Capstone-Modelling-Part-2 .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling -Part -2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weekly sales is being precited using features in dataset.(Not using time series approach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required packages\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from datetime import datetime as dt\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor \n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.svm import SVR\n",
    "from sklearn import linear_model\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import KFold\n",
    "import matplotlib.pyplot as plt\n",
    "import xgboost as xgb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading data\n",
    "train=pd.read_csv('train.csv')\n",
    "test=pd.read_csv('test.csv')\n",
    "store=pd.read_csv('stores.csv')\n",
    "feature=pd.read_csv('features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging train and features dataset\n",
    "merge_df=pd.merge(train,feature, on=['Store','Date'], how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2010-02-05\n",
       "1   2010-02-05\n",
       "2   2010-02-05\n",
       "3   2010-02-05\n",
       "4   2010-02-05\n",
       "Name: DateTimeObj, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Changing date as index\n",
    "merge_df['DateTimeObj']=[dt.strptime(x,'%Y-%m-%d') for x in list(merge_df['Date'])]\n",
    "merge_df['DateTimeObj'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday_x</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday_y</th>\n",
       "      <th>DateTimeObj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>24924.50</td>\n",
       "      <td>False</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>50605.27</td>\n",
       "      <td>False</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>13740.12</td>\n",
       "      <td>False</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  Dept        Date  Weekly_Sales  IsHoliday_x  Temperature  \\\n",
       "0      1     1  2010-02-05      24924.50        False        42.31   \n",
       "1      1     2  2010-02-05      50605.27        False        42.31   \n",
       "2      1     3  2010-02-05      13740.12        False        42.31   \n",
       "\n",
       "   Fuel_Price  MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5  \\\n",
       "0       2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "1       2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "2       2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "          CPI  Unemployment  IsHoliday_y DateTimeObj  \n",
       "0  211.096358         8.106        False  2010-02-05  \n",
       "1  211.096358         8.106        False  2010-02-05  \n",
       "2  211.096358         8.106        False  2010-02-05  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting  weekly sales of store 1 versus date \n",
    "plt.plot(merge_df[(merge_df.Store==1)].DateTimeObj, merge_df[(merge_df.Store==1)].Weekly_Sales, 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store  Date      \n",
       "1      2010-02-05    1643690.90\n",
       "       2010-02-12    1641957.44\n",
       "       2010-02-19    1611968.17\n",
       "       2010-02-26    1409727.59\n",
       "Name: Weekly_Sales, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plotting weekly sales storewise\n",
    "weeklysales=merge_df.groupby(['Store','Date'])['Weekly_Sales'].apply(lambda x:np.sum(x))\n",
    "weeklysales[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store  Dept\n",
       "1      1       3219405.18\n",
       "       2       6592598.93\n",
       "       3       1880518.36\n",
       "       4       5285874.09\n",
       "       5       3468885.58\n",
       "Name: Weekly_Sales, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Plotting weekly sales departmentwise\n",
    "weeklysaledept=merge_df.groupby(['Store','Dept'])['Weekly_Sales'].apply(lambda x:np.sum(x))\n",
    "weeklysaledept[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>1643690.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1641957.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1611968.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>1409727.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1554806.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales\n",
       "0      1  2010-02-05    1643690.90\n",
       "1      1  2010-02-12    1641957.44\n",
       "2      1  2010-02-19    1611968.17\n",
       "3      1  2010-02-26    1409727.59\n",
       "4      1  2010-03-05    1554806.68"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Our approach is storewise,so taking that dataframe and resetting index\n",
    "weeklyscale=weeklysales.reset_index()\n",
    "weeklyscale[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Temperature  Fuel_Price  MarkDown1  \\\n",
       "0      1  2010-02-05    1643690.90        42.31       2.572        NaN   \n",
       "1      1  2010-02-12    1641957.44        38.51       2.548        NaN   \n",
       "2      1  2010-02-19    1611968.17        39.93       2.514        NaN   \n",
       "3      1  2010-02-26    1409727.59        46.63       2.561        NaN   \n",
       "4      1  2010-03-05    1554806.68        46.50       2.625        NaN   \n",
       "\n",
       "   MarkDown2  MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  \\\n",
       "0        NaN        NaN        NaN        NaN  211.096358         8.106   \n",
       "1        NaN        NaN        NaN        NaN  211.242170         8.106   \n",
       "2        NaN        NaN        NaN        NaN  211.289143         8.106   \n",
       "3        NaN        NaN        NaN        NaN  211.319643         8.106   \n",
       "4        NaN        NaN        NaN        NaN  211.350143         8.106   \n",
       "\n",
       "   IsHoliday  \n",
       "0      False  \n",
       "1       True  \n",
       "2      False  \n",
       "3      False  \n",
       "4      False  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging feature and weekly salers storewise\n",
    "walmartstore=pd.merge(weeklyscale, feature, on=['Store', 'Date'], how='inner')\n",
    "walmartstore.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing markdowns offer\n",
    "walmartstoredf = walmartstore.iloc[:, list(range(5)) + list(range(10,13))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Resetting index\n",
    "walmartstoredf['DateTimeObj'] = [dt.strptime(x, '%Y-%m-%d') for x in list(walmartstoredf['Date'])]\n",
    "weekNo=walmartstoredf.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding an additional colum specifying week number\n",
    "weekNo = [(x - walmartstoredf['DateTimeObj'][0]) for x in list(walmartstoredf['DateTimeObj'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "walmartstoredf['Week'] = [np.timedelta64(x, 'D').astype(int)/7 for x in weekNo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>DateTimeObj</th>\n",
       "      <th>Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Temperature  Fuel_Price         CPI  \\\n",
       "0      1  2010-02-05    1643690.90        42.31       2.572  211.096358   \n",
       "1      1  2010-02-12    1641957.44        38.51       2.548  211.242170   \n",
       "2      1  2010-02-19    1611968.17        39.93       2.514  211.289143   \n",
       "3      1  2010-02-26    1409727.59        46.63       2.561  211.319643   \n",
       "4      1  2010-03-05    1554806.68        46.50       2.625  211.350143   \n",
       "\n",
       "   Unemployment  IsHoliday DateTimeObj  Week  \n",
       "0         8.106      False  2010-02-05   0.0  \n",
       "1         8.106       True  2010-02-12   1.0  \n",
       "2         8.106      False  2010-02-19   2.0  \n",
       "3         8.106      False  2010-02-26   3.0  \n",
       "4         8.106      False  2010-03-05   4.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmartstoredf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(walmartstoredf.DateTimeObj, walmartstoredf.Weekly_Sales, 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>DateTimeObj</th>\n",
       "      <th>Week</th>\n",
       "      <th>IsHolidayInt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-12</td>\n",
       "      <td>1439541.59</td>\n",
       "      <td>57.79</td>\n",
       "      <td>2.667</td>\n",
       "      <td>211.380643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-03-12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Temperature  Fuel_Price         CPI  \\\n",
       "0      1  2010-02-05    1643690.90        42.31       2.572  211.096358   \n",
       "1      1  2010-02-12    1641957.44        38.51       2.548  211.242170   \n",
       "2      1  2010-02-19    1611968.17        39.93       2.514  211.289143   \n",
       "3      1  2010-02-26    1409727.59        46.63       2.561  211.319643   \n",
       "4      1  2010-03-05    1554806.68        46.50       2.625  211.350143   \n",
       "5      1  2010-03-12    1439541.59        57.79       2.667  211.380643   \n",
       "\n",
       "   Unemployment  IsHoliday DateTimeObj  Week  IsHolidayInt  \n",
       "0         8.106      False  2010-02-05   0.0             0  \n",
       "1         8.106       True  2010-02-12   1.0             1  \n",
       "2         8.106      False  2010-02-19   2.0             0  \n",
       "3         8.106      False  2010-02-26   3.0             0  \n",
       "4         8.106      False  2010-03-05   4.0             0  \n",
       "5         8.106      False  2010-03-12   5.0             0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Integer value for is_holiday \n",
    "walmartstoredf['IsHolidayInt'] = [int(x) for x in list(walmartstoredf.IsHoliday)]\n",
    "walmartstoredf.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train test split\n",
    "train_WM, test_WM = train_test_split(walmartstoredf, test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "XTrain = train_WM[['Temperature', 'Fuel_Price', 'CPI', 'Unemployment', 'Week', 'IsHolidayInt']]\n",
    "YTrain = train_WM['Weekly_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "XTest = test_WM[['Temperature', 'Fuel_Price', 'CPI', 'Unemployment', 'Week', 'IsHolidayInt']]\n",
    "YTest = test_WM['Weekly_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Instantiate\n",
    "wmLinear = linear_model.LinearRegression(normalize=True)\n",
    "#Fit\n",
    "wmLinear.fit(XTrain, YTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Performance/Predict on the test data sets\n",
    "YHatTest = wmLinear.predict(XTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Predicted and actual values for test and train\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.018399963104079542\n",
      "0.6647628870821394 313837794216.65875\n"
     ]
    }
   ],
   "source": [
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy is very low using only numerical columns.Lets use entire dataset for modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get dummies\n",
    "Store_Dummies = pd.get_dummies(walmartstoredf.Store, prefix='Store').iloc[:,1:]\n",
    "walmartstoredf = pd.concat([walmartstoredf, Store_Dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>DateTimeObj</th>\n",
       "      <th>Week</th>\n",
       "      <th>...</th>\n",
       "      <th>Store_36</th>\n",
       "      <th>Store_37</th>\n",
       "      <th>Store_38</th>\n",
       "      <th>Store_39</th>\n",
       "      <th>Store_40</th>\n",
       "      <th>Store_41</th>\n",
       "      <th>Store_42</th>\n",
       "      <th>Store_43</th>\n",
       "      <th>Store_44</th>\n",
       "      <th>Store_45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>True</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-12</td>\n",
       "      <td>1439541.59</td>\n",
       "      <td>57.79</td>\n",
       "      <td>2.667</td>\n",
       "      <td>211.380643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>2010-03-12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Temperature  Fuel_Price         CPI  \\\n",
       "0      1  2010-02-05    1643690.90        42.31       2.572  211.096358   \n",
       "1      1  2010-02-12    1641957.44        38.51       2.548  211.242170   \n",
       "2      1  2010-02-19    1611968.17        39.93       2.514  211.289143   \n",
       "3      1  2010-02-26    1409727.59        46.63       2.561  211.319643   \n",
       "4      1  2010-03-05    1554806.68        46.50       2.625  211.350143   \n",
       "5      1  2010-03-12    1439541.59        57.79       2.667  211.380643   \n",
       "\n",
       "   Unemployment  IsHoliday DateTimeObj  Week  ...  Store_36  Store_37  \\\n",
       "0         8.106      False  2010-02-05   0.0  ...         0         0   \n",
       "1         8.106       True  2010-02-12   1.0  ...         0         0   \n",
       "2         8.106      False  2010-02-19   2.0  ...         0         0   \n",
       "3         8.106      False  2010-02-26   3.0  ...         0         0   \n",
       "4         8.106      False  2010-03-05   4.0  ...         0         0   \n",
       "5         8.106      False  2010-03-12   5.0  ...         0         0   \n",
       "\n",
       "   Store_38  Store_39  Store_40  Store_41  Store_42  Store_43  Store_44  \\\n",
       "0         0         0         0         0         0         0         0   \n",
       "1         0         0         0         0         0         0         0   \n",
       "2         0         0         0         0         0         0         0   \n",
       "3         0         0         0         0         0         0         0   \n",
       "4         0         0         0         0         0         0         0   \n",
       "5         0         0         0         0         0         0         0   \n",
       "\n",
       "   Store_45  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  \n",
       "\n",
       "[6 rows x 55 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmartstoredf.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train test split\n",
    "train_WM, test_WM = train_test_split(walmartstoredf, test_size=0.3,random_state=42)\n",
    "XTrain = train_WM.iloc[:,([3,4,5,6] + [9,10]) + list(range(11,walmartstoredf.shape[1]))]\n",
    "yTrain = train_WM.Weekly_Sales\n",
    "                                                    \n",
    "XTest = test_WM.iloc[:,([3,4,5,6] + [9,10]) + list(range(11,walmartstoredf.shape[1]))]\n",
    "yTest=test_WM.Weekly_Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.StandardScaler()\n",
    "scaler.fit(XTrain)\n",
    "X_transformed_train= scaler.transform(XTrain)\n",
    "X_transformed_test= scaler.transform(XTest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Instantiate \n",
    "wmLinear = linear_model.LinearRegression(normalize=True)\n",
    "#fit\n",
    "wmLinear.fit(XTrain, YTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/rg5efdVUFBJ63XQkukRIDScnSaIQIlLgZmZZ9t/3+cJvdKE3v969zSORG2G0xGNZfHXtzfh8cOGF5tR555kF/aqrHfwcmUp0TkkNx3mijFcu0wnXsqy8e6xfv97yKi/HFipX4rQsl2NduNCyysosy9zK3HlcfnkwzoULLauy0pwvLAz/N+Ixk6vCTn247zHh5SsqzMPnM6+5cGH4zxJ4n8hHRYX5XpTnpe13Hy2eysqkXi7n/kbt/rbKyjr/3uKVc/o6SVi9evVqy+aemvWbejoeXv4D8nJsoXIlTstyOdZoNzMXHu0Oy23mwLBTV3Kr8/epqIh9Qykpsazi4pg3mbT97n0++5h9vqReLuf+Rp0mynjlXE64oaIlCml6El1bZBV+06a0vZWTLR9+w0z6saXj+FP6MYurnL/Jtm3BjvCaGrjnHqisNM1llZXQqxfs3Rv+nEz1WwwcmNj5fOO0KTBeuSw0KUqiEF2XXZt5lnbw2cA38WFxK78B4BauxsIXljQcs0I6wmtq4KOPTMfGRx+ZKdt2MtFvMWMGlJWFnysrM+e7AqeJMl65LCRcSRSia6qtNT3EdqONMuzn1HIoGzqOd7APVzMztRe1LPtaQjY/1dvVcO65x5zvCpwmynjlspBwJVGIridQk2hry2oYa/guPiz+zM8BmM9FWPjYh53uvIFdLcHuJuPzmdpUJkbPRNZwukqSAOeJMl65LCRcSRSi67GbX5BB7fg4iZepZg0A+7GdPXTjIh5y943sagmhNxkwN5pALco/XLV88eL4rx3o2/H5zDRxn0/mRTjhNFHGKxf5fUjrcNmYq8cqpYqBB4BBQCkwHVgPPAhYwDpgvNa6XSk1FTgTaAUmaq1XKaUOTUdZ93580SXFao8vKwuf2eayfzCcU/hHx/FfOYuz+Jv7bxSrKaKmxjzsOu8bG+l/9dVmg+0ZM+xvZIEaWSDZBmpmgXkRgfcQmRH5+0jD7yFejWIMsE1rPQwYBdwJzAKm+M/5gNFKqWrgJGAocB4wx//8dJUVwrnIkU37729frrAwOLPNZXsp4lA+7EgS32EteylKT5IIbYoYNy74ib+oKHxZkCgJ0wexJ8PFqpFle+Z3V5SBGfjxEsXjwPUhx63AEOAV//ES4FTgBGCp1trSWjcARUqpPmksK4QzdiObduzoXM7nM5+M77rL9drEE5xNCXvZwKEAvMbxrGUwRaSpjyRQExg3zuxeFPjE39Zmjk89NfbM84BoN5t4I6SyNfO7q8rAcNmYTU9a690ASqlewCJgCvBHrXXgL2wXsA9QDoT+7wqc96Wp7Bex4m5ubqa+vj5WkaxpamrybGyhciVOiB3rIZMmURL5aSuiE9sCfIGbpoujnhrpzv5sp5luAIzkeZYwytF8imisggJ87e0xy7RfdBGbP/2U/nffbfte1rJljmOwGhp4P+Tali9eTH+fL3i9bLT07cuGDPztePlvtHzxYvrcdhvFW7awt29fuo8fT/3ZZ6flvQ7p25eSzZs7nXfz9xB3hzul1EHAU8BcrfUjSqk/hHy7F7AD2On/OvJ8e5rKxlRaWkpVVVW8YllRX1/v2dhC5UqcECfWLfHnIaRj5sTdXMpl3N1xvJbv8B3eS/l14yUJgILWVgbMmWM6Ou1eI5H3GzgweG1ra2HatKivC0BZGSUzZ2bkb8ezf6OB6+T/gFKyeTMDp0+nYNCg9PTdzJwZ3kcBSf8e6urqbM/HbHpSSh0ILAWu0Vo/4D+9Rik13P/1KGA5sAIYqZQqUEoNBAq01lvTWFYIZzI863c7++HD6kgSY7kfC58rSQKfz/kG2A0Nps8lEfHG5scbLdbV5kVEY3OdCpqa0td3k4HhsvH6KK4D9gOuV0q9rJR6GdP8dKNSaiVQAizSWtdhbuwrgSeA8f7nX5WmsqKrSXa1zBkzMjbb+ndMoYLgzOePqOR+LnHvDSwr+szqSAMHBke+OOW/2VjRbjbR2rwDQ2y72ryIaLKxam+656fYLQCV6w8vLxbm5dhCeSrOOKtlxo01TYv8BR4fMyDs1GR+l/b3jPkoLg4u8nf55eGr1JaU2D+noqLjckW9nmlcjC4ZnvobDeWx65QIWRRQ5K5Uh/8FJpelwTjmcBCfdBx/Th+mhw0UTFCizUWRKipg/vzgJ8q5c6G11dyqWlvhgQc675laXAyzZ8d/7a6+VpNTNtepvVu3nL5OkiiE96ValU/Df9B6DsOHxTzMvIQ7uAILH31IoQutpCT5ZUXKymDhQti6NXazQ02NSSSh7dmhiSWWrr5Wk1M212nzTTfl9HWSRCEyJ7KfYdw4+34HpxPknHRU19a62oloAT/iGQ7HDDv00c4uenJFqnNBKyrMp32ntZ+ePZO/YafSnt2V12pKRMR12hnY6DxHxR0eK4Qr7JYZmDcv+P3ATOAVK+Chh8LLlZSY5pHQfRTKyuCMM2DQIA5raDBJI3LJidpaGDsWWlpc+RHe4Pscyxsdx3/hXM7lseRfsLLSfpmMyKGOdpqboy+xIYTLpEYhMsPJQnyNjeaTcWS5lhYoLw//BH3hhSahbNpkJn9t2mQ27AnMOi4ogAsucCVJtFHAEFZ3JIlv8DHNlMRPEiNGRB/OWlFhf6O3a97p0aPz8/fulaUyRMZIohCZ4bQ/IVob/fbt4U0ezz1nv5fEsmXB5TocTE6L53lGUkQbbzEEgKX8gI8ZSAl7Yz+xuBguvtj0GSxcCBUVhM1l3rYNxoyB3r07D/WNbN6JlmBlqQyRIZIoRGY4nfgWbdRP5PPTfJNspoS+bGYUzwNwLCtpo4Af8KKzFwj9xF9TAz172s+I3rYt+uJ7AV19C1GRdZIoRGbYDa2MVFZmbppOhmCm8SZZy8/pRjOf0ReANzmGlRxHQXidIL7QZBYrscUb6ivDUkWWSaIQmWHX9n755Z1H7syd62wIZhpmXO+kFz4sxmA+3Z/DItrxcQz269/EFZrM4iW2WIlEhqWKLJNRTyJzAhvmuFGupsaMkLrrrtgrvhYVmYlm0fToAV9/ze1M4Epu7zit+Tbf5sP4sQaWrwjdKQ46f+KfMYP2Sy4xa/7YiZdInF47IdJAahQid82dCwsW0NKvX/RayoMPdnQmd6ioMOcsi8837saH1ZEkruAOLHzxk0RpqXmN9naTIBYsiP2Jv6bGTLqyGwUlzUjC6+zW9cj1h2fXgLE8vD5NhKzEuXChWQ/H5zP/Llxofy5CsrFec034Ujz/GfA9+zV6Dj88fL2kyy9P6v064nTwM8XlxmvEi9PjciVOy8qdWKOt9SRNT8Ib7CbkjR1rbtWBiXaRewH7Z11HnXAXxb//Dd/8ZvB4xgy47jqADG3HnmozUgb2SBYilDQ9CW+wm5DX0hI+GxtMmQkTzPyDMWPCJ9zFG2YKXDBsY1iS2H734/4kkUMysEeyEKEkUYjsiFzPadMm58/dts1+X+sYN8t33zXdBwteM1niXi7Bwsd+l51rvpHIHheREtkrI9l9NUJlY78D0aVJohCZEbhB+nxmUp2/NkCgNuCWTZvMe/hnPFuWWUnjqKPMt3uyi0a6cwn3mxOBkUqbNpmZ1JE37tC4i4o6J5Vx48zSIaE/S5SaTfnixeZ7DsrGJBPwRIZJohDpF2hTDyQEF5bWiGvbNl4dcw8FBfDSS+bUU/yYXZTTnShDVPfuNc1aockhkAQguLxI6LpSdsNzo9Rs+tx2mztNRjIBT2SYdGaL9HOyIKCLWinkO6xDcxgAivdZx3cowsFeD4ElNQLxRpujEVhXKhqbZqDiLVscl40p0GE9ebJ5bgId+UIkQ2oUIv0y2Hb+NKMpprUjSbzCibxPlbMkEeBGUrNpBtrbt6+zsk76MWLtC+FGP4gQISRRiPQIvVkVpP/PbA/d6MkufsLTAJzCMtrxcSLL0/7etmyagb648sr4TUahzXTJ9GOk+nwhbDhqelJKDQVu0VoPV0pVA3+Fjqmr87TWjyqlpgJnAq3ARK31KqXUocCDmI3B1gHjtdbtqZZ15ScX6RM5zj/Z7T0dup+xwc5p4G2O4ijeTet7xhW6cqzfzrPOYkD//uZ7mzaZTv3QPoqamthDX500LaX6fCFsxP2op5S6GrgP6OY/VQ3M0loP9z8e9SePk4ChwHnQsS/kLGCK1noY4ANGu1RWeFVtrdlUKAN9El+yLz6sjiRxPg9j4Us9SThdbDBWucAn+ZDtXg8ZMcJ8L9AZHdo5HvjUn+rQVxk6K9LASY1iA3A2sMB/PARQSqnRmFrFROAEYKnW2gIalFJFSqk+/rKv+J+3BDgN0KmW1Vp/ESvg5uZm6uvrnfz8GdfU1OTZ2EIlGmf54sX0nTqVgj177PddCGEB7d27Y5WUUPjVVwBxnxPp//gt1/F/HccbOZiD+SjBV7GPzSouxheyM54v4vsAe/v1Y/dJJ7HvE09QEDkpMKCxEWvevI7nl2zeTPsll9BeWkqRzaf+lkmToG9fSjZv7vRSLX37ssHB7+OQFJ8P+fs3mk25FKuduIlCa/2EUmpQyKlVwH1a6zql1GRgKrADCJ0BtQvYB/D5b/Kh58pdKBszUZSWllJVVRXvR8uK+vp6z8YWKqE4Tz019gigCD6gcM8e2LMn4bg+pR8D+LTj+Bpu5mauTfh1wgMKrvzqg7Ak0akoQGUlJR99xP5g5mvYTf4LLR+ioKkp6gqyJVu2mMUFI/fMLiujZOZMZ7+PmTNTez4xfvf+JVO8MtIqV/4vQe7EWldnv6R+Mr2MT2mtA6/2FHA0sBPoFVKmFyZ5tNucc6OsyJbIiXM+X0JJIhW/YnZYkviMA1JPEpWViU9UC23G2b49tfcPtf/+qe89ka69K6STvEtLJlG8oJT6vv/rEUAdsAIYqZQqUEoNBAq01luBNUqp4f6yo4DlLpUVmRI6eql3b7joosxOnAM+4Fv4sLiDXwEwiyux8HFA7IqlM5s2JT4zPJENiexUVJg9tSPt2mWud6yhr06k+nw7sr5Ul5ZMorgcuF0p9TJwPDDdX8NYDqwEngDG+8teBdyolFoJlACLXCorMiHyU+S2bbE3AXKZBfyUx1F80HHuK8rDNhhKWaK75NlsSBRzi9fI1y8rg9mzoby8c9mWFu/eeKWTvGuzW3s81x9eXvvdy7GFWr9+vdnnwG5/hgw83mRI2KmF/DxrsVg+n/k32r4PCxfGfr5/34jmfv2Czw+8pt17ZZnt32i0v4XKykyH1yFX/i9ZVu7EGm0/CplwJ6LLwqfFdnwcy0q+x2oADmQLTZRSwyOpv3iPHs7LVlQE2/gXLDC3xWjNODU1ppydysqOZqANy5YFn59rC/vJ+lJdmiQKEV2Gb1p/51QKaecNjgVgCaezhX6UEn0UUkIsy34r0kiVlbB1a2Jt/IneSHPtxpuuTnKREyRRiOhmzEi8DT8JLRRzEA2cxt8BqKaOVgo5nRfcfaPGxphDWTvs3p34aB4HN9LyxYuDAwMmTzYTE3PpxpuOTnKREyRRiOhqauCyy9KaLB7lZ5TSwiccBMBKjqWOYygkMyOqbAVWkE0mWcRYqK/fDTeEDy996CGTjJO98crifyJDJFGI2ObONW30oU02LiSO3fSggDbO41EAfsQz/v6JN1J+7ZicLlDo9tDPyZM7T7RL5T1kXoPIIEkUwpnQWdRWlD0aHLqT8fRiN4GxFOup4hl+nPAyHgkrK0ts7oebnfluDy+VeQ0igyRRiPhc2nhoKxX4sPhf7gTgMuZh4aOK91N+7ZhC+wCijU6y42ZnvtujnGReg8ggSRQiPhduPtdzE30ITqpv4CDmMS7l142roiK8DyDaBLnCwvBjt0cgzZhBe7du4edSeY9cG14rcpokChFfCjefTQzEh8V0rgdgGlOx8HEQn7gVXXQlJWYWdCi70UkLF5qO5XSOQKqpYfNNN7n3Hrk2vFbkNEkUIr54y1REcQn3MojgOkrb2J+p3ORmZFBRQbvdukkAvXp1PhdtBVQ3h35GGY2086yz3HsPmdcgMkgSheisttZsshO40YEZ8+9wtNM6jsCHxf1cAsA8LsPCx/586W6c/nWTNs+YEex7CI0xcphrJkYKZXI0ksxrEBkiiUKE89/oSjZvDt7oxo6FefPijnaygNNZwmPFfDsAABJzSURBVJGsA6CUJnbTg8u4O7WYunULJoJAX0LIJ+iOT+qVlZ1jDB0JlImRQjIaSeQhSRT5KpnJWOPGwZgxnW90MTbyCVjBcRRg8QKnA7CIc2iiOz1wYUvU5maTCCzLrF4bbd2leCOBMjFSSEYjiTwkiSJfRO4bMXZsYs0fp55qag0JasfHufyFE1gBwDfZQAvFnMOTsZ94+eWmEzm0jT3aOkxOO9OjlSsoMD97JkYKyWgkkYckUeQDu30jImsBjY1wwQXmpuzzmZtn4Oskd6n7gG8xnJd5jHMBeImT2cChFONgz4rHHuvcxj57dmojeaJ1ure1wfnnw6GHpn+kkIxGEnlIEkU+cDohLnRWcgqzq1sp5BauZjDvspYjeYCLacfHybzs/EW2betcw3FrG9DIORFgft6XXkr/QnwyGknkIUkU+SCD7d9vcxRDeYPfcgtn8BzrOZyLeTC55TfGjOncfxJnYb24/S41NdGX6bAseO651EcKxYtDRiOJPCOJIh9koP27iVImM51jWM1/GMAizuFJzqEfW1J7YafDRxMZdhrreqSaVJ3GISu7ijwiiSIfzJgB0SaduWAFx/Fd3ub3TGYMC1nP4fE7qxPhZPhoIsNOzzgj+uukmlSdxBElmZQvXpzaewuRJUVOCimlhgK3aK2HK6UOBR7EDJtfB4zXWrcrpaYCZwKtwESt9ap0lXXpZ88fK1bA3r2uv+xuenAdv+dOruAgPuZ5RjKSpa6/D2BuprE4HXZaW2uW47BTUhK9UznajO1k4oiSTPrcdhtMmmT/fCE8LG6NQil1NXAfEFjRbBYwRWs9DPABo5VS1cBJwFDgPGBOmsuKgNrapIa1xvMCp3EE73EnV3AFd7KO76QvSYB9B3Qop8NOY3XsR+vAd6NZK/R8lGRSvCXFZjohssRJ09MG4OyQ4yHAK/6vlwCnAicAS7XWlta6AShSSvVJY1kR4PKM3+3sx0XM53ReoDt7WM4w/sQEerHb1ffppK0t9vedDjuN1Qexd6/99UqkWctJHFGSyd6+faPHJoSHxW160lo/oZQaFHLKp7UOfDTbBewDlAOhmxEHzqer7BexYm5ubqa+vj7ej5YVTU1NrsVWvngx/Tdtcm3Dn0Wcw3jmsI0KJjOdKUynG80uvXpsVkEBn86caZbjsFNdTfm0afS57TaKt2xhb9++fHHlleysru64puWLF9Pf58MXY+iv1dDA+xHX/7CGBttraFc2Vhz4y5aPH0+/G24I29GuvVs3Ph0/nj0e/bsM5ebfaDrlSpyQW7HacdRHESF07GEvYAew0/915Pl0lY2ptLSUqqqqeMWyor6+3nlssdrNa2thyhRXYtpMX8Yzh6c4m6N5ixcYyXd5x5XXtuXzdWoG8rW3M2DaNAb07x99OGlVVUcbfwkwABhQW0vLpElmbSqb1+301gMHdr7+Awfa9pHYlo0WR+T3+/cP+90VzJjBnupqz/5dhkrobzSLciVOyJ1Y6+rqbM8nM+ppjVJquP/rUcByYAUwUilVoJQaCBRorbemsWz+i9duPmGCozWYYrGA+VzE4aznOc7gZq5hFd9Pb5IAOOUU+z6JRBfPC13AEOJPIow2Qzods6llLoXII8kkiquAG5VSKzEfqBZpreswN/aVwBPA+DSXzX/R2s0nTDDj8rdts32aU/9mEKexlLHM50jW8i6DuYY/UEScvgI3vPRS9D6JROY5OJ2RHm+GtMymFiI2y7Ly7rF+/XrLqxzH5vNZlvmM7OqjlQLrdn5llbHb6slOay6XWW2k572SelRWOr+YTq5RIq+XZl7+uwwlcbovV2JdvXr1asvmnioT7rwqDbOt11PFCbzGRGZzEq/wHkdwOXdRQPLrPrkq0eaeeNdIFuMTwhWSKLzqjDMc7ygXTwvF/I4pHM0aPuRbLGAMf+NMBvKxK6/vimSae+z6FgLXTJqPhHBNMqOeRLoFZheHds76fNCjB+xObD7DaobwC+7nXY7iXP7Cn/gVB8QeXZx5lZWmwzdR/iTQMmkSJVu2xJ5RLYRImtQovCBy06Hzz+/cSWtZCSWJRrpzNbcwlDfYSm+eZjR/4X/cSxIVFdCzZ/hxtI2HAntfVFR0XpPKhdFFG5YtS3x0kSzaJ4RjkiiyzW7ToXjDPON4mZM4ineYydX8gvt5jyMYzbMJvYZtBD6f2ZnOsmDrVti1K9htvHVr9I2HHn7Y3Mi3boX587M/uiiRJTuEEJIoss7pEE8HvqKcy5jHybxMOwUs4xTu4Zfsy1fRn9Szp+14ob39+nUua/n3c4jGyTBTL8wvSGTJDiGEJIqMqq3lkBEjwps7XNp0aDFncgTvcS//j19zK2s5klP4R/wnfv11pxgZNIjiwCS2SPHidZIIst3s43QlWiEEIIkic0JnEQeaO8aMSbmZ6Qt683Nq+SGL2ZcdvM5x3MpvKGOPsxcoKAjeqEOaZKKOt0p12K4Xmn2crkQrhAAkUWSOi01MYPoQHuF/qKKeRfyUaUzlLaoZSoLbdbS1BW/U8WJ0Y16CF5p90rFkhxB5TBJFprjYrPEJA/gRz1LDIxzCBt6imqncRAlJbl4UuFHHitGtjmcvNPvIkh1CJEQSRbpEtsNHfoJNQjs+7uZSDmc9yxjBLK7kdY7jO7yX8mt3rFBrJzDPwY0bqVeafbzQqS5EjpBEkQ61tTB2bHg7fGSncYI+5FBO4SUu426+x5us5Uiu5HYKw1Znj2PhQnPTtxOYp+H2PIdI0uwjRM6RRJEOLiwBHtBKITP5DYN5lzUczb1cwoucyiFsTPzFamrsb9QB27Z1TIyz0tUkI80+QuQcWcIjHVJcAjzgHQbzC+6njmP4Ec8wl3EM4NPkXixQkwjckCdPtt2sh5YW6NmT95cvT99GKzU1khiEyCFSo/CgZkq4nps4htU0MJBH+RlP8+Pkk0Rk006gfT7aooMyn0AIEUIShces5FiOZg3TuZ7/4c/UU8XPeDy5fbHtmnZCO9kLovz6ZT6BECKEJAo3BW7CSdhNDyZyG8ezgt305DlG8TAXUsH25GKprOw8oidyspvdLnP+2kf54sWyaJ4QApBE4Z7Qm3CC/s6pHMlaZjORcczlPY5gFM8nH0u0UUTRJtQVFobXPoB+N9wQf/Z0tpfiEEJkhHRmu2XChIRnXm/hQCZyO49yHt9G8yrDGMZrqcdy4YX2ncXR+h7a280joHdvCpqawssEJuVF1k4CP3MgmYB0VAuRZ5JOFEqpNdCxLOm/gbuB2UArsFRrfaNSqgCYCxwFNAOXaK3/pZQ6NpWyycacFuPGwV13Jbxm0wU8xAIuAOC3/B9TuZFuNLsTU7QVXgcOtK/xhPZJ1NZGH7UVmmhiLcUhiUKIvJJUolBKdQPQWg8POfc2cA6wEfibUqoaGAR001r/l/+GfyswGrgrlbJa67eSidt148bBvHkJPeUdBvNd3uk4fogLuIAF7sYVreYwY0Z4LQA6N1PFWnMpNKF4YSkOIURGJNtHcRRQppRaqpR6SSl1IlCqtd6gtbaAF4ARwAlgGtu11v8EjlFKlbtQNvMC7fE+X7BNP4EkYQGnsKwjSfRiJ410dz9JQPRRS04mu8W60YcmFK8sxSGESLtkm54agT8C9wHfApYAO0K+vwv4JlAOYbvmtPnP7UyxbEzNzc3U19c7/FHiK1+8mH433BBst29PYNkM4BVOZDivdBw/zeiEdpyzwHZ4bHtxMVgWBa2twXPdurF5/Hh2Rvv5q6thyZLwcyFlD+nb1yyFHqF13335sLq6o2z5+PHh18TJe6dJU1OTq7/vdJE43ZUrcUJuxWon2UTxAfAv/6f8D5RSXwH7h3y/FyZxlPm/DijA3Ph7pVg2ptLSUndnFY8aBZGduw60UsjhrOdDvg3AYdSzliMpwmZYagw+MJ/+N20ytZm2NqispCDwCT+w8uvAgRTMmMGAmhoGJByt38yZts1TRXfeGX5Nq6qgf3933ztJ9fX16ZtF7iKJ0125EifkTqx1dXW255NNFGOBI4FxSqn+mJv810qpQzB9CSOBG4FvAD8EHvP3O6zVWu9USrWkWDazkmh3f5rR/ISnO45f4UROZHly719RYeZDROP2WkxAy6RJlGzZYpqSZsywfw9ZikOILiHZRHE/8KBS6jVMy8hYoB2oBQoxo5PeUEq9CfxAKfU65oPxxf7nX5ZK2SRjdiawgU9DA/ToYVZUTUAj3TmAz/mangCM4EX+zg+Sm1kNZo7C7NnJPjs5NTVsqK7OiU9AQoj0SypRaK1bgJ/bfOvYiHLtmBt95PP/mUrZtImcG5BgkriPX/D/uK/j+B0GM5i1ycdTUWGShHxqF0JkkUy4C5XkdqVfsi/782XH8QU8xENcFPtJKe6VLYQQmSJLeIRKoi/i91wbliQ2cnD8JCGEEDlEEkWo/fePX8bvP/THh8Vkfg/ANdyMhY+D+ShNwQkhRHZI01MS/pc/cSf/23H8GQdwAF9kMSIhhEgfqVGEirMznebb+LA6ksRtTMTCl3iSiLZvtRBCeJDUKAJiLJFtAefwBE9xdse5nfSiF4mNigIze7nAbglwIYTwKKlRBPzyl7an3+QYCrA6ksRCarDwJZYkAluOVlay+aabZLirECKnSI0CTG3i66/DTrXj479YySqGAtCXzXzEIEppif96RUXw4IO2CWFnfX3Gl7gQQohUSI0COi2t/XdOpZD2jiSxhNPZTP/4SaKiAhYuhL17pdYghMgbUqOAjvkTLRTzTTbyH74BwBBW8wZDKSTOarFJLPUhhBC5QmoU0LGHwhgWdiSJfzKU1XwvfpKApGZzCyFErpBEAR0b8kxhOrfya9rxMZRVzp8vm/UIIfKYJAow/QkjRjCYtfya2xJb6TVyK1EhhMgzkigCXnwRRiS4y6rdVqJCCJFnJFGEevFFM2opMHO6sND8W1lpzltW+OOjjyRJCCHynox6iiS7tgkhRBipUQghhIhJEoUQQoiYJFEIIYSISRKFEEKImCRRCCGEiMlnWVa2Y3BdXV3dF8CmbMchhBA5pnLIkCF9Ik/mZaIQQgjhHml6EkIIEZMkCiGEEDFJohBCCBGTJAohhBAxSaIQQggRkyQKIYQQMcnqsUlQSq0BvvIf/hu4G5gNtAJLtdY3KqUKgLnAUUAzcInW+l9KqWNTKeswvqHALVrr4UqpQ4EHAQtYB4zXWrcrpaYCZ/pfe6LWelW6yjqMsxr4K/Ch/9vztNaPZjtOpVQx8AAwCCgFpgPrvXZNo8T5ideuqVKqELgXUEAbcDHg89r1jBHrPl67ppkgNYoEKaW6AWith/sfFwN3AT8HTgCG+m96Pwa6aa3/C/gtcKv/JVItGy++q4H7gG7+U7OAKVrrYZj/kKP9r3MSMBQ4D5iT5rJO4qwGZoVc10e9ECcwBtjmf/4o4E6PXlO7OL14TX8IoLU+HrjB/1pevJ7RYvXiNU07SRSJOwooU0otVUq9pJQ6ESjVWm/QWlvAC8AIzM39eQCt9T+BY5RS5S6UjWcDcHbI8RDgFf/XS4BT/e+3VGttaa0bgCKlVJ80lnUa55lKqVeVUvcrpXp5JM7HgetDjls9ek2jxempa6q1fhq41H9YCXzm0esZK1ZPXdNMkESRuEbgj8BI4DJgvv9cwC5M9bScYPMUmKprObAzxbIxaa2fAPaGnPL5E02s9wucT1dZJ3GuAiZprU8ENgJTPRLnbq31Lv8NYREwJY3vn3SsUeL06jVtVUo9BNzhj9Vz1zNGrJ68pukmiSJxHwAL/Vn+A8wvcv+Q7/cCdmBu8r1CzhfYnEumbKLaHbxf4Hy6yjrxlNa6LvA1cLRX4lRKHQT8A1igtX4kje+fUqw2cXr2mmqtLwS+jekD6J6m93blbzQi1qVevabpJIkicWPx9yEopfoDZcDXSqlDlFI+TE1jObACOMNf7lhgrdZ6J9CSYtlErVFKDfd/PSrk/UYqpQqUUgOBAq311jSWdeIFpdT3/V+PAOq8EKdS6kBgKXCN1voBr17TKHF67poqpc5XSl3rP2zE3CBXe+16xoj1Sa9d00yQUU+Jux94UCn1GmaEwljMH1AtUIj5xPGGUupN4AdKqdcxnVMX+59/WSplk4j3KuBepVQJUA8s0lq3KaWWAysxHxbGp7msE5cDdyqlWoAtwKVa650eiPM6YD/geqVUoA9gAvAnj11Tuzh/DdzusWv6JDBfKfUqUAxM9L+GF/9G7WL9GG/+naaVrB4rhBAiJml6EkIIEZMkCiGEEDFJohBCCBGTJAohhBAxSaIQQggRkyQKIYQQMUmiEEIIEdP/Bw5RFPfxOIgPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Performance on the test data sets\n",
    "YHatTest = wmLinear.predict(XTest)\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9164906274809529\n",
      "0.08956881121002612 26699670214.63539\n",
      "0.08956881121002612 26699670214.63539\n"
     ]
    }
   ],
   "source": [
    "# calculate the accuracy of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)\n",
    "print(MAPE, MSSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using scaled data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9164906274809529\n",
      "0.08956881121002612 26699670214.63539\n",
      "0.08956881121002612 26699670214.63539\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instantiate \n",
    "wmLinear = linear_model.LinearRegression(normalize=True)\n",
    "#fir\n",
    "wmLinear.fit(X_transformed_train, YTrain)\n",
    "#Performance on the test data sets\n",
    "YHatTest = wmLinear.predict(X_transformed_test)\n",
    "\n",
    "# calculate the accuracy of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)\n",
    "print(MAPE, MSSE)\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56023494849469.734, tolerance: 143142991402.9765\n",
      "  positive)\n",
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55365629274480.0, tolerance: 143142991402.9765\n",
      "  positive)\n",
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53671765333390.85, tolerance: 143142991402.9765\n",
      "  positive)\n",
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44955307076773.16, tolerance: 143142991402.9765\n",
      "  positive)\n",
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44788915143372.734, tolerance: 143142991402.9765\n",
      "  positive)\n",
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53047757803783.734, tolerance: 143142991402.9765\n",
      "  positive)\n",
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54916773478650.86, tolerance: 143142991402.9765\n",
      "  positive)\n",
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25797001951737.766, tolerance: 143142991402.9765\n",
      "  positive)\n",
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 762561604542.9688, tolerance: 143142991402.9765\n",
      "  positive)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alphas = np.linspace(10, 20, 10)\n",
    "#Hyper parameter optimisation\n",
    "testError = np.empty(10)\n",
    "\n",
    "for i, alpha in enumerate(alphas) :\n",
    "    \n",
    "    lasso = Lasso(alpha=alpha)\n",
    "    lasso.fit(XTrain, YTrain)\n",
    "    testError[i] = mean_squared_error(YTest, lasso.predict(XTest))\n",
    "plt.plot(alphas, testError, 'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "17 seems to be ideal value for hyperparameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55237662443319.19, tolerance: 143142991402.9765\n",
      "  positive)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=17, copy_X=True, fit_intercept=True, max_iter=1000, normalize=False,\n",
       "      positive=False, precompute=False, random_state=None, selection='cyclic',\n",
       "      tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso = Lasso(alpha=17)\n",
    "lasso.fit(XTrain, YTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Performance on the test data sets\n",
    "YHatTest =lasso.predict(XTest)\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9164472451863696\n",
      "0.08969639433151724 26713540429.721313\n"
     ]
    }
   ],
   "source": [
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.KNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instantiate\n",
    "knn = KNeighborsRegressor()\n",
    "#Fit\n",
    "knn.fit(XTrain, YTrain)\n",
    "#Predict\n",
    "YHatTest=knn.predict(XTest)\n",
    "\n",
    "#Plotting \n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.004160742946214158\n",
      "0.6585075525101509 318390366830.49176\n"
     ]
    }
   ],
   "source": [
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using scaled data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9162375208129622\n",
      "0.07496902413625751 26780593641.079945\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instantiate\n",
    "knn = KNeighborsRegressor()\n",
    "#Fit\n",
    "knn.fit(X_transformed_train, YTrain)\n",
    "#Predict\n",
    "YHatTest=knn.predict(X_transformed_test)\n",
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)\n",
    "#Plotting \n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Choosing best n_neighbors value\n",
    "i=np.arange(1,25,2)\n",
    "r2_test=[]\n",
    "r2_train=[]\n",
    "\n",
    "for x in i:\n",
    "    KNN_model = KNeighborsRegressor(n_neighbors=x,n_jobs=-1)\n",
    "    KNN_model.fit(X_transformed_train, YTrain)\n",
    "    YHatTest = KNN_model.predict(X_transformed_test)\n",
    "    r_test = r2_score(YTest,YHatTest)\n",
    "    r2_test.append(r_test)\n",
    "    prediction2 = KNN_model.predict(X_transformed_train)\n",
    "    r_train = r2_score(YTrain,prediction2)\n",
    "    r2_train.append(r_train)\n",
    "#Plotting the accuracy versus number of neighbors value to find best value\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(r2_test,label='test')\n",
    "plt.plot(r2_train,label='train')\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('R2 Score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After 10,there is no improvement in test scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KNN on ideal value\n",
    "knn = KNeighborsRegressor(n_neighbors=10)\n",
    "knn.fit(X_transformed_train, YTrain)\n",
    "YHatTest=knn.predict(X_transformed_test)\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9173431540849879\n",
      "0.07327603009202559 26427099861.268818\n"
     ]
    }
   ],
   "source": [
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.Extra  Trees Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07034253983472535 21876166247.920254\n",
      "0.93157724789107\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instantaite\n",
    "clf = ExtraTreesRegressor()\n",
    "#Fit\n",
    "clf.fit(XTrain, YTrain)\n",
    "#Predict\n",
    "YHatTest=clf.predict(XTest)\n",
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(MAPE, MSSE)\n",
    "print(r2_score(YTest,YHatTest))\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06840815238667336 21421398135.99923\n",
      "0.9329996399791708\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instantaite\n",
    "clf = ExtraTreesRegressor()\n",
    "#Fit\n",
    "clf.fit(X_transformed_train, YTrain)\n",
    "#Predict\n",
    "YHatTest=clf.predict(X_transformed_test)\n",
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(MAPE, MSSE)\n",
    "print(r2_score(YTest,YHatTest))\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Choosing best estimator value\n",
    "i=np.arange(1,70,2)\n",
    "r2_test=[]\n",
    "r2_train=[]\n",
    "\n",
    "for x in i:\n",
    "    ET_model = ExtraTreesRegressor(n_estimators=x,n_jobs=-1)\n",
    "    ET_model.fit(X_transformed_train, YTrain)\n",
    "    YHatTest = ET_model.predict(X_transformed_test)\n",
    "    r_test = r2_score(YTest,YHatTest)\n",
    "    r2_test.append(r_test)\n",
    "    prediction2 = ET_model.predict(X_transformed_train)\n",
    "    r_train = r2_score(YTrain,prediction2)\n",
    "    r2_train.append(r_train)\n",
    "#Plotting the accuracy versus number of neighbors value to find best value\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(r2_test,label='test')\n",
    "plt.plot(r2_train,label='train')\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('R2 Score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ideal n_estimators=10(There is no significant improvement in test scores after this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9347271805221486\n",
      "0.06934092532415362 20869067764.107124\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using ideal value\n",
    "clf = ExtraTreesRegressor(n_estimators=10,max_features='auto', verbose=1, n_jobs=1)\n",
    "clf.fit(X_transformed_train, YTrain)\n",
    "YHatTest=clf.predict(X_transformed_test)\n",
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9270933468950018\n",
      "0.06991555159865692 23309761954.112305\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instantiate \n",
    "clf = RandomForestRegressor()\n",
    "#Fit \n",
    "clf.fit(X_transformed_train, YTrain)\n",
    "#Predict\n",
    "YHatTest=clf.predict(X_transformed_test)\n",
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Find ideal value\n",
    "i=np.arange(1,102,2)\n",
    "r2_test=[]\n",
    "r2_train=[]\n",
    "\n",
    "for x in i:\n",
    "    RF_model = RandomForestRegressor(n_estimators=x,n_jobs=-1)\n",
    "    RF_model.fit(X_transformed_train, YTrain)\n",
    "    YHatTest = RF_model.predict(X_transformed_test)\n",
    "    r_test = r2_score(YTest,YHatTest)\n",
    "    r2_test.append(r_test)\n",
    "    prediction2 = RF_model.predict(XTrain)\n",
    "    r_train = r2_score(YTrain,prediction2)\n",
    "    r2_train.append(r_train)\n",
    "#Plotting the accuracy versus number of neighbors value to find best value\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(r2_test,label='test')\n",
    "plt.plot(r2_train,label='train')\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('R2 Score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using ideal value=50 after which there is no significant improvement in test scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9416601176870485\n",
      "0.0704670078713485 18652464641.153557\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Running model for ideal value\n",
    "clf = RandomForestRegressor(n_estimators=50,max_features='log2', verbose=1)\n",
    "clf.fit(X_transformed_train, YTrain)\n",
    "YHatTest=clf.predict(X_transformed_test)\n",
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.03459785662169024\n",
      "0.6100983494917479 330782291176.5659\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instantiate\n",
    "clf = SVR(kernel='rbf', gamma='auto')\n",
    "#Fit\n",
    "clf.fit(X_transformed_train, YTrain)\n",
    "#Predict\n",
    "YHatTest=clf.predict(X_transformed_test)\n",
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)\n",
    "\n",
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.MLP Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 701017048801.70947266\n",
      "Iteration 2, loss = 701003338069.93420410\n",
      "Iteration 3, loss = 700990119835.06945801\n",
      "Iteration 4, loss = 700976867414.22497559\n",
      "Iteration 5, loss = 700962658101.59875488\n",
      "Iteration 6, loss = 700947198299.93994141\n",
      "Iteration 7, loss = 700930378756.58081055\n",
      "Iteration 8, loss = 700912289043.82336426\n",
      "Iteration 9, loss = 700892701752.94604492\n",
      "Iteration 10, loss = 700871726075.51159668\n",
      "Iteration 11, loss = 700849313310.16625977\n",
      "Iteration 12, loss = 700825345781.76538086\n",
      "Iteration 13, loss = 700799964790.53088379\n",
      "Iteration 14, loss = 700773054262.44519043\n",
      "Iteration 15, loss = 700744434904.80883789\n",
      "Iteration 16, loss = 700714475475.10754395\n",
      "Iteration 17, loss = 700682758766.51708984\n",
      "Iteration 18, loss = 700649686238.15637207\n",
      "Iteration 19, loss = 700614906928.57019043\n",
      "Iteration 20, loss = 700578576157.72619629\n",
      "Iteration 21, loss = 700540761181.86669922\n",
      "Iteration 22, loss = 700501356722.61999512\n",
      "Iteration 23, loss = 700460286918.02990723\n",
      "Iteration 24, loss = 700417751492.22900391\n",
      "Iteration 25, loss = 700373556456.29125977\n",
      "Iteration 26, loss = 700327882995.41088867\n",
      "Iteration 27, loss = 700280613499.35131836\n",
      "Iteration 28, loss = 700231720760.02062988\n",
      "Iteration 29, loss = 700181482118.92871094\n",
      "Iteration 30, loss = 700129543858.35314941\n",
      "Iteration 31, loss = 700076099976.44018555\n",
      "Iteration 32, loss = 700021222488.85791016\n",
      "Iteration 33, loss = 699964850649.66308594\n",
      "Iteration 34, loss = 699906826345.59960938\n",
      "Iteration 35, loss = 699847284534.89270020\n",
      "Iteration 36, loss = 699786147308.64477539\n",
      "Iteration 37, loss = 699723828460.00549316\n",
      "Iteration 38, loss = 699659740401.01843262\n",
      "Iteration 39, loss = 699594349050.50183105\n",
      "Iteration 40, loss = 699527377618.52355957\n",
      "Iteration 41, loss = 699459154489.63049316\n",
      "Iteration 42, loss = 699389340173.00158691\n",
      "Iteration 43, loss = 699318055344.31835938\n",
      "Iteration 44, loss = 699245591521.14453125\n",
      "Iteration 45, loss = 699169261329.73095703\n",
      "Iteration 46, loss = 699081640998.68078613\n",
      "Iteration 47, loss = 698984465255.00329590\n",
      "Iteration 48, loss = 698880218771.05334473\n",
      "Iteration 49, loss = 698771575985.44665527\n",
      "Iteration 50, loss = 698659402262.35070801\n",
      "Iteration 51, loss = 698543273942.53393555\n",
      "Iteration 52, loss = 698424471260.59008789\n",
      "Iteration 53, loss = 698303104369.02978516\n",
      "Iteration 54, loss = 698179431521.33496094\n",
      "Iteration 55, loss = 698052763193.42602539\n",
      "Iteration 56, loss = 697925020524.76330566\n",
      "Iteration 57, loss = 697794693526.05981445\n",
      "Iteration 58, loss = 697661841043.05004883\n",
      "Iteration 59, loss = 697527581975.63354492\n",
      "Iteration 60, loss = 697390878207.65356445\n",
      "Iteration 61, loss = 697252746175.61877441\n",
      "Iteration 62, loss = 697111886335.36840820\n",
      "Iteration 63, loss = 696969827595.12512207\n",
      "Iteration 64, loss = 696825258733.28552246\n",
      "Iteration 65, loss = 696678559686.23364258\n",
      "Iteration 66, loss = 696530727716.31994629\n",
      "Iteration 67, loss = 696380721733.79028320\n",
      "Iteration 68, loss = 696228531751.46118164\n",
      "Iteration 69, loss = 696075308405.14233398\n",
      "Iteration 70, loss = 695919751835.30566406\n",
      "Iteration 71, loss = 695762838236.25622559\n",
      "Iteration 72, loss = 695603695084.03125000\n",
      "Iteration 73, loss = 695443291179.86047363\n",
      "Iteration 74, loss = 695281366631.40368652\n",
      "Iteration 75, loss = 695117392413.30664062\n",
      "Iteration 76, loss = 694951425324.93322754\n",
      "Iteration 77, loss = 694784085345.48583984\n",
      "Iteration 78, loss = 694614982690.20422363\n",
      "Iteration 79, loss = 694444099337.66625977\n",
      "Iteration 80, loss = 694272008041.25500488\n",
      "Iteration 81, loss = 694097462783.42187500\n",
      "Iteration 82, loss = 693922394563.01940918\n",
      "Iteration 83, loss = 693745330179.32409668\n",
      "Iteration 84, loss = 693566653543.38745117\n",
      "Iteration 85, loss = 693385672086.42285156\n",
      "Iteration 86, loss = 693203844388.53918457\n",
      "Iteration 87, loss = 693020964900.41223145\n",
      "Iteration 88, loss = 692835918239.35974121\n",
      "Iteration 89, loss = 692650173183.54553223\n",
      "Iteration 90, loss = 692461857723.68310547\n",
      "Iteration 91, loss = 692272327532.47766113\n",
      "Iteration 92, loss = 692081796887.59130859\n",
      "Iteration 93, loss = 691888908262.47326660\n",
      "Iteration 94, loss = 691695502339.85229492\n",
      "Iteration 95, loss = 691499590513.30603027\n",
      "Iteration 96, loss = 691303230304.44750977\n",
      "Iteration 97, loss = 691104141822.01440430\n",
      "Iteration 98, loss = 690904630133.71484375\n",
      "Iteration 99, loss = 690702882142.48596191\n",
      "Iteration 100, loss = 690500225144.45666504\n",
      "Iteration 101, loss = 690295336420.06335449\n",
      "Iteration 102, loss = 690089394751.15783691\n",
      "Iteration 103, loss = 689881708065.09521484\n",
      "Iteration 104, loss = 689672968042.22265625\n",
      "Iteration 105, loss = 689463039377.43054199\n",
      "Iteration 106, loss = 689251523065.78405762\n",
      "Iteration 107, loss = 689039225614.30883789\n",
      "Iteration 108, loss = 688824136701.47875977\n",
      "Iteration 109, loss = 688608573235.90600586\n",
      "Iteration 110, loss = 688391355928.23999023\n",
      "Iteration 111, loss = 688172991908.52563477\n",
      "Iteration 112, loss = 687952839370.42114258\n",
      "Iteration 113, loss = 687731803100.66259766\n",
      "Iteration 114, loss = 687509594769.56835938\n",
      "Iteration 115, loss = 687285732633.74914551\n",
      "Iteration 116, loss = 687060798707.11145020\n",
      "Iteration 117, loss = 686833791337.45031738\n",
      "Iteration 118, loss = 686606388809.54980469\n",
      "Iteration 119, loss = 686376675194.38891602\n",
      "Iteration 120, loss = 686146389796.91271973\n",
      "Iteration 121, loss = 685914628799.36572266\n",
      "Iteration 122, loss = 685681828032.27746582\n",
      "Iteration 123, loss = 685446401904.63830566\n",
      "Iteration 124, loss = 685210907526.62854004\n",
      "Iteration 125, loss = 684973365275.08276367\n",
      "Iteration 126, loss = 684735515304.54895020\n",
      "Iteration 127, loss = 684495472939.85266113\n",
      "Iteration 128, loss = 684254797013.19580078\n",
      "Iteration 129, loss = 684011874717.90551758\n",
      "Iteration 130, loss = 683768995600.35241699\n",
      "Iteration 131, loss = 683523593598.38293457\n",
      "Iteration 132, loss = 683277950280.63757324\n",
      "Iteration 133, loss = 683030525884.14233398\n",
      "Iteration 134, loss = 682782289413.17346191\n",
      "Iteration 135, loss = 682533120956.20227051\n",
      "Iteration 136, loss = 682281968454.17004395\n",
      "Iteration 137, loss = 682029775302.52416992\n",
      "Iteration 138, loss = 681776443895.71276855\n",
      "Iteration 139, loss = 681522406557.17395020\n",
      "Iteration 140, loss = 681266048622.86218262\n",
      "Iteration 141, loss = 681009687545.54077148\n",
      "Iteration 142, loss = 680751327451.48034668\n",
      "Iteration 143, loss = 680492329191.83154297\n",
      "Iteration 144, loss = 680231412967.69982910\n",
      "Iteration 145, loss = 679968954497.90185547\n",
      "Iteration 146, loss = 679706076472.88586426\n",
      "Iteration 147, loss = 679441121165.55664062\n",
      "Iteration 148, loss = 679175776258.93005371\n",
      "Iteration 149, loss = 678908782580.43957520\n",
      "Iteration 150, loss = 678640763879.17370605\n",
      "Iteration 151, loss = 678371298140.33703613\n",
      "Iteration 152, loss = 678101827951.47521973\n",
      "Iteration 153, loss = 677829833997.16723633\n",
      "Iteration 154, loss = 677557101065.95971680\n",
      "Iteration 155, loss = 677284024436.73889160\n",
      "Iteration 156, loss = 677008640243.21386719\n",
      "Iteration 157, loss = 676732776987.18078613\n",
      "Iteration 158, loss = 676456010523.47497559\n",
      "Iteration 159, loss = 676177061965.45861816\n",
      "Iteration 160, loss = 675897472408.77392578\n",
      "Iteration 161, loss = 675617131002.98461914\n",
      "Iteration 162, loss = 675334848873.59753418\n",
      "Iteration 163, loss = 675051592979.75732422\n",
      "Iteration 164, loss = 674767878788.84082031\n",
      "Iteration 165, loss = 674482687674.85791016\n",
      "Iteration 166, loss = 674195876815.71630859\n",
      "Iteration 167, loss = 673908980322.62036133\n",
      "Iteration 168, loss = 673620698767.97814941\n",
      "Iteration 169, loss = 673331234969.83471680\n",
      "Iteration 170, loss = 673041013254.27124023\n",
      "Iteration 171, loss = 672749213607.52709961\n",
      "Iteration 172, loss = 672456218289.66064453\n",
      "Iteration 173, loss = 672161972298.38037109\n",
      "Iteration 174, loss = 671867620408.80590820\n",
      "Iteration 175, loss = 671571252925.06188965\n",
      "Iteration 176, loss = 671274481564.96655273\n",
      "Iteration 177, loss = 670976838842.43176270\n",
      "Iteration 178, loss = 670677766065.85339355\n",
      "Iteration 179, loss = 670377522636.99182129\n",
      "Iteration 180, loss = 670075515312.50488281\n",
      "Iteration 181, loss = 669773255524.05651855\n",
      "Iteration 182, loss = 669468859256.42187500\n",
      "Iteration 183, loss = 669163472464.66577148\n",
      "Iteration 184, loss = 668857332232.60559082\n",
      "Iteration 185, loss = 668549986305.47290039\n",
      "Iteration 186, loss = 668241830340.74389648\n",
      "Iteration 187, loss = 667932027506.08642578\n",
      "Iteration 188, loss = 667622397735.30615234\n",
      "Iteration 189, loss = 667310485661.46350098\n",
      "Iteration 190, loss = 666998556978.29199219\n",
      "Iteration 191, loss = 666685015123.06408691\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 192, loss = 666371389116.98779297\n",
      "Iteration 193, loss = 666055299020.83239746\n",
      "Iteration 194, loss = 665739127649.84533691\n",
      "Iteration 195, loss = 665421969867.57214355\n",
      "Iteration 196, loss = 665102893141.63598633\n",
      "Iteration 197, loss = 664784270163.30651855\n",
      "Iteration 198, loss = 664462791422.83520508\n",
      "Iteration 199, loss = 664140614406.34863281\n",
      "Iteration 200, loss = 663818630688.27185059\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "#Instantiate\n",
    "clf = MLPRegressor(hidden_layer_sizes=(10,),  activation='relu', verbose=3)\n",
    "#fit\n",
    "clf.fit(XTrain, YTrain)\n",
    "#predict\n",
    "YHatTest=clf.predict(XTest)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.2783308053143854\n",
      "0.9507651941089227 1367870672779.3403\n"
     ]
    }
   ],
   "source": [
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 701063199407.46948242\n",
      "Iteration 2, loss = 701062788949.30065918\n",
      "Iteration 3, loss = 701062358288.14587402\n",
      "Iteration 4, loss = 701061892103.36254883\n",
      "Iteration 5, loss = 701061377250.09020996\n",
      "Iteration 6, loss = 701060810905.27819824\n",
      "Iteration 7, loss = 701060184740.07092285\n",
      "Iteration 8, loss = 701059499226.85815430\n",
      "Iteration 9, loss = 701058743282.19311523\n",
      "Iteration 10, loss = 701057920827.87524414\n",
      "Iteration 11, loss = 701057019257.31896973\n",
      "Iteration 12, loss = 701056047755.29821777\n",
      "Iteration 13, loss = 701055001750.67089844\n",
      "Iteration 14, loss = 701053874899.77563477\n",
      "Iteration 15, loss = 701052657784.20056152\n",
      "Iteration 16, loss = 701051347170.07202148\n",
      "Iteration 17, loss = 701049937954.12170410\n",
      "Iteration 18, loss = 701048429180.68603516\n",
      "Iteration 19, loss = 701046823812.51306152\n",
      "Iteration 20, loss = 701045127662.25598145\n",
      "Iteration 21, loss = 701043331763.16528320\n",
      "Iteration 22, loss = 701041437421.43261719\n",
      "Iteration 23, loss = 701039464535.24450684\n",
      "Iteration 24, loss = 701037390686.83361816\n",
      "Iteration 25, loss = 701035219220.55895996\n",
      "Iteration 26, loss = 701032985577.19604492\n",
      "Iteration 27, loss = 701030656034.58422852\n",
      "Iteration 28, loss = 701028229161.38232422\n",
      "Iteration 29, loss = 701025733514.17712402\n",
      "Iteration 30, loss = 701023158846.92443848\n",
      "Iteration 31, loss = 701020485877.47241211\n",
      "Iteration 32, loss = 701017740112.11560059\n",
      "Iteration 33, loss = 701014919933.68249512\n",
      "Iteration 34, loss = 701012001912.75610352\n",
      "Iteration 35, loss = 701009016724.32751465\n",
      "Iteration 36, loss = 701005956016.23010254\n",
      "Iteration 37, loss = 701002801388.20324707\n",
      "Iteration 38, loss = 700999583714.78515625\n",
      "Iteration 39, loss = 700996271787.25341797\n",
      "Iteration 40, loss = 700992903191.58105469\n",
      "Iteration 41, loss = 700989435670.00915527\n",
      "Iteration 42, loss = 700985899491.31921387\n",
      "Iteration 43, loss = 700982284454.22521973\n",
      "Iteration 44, loss = 700978614139.04760742\n",
      "Iteration 45, loss = 700974861918.35290527\n",
      "Iteration 46, loss = 700971026196.77465820\n",
      "Iteration 47, loss = 700967133698.87023926\n",
      "Iteration 48, loss = 700963161047.65209961\n",
      "Iteration 49, loss = 700959126006.65527344\n",
      "Iteration 50, loss = 700955024469.49633789\n",
      "Iteration 51, loss = 700950844568.40795898\n",
      "Iteration 52, loss = 700946592018.92053223\n",
      "Iteration 53, loss = 700942290218.22888184\n",
      "Iteration 54, loss = 700937894122.35913086\n",
      "Iteration 55, loss = 700933452910.43310547\n",
      "Iteration 56, loss = 700928932419.23132324\n",
      "Iteration 57, loss = 700924339620.16894531\n",
      "Iteration 58, loss = 700919700019.19494629\n",
      "Iteration 59, loss = 700914956997.01464844\n",
      "Iteration 60, loss = 700910189669.44824219\n",
      "Iteration 61, loss = 700905331301.49304199\n",
      "Iteration 62, loss = 700900411257.38769531\n",
      "Iteration 63, loss = 700895444190.57885742\n",
      "Iteration 64, loss = 700890387599.66723633\n",
      "Iteration 65, loss = 700885293148.42260742\n",
      "Iteration 66, loss = 700880096032.86547852\n",
      "Iteration 67, loss = 700874872339.59973145\n",
      "Iteration 68, loss = 700869586222.18957520\n",
      "Iteration 69, loss = 700864200127.31018066\n",
      "Iteration 70, loss = 700858817899.15869141\n",
      "Iteration 71, loss = 700853318822.12536621\n",
      "Iteration 72, loss = 700847804964.64160156\n",
      "Iteration 73, loss = 700842206881.02587891\n",
      "Iteration 74, loss = 700836570175.62890625\n",
      "Iteration 75, loss = 700830840000.04077148\n",
      "Iteration 76, loss = 700825081309.22058105\n",
      "Iteration 77, loss = 700819250165.45825195\n",
      "Iteration 78, loss = 700813333388.58679199\n",
      "Iteration 79, loss = 700807413073.70153809\n",
      "Iteration 80, loss = 700801379458.36279297\n",
      "Iteration 81, loss = 700795340961.62500000\n",
      "Iteration 82, loss = 700789245804.33703613\n",
      "Iteration 83, loss = 700783055437.23181152\n",
      "Iteration 84, loss = 700776815560.30480957\n",
      "Iteration 85, loss = 700770564565.18957520\n",
      "Iteration 86, loss = 700764167034.86608887\n",
      "Iteration 87, loss = 700757781846.45898438\n",
      "Iteration 88, loss = 700751343136.43652344\n",
      "Iteration 89, loss = 700744810299.08654785\n",
      "Iteration 90, loss = 700738225351.08776855\n",
      "Iteration 91, loss = 700731586581.86865234\n",
      "Iteration 92, loss = 700724939535.40893555\n",
      "Iteration 93, loss = 700718165063.66271973\n",
      "Iteration 94, loss = 700711393142.08032227\n",
      "Iteration 95, loss = 700704552674.27880859\n",
      "Iteration 96, loss = 700697644234.32678223\n",
      "Iteration 97, loss = 700690691629.99023438\n",
      "Iteration 98, loss = 700683685773.20263672\n",
      "Iteration 99, loss = 700676654044.62646484\n",
      "Iteration 100, loss = 700669571577.04650879\n",
      "Iteration 101, loss = 700662389714.96289062\n",
      "Iteration 102, loss = 700655163631.09997559\n",
      "Iteration 103, loss = 700647923722.66833496\n",
      "Iteration 104, loss = 700640608724.53491211\n",
      "Iteration 105, loss = 700633264144.43103027\n",
      "Iteration 106, loss = 700625851526.51586914\n",
      "Iteration 107, loss = 700618377467.12805176\n",
      "Iteration 108, loss = 700610873127.92834473\n",
      "Iteration 109, loss = 700603325541.30627441\n",
      "Iteration 110, loss = 700595709394.77514648\n",
      "Iteration 111, loss = 700588010740.28186035\n",
      "Iteration 112, loss = 700580292406.31726074\n",
      "Iteration 113, loss = 700572507999.54541016\n",
      "Iteration 114, loss = 700564727595.33776855\n",
      "Iteration 115, loss = 700556832643.49938965\n",
      "Iteration 116, loss = 700548891913.36145020\n",
      "Iteration 117, loss = 700540936963.63952637\n",
      "Iteration 118, loss = 700532882894.37255859\n",
      "Iteration 119, loss = 700524832061.42858887\n",
      "Iteration 120, loss = 700516715086.42980957\n",
      "Iteration 121, loss = 700508545498.28454590\n",
      "Iteration 122, loss = 700500344585.91088867\n",
      "Iteration 123, loss = 700492070552.14123535\n",
      "Iteration 124, loss = 700483775118.31628418\n",
      "Iteration 125, loss = 700475406333.39746094\n",
      "Iteration 126, loss = 700466977842.51403809\n",
      "Iteration 127, loss = 700458522784.16638184\n",
      "Iteration 128, loss = 700450086937.87780762\n",
      "Iteration 129, loss = 700441478783.66638184\n",
      "Iteration 130, loss = 700432905859.91015625\n",
      "Iteration 131, loss = 700424242308.06604004\n",
      "Iteration 132, loss = 700415541473.32714844\n",
      "Iteration 133, loss = 700406802145.35681152\n",
      "Iteration 134, loss = 700397989740.23266602\n",
      "Iteration 135, loss = 700389202776.90832520\n",
      "Iteration 136, loss = 700380338561.25793457\n",
      "Iteration 137, loss = 700371396768.14599609\n",
      "Iteration 138, loss = 700362479921.72021484\n",
      "Iteration 139, loss = 700353455067.64526367\n",
      "Iteration 140, loss = 700344362455.81579590\n",
      "Iteration 141, loss = 700335309168.58898926\n",
      "Iteration 142, loss = 700326137783.85974121\n",
      "Iteration 143, loss = 700316981781.21020508\n",
      "Iteration 144, loss = 700307706681.16857910\n",
      "Iteration 145, loss = 700298412429.34924316\n",
      "Iteration 146, loss = 700289079701.66479492\n",
      "Iteration 147, loss = 700279705090.31494141\n",
      "Iteration 148, loss = 700270250571.30371094\n",
      "Iteration 149, loss = 700260769968.08239746\n",
      "Iteration 150, loss = 700251266678.15661621\n",
      "Iteration 151, loss = 700241688133.81555176\n",
      "Iteration 152, loss = 700232126022.72119141\n",
      "Iteration 153, loss = 700222448333.94116211\n",
      "Iteration 154, loss = 700212762500.20104980\n",
      "Iteration 155, loss = 700203035429.15100098\n",
      "Iteration 156, loss = 700193238650.23413086\n",
      "Iteration 157, loss = 700183473867.60644531\n",
      "Iteration 158, loss = 700173575459.85095215\n",
      "Iteration 159, loss = 700163667318.05004883\n",
      "Iteration 160, loss = 700153675176.00659180\n",
      "Iteration 161, loss = 700143679775.69592285\n",
      "Iteration 162, loss = 700133608222.27270508\n",
      "Iteration 163, loss = 700123544836.60864258\n",
      "Iteration 164, loss = 700113430215.84948730\n",
      "Iteration 165, loss = 700103200960.35998535\n",
      "Iteration 166, loss = 700093014633.18530273\n",
      "Iteration 167, loss = 700082733905.84155273\n",
      "Iteration 168, loss = 700072447063.55590820\n",
      "Iteration 169, loss = 700062087781.84655762\n",
      "Iteration 170, loss = 700051724570.17175293\n",
      "Iteration 171, loss = 700041263156.35827637\n",
      "Iteration 172, loss = 700030800597.20300293\n",
      "Iteration 173, loss = 700020289602.43713379\n",
      "Iteration 174, loss = 700009710394.30859375\n",
      "Iteration 175, loss = 699999074785.52893066\n",
      "Iteration 176, loss = 699988477892.64245605\n",
      "Iteration 177, loss = 699977743053.70874023\n",
      "Iteration 178, loss = 699967000125.91125488\n",
      "Iteration 179, loss = 699956272811.71875000\n",
      "Iteration 180, loss = 699945410421.33129883\n",
      "Iteration 181, loss = 699934578573.37182617\n",
      "Iteration 182, loss = 699923684188.82946777\n",
      "Iteration 183, loss = 699912734341.04907227\n",
      "Iteration 184, loss = 699901772118.06481934\n",
      "Iteration 185, loss = 699890687892.94665527\n",
      "Iteration 186, loss = 699879651068.02514648\n",
      "Iteration 187, loss = 699868521865.35412598\n",
      "Iteration 188, loss = 699857349119.97949219\n",
      "Iteration 189, loss = 699846120679.38427734\n",
      "Iteration 190, loss = 699834929765.94299316\n",
      "Iteration 191, loss = 699823621904.90112305\n",
      "Iteration 192, loss = 699812313723.68078613\n",
      "Iteration 193, loss = 699800931727.79699707\n",
      "Iteration 194, loss = 699789514792.99804688\n",
      "Iteration 195, loss = 699778087706.90380859\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 196, loss = 699766578124.82482910\n",
      "Iteration 197, loss = 699755082344.48974609\n",
      "Iteration 198, loss = 699743553566.24707031\n",
      "Iteration 199, loss = 699731948001.92053223\n",
      "Iteration 200, loss = 699720252615.00842285\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\b16z\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "#Instantiate\n",
    "clf = MLPRegressor(hidden_layer_sizes=(10,),  activation='relu', verbose=3)\n",
    "#fit\n",
    "clf.fit(X_transformed_train, YTrain)\n",
    "#predict\n",
    "YHatTest=clf.predict(X_transformed_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.5066572299096244\n",
      "0.9990612219801895 1440871343890.7085\n"
     ]
    }
   ],
   "source": [
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Instantiate\n",
    "clf = GradientBoostingRegressor(loss=\"huber\")\n",
    "#Fit\n",
    "clf.fit(X_transformed_train, YTrain)\n",
    "#Predict\n",
    "YHatTest=clf.predict(X_transformed_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8005028244803417\n",
      "0.2347457327380229 63783365081.70049\n"
     ]
    }
   ],
   "source": [
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.Elastic Net "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True)\n",
    "#Fit\n",
    "clf.fit(X_transformed_train, YTrain)\n",
    "#Predict\n",
    "YHatTest=clf.predict(X_transformed_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.803675512917605\n",
      "0.24853568721449218 62768991096.91937\n"
     ]
    }
   ],
   "source": [
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. XG Boost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:34:35] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "#Instantiate\n",
    "clf = xgb.XGBRegressor()\n",
    "#Fit\n",
    "clf.fit(X_transformed_train, YTrain)\n",
    "#Predict\n",
    "YHatTest=clf.predict(X_transformed_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8071904926981709\n",
      "0.2486015240557512 61645179504.02523\n"
     ]
    }
   ],
   "source": [
    "# calculate the accuray of the model by sum of Square and mean absolute prediction error\n",
    "MAPE = np.mean(abs((YTest - YHatTest)/YTest))\n",
    "MSSE = np.mean(np.square(YHatTest - YTest))\n",
    "print(r2_score(YTest,YHatTest))\n",
    "print(MAPE, MSSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(YTest, YHatTest,'ro')\n",
    "plt.plot(YTest, YTest,'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these models,the models with most accurate models are :\n",
    "* Random Forest Regressor\n",
    "* Extra Trees Regressor \n",
    "* Lasso Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here these models are predicting the sales based on input variables provided.But time factor is not considered."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}