <td>220.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>51.2</td>\n",
       "      <td>39.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>257.9</td>\n",
       "      <td>244.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25.9</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>72.6</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30.8</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>329.5</td>\n",
       "      <td>216.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>287.8</td>\n",
       "      <td>304.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>104.2</td>\n",
       "      <td>106.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60.9</td>\n",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>232.7</td>\n",
       "      <td>289.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.7</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>182.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>284.9</td>\n",
       "      <td>401.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>62.0</td>\n",
       "      <td>59.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>285.8</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>284.5</td>\n",
       "      <td>321.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>15.3</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9.0</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>23.3</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>29.2</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>42.5</td>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>226.0</td>\n",
       "      <td>385.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>375.5</td>\n",
       "      <td>447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>169.6</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>41.4</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>13.2</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>11.4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>67.2</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>14.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>127.9</td>\n",
       "      <td>120.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>215.7</td>\n",
       "      <td>280.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>84.5</td>\n",
       "      <td>75.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>225.2</td>\n",
       "      <td>307.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>12.5</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>7.5</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>16.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>736.8</td>\n",
       "      <td>576.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>3.2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>55.0</td>\n",
       "      <td>54.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>41.2</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>6.4</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>75.9</td>\n",
       "      <td>72.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>48.2</td>\n",
       "      <td>50.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>14.5</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>292.2</td>\n",
       "      <td>239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>177.5</td>\n",
       "      <td>176.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>13.2</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>387 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predicted  Actual\n",
       "0        171.7   190.6\n",
       "1         52.0    56.1\n",
       "2        263.1   223.5\n",
       "3          8.9    14.5\n",
       "4         81.3    37.0\n",
       "5         90.4    14.2\n",
       "6        190.0   204.0\n",
       "7        211.5   255.0\n",
       "8         53.8    67.3\n",
       "9        172.9   220.2\n",
       "10        51.2    39.5\n",
       "11       257.9   244.0\n",
       "12        25.9    18.2\n",
       "13        72.6    81.0\n",
       "14        30.8    42.0\n",
       "15       329.5   216.8\n",
       "16       287.8   304.9\n",
       "17       104.2   106.8\n",
       "18        60.9    45.3\n",
       "19       232.7   289.4\n",
       "20         3.7     0.2\n",
       "21       182.0   202.0\n",
       "22       284.9   401.5\n",
       "23        62.0    59.2\n",
       "24       285.8   224.0\n",
       "25       284.5   321.6\n",
       "26         3.2     3.0\n",
       "27        15.3    18.5\n",
       "28         9.0    39.2\n",
       "29        23.3    20.6\n",
       "..         ...     ...\n",
       "357       29.2    18.0\n",
       "358        9.0     6.0\n",
       "359       42.5    48.3\n",
       "360      226.0   385.7\n",
       "361      375.5   447.0\n",
       "362      169.6   182.0\n",
       "363       41.4    34.5\n",
       "364       13.2    16.2\n",
       "365       11.4     5.0\n",
       "366       67.2    57.0\n",
       "367       14.7     3.2\n",
       "368      127.9   120.7\n",
       "369      215.7   280.5\n",
       "370        0.3     0.0\n",
       "371       84.5    75.9\n",
       "372      225.2   307.4\n",
       "373       12.5     9.8\n",
       "374        7.5     2.5\n",
       "375       16.0     1.6\n",
       "376      736.8   576.6\n",
       "377        3.2     0.4\n",
       "378       55.0    54.1\n",
       "379       41.2     4.9\n",
       "380        6.4    19.5\n",
       "381       75.9    72.7\n",
       "382       48.2    50.6\n",
       "383       14.5    10.5\n",
       "384      292.2   239.0\n",
       "385      177.5   176.3\n",
       "386       13.2     8.4\n",
       "\n",
       "[387 rows x 2 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.24831068781849"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now lets compare with other feature combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just the 4 original features (no soil data)\n",
    "X_old = X[[\"X1_zscore\", \"X2_zscore\", \"X3_zscore\", \"X4_zscore\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seed is fixed, so the vector y_test is going to same as before\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_old, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alg = LinearRegression()\n",
    "alg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef = alg.coef_\n",
    "intercept = alg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([[ 132450.58,   80889.02,    7144.62,   -7681.96]])\n",
      "array([ 156944.0755844])\n"
     ]
    }
   ],
   "source": [
    "coef = coef.round(decimals=2)\n",
    "pp = pprint.PrettyPrinter()\n",
    "pp.pprint(coef)\n",
    "pp.pprint(intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54259.34353018352\n"
     ]
    }
   ],
   "source": [
    "y_predict = alg.predict(X_test)\n",
    "yp = y_predict\n",
    "yt = y_test.as_matrix()\n",
    "rmse = sqrt(mean_squared_error(y_predict, y_test))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average RMSE (Cross Validation, 5-Fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LinearRegression()\n",
    "scores = cross_val_score(clf, X_old, y, cv=5, scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,5):\n",
    "    scores[i] = sqrt(-1*scores[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 76204.73868727  27581.31938911  61253.46984196  54898.98784947\n",
      "  39436.16221218]\n",
      "\n",
      "\n",
      "Avg RMSE is  51874.935596\n"
     ]
    }
   ],
   "source": [
    "print(scores)\n",
    "avg_rmse_orig = scores.mean()\n",
    "print(\"\\n\\nAvg RMSE is \",scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Avg RMSE with original 4 features  :  51874.9                                               \n",
    "Avg RMSE with 5 features(soil too) : 52272.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So there was NO improvement in the RMSE, due to the new feature.\n",
    "## Now lets try by removing the rainfall features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_no_rain = X[[\"X1_zscore\", \"X2_zscore\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_no_rain, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alg = LinearRegression()\n",
    "alg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef = alg.coef_\n",
    "intercept = alg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([[ 131375.8 ,   82072.26]])\n",
      "array([ 157003.90363578])\n"
     ]
    }
   ],
   "source": [
    "coef = coef.round(decimals=2)\n",
    "pp = pprint.PrettyPrinter()\n",
    "pp.pprint(coef)\n",
    "pp.pprint(intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54408.104038638914\n"
     ]
    }
   ],
   "source": [
    "y_predict = alg.predict(X_test)\n",
    "yp = y_predict\n",
    "yt = y_test.as_matrix()\n",
    "rmse = sqrt(mean_squared_error(y_predict, y_test))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avg RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LinearRegression()\n",
    "scores = cross_val_score(clf, X_no_rain, y, cv=5, scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,5):\n",
    "    scores[i] = sqrt(-1*scores[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 76443.90121968  27973.46173293  60887.42884253  54851.03966512\n",
      "  39454.09118802]\n",
      "\n",
      "\n",
      "Avg RMSE is  51921.9845297\n"
     ]
    }
   ],
   "source": [
    "print(scores)\n",
    "avg_rmse_no_rain = scores.mean()\n",
    "print(\"\\n\\nAvg RMSE is \",scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avg RMSE with the original 4 features : 51874.9\n",
    "#### Avg RMSE without the 2  rain features : 51921.9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0999999999999996"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "reg = linear_model.RidgeCV(alphas=[1,2,3,4,5,6,7,7.1,7.2,7.3,8,9,10])\n",
    "reg.fit(X_old, y)\n",
    "reg.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 129388.47233661   83479.38823733    6873.59978818   -7434.09039496]]\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_old, y, test_size=0.2, random_state=1)\n",
    "\n",
    "reg = linear_model.Ridge(alpha = 7.1)\n",
    "reg.fit (X_train, y_train)\n",
    "print(reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54181.93424418657\n"
     ]
    }
   ],
   "source": [
    "y_predict = reg.predict(X_test)\n",
    "rmse = sqrt(mean_squared_error(y_predict, y_test))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avg RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 76134.52790453  27507.68332601  61457.10987059  54957.99018935\n",
      "  39147.52019336]\n",
      "\n",
      "\n",
      "Avg RMSE is  51840.9662968\n"
     ]
    }
   ],
   "source": [
    "clf = linear_model.Ridge(alpha = 7.1)\n",
    "scores = cross_val_score(clf, X_old, y, cv=5, scoring='neg_mean_squared_error')\n",
    "\n",
    "for i in range(0,5):\n",
    "    scores[i] = sqrt(-1*scores[i])\n",
    "    \n",
    "print(scores)\n",
    "avg_rmse_ridge = scores.mean()\n",
    "print(\"\\n\\nAvg RMSE is \",scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avg RMSE with Linear Regression : 51874.9\n",
    "#### Avg RMSE with Ridge Regression : 51840.9\n",
    "### So Ridge is slightly better than Linear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.01"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "reg = linear_model.LassoCV(alphas=[0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1])\n",
    "reg.fit(X_old, y)\n",
    "reg.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 132450.83956189   80888.75823956    7144.57227528   -7681.91765549]\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_old, y, test_size=0.2, random_state=1)\n",
    "\n",
    "las = linear_model.Lasso(alpha = 0.01)\n",
    "las.fit (X_train, y_train)\n",
    "print(las.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54259.33551624823\n"
     ]
    }
   ],
   "source": [
    "y_predict = las.predict(X_test)\n",
    "rmse = sqrt(mean_squared_error(y_predict, y_test))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avg RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 76204.74665396  27581.32715608  61253.46437277  54898.96269897\n",
      "  39436.18029115]\n",
      "\n",
      "\n",
      "Avg RMSE is  51874.9362346\n"
     ]
    }
   ],
   "source": [
    "clf = linear_model.Lasso(alpha = 0.01)\n",
    "scores = cross_val_score(clf, X_old, y, cv=5, scoring='neg_mean_squared_error')\n",
    "\n",
    "for i in range(0,5):\n",
    "    scores[i] = sqrt(-1*scores[i])\n",
    "    \n",
    "print(scores)\n",
    "avg_rmse_las = scores.mean()\n",
    "print(\"\\n\\nAvg RMSE is \",scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avg RMSE with Linear Regression : 51874.9\n",
    "#### Avg RMSE with Ridge Regression : 51840.9\n",
    "#### Avg RMSE with Lasso Regression : 51874.9"
   ]
  }
 ],
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