WEELY_SALES_CODE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bdcb59d5-5f78-4bb2-b9d0-392eb0b3fc6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "\n",
    "from statsmodels.formula.api import ols\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import Ridge, Lasso, ElasticNet, LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [10,6]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd5d53bf-81ad-4e66-b6fc-b4607296567d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>05-02-2010</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>0</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12-02-2010</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>1</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>19-02-2010</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>0</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>26-02-2010</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>0</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>05-03-2010</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0      1  05-02-2010    1643690.90             0        42.31       2.572   \n",
       "1      1  12-02-2010    1641957.44             1        38.51       2.548   \n",
       "2      1  19-02-2010    1611968.17             0        39.93       2.514   \n",
       "3      1  26-02-2010    1409727.59             0        46.63       2.561   \n",
       "4      1  05-03-2010    1554806.68             0        46.50       2.625   \n",
       "\n",
       "          CPI  Unemployment  \n",
       "0  211.096358         8.106  \n",
       "1  211.242170         8.106  \n",
       "2  211.289143         8.106  \n",
       "3  211.319643         8.106  \n",
       "4  211.350143         8.106  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mInference:\u001b[0m The Datset consists of 8 features & 6435 samples.\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"D:\\walmarts datasheet\\Walmart.csv\")\n",
    "\n",
    "#df.drop(['car name'], axis=1, inplace=True)\n",
    "display(df.head())\n",
    "\n",
    "original_df = df.copy(deep=True)\n",
    "\n",
    "print('\\n\\033[1mInference:\\033[0m The Datset consists of {} features & {} samples.'.format(df.shape[1], df.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc1f6f06-9aac-440a-b5e1-3c4554505ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>0</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>1</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>0</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>0</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price         CPI  \\\n",
       "0      1    1643690.90             0        42.31       2.572  211.096358   \n",
       "1      1    1641957.44             1        38.51       2.548  211.242170   \n",
       "2      1    1611968.17             0        39.93       2.514  211.289143   \n",
       "3      1    1409727.59             0        46.63       2.561  211.319643   \n",
       "4      1    1554806.68             0        46.50       2.625  211.350143   \n",
       "\n",
       "   Unemployment  weekday  month  year  \n",
       "0         8.106        4      2  2010  \n",
       "1         8.106        4      2  2010  \n",
       "2         8.106        4      2  2010  \n",
       "3         8.106        4      2  2010  \n",
       "4         8.106        4      3  2010  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming df is your DataFrame\n",
    "\n",
    "# Convert 'Date' column to datetime with the correct format\n",
    "df['Date'] = pd.to_datetime(df['Date'], format='%d-%m-%Y')\n",
    "\n",
    "# Extract additional date-related features\n",
    "df['weekday'] = df['Date'].dt.weekday\n",
    "df['month'] = df['Date'].dt.month\n",
    "df['year'] = df['Date'].dt.year\n",
    "\n",
    "# Drop the 'Date' column\n",
    "df.drop(['Date'], axis=1, inplace=True)\n",
    "\n",
    "# Define target variable and features\n",
    "target = 'Weekly_Sales'\n",
    "features = [col for col in df.columns if col != target]\n",
    "\n",
    "# Create a copy of the original DataFrame for reference\n",
    "original_df = df.copy(deep=True)\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "630ded67-1df3-4d16-8d12-1de959f05a00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6435 entries, 0 to 6434\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Store         6435 non-null   int64  \n",
      " 1   Weekly_Sales  6435 non-null   float64\n",
      " 2   Holiday_Flag  6435 non-null   int64  \n",
      " 3   Temperature   6435 non-null   float64\n",
      " 4   Fuel_Price    6435 non-null   float64\n",
      " 5   CPI           6435 non-null   float64\n",
      " 6   Unemployment  6435 non-null   float64\n",
      " 7   weekday       6435 non-null   int32  \n",
      " 8   month         6435 non-null   int32  \n",
      " 9   year          6435 non-null   int32  \n",
      "dtypes: float64(5), int32(3), int64(2)\n",
      "memory usage: 427.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "65c4b44b-6f95-4b7d-98d2-ab142645540d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekday            1\n",
       "Holiday_Flag       2\n",
       "year               3\n",
       "month             12\n",
       "Store             45\n",
       "Unemployment     349\n",
       "Fuel_Price       892\n",
       "CPI             2145\n",
       "Temperature     3528\n",
       "Weekly_Sales    6435\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10dd42b9-c3e2-4b0c-ae27-cf544dea49e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mInference:\u001b[0m The Datset has 4 numerical & 5 categorical features.\n"
     ]
    }
   ],
   "source": [
    "nu = df[features].nunique().sort_values()\n",
    "nf = []; cf = []; nnf = 0; ncf = 0; #numerical & categorical features\n",
    "\n",
    "for i in range(df[features].shape[1]):\n",
    "    if nu.values[i]<=45:cf.append(nu.index[i])\n",
    "    else: nf.append(nu.index[i])\n",
    "\n",
    "print('\\n\\033[1mInference:\\033[0m The Datset has {} numerical & {} categorical features.'.format(len(nf),len(cf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee282b10-c804-47ba-a8ac-3ca577a925c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6.435000e+03</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6435.0</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.046965e+06</td>\n",
       "      <td>0.069930</td>\n",
       "      <td>60.663782</td>\n",
       "      <td>3.358607</td>\n",
       "      <td>171.578394</td>\n",
       "      <td>7.999151</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.447552</td>\n",
       "      <td>2010.965035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.988182</td>\n",
       "      <td>5.643666e+05</td>\n",
       "      <td>0.255049</td>\n",
       "      <td>18.444933</td>\n",
       "      <td>0.459020</td>\n",
       "      <td>39.356712</td>\n",
       "      <td>1.875885</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.238308</td>\n",
       "      <td>0.797019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.099862e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.060000</td>\n",
       "      <td>2.472000</td>\n",
       "      <td>126.064000</td>\n",
       "      <td>3.879000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.533501e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.460000</td>\n",
       "      <td>2.933000</td>\n",
       "      <td>131.735000</td>\n",
       "      <td>6.891000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>9.607460e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.670000</td>\n",
       "      <td>3.445000</td>\n",
       "      <td>182.616521</td>\n",
       "      <td>7.874000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>1.420159e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>74.940000</td>\n",
       "      <td>3.735000</td>\n",
       "      <td>212.743293</td>\n",
       "      <td>8.622000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>3.818686e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.140000</td>\n",
       "      <td>4.468000</td>\n",
       "      <td>227.232807</td>\n",
       "      <td>14.313000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Store  Weekly_Sales  Holiday_Flag  Temperature   Fuel_Price  \\\n",
       "count  6435.000000  6.435000e+03   6435.000000  6435.000000  6435.000000   \n",
       "mean     23.000000  1.046965e+06      0.069930    60.663782     3.358607   \n",
       "std      12.988182  5.643666e+05      0.255049    18.444933     0.459020   \n",
       "min       1.000000  2.099862e+05      0.000000    -2.060000     2.472000   \n",
       "25%      12.000000  5.533501e+05      0.000000    47.460000     2.933000   \n",
       "50%      23.000000  9.607460e+05      0.000000    62.670000     3.445000   \n",
       "75%      34.000000  1.420159e+06      0.000000    74.940000     3.735000   \n",
       "max      45.000000  3.818686e+06      1.000000   100.140000     4.468000   \n",
       "\n",
       "               CPI  Unemployment  weekday        month         year  \n",
       "count  6435.000000   6435.000000   6435.0  6435.000000  6435.000000  \n",
       "mean    171.578394      7.999151      4.0     6.447552  2010.965035  \n",
       "std      39.356712      1.875885      0.0     3.238308     0.797019  \n",
       "min     126.064000      3.879000      4.0     1.000000  2010.000000  \n",
       "25%     131.735000      6.891000      4.0     4.000000  2010.000000  \n",
       "50%     182.616521      7.874000      4.0     6.000000  2011.000000  \n",
       "75%     212.743293      8.622000      4.0     9.000000  2012.000000  \n",
       "max     227.232807     14.313000      4.0    12.000000  2012.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dec7bebc-a035-450a-b3e7-4d90ea2704fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,4])\n",
    "sns.distplot(df[target], color='g',hist_kws=dict(edgecolor=\"black\", linewidth=2), bins=30)\n",
    "plt.title('Target Variable Distribution - Median Value of Homes ($1Ms)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ae02c0b-f0ed-48e9-bca0-9ac782b82700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mInference:\u001b[0m The dataset doesn't have any duplicates\n"
     ]
    }
   ],
   "source": [
    "#Removal of any Duplicate rows (if any)\n",
    "\n",
    "counter = 0\n",
    "rs,cs = original_df.shape\n",
    "\n",
    "df.drop_duplicates(inplace=True)\n",
    "\n",
    "if df.shape==(rs,cs):\n",
    "    print('\\n\\033[1mInference:\\033[0m The dataset doesn\\'t have any duplicates')\n",
    "else:\n",
    "    print(f'\\n\\033[1mInference:\\033[0m Number of duplicates dropped/fixed ---> {rs-df.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5c3aecc-6cee-44fe-a13d-79e4679bfebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Total Null Values  Percentage\n",
      "Store                         0         0.0\n",
      "Weekly_Sales                  0         0.0\n",
      "Holiday_Flag                  0         0.0\n",
      "Temperature                   0         0.0\n",
      "Fuel_Price                    0         0.0\n",
      "CPI                           0         0.0\n",
      "Unemployment                  0         0.0\n",
      "weekday                       0         0.0\n",
      "month                         0         0.0\n",
      "year                          0         0.0\n"
     ]
    }
   ],
   "source": [
    "#Check for empty elements\n",
    "\n",
    "nvc = pd.DataFrame(df.isnull().sum().sort_values(), columns=['Total Null Values'])\n",
    "nvc['Percentage'] = round(nvc['Total Null Values']/df.shape[0],3)*100\n",
    "print(nvc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2cdc1286-507c-4f5a-a062-13efb3c017d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mOne-Hot Encoding on features:\u001b[0m\n",
      "Holiday_Flag\n",
      "\n",
      "\u001b[1mDummy Encoding on features:\u001b[0m\n",
      "year\n",
      "month\n",
      "Store\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(6435, 64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df.copy()\n",
    "\n",
    "ecc = nvc[nvc['Percentage']!=0].index.values\n",
    "fcc = [i for i in cf if i not in ecc]\n",
    "#One-Hot Binay Encoding\n",
    "oh=True\n",
    "dm=True\n",
    "for i in fcc:\n",
    "    #print(i)\n",
    "    if df3[i].nunique()==2:\n",
    "        if oh==True: print(\"\\033[1mOne-Hot Encoding on features:\\033[0m\")\n",
    "        print(i);oh=False\n",
    "        df3[i]=pd.get_dummies(df3[i], drop_first=True, prefix=str(i))\n",
    "    if (df3[i].nunique()>2):\n",
    "        if dm==True: print(\"\\n\\033[1mDummy Encoding on features:\\033[0m\")\n",
    "        print(i);dm=False\n",
    "        df3 = pd.concat([df3.drop([i], axis=1), pd.DataFrame(pd.get_dummies(df3[i], drop_first=True, prefix=str(i)))],axis=1)\n",
    "        \n",
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e43a1e4f-6aa2-4a9d-bb2e-ab3625e3ef60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>weekday</th>\n",
       "      <th>year_2011</th>\n",
       "      <th>year_2012</th>\n",
       "      <th>month_2</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>1643690.90</td>\n",
       "      <td>False</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1641957.44</td>\n",
       "      <td>True</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1611968.17</td>\n",
       "      <td>False</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1409727.59</td>\n",
       "      <td>False</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1554806.68</td>\n",
       "      <td>False</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price         CPI  \\\n",
       "0    1643690.90         False        42.31       2.572  211.096358   \n",
       "1    1641957.44          True        38.51       2.548  211.242170   \n",
       "2    1611968.17         False        39.93       2.514  211.289143   \n",
       "3    1409727.59         False        46.63       2.561  211.319643   \n",
       "4    1554806.68         False        46.50       2.625  211.350143   \n",
       "\n",
       "   Unemployment  weekday  year_2011  year_2012  month_2  ...  Store_36  \\\n",
       "0         8.106        4      False      False     True  ...     False   \n",
       "1         8.106        4      False      False     True  ...     False   \n",
       "2         8.106        4      False      False     True  ...     False   \n",
       "3         8.106        4      False      False     True  ...     False   \n",
       "4         8.106        4      False      False    False  ...     False   \n",
       "\n",
       "   Store_37  Store_38  Store_39  Store_40  Store_41  Store_42  Store_43  \\\n",
       "0     False     False     False     False     False     False     False   \n",
       "1     False     False     False     False     False     False     False   \n",
       "2     False     False     False     False     False     False     False   \n",
       "3     False     False     False     False     False     False     False   \n",
       "4     False     False     False     False     False     False     False   \n",
       "\n",
       "   Store_44  Store_45  \n",
       "0     False     False  \n",
       "1     False     False  \n",
       "2     False     False  \n",
       "3     False     False  \n",
       "4     False     False  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mInference:\u001b[0m\n",
      "Before removal of outliers, The dataset had 6435 samples.\n",
      "After removal of outliers, The dataset now has 5953 samples.\n"
     ]
    }
   ],
   "source": [
    "df1 = df3.copy()\n",
    "\n",
    "#features1 = [i for i in features if i not in ['CHAS','RAD']]\n",
    "features1 = nf\n",
    "\n",
    "for i in features1:\n",
    "    Q1 = df1[i].quantile(0.25)\n",
    "    Q3 = df1[i].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    df1 = df1[df1[i] <= (Q3+(1.5*IQR))]\n",
    "    df1 = df1[df1[i] >= (Q1-(1.5*IQR))]\n",
    "    df1 = df1.reset_index(drop=True)\n",
    "display(df1.head())\n",
    "print('\\n\\033[1mInference:\\033[0m\\nBefore removal of outliers, The dataset had {} samples.'.format(df3.shape[0]))\n",
    "print('After removal of outliers, The dataset now has {} samples.'.format(df1.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de25f769-89b2-41c8-b810-24b7f7247e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mInference:\u001b[0m After the cleanup process, 482 samples were dropped, while retaining 7.49% of the data.\n"
     ]
    }
   ],
   "source": [
    "df = df1.copy()\n",
    "df.columns=[i.replace('-','_') for i in df.columns]\n",
    "\n",
    "plt.title('Final Dataset')\n",
    "plt.pie([df.shape[0], original_df.shape[0]-df.shape[0]], radius = 1, labels=['Retained','Dropped'], counterclock=False, \n",
    "        autopct='%1.1f%%', pctdistance=0.9, explode=[0,0], shadow=True)\n",
    "plt.pie([df.shape[0]], labels=['100%'], labeldistance=-0, radius=0.78)\n",
    "plt.show()\n",
    "\n",
    "print(f'\\n\\033[1mInference:\\033[0m After the cleanup process, {original_df.shape[0]-df.shape[0]} samples were dropped, \\\n",
    "while retaining {round(100 - (df.shape[0]*100/(original_df.shape[0])),2)}% of the data.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aaa1c210-03c9-4257-a068-2b3c89e88752",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original set  --->  (5953, 63) (5953,) \n",
      "Training set  --->  (4762, 63) (4762,) \n",
      "Testing set   --->  (1191, 63)  (1191,)\n"
     ]
    }
   ],
   "source": [
    "m=[]\n",
    "for i in df.columns.values:\n",
    "    m.append(i.replace(' ','_'))\n",
    "    \n",
    "df.columns = m\n",
    "X = df.drop([target],axis=1)\n",
    "Y = df[target]\n",
    "Train_X, Test_X, Train_Y, Test_Y = train_test_split(X, Y, train_size=0.8, test_size=0.2, random_state=100)\n",
    "Train_X.reset_index(drop=True,inplace=True)\n",
    "\n",
    "print('Original set  ---> ',X.shape,Y.shape,'\\nTraining set  ---> ',Train_X.shape,Train_Y.shape,'\\nTesting set   ---> ', Test_X.shape,'', Test_Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3a8798ed-2b95-4448-9475-7671897a5d46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         \u001b[1mStandardardization on Training set                                         \n"
     ]
    },
    {
     "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>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>weekday</th>\n",
       "      <th>year_2011</th>\n",
       "      <th>year_2012</th>\n",
       "      <th>month_2</th>\n",
       "      <th>month_3</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>count</th>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4762.0</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "      <td>4.762000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.492110e-18</td>\n",
       "      <td>-1.305596e-16</td>\n",
       "      <td>-2.991680e-16</td>\n",
       "      <td>-2.762268e-16</td>\n",
       "      <td>-4.267434e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.520807e-17</td>\n",
       "      <td>1.939743e-17</td>\n",
       "      <td>7.460550e-18</td>\n",
       "      <td>3.618367e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>1.529413e-17</td>\n",
       "      <td>4.923963e-17</td>\n",
       "      <td>8.952659e-18</td>\n",
       "      <td>1.342899e-17</td>\n",
       "      <td>-3.730275e-18</td>\n",
       "      <td>-1.492110e-17</td>\n",
       "      <td>3.730275e-17</td>\n",
       "      <td>-2.984220e-17</td>\n",
       "      <td>-4.774752e-17</td>\n",
       "      <td>2.499284e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "      <td>1.000105e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.742012e-01</td>\n",
       "      <td>-2.961575e+00</td>\n",
       "      <td>-1.871814e+00</td>\n",
       "      <td>-1.248731e+00</td>\n",
       "      <td>-2.762670e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.526270e-01</td>\n",
       "      <td>-6.371530e-01</td>\n",
       "      <td>-2.982532e-01</td>\n",
       "      <td>-3.166659e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.573123e-01</td>\n",
       "      <td>-1.587085e-01</td>\n",
       "      <td>-4.351484e-02</td>\n",
       "      <td>-1.580118e-01</td>\n",
       "      <td>-1.307162e-01</td>\n",
       "      <td>-1.537717e-01</td>\n",
       "      <td>-1.573123e-01</td>\n",
       "      <td>-1.628322e-01</td>\n",
       "      <td>-1.551967e-01</td>\n",
       "      <td>-1.523346e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-2.742012e-01</td>\n",
       "      <td>-7.314248e-01</td>\n",
       "      <td>-9.886990e-01</td>\n",
       "      <td>-1.076949e+00</td>\n",
       "      <td>-6.783836e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.526270e-01</td>\n",
       "      <td>-6.371530e-01</td>\n",
       "      <td>-2.982532e-01</td>\n",
       "      <td>-3.166659e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.573123e-01</td>\n",
       "      <td>-1.587085e-01</td>\n",
       "      <td>-4.351484e-02</td>\n",
       "      <td>-1.580118e-01</td>\n",
       "      <td>-1.307162e-01</td>\n",
       "      <td>-1.537717e-01</td>\n",
       "      <td>-1.573123e-01</td>\n",
       "      <td>-1.628322e-01</td>\n",
       "      <td>-1.551967e-01</td>\n",
       "      <td>-1.523346e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.742012e-01</td>\n",
       "      <td>1.062547e-01</td>\n",
       "      <td>1.663112e-01</td>\n",
       "      <td>3.842133e-01</td>\n",
       "      <td>9.596435e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.526270e-01</td>\n",
       "      <td>-6.371530e-01</td>\n",
       "      <td>-2.982532e-01</td>\n",
       "      <td>-3.166659e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.573123e-01</td>\n",
       "      <td>-1.587085e-01</td>\n",
       "      <td>-4.351484e-02</td>\n",
       "      <td>-1.580118e-01</td>\n",
       "      <td>-1.307162e-01</td>\n",
       "      <td>-1.537717e-01</td>\n",
       "      <td>-1.573123e-01</td>\n",
       "      <td>-1.628322e-01</td>\n",
       "      <td>-1.551967e-01</td>\n",
       "      <td>-1.523346e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-2.742012e-01</td>\n",
       "      <td>7.731979e-01</td>\n",
       "      <td>8.427860e-01</td>\n",
       "      <td>9.933828e-01</td>\n",
       "      <td>6.138095e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.328679e+00</td>\n",
       "      <td>1.569482e+00</td>\n",
       "      <td>-2.982532e-01</td>\n",
       "      <td>-3.166659e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.573123e-01</td>\n",
       "      <td>-1.587085e-01</td>\n",
       "      <td>-4.351484e-02</td>\n",
       "      <td>-1.580118e-01</td>\n",
       "      <td>-1.307162e-01</td>\n",
       "      <td>-1.537717e-01</td>\n",
       "      <td>-1.573123e-01</td>\n",
       "      <td>-1.628322e-01</td>\n",
       "      <td>-1.551967e-01</td>\n",
       "      <td>-1.523346e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.646958e+00</td>\n",
       "      <td>2.170008e+00</td>\n",
       "      <td>2.469806e+00</td>\n",
       "      <td>1.340791e+00</td>\n",
       "      <td>2.575491e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.328679e+00</td>\n",
       "      <td>1.569482e+00</td>\n",
       "      <td>3.352856e+00</td>\n",
       "      <td>3.157903e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>6.356783e+00</td>\n",
       "      <td>6.300861e+00</td>\n",
       "      <td>2.298067e+01</td>\n",
       "      <td>6.328643e+00</td>\n",
       "      <td>7.650163e+00</td>\n",
       "      <td>6.503146e+00</td>\n",
       "      <td>6.356783e+00</td>\n",
       "      <td>6.141290e+00</td>\n",
       "      <td>6.443435e+00</td>\n",
       "      <td>6.564495e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Holiday_Flag   Temperature    Fuel_Price           CPI  Unemployment  \\\n",
       "count  4.762000e+03  4.762000e+03  4.762000e+03  4.762000e+03  4.762000e+03   \n",
       "mean  -1.492110e-18 -1.305596e-16 -2.991680e-16 -2.762268e-16 -4.267434e-16   \n",
       "std    1.000105e+00  1.000105e+00  1.000105e+00  1.000105e+00  1.000105e+00   \n",
       "min   -2.742012e-01 -2.961575e+00 -1.871814e+00 -1.248731e+00 -2.762670e+00   \n",
       "25%   -2.742012e-01 -7.314248e-01 -9.886990e-01 -1.076949e+00 -6.783836e-01   \n",
       "50%   -2.742012e-01  1.062547e-01  1.663112e-01  3.842133e-01  9.596435e-02   \n",
       "75%   -2.742012e-01  7.731979e-01  8.427860e-01  9.933828e-01  6.138095e-01   \n",
       "max    3.646958e+00  2.170008e+00  2.469806e+00  1.340791e+00  2.575491e+00   \n",
       "\n",
       "       weekday     year_2011     year_2012       month_2       month_3  ...  \\\n",
       "count   4762.0  4.762000e+03  4.762000e+03  4.762000e+03  4.762000e+03  ...   \n",
       "mean       0.0 -5.520807e-17  1.939743e-17  7.460550e-18  3.618367e-17  ...   \n",
       "std        0.0  1.000105e+00  1.000105e+00  1.000105e+00  1.000105e+00  ...   \n",
       "min        0.0 -7.526270e-01 -6.371530e-01 -2.982532e-01 -3.166659e-01  ...   \n",
       "25%        0.0 -7.526270e-01 -6.371530e-01 -2.982532e-01 -3.166659e-01  ...   \n",
       "50%        0.0 -7.526270e-01 -6.371530e-01 -2.982532e-01 -3.166659e-01  ...   \n",
       "75%        0.0  1.328679e+00  1.569482e+00 -2.982532e-01 -3.166659e-01  ...   \n",
       "max        0.0  1.328679e+00  1.569482e+00  3.352856e+00  3.157903e+00  ...   \n",
       "\n",
       "           Store_36      Store_37      Store_38      Store_39      Store_40  \\\n",
       "count  4.762000e+03  4.762000e+03  4.762000e+03  4.762000e+03  4.762000e+03   \n",
       "mean   1.529413e-17  4.923963e-17  8.952659e-18  1.342899e-17 -3.730275e-18   \n",
       "std    1.000105e+00  1.000105e+00  1.000105e+00  1.000105e+00  1.000105e+00   \n",
       "min   -1.573123e-01 -1.587085e-01 -4.351484e-02 -1.580118e-01 -1.307162e-01   \n",
       "25%   -1.573123e-01 -1.587085e-01 -4.351484e-02 -1.580118e-01 -1.307162e-01   \n",
       "50%   -1.573123e-01 -1.587085e-01 -4.351484e-02 -1.580118e-01 -1.307162e-01   \n",
       "75%   -1.573123e-01 -1.587085e-01 -4.351484e-02 -1.580118e-01 -1.307162e-01   \n",
       "max    6.356783e+00  6.300861e+00  2.298067e+01  6.328643e+00  7.650163e+00   \n",
       "\n",
       "           Store_41      Store_42      Store_43      Store_44      Store_45  \n",
       "count  4.762000e+03  4.762000e+03  4.762000e+03  4.762000e+03  4.762000e+03  \n",
       "mean  -1.492110e-17  3.730275e-17 -2.984220e-17 -4.774752e-17  2.499284e-17  \n",
       "std    1.000105e+00  1.000105e+00  1.000105e+00  1.000105e+00  1.000105e+00  \n",
       "min   -1.537717e-01 -1.573123e-01 -1.628322e-01 -1.551967e-01 -1.523346e-01  \n",
       "25%   -1.537717e-01 -1.573123e-01 -1.628322e-01 -1.551967e-01 -1.523346e-01  \n",
       "50%   -1.537717e-01 -1.573123e-01 -1.628322e-01 -1.551967e-01 -1.523346e-01  \n",
       "75%   -1.537717e-01 -1.573123e-01 -1.628322e-01 -1.551967e-01 -1.523346e-01  \n",
       "max    6.503146e+00  6.356783e+00  6.141290e+00  6.443435e+00  6.564495e+00  \n",
       "\n",
       "[8 rows x 63 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                                          \u001b[1mStandardardization on Testing set                                          \n"
     ]
    },
    {
     "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>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>weekday</th>\n",
       "      <th>year_2011</th>\n",
       "      <th>year_2012</th>\n",
       "      <th>month_2</th>\n",
       "      <th>month_3</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>count</th>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.005646</td>\n",
       "      <td>0.044406</td>\n",
       "      <td>0.075113</td>\n",
       "      <td>0.021041</td>\n",
       "      <td>-0.050953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.052984</td>\n",
       "      <td>0.065042</td>\n",
       "      <td>0.026699</td>\n",
       "      <td>-0.019096</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004168</td>\n",
       "      <td>-0.017693</td>\n",
       "      <td>0.111140</td>\n",
       "      <td>-0.010959</td>\n",
       "      <td>-0.000055</td>\n",
       "      <td>0.030677</td>\n",
       "      <td>-0.004168</td>\n",
       "      <td>-0.056970</td>\n",
       "      <td>0.016556</td>\n",
       "      <td>0.045053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.009885</td>\n",
       "      <td>1.000220</td>\n",
       "      <td>0.971917</td>\n",
       "      <td>1.004644</td>\n",
       "      <td>1.010206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.014188</td>\n",
       "      <td>1.028250</td>\n",
       "      <td>1.040072</td>\n",
       "      <td>0.972715</td>\n",
       "      <td>...</td>\n",
       "      <td>0.987401</td>\n",
       "      <td>0.944330</td>\n",
       "      <td>1.881448</td>\n",
       "      <td>0.965939</td>\n",
       "      <td>1.000214</td>\n",
       "      <td>1.093088</td>\n",
       "      <td>0.987401</td>\n",
       "      <td>0.810380</td>\n",
       "      <td>1.051077</td>\n",
       "      <td>1.134875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.274201</td>\n",
       "      <td>-2.857425</td>\n",
       "      <td>-1.780457</td>\n",
       "      <td>-1.248731</td>\n",
       "      <td>-2.762670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.752627</td>\n",
       "      <td>-0.637153</td>\n",
       "      <td>-0.298253</td>\n",
       "      <td>-0.316666</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.157312</td>\n",
       "      <td>-0.158708</td>\n",
       "      <td>-0.043515</td>\n",
       "      <td>-0.158012</td>\n",
       "      <td>-0.130716</td>\n",
       "      <td>-0.153772</td>\n",
       "      <td>-0.157312</td>\n",
       "      <td>-0.162832</td>\n",
       "      <td>-0.155197</td>\n",
       "      <td>-0.152335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.274201</td>\n",
       "      <td>-0.657516</td>\n",
       "      <td>-0.852751</td>\n",
       "      <td>-1.077025</td>\n",
       "      <td>-0.699355</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.752627</td>\n",
       "      <td>-0.637153</td>\n",
       "      <td>-0.298253</td>\n",
       "      <td>-0.316666</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.157312</td>\n",
       "      <td>-0.158708</td>\n",
       "      <td>-0.043515</td>\n",
       "      <td>-0.158012</td>\n",
       "      <td>-0.130716</td>\n",
       "      <td>-0.153772</td>\n",
       "      <td>-0.157312</td>\n",
       "      <td>-0.162832</td>\n",
       "      <td>-0.155197</td>\n",
       "      <td>-0.152335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.274201</td>\n",
       "      <td>0.187351</td>\n",
       "      <td>0.298996</td>\n",
       "      <td>0.393492</td>\n",
       "      <td>0.058860</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.752627</td>\n",
       "      <td>-0.637153</td>\n",
       "      <td>-0.298253</td>\n",
       "      <td>-0.316666</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.157312</td>\n",
       "      <td>-0.158708</td>\n",
       "      <td>-0.043515</td>\n",
       "      <td>-0.158012</td>\n",
       "      <td>-0.130716</td>\n",
       "      <td>-0.153772</td>\n",
       "      <td>-0.157312</td>\n",
       "      <td>-0.162832</td>\n",
       "      <td>-0.155197</td>\n",
       "      <td>-0.152335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.274201</td>\n",
       "      <td>0.818764</td>\n",
       "      <td>0.844961</td>\n",
       "      <td>1.019967</td>\n",
       "      <td>0.611390</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.328679</td>\n",
       "      <td>1.569482</td>\n",
       "      <td>-0.298253</td>\n",
       "      <td>-0.316666</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.157312</td>\n",
       "      <td>-0.158708</td>\n",
       "      <td>-0.043515</td>\n",
       "      <td>-0.158012</td>\n",
       "      <td>-0.130716</td>\n",
       "      <td>-0.153772</td>\n",
       "      <td>-0.157312</td>\n",
       "      <td>-0.162832</td>\n",
       "      <td>-0.155197</td>\n",
       "      <td>-0.152335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.646958</td>\n",
       "      <td>2.035481</td>\n",
       "      <td>2.469806</td>\n",
       "      <td>1.345814</td>\n",
       "      <td>2.575491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.328679</td>\n",
       "      <td>1.569482</td>\n",
       "      <td>3.352856</td>\n",
       "      <td>3.157903</td>\n",
       "      <td>...</td>\n",
       "      <td>6.356783</td>\n",
       "      <td>6.300861</td>\n",
       "      <td>22.980668</td>\n",
       "      <td>6.328643</td>\n",
       "      <td>7.650163</td>\n",
       "      <td>6.503146</td>\n",
       "      <td>6.356783</td>\n",
       "      <td>6.141290</td>\n",
       "      <td>6.443435</td>\n",
       "      <td>6.564495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Holiday_Flag  Temperature   Fuel_Price          CPI  Unemployment  \\\n",
       "count   1191.000000  1191.000000  1191.000000  1191.000000   1191.000000   \n",
       "mean       0.005646     0.044406     0.075113     0.021041     -0.050953   \n",
       "std        1.009885     1.000220     0.971917     1.004644      1.010206   \n",
       "min       -0.274201    -2.857425    -1.780457    -1.248731     -2.762670   \n",
       "25%       -0.274201    -0.657516    -0.852751    -1.077025     -0.699355   \n",
       "50%       -0.274201     0.187351     0.298996     0.393492      0.058860   \n",
       "75%       -0.274201     0.818764     0.844961     1.019967      0.611390   \n",
       "max        3.646958     2.035481     2.469806     1.345814      2.575491   \n",
       "\n",
       "       weekday    year_2011    year_2012      month_2      month_3  ...  \\\n",
       "count   1191.0  1191.000000  1191.000000  1191.000000  1191.000000  ...   \n",
       "mean       0.0     0.052984     0.065042     0.026699    -0.019096  ...   \n",
       "std        0.0     1.014188     1.028250     1.040072     0.972715  ...   \n",
       "min        0.0    -0.752627    -0.637153    -0.298253    -0.316666  ...   \n",
       "25%        0.0    -0.752627    -0.637153    -0.298253    -0.316666  ...   \n",
       "50%        0.0    -0.752627    -0.637153    -0.298253    -0.316666  ...   \n",
       "75%        0.0     1.328679     1.569482    -0.298253    -0.316666  ...   \n",
       "max        0.0     1.328679     1.569482     3.352856     3.157903  ...   \n",
       "\n",
       "          Store_36     Store_37     Store_38     Store_39     Store_40  \\\n",
       "count  1191.000000  1191.000000  1191.000000  1191.000000  1191.000000   \n",
       "mean     -0.004168    -0.017693     0.111140    -0.010959    -0.000055   \n",
       "std       0.987401     0.944330     1.881448     0.965939     1.000214   \n",
       "min      -0.157312    -0.158708    -0.043515    -0.158012    -0.130716   \n",
       "25%      -0.157312    -0.158708    -0.043515    -0.158012    -0.130716   \n",
       "50%      -0.157312    -0.158708    -0.043515    -0.158012    -0.130716   \n",
       "75%      -0.157312    -0.158708    -0.043515    -0.158012    -0.130716   \n",
       "max       6.356783     6.300861    22.980668     6.328643     7.650163   \n",
       "\n",
       "          Store_41     Store_42     Store_43     Store_44     Store_45  \n",
       "count  1191.000000  1191.000000  1191.000000  1191.000000  1191.000000  \n",
       "mean      0.030677    -0.004168    -0.056970     0.016556     0.045053  \n",
       "std       1.093088     0.987401     0.810380     1.051077     1.134875  \n",
       "min      -0.153772    -0.157312    -0.162832    -0.155197    -0.152335  \n",
       "25%      -0.153772    -0.157312    -0.162832    -0.155197    -0.152335  \n",
       "50%      -0.153772    -0.157312    -0.162832    -0.155197    -0.152335  \n",
       "75%      -0.153772    -0.157312    -0.162832    -0.155197    -0.152335  \n",
       "max       6.503146     6.356783     6.141290     6.443435     6.564495  \n",
       "\n",
       "[8 rows x 63 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Feature Scaling (Standardization)\n",
    "\n",
    "std = StandardScaler()\n",
    "\n",
    "print('\\033[1mStandardardization on Training set'.center(120))\n",
    "Train_X_std = std.fit_transform(Train_X)\n",
    "Train_X_std = pd.DataFrame(Train_X_std, columns=X.columns)\n",
    "display(Train_X_std.describe())\n",
    "\n",
    "print('\\n','\\033[1mStandardardization on Testing set'.center(120))\n",
    "Test_X_std = std.transform(Test_X)\n",
    "Test_X_std = pd.DataFrame(Test_X_std, columns=X.columns)\n",
    "display(Test_X_std.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f903a4a3-cc9f-42c7-b849-c351c6eecd8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           Weekly_Sales   R-squared:                       0.938\n",
      "Model:                            OLS   Adj. R-squared:                  0.938\n",
      "Method:                 Least Squares   F-statistic:                     1155.\n",
      "Date:                Mon, 01 Apr 2024   Prob (F-statistic):               0.00\n",
      "Time:                        14:05:21   Log-Likelihood:                -63223.\n",
      "No. Observations:                4762   AIC:                         1.266e+05\n",
      "Df Residuals:                    4699   BIC:                         1.270e+05\n",
      "Df Model:                          62                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept     1.048e+06   2059.199    508.743      0.000    1.04e+06    1.05e+06\n",
      "Holiday_Flag  8059.9214   2279.678      3.536      0.000    3590.684    1.25e+04\n",
      "Temperature   1.769e+04   7235.809      2.445      0.015    3504.204    3.19e+04\n",
      "Fuel_Price   -1296.0299   6741.498     -0.192      0.848   -1.45e+04    1.19e+04\n",
      "CPI           1.577e+05   7.06e+04      2.233      0.026    1.93e+04    2.96e+05\n",
      "Unemployment -5.424e+04   7171.270     -7.564      0.000   -6.83e+04   -4.02e+04\n",
      "weekday      -2.329e-10   7.57e-10     -0.308      0.758   -1.72e-09    1.25e-09\n",
      "year_2011    -1.523e+04   7195.844     -2.116      0.034   -2.93e+04   -1120.441\n",
      "year_2012    -2.805e+04   1.04e+04     -2.700      0.007   -4.84e+04   -7678.312\n",
      "month_2       3.105e+04   3383.632      9.177      0.000    2.44e+04    3.77e+04\n",
      "month_3       2.123e+04   4074.185      5.211      0.000    1.32e+04    2.92e+04\n",
      "month_4       2.326e+04   4956.542      4.693      0.000    1.35e+04     3.3e+04\n",
      "month_5       2.165e+04   5142.688      4.210      0.000    1.16e+04    3.17e+04\n",
      "month_6        2.84e+04   5716.385      4.968      0.000    1.72e+04    3.96e+04\n",
      "month_7       1.747e+04   6274.423      2.785      0.005    5171.630    2.98e+04\n",
      "month_8        2.02e+04   6207.060      3.255      0.001    8034.143    3.24e+04\n",
      "month_9       2761.7459   5723.463      0.483      0.629   -8458.925     1.4e+04\n",
      "month_10      9450.4348   5144.900      1.837      0.066    -635.983    1.95e+04\n",
      "month_11      4.162e+04   3902.022     10.666      0.000     3.4e+04    4.93e+04\n",
      "month_12      8.575e+04   3975.296     21.570      0.000     7.8e+04    9.35e+04\n",
      "Store_2       5.429e+04   2849.085     19.056      0.000    4.87e+04    5.99e+04\n",
      "Store_3      -1.816e+05   3037.660    -59.778      0.000   -1.88e+05   -1.76e+05\n",
      "Store_4       1.148e+05    2.3e+04      4.999      0.000    6.97e+04     1.6e+05\n",
      "Store_5      -1.899e+05   3023.242    -62.806      0.000   -1.96e+05   -1.84e+05\n",
      "Store_6      -7322.3757   3058.926     -2.394      0.017   -1.33e+04   -1325.446\n",
      "Store_7      -1.284e+05   7132.924    -18.005      0.000   -1.42e+05   -1.14e+05\n",
      "Store_8      -1.142e+05   3392.659    -33.653      0.000   -1.21e+05   -1.08e+05\n",
      "Store_9      -1.589e+05   3194.275    -49.741      0.000   -1.65e+05   -1.53e+05\n",
      "Store_10      1.075e+05   2.39e+04      4.503      0.000    6.07e+04    1.54e+05\n",
      "Store_11     -3.776e+04   3058.338    -12.347      0.000   -4.38e+04   -3.18e+04\n",
      "Store_12     -2928.8960   9460.509     -0.310      0.757   -2.15e+04    1.56e+04\n",
      "Store_13      1.176e+05    2.4e+04      4.891      0.000    7.04e+04    1.65e+05\n",
      "Store_14      9.566e+04   8660.998     11.045      0.000    7.87e+04    1.13e+05\n",
      "Store_15     -9.031e+04   2.27e+04     -3.975      0.000   -1.35e+05   -4.58e+04\n",
      "Store_16     -1.522e+05   7079.401    -21.498      0.000   -1.66e+05   -1.38e+05\n",
      "Store_17     -5.366e+04   2.43e+04     -2.205      0.027   -1.01e+05   -5960.440\n",
      "Store_18     -1.322e+04   2.29e+04     -0.578      0.563    -5.8e+04    3.16e+04\n",
      "Store_19      3.508e+04   2.25e+04      1.563      0.118   -8931.997    7.91e+04\n",
      "Store_20      8.718e+04   3534.911     24.662      0.000    8.02e+04    9.41e+04\n",
      "Store_21     -1.224e+05   2867.959    -42.690      0.000   -1.28e+05   -1.17e+05\n",
      "Store_22     -2.898e+04   2.19e+04     -1.326      0.185   -7.18e+04    1.39e+04\n",
      "Store_23      4941.7850   1.95e+04      0.253      0.800   -3.33e+04    4.32e+04\n",
      "Store_24       2.53e+04   2.23e+04      1.137      0.256   -1.83e+04    6.89e+04\n",
      "Store_25     -1.314e+05   3682.615    -35.672      0.000   -1.39e+05   -1.24e+05\n",
      "Store_26     -3.147e+04   2.29e+04     -1.377      0.169   -7.63e+04    1.33e+04\n",
      "Store_27      8.583e+04    2.2e+04      3.904      0.000    4.27e+04    1.29e+05\n",
      "Store_28      1.034e+04   8842.030      1.170      0.242   -6993.236    2.77e+04\n",
      "Store_29     -9.329e+04   2.34e+04     -3.984      0.000   -1.39e+05   -4.74e+04\n",
      "Store_30     -1.798e+05   2932.260    -61.308      0.000   -1.86e+05   -1.74e+05\n",
      "Store_31     -2.421e+04   2849.075     -8.497      0.000   -2.98e+04   -1.86e+04\n",
      "Store_32     -4.007e+04   7083.026     -5.657      0.000    -5.4e+04   -2.62e+04\n",
      "Store_33     -1.407e+05   2.43e+04     -5.791      0.000   -1.88e+05   -9.31e+04\n",
      "Store_34     -2.012e+04   2.45e+04     -0.822      0.411   -6.81e+04    2.79e+04\n",
      "Store_35     -4.127e+04   2.13e+04     -1.935      0.053   -8.31e+04     543.578\n",
      "Store_36     -1.807e+05   2913.015    -62.043      0.000   -1.86e+05   -1.75e+05\n",
      "Store_37     -1.599e+05   2927.859    -54.613      0.000   -1.66e+05   -1.54e+05\n",
      "Store_38     -2.534e+04   7446.299     -3.402      0.001   -3.99e+04   -1.07e+04\n",
      "Store_39     -1.445e+04   2916.005     -4.954      0.000   -2.02e+04   -8729.366\n",
      "Store_40     -4.829e+04   1.89e+04     -2.549      0.011   -8.54e+04   -1.12e+04\n",
      "Store_41     -3.454e+04   6812.723     -5.070      0.000   -4.79e+04   -2.12e+04\n",
      "Store_42     -9.619e+04   2.44e+04     -3.946      0.000   -1.44e+05   -4.84e+04\n",
      "Store_43     -1.267e+05   4349.375    -29.123      0.000   -1.35e+05   -1.18e+05\n",
      "Store_44     -1.409e+05   2.41e+04     -5.845      0.000   -1.88e+05   -9.37e+04\n",
      "Store_45     -9.132e+04   8556.624    -10.673      0.000   -1.08e+05   -7.45e+04\n",
      "==============================================================================\n",
      "Omnibus:                     3276.984   Durbin-Watson:                   2.006\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           115569.372\n",
      "Skew:                           2.817   Prob(JB):                         0.00\n",
      "Kurtosis:                      26.467   Cond. No.                     6.66e+15\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 2.49e-28. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "# Concatenating scaled features and target variable into Train_xy\n",
    "Train_xy = pd.concat([Train_X_std, Train_Y.reset_index(drop=True)], axis=1)\n",
    "\n",
    "# Constructing the formula for linear regression\n",
    "formula = '{} ~ {}'.format(target, ' + '.join(i for i in Train_X_std.columns))\n",
    "\n",
    "# Fitting the linear regression model\n",
    "API = ols(formula=formula, data=Train_xy).fit()\n",
    "\n",
    "# Printing the summary of the model\n",
    "print(API.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4fd7f7d4-5323-4c09-b1f5-9ab98b919613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped Features -->  ['CPI', 'year_2012', 'Temperature', 'Unemployment', 'month_4', 'Store_30', 'month_2', 'Store_27', 'Fuel_Price', 'Store_29', 'month_7', 'Store_32', 'month_9', 'Store_8', 'Store_25', 'month_8', 'Store_43', 'Store_17', 'month_10', 'Store_18', 'Holiday_Flag', 'Store_19', 'Store_20', 'Store_16', 'Store_21', 'Store_22', 'month_3', 'Store_15', 'Store_36', 'Store_14', 'Store_45', 'Store_13', 'month_5', 'year_2011', 'month_6', 'Store_24', 'Store_26', 'Store_44', 'Store_42', 'Store_41', 'month_11', 'Store_40', 'Store_39', 'Store_11', 'Store_35', 'Store_10', 'Store_2', 'Store_3', 'Store_4', 'Store_5', 'Store_6']\n"
     ]
    },
    {
     "data": {
      "image/png": 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yyCPccccdFBcXN8j5J0+ezIMPPsjjjz9OfHx8jf1t27Y963t79epFcHAwU6ZMIS8vD6vVymWXXcarr75KWlqaR2z1bQRO9+677/KjH/3o0j+EiIiISEv29SfwjzRwVp1q8wuEjv1PFlUDIfYqsDSP8qR5fAq5KOnp6aSnp7tfd+7cudb7bbVp04Z//etf5zzWqlWrPF7n5ubWiNm8efM5jzF//vxa21NTU0lNTT3nsU9XVFTk3u7atet57891ts8tIiIiInWg7Bh8/HNXgRV7FVye4iqsOlwLflZfZ1cvVGSJiIiIiEj9yXjKNTUwMgEezmi2hdXp6m+JOhERERERadm+/S9sXgCYYNS8FlFggYosERERERGpD7ZS1zRBgOsegY79fJtPA1KRJSIiIiIide+zma4VBcPj4eanfZ1Ng1KRJSIiIiIidStvI2S97Noe+SJYW/k0nYamIktEREREROpOlQ0+egww4Mp7oXvN2+Q0dyqyRERERESk7nw+Fw5/DSHtIGWWr7PxCRVZIiIiIiJSNw5ug8+fd23f8hwEt/FtPj6iIktERERERC6d0wEfTQCnHRJugyvu9HVGPqMiq4UxmUznfKSnp1/0sTt37syLL754QXHV5wsKCqJHjx784Q9/wDAMd0xubi4mkwk/Pz++//57j/fn5+fj5+eHyWQiNzfX3f7BBx/Qr18/wsPDCQ0N5YorrmDKlCnu/fPnz6/1MwcGBl70ZxYRERGRk9b/Fb7PBms43PY8mEyXfMgKu4PFmw6w9dilH6sh+fk6AWlY+fn57u333nuPp59+mp07d7rbgoKCGiSPZ555hrFjx1JRUcGKFSv42c9+RlhYGI8++qhHXFxcHG+99RbTpk1zt7355pu0b9+effv2udtWrFjBj370I2bNmsWoUaMwmUxs376dlStXehwvLCzM4/OCq/AUERERkUtwbA+s/K1re9gzEBZ7SYfbWXCcdzfs48Mv91NSUUWHEDOP10GaDUUjWS1MTEyM+xEeHo7JZPJoW7NmDX369CEwMJCuXbsyY8YMqqqq3O+fPn06HTt2xGq1EhcXx8SJEwG46aab2Lt3L7/4xS/cI0TnEhoaSkxMDJ07d+YnP/kJvXv3JiMjo0bcAw88wBtvvOHRNn/+fB544AGPtiVLlnDDDTfwy1/+koSEBC6//HLuuOMO5s2b5xF35ueNiYkhOjraq+9QRERERE5jGK6bDleVQ+cfwDUPnP89tSivdPDP7P2M/sv/SHlxDfPX5boKrNaBXNnGSZXDWceJ1x+NZNUlwwB7Wf0d3+l0Hb/SAuYz6mP/4Esekv3000+5//77+dOf/sQPfvADvv32Wx555BEAfvOb3/DPf/6TF154gUWLFnHFFVdQUFDAV199BcCHH37IlVdeySOPPMLYsWMv+JyGYbB69Wp27NjBZZddVmP/qFGj+Otf/8ratWu54YYbWLt2LceOHWPkyJH89re/dcfFxMTwzjvvkJOTQ2Ji4iV9DyIiIiLihU0LYM9q8AuEkX/0+m/SrwtKeHf9Pj7c9D3HK1z/c9/PbGJIz2ju7deR/p3CWbbsP/hZms74kIqsumQvg1lx9XZ4M9D6bDufOAABIZd0/JkzZ/J///d/7lGirl278tvf/pZf/epX/OY3v2Hfvn3ExMQwZMgQ/P396dixI9dddx0Abdq0wWKxuEeozufxxx/n17/+NZWVldjtdgIDA92jYqfz9/fn/vvv5/XXX+eGG27g9ddf5/7778ff398jbsKECXz++eckJSXRqVMn+vfvz7Bhw7jvvvuwWq3uuOLiYlq18rwZ3oABA2odRRMRERGR8zheAJ8+6doe9CS07XZBbyurrGLJlnze3bCPTfuK3O0d2wRzz7XxjOnTgagw13Xzdru9rrOudyqyxC07O5uNGzcyc+ZMd5vD4aCiooKysjLGjBnDiy++SNeuXRk+fDi33norI0eOxM/P+270y1/+kvT0dA4fPsyTTz7J4MGDGTBgQK2xDz/8MMnJycyaNYv333+fzMxMjymMACEhISxdupRvv/2Wzz77jKysLKZMmcIf//hHMjMzCQ4OBlzTFL/88kuP9zbUdWgiIiIizc4nU8FWDHFXQ/9x5w3ffqCERRv3sfjL7zluOzVqNeyKaO69riPXd4vEbG7618uryKpL/sGuEaV64nQ6KTl+nLDQUMy1TResg+PPmDGD0aNH19gXGBhIfHw8O3fuZPny5axYsYJx48bxhz/8gdWrV9cYWTqfyMhIunfvTvfu3fnggw/o3r07/fv3Z8iQmncET0xMpEePHtx777307NmTxMRENm/eXOtxu3XrRrdu3fjJT37Ck08+yeWXX857773Hgw8+CIDZbKZ79+5e5SoiIiIitdj+b9jxMZj9YNRLYDl7afHNoVJ+9c+v+PK0UatObYP50bUduatPB9qFWs/63qZIRVZdMpkuecreOTmd4O9wnePMIqsOXHPNNezcufOcRUhQUBCjRo1i1KhRjB8/nh49erB161auueYaAgICcDgcXp83IiKCCRMmMHXqVDZt2lTrohkPPfQQ48aN4+WXX77g43bu3Jng4GBOnDjhdU4iIiIicg5lx2DpVNf2Db+AmLNfE5/zfTE/fn0Dx05U4m8xMeyKGFKv60hy17bNYtSqNiqyxO3pp59mxIgRxMfHM2bMGMxmM1u2bGHr1q08++yzzJ8/H4fDQb9+/QgODubtt98mKCiITp06Aa6iZs2aNfzoRz/CarUSGRl5weceP348v//97/nggw+46667auwfO3YsY8aMoXXr1rW+f/r06ZSVlXHrrbfSqVMnioqK+NOf/oTdbmfo0KHuOMMwKCgoqPH+qKiomqODIiIiIlK7jKfgxCGIvBxu/OVZwzbmHuOhNzZy3FZFUvtwXn2gL9Fhzf8epfqrUtxSUlJYsmQJy5cv59prr6V///7MnTvXXUS1bt2aV155heuvv57evXuzcuVKPv74Y9q2bQu47n2Vm5tLt27daNeunVfnbteuHWlpaUyfPh2ns+bynH5+fkRGRp71+q+BAwfy3Xff8eMf/5gePXpwyy23UFBQQEZGBgkJCe64kpISYmNjazwOHTrkVb4iIiIiLda3/4XNCwCTa5qgX+1T/VbtPETaa+s5bqviui5teGdsvxZRYIFGslq09PR00tPTPdpSUlJISUmpNf6OO+7gjjvuOOvx+vfv717S/Vxyc3Nrbf/73//u3u7cuTOGYZz1GFdddZXH/kGDBjFo0KBznre2zysiIiIiXrCVuu6JBXDdI9CxX61hS7fkM+m9TdgdBoMS2vHy/X0I9Lc0YKK+pSJLREREREQuzKrZULQPwuPh5qdrDfnHxjz+78MtOA0Y0TuWuXdfRYBfy5pApyJLRERERETOr2ArZJ1chOy2uWBtVSPk1c+/49mlOwC497qOPHtHIpZmurjFuajIEhERERGRc3M6YckvwHBAr9vh8mEeuw3D4IUVu/nTyt0APHpjV/7vlh61rhrdEqjIEhERERGRc/tyPuzfCAGhMPx3HrucToNnlmxn/rpcAH6ZksC4m7q12AILVGSJiIiIiMi5lB6CFdNd24N/DWFx7l1VDif/9+FW/pm9H4Df3n4FacmdGz7HRkZFVh2obclxad70MxcREZEWI+PXUFEMsVfCtT9xN9uqHPz83c0s21aAxWxizpje3Hl1Bx8m2nioyLoEAQEBmM1mDhw4QLt27QgICKjXYVGn00llZSUVFRW6ca6PGIZBZWUlhw8fxmw2ExAQ4OuUREREROrPd6thy3uACUa8ABZX+VBWWcWjb2fz+e4jBFjMvJR6NcOuiPFtro2IiqxLYDab6dKlC/n5+Rw4cKDez2cYBuXl5QQFBbXoOa6NQXBwMB07dlSxKyIiIs1XlQ2WTnZtX/sTaN8HgOJyOw/N30j23kKCAyy88uO+XN890oeJNj4qsi5RQEAAHTt2pKqqCofDUa/nstvtrFmzhhtvvBF/f/96PZecncViwc/PT4WuiIiING9rX4Sj30CraLj5KQCOltq4/7UN7MgvITzIn/kPXsvVHSN8m2cjpCKrDphMJvz9/eu98LFYLFRVVREYGKgiS0RERETqz9Fv4fPnXdspsyAwHMMwePyDLezIL6FdqJW3H76OHjFhvs2zkdJcJxEREREROcUwYOkUcNig22BI/CEA/9r8PSt2HMLfYuKth1RgnYuKLBEREREROSXnA/juM7BY4dY5YDJxqKSC6R9tB+DnN19Gz1gVWOeiIktERERERFzKi+DTJ1zbN06Ftt0wDIMnFudQXG4nqX04Px3YzacpNgUqskRERERExOW/z0LpQWjbHa7/OQD/3nyAFTsO4m8x8YcxvfGzqIQ4H31DIiIiIiIC+7Nh46uu7dvmgp+VQ8cr+M1H2wCYOPgyXYd1gbwqsqZPn47JZPJ4xMScuumYYRhMnz6duLg4goKCuOmmm9i2bZvHMWw2GxMmTCAyMpKQkBBGjRrF/v37PWIKCwtJS0sjPDyc8PBw0tLSKCoq8ojZt28fI0eOJCQkhMjISCZOnEhlZaVHzNatWxk4cCBBQUG0b9+eZ555BsMwvPnIIiIiIiLNn6MKlkwCDOh9D3QdiGEYPHlymmBi+zB+epOmCV4or0eyrrjiCvLz892PrVu3uvc999xzzJ07l5deeomNGzcSExPD0KFDOX78uDtm0qRJLF68mEWLFrF27VpKS0sZMWKExz2mUlNT2bx5M8uWLWPZsmVs3ryZtLQ0936Hw8Ftt93GiRMnWLt2LYsWLeKDDz5gypQp7piSkhKGDh1KXFwcGzduZN68ecyZM4e5c+d6/SWJiIiIiDRrG1+Bgi0QGA7DngXgo68OsHz7yWmCd12Jv6YJXjCv75Pl5+fnMXpVzTAMXnzxRZ588klGjx4NwJtvvkl0dDTvvPMOjz76KMXFxbz22mu8/fbbDBkyBIAFCxYQHx/PihUrSElJYceOHSxbtoysrCz69esHwCuvvEJycjI7d+4kISGBjIwMtm/fTl5eHnFxcQA8//zzpKenM3PmTMLCwli4cCEVFRXMnz8fq9VKYmIiu3btYu7cuUyePFk3khURERERASg54LoWC2DIDGgV5TFNcMJgrSboLa+LrN27dxMXF4fVaqVfv37MmjWLrl27smfPHgoKChg2bJg71mq1MnDgQNatW8ejjz5KdnY2drvdIyYuLo7ExETWrVtHSkoKmZmZhIeHuwssgP79+xMeHs66detISEggMzOTxMREd4EFkJKSgs1mIzs7m0GDBpGZmcnAgQOxWq0eMdOmTSM3N5cuXbrU+vlsNhs2m839uqSkBAC73Y7dbvf266pT1ef3dR7SdKjPiLfUZ8Rb6jPiLfWZxsfyyeOYK0txtu+Lo3cqRmUlT364laIyO71iQ/nJ9R19+vNqTH3mQnPwqsjq168fb731FpdffjkHDx7k2WefZcCAAWzbto2CggIAoqOjPd4THR3N3r17ASgoKCAgIICIiIgaMdXvLygoICoqqsa5o6KiPGLOPE9ERAQBAQEeMZ07d65xnup9ZyuyZs+ezYwZM2q0Z2RkEBwcXOt7Gtry5ct9nYI0Meoz4i31GfGW+ox4S32mcYgq/ork7z7CiZnVre6g5D/L+PKIieW7LZhNBiPaFbL802W+ThNoHH2mrKzsguK8KrJuueUW93ZSUhLJycl069aNN998k/79+wPUmIZnGMZ5p+adGVNbfF3EVC96ca58pk2bxuTJk92vS0pKiI+PZ9iwYYSF+XaY1G63s3z5coYOHYq/v79Pc5GmQX1GvKU+I95SnxFvqc80IvYy/P72awCMfj/lhiE/5Uipjenz1gF2HhvUnbGDfL/YRWPqM9Wz3M7H6+mCpwsJCSEpKYndu3dzxx13AK5RotjYWHfMoUOH3CNIMTExVFZWUlhY6DGadejQIQYMGOCOOXjwYI1zHT582OM469ev99hfWFiI3W73iKke1Tr9PFBztO10VqvVY4phNX9/f5//UKs1plykaVCfEW+pz4i31GfEW+ozjcDqF6F4H4S1xzL4Scx+fkxf8hWFZXZ6xYYx4ebLG9ViF42hz1zo+S/pW7PZbOzYsYPY2Fi6dOlCTEyMxzBeZWUlq1evdhdQffr0wd/f3yMmPz+fnJwcd0xycjLFxcVs2LDBHbN+/XqKi4s9YnJycsjPz3fHZGRkYLVa6dOnjztmzZo1Hsu6Z2RkEBcXV2MaoYiIiIhIi3JoB6z7k2v7lufA2oolW/L5dNtB/Myumw43pgKrqfHqm5s6dSqrV69mz549rF+/nrvuuouSkhIeeOABTCYTkyZNYtasWSxevJicnBzS09MJDg4mNTUVgPDwcB5++GGmTJnCypUr2bRpE/fffz9JSUnu1QZ79uzJ8OHDGTt2LFlZWWRlZTF27FhGjBhBQkICAMOGDaNXr16kpaWxadMmVq5cydSpUxk7dqx7Sl9qaipWq5X09HRycnJYvHgxs2bN0sqCIiIiItKyFeXBO3eDswoSboWeIzh83MbT/84B4LHB3bkiLtzHSTZtXk0X3L9/P/feey9HjhyhXbt29O/fn6ysLDp16gTAr371K8rLyxk3bhyFhYX069ePjIwMQkND3cd44YUX8PPz4+6776a8vJybb76Z+fPnY7FY3DELFy5k4sSJ7lUIR40axUsvveTeb7FYWLp0KePGjeP6668nKCiI1NRU5syZ444JDw9n+fLljB8/nr59+xIREcHkyZM9rrcSEREREWlRSvLhrVFQtA/adIURL2IYBk/9K4fCMjs9Y8MYd1N3X2fZ5HlVZC1atOic+00mE9OnT2f69OlnjQkMDGTevHnMmzfvrDFt2rRhwYIF5zxXx44dWbJkyTljkpKSWLNmzTljRERERERahNLDrgLr2HfQuiM88DGERrN0ywGWbSvAz2xizpjeBPhpmuCl0jcoIiIiItLclR2Dt26HI7sgrL2rwArvwJFSG0//23XT4XGDNE2wrqjIEhERERFpzsqL4O074dA2aBXtKrAiOgPw9L9zOHaikh4xoTw2SNME64qKLBERERGR5sp2HBbeBfmbIbgt/PgjaOu699XSLfl8srUAi9nEnDFXappgHdI3KSIiIiLSHFWegIV3w/6NENgafvxviOoBwNFSG0+dXE1w/E3dSGyvaYJ1SUWWiIiIiEhzYy+Hd++FfevAGgZpiyEmCYD84nLuf23DqWmCgy/zcbLNj1erC4qIiIiISCNXVQn/+DHsWQ3+IXD/B9D+GgByvi/m4Tc3crDERmSrAF780VWaJlgPVGSJiIiIiDQXDjv880HYnQF+QXDfPyD+OgBWbD/IxEWbKKt0cFlUK15Pv5b4NsE+Trh5UpElIiIiItIcOB3w4SPw9RKwWOHed6DzDQC88b89/HbJdpwG3NA9kj/fdw3hQf4+Trj5UpElIiIiItLUOZ3w7/Gw7UMw+8Pdb0G3wTicBr9dsp3563IB+NG18fz2jkT8LZoiWJ9UZImIiIiINGWGAUt/AV+9CyYL3PU6JAznhK2Kie9uYuXXhwD4v1t68OiNXTGZTD5OuPlTkSUiIiIi0lQZBiz7P8ieDyYzjP479BpFQXEFD83fyPb8Eqx+Zl645ypuTYr1dbYthoosEREREZGmas0cWP9X1/btf4aku9h2oJiH539BQUkFka0C+PuP+3JNxwjf5tnCqMgSEREREWmK8jbAqlmu7dueh6tS+e/XB3nsHdcKgt2jWvGGVhD0CRVZIiIiIiJNja3UtZKg4YSku+Han/DmulxmfLwNpwEDurXl5fv7aAVBH1GRJSIiIiLS1Hw6DQr3QFgHHLc8x7Mfb+ON/+UCcHffDjx7R5JuMuxDKrJERERERJqSr5fCl28BJspH/JkJ73/Dih2uFQR/NTyBnw3sphUEfUxFloiIiIhIU3H8IHw0AQBn8gQeXRvMml2HCPAzM/fuKxnRO87HCQqoyBIRERERaRoMAz56DMqOQnQSfzLuZs2ufQT6m1n4k3706dTG1xnKSZqoKSIiIiLSFHzxOuzOAIuVrKtm8+KqfQDMHp2kAquRUZElIiIiItLYHfkGMn4NwLHkaYz9tAyAHyd34s6rO/gyM6mFiiwRERERkcbMYYcPx4K9DEfngdyfcw3HK6q4umNrfn1bL19nJ7VQkSUiIiIi0pitfg4OfIkRGM6z/o+xvaCUyFYB/OW+a7RMeyOln4qIiIiISGOVtwE+nwPA2oQneWOrHbMJ/nTv1cSGB/k4OTkbFVkiIiIiIo2RrRQ+fAQMJ8e63cFDX8QD8PjwHgzoFunj5ORcVGSJiIiIiDRGn06Dwj04QttzT94PsTsMhl8RwyM3dvV1ZnIeKrJERERERBqbr5fCl29hYGK29efsLrHQNTKEP4zpjclk8nV2ch4qskREREREGpPjB+GjCQBsiL2PV/d3IDjAwl/T+hAa6O/j5ORCqMgSEREREWksDAM+egzKjlIS3oO0PUMB+P0Pe3N5dKiPk5MLpSJLRERERKSx+OJ12J2B02LlgaKfUIk/D13fhZFXxvk6M/GCiiwRERERkcbgyDeQ8WsA/u5/P5tscVzbOYJpt/bwcWLiLRVZIiIiIiK+5rDDh2PBXsbO4Gv4fdEg2oVa+XPqNfhb9Cd7U6OfmIiIiIiIr635Axz4EptfKA8cexCz2cKfU68hKizQ15nJRVCRJSIiIiLiSwVb4fPnAfhVRToFtGXaLT24rksbHycmF0tFloiIiIiIrzjs8K9x4KziM1M//l2VzG29Y3n4hi6+zkwugYosERERERFfWfcnKNhCqakVvyp/gO5RrXjuh7rhcFPn5+sERERERERapMO7YNXvAXjadj8nAtry7v19CLHqT/SmTj9BEREREZGG5nS4bjrssLHKeSUfOn/AH0cn0T2qla8zkzqg6YIiIiIiIg1twyuQt54TBPJE5cPce10nbr+qva+zkjqiIktEREREpCEV5mKsnAHALHsqYTFd+M3IXj5OSuqSpguKiIiIiDQUw4CPJmKyl5Hl7Mm/LUP56L5rCPS3+DozqUMayRIRERERaShfvgV7VlNuBPC4fSwzR19J13a6Dqu5uaQia/bs2ZhMJiZNmuRuS09Px2QyeTz69+/v8T6bzcaECROIjIwkJCSEUaNGsX//fo+YwsJC0tLSCA8PJzw8nLS0NIqKijxi9u3bx8iRIwkJCSEyMpKJEydSWVnpEbN161YGDhxIUFAQ7du355lnnsEwjEv52CIiIiIi3is5gPPTJwF4vmoM1193na7DaqYuerrgxo0b+fvf/07v3r1r7Bs+fDhvvPGG+3VAQIDH/kmTJvHxxx+zaNEi2rZty5QpUxgxYgTZ2dlYLK6h0tTUVPbv38+yZcsAeOSRR0hLS+Pjjz8GwOFwcNttt9GuXTvWrl3L0aNHeeCBBzAMg3nz5gFQUlLC0KFDGTRoEBs3bmTXrl2kp6cTEhLClClTLvaji4iIiIh4xzBwfvwLzJXH2ezsRma7u/lghK7Daq4uqsgqLS3lvvvu45VXXuHZZ5+tsd9qtRITE1Pre4uLi3nttdd4++23GTJkCAALFiwgPj6eFStWkJKSwo4dO1i2bBlZWVn069cPgFdeeYXk5GR27txJQkICGRkZbN++nby8POLi4gB4/vnnSU9PZ+bMmYSFhbFw4UIqKiqYP38+VquVxMREdu3axdy5c5k8ebJu8iYiIiIiDSPnA8y7l1FpWHianzHvvr66DqsZu6gia/z48dx2220MGTKk1iJr1apVREVF0bp1awYOHMjMmTOJiooCIDs7G7vdzrBhw9zxcXFxJCYmsm7dOlJSUsjMzCQ8PNxdYAH079+f8PBw1q1bR0JCApmZmSQmJroLLICUlBRsNhvZ2dkMGjSIzMxMBg4ciNVq9YiZNm0aubm5dOnSpUbuNpsNm83mfl1SUgKA3W7HbrdfzNdVZ6rP7+s8pOlQnxFvqc+It9RnxFstss+cOIzx8RQCgHlVd/Lg6BTiW1tb1ndwCRpTn7nQHLwushYtWsSXX37Jxo0ba91/yy23MGbMGDp16sSePXt46qmnGDx4MNnZ2VitVgoKCggICCAiIsLjfdHR0RQUFABQUFDgLspOFxUV5RETHR3tsT8iIoKAgACPmM6dO9c4T/W+2oqs2bNnM2PGjBrtGRkZBAcH1/qZG9ry5ct9nYI0Meoz4i31GfGW+ox4qyX1md7f/pkulUXscHbkq7Yj6L5/E5/s3+TrtJqcxtBnysrKLijOqyIrLy+Pn//852RkZBAYGFhrzD333OPeTkxMpG/fvnTq1ImlS5cyevTosx7bMAyP6Xu1TeWri5jqRS/ONlVw2rRpTJ482f26pKSE+Ph4hg0bRlhY2Fnzbwh2u53ly5czdOhQ/P39fZqLNA3qM+It9RnxlvqMeKul9RnnjqVYN62nyjDz57Bf8Jef3oJV0wS90pj6TPUst/PxqsjKzs7m0KFD9OnTx93mcDhYs2YNL730Ejabzb1wRbXY2Fg6derE7t27AYiJiaGyspLCwkKP0axDhw4xYMAAd8zBgwdrnP/w4cPukaiYmBjWr1/vsb+wsBC73e4RUz2qdfp5gBqjYNWsVqvH9MJq/v7+Pv+hVmtMuUjToD4j3lKfEW+pz4i3WkSfKS+k9OMpWIH5jGJK+j20Cq59oELOrzH0mQs9v1dLuN98881s3bqVzZs3ux99+/blvvvuY/PmzTUKLICjR4+Sl5dHbGwsAH369MHf399juC8/P5+cnBx3kZWcnExxcTEbNmxwx6xfv57i4mKPmJycHPLz890xGRkZWK1WdxGYnJzMmjVrPJZ1z8jIIC4ursY0QhERERGRupT//lRa2Y/wrTOW2Nun0yUyxNcpSQPxqsgKDQ0lMTHR4xESEkLbtm1JTEyktLSUqVOnkpmZSW5uLqtWrWLkyJFERkZy5513AhAeHs7DDz/MlClTWLlyJZs2beL+++8nKSnJvdpgz549GT58OGPHjiUrK4usrCzGjh3LiBEjSEhIAGDYsGH06tWLtLQ0Nm3axMqVK5k6dSpjx451T+tLTU3FarWSnp5OTk4OixcvZtasWVpZUERERETq1bEty4j97p84DRMrL3+K266puRaANF8XfZ+s2lgsFrZu3cpbb71FUVERsbGxDBo0iPfee4/Q0FB33AsvvICfnx9333035eXl3HzzzcyfP99jJGzhwoVMnDjRvQrhqFGjeOmllzzOtXTpUsaNG8f1119PUFAQqampzJkzxx0THh7O8uXLGT9+PH379iUiIoLJkyd7XHMlIiIiIlKXqspLqPrXBAA+DhzBj+/5kY8zkoZ2yUXWqlWr3NtBQUF8+umn531PYGAg8+bNc980uDZt2rRhwYIF5zxOx44dWbJkyTljkpKSWLNmzXlzEhERERGpC1/Nn0wf5yH2G+246oG5uh9WC1SnI1kiIiIiIs2J02lgq3JSYXe4nyuqHNjsTioqq6i0ncBRXoKjvARnxXGMQ18z7OD7AOz/we/oH1fztkTS/KnIEhEREZEW70ipjW37C8n7bgcledswHdlFUMUhgoxyWpnKCKWcEFMFrSgn1FROLOW0ohyLyaj1eF+0GUH/IXc18KeQxkJFloiIiIi0GIZhUHDkKLk7t1C4LwfHoZ0ElXxHh6o8+pvyGWiqOhV8gbP8nJgoNwVjMwdTYQmmrFVnEh88+2Ux0vypyBIRERGR5qmyjGPfZbP/6y+wFXyNtegbIiv2EsdhYs+MPbnmdqUpgJKQzhhtL8Ma2QVLcDh+QWH4BYZhCQoDayhYW4H15HZAK8wBIYSYTGiBdqmmIktEREREmr4qGxzcBge+pGr/l5Tt2Uirkm9og5M2tYQXmcI4FtQZe0R3guJ60q5zEkFxPQkIjyfSrIUq5NKoyBIRERGRpsVRBYe/hgNfwoFNcGATRkEOJqcdcP2BG3Yy9JDRmn0B3alo3Q3/6B607ZxEh8uupHV4FK19lb80eyqyRERERKRhOB1QtJewsn2YDmwCswmcdnDYwVnlepy+7X5tdxVWx751FVX5W6Cq3OPQJqDQaMUWZ1e2GF3ZH9iDblfeQEry1fSNbOWbzystloosEREREak7hgGlB+Hot3D0m5OPk9uFe/B3VDIIYOelnabKvxXfWLrz+YkObHK4CqtDlmiGXxHLmL4dGNctEovZVBefSMRrKrJERERExHsVxZ4F1OkFVWXpWd9mWKzYTIFYg0IwWfzB4g9mPzD7g8Wv9m2zH1j8KPKLZM2JeN7a24bs420wTq5W0btDOI/2jWdU7zjCg/0b6hsQOSsVWSIiIiJSO8OAE4fh8E7XNVBHdp3c3gmlBWd9mxMzxwNjOWrtyKGADhywtCfPFMd3Riz77OEcLjpOmF8YYMJwguEwMAwwcD07DQMDwAAD17LrdofB90Wnpgi2DQngzqvbM6ZvPAkxofX9TYh4RUWWiIiISEtnGFC8Hw7vxDj8Nbb8HTgOfU1A4Tf4Vxad9W0HjdbsMWL5zhnDHiP25COGPCOKyoqzjSiVAiY4cdzrNC1mE4MSohjTtwODEqII8DN7fQyRhqAiS0RERKQlMgwOrf8H9v+9RGTpLqxGBeBaQCLwtDCnYSLPaMduoz3fVD+c7fnWiKPCEkKI1Y+QAD9aWf1oFehHe6sfCVYLIQF+hFj9CA10PYdY/WhltRBoMbH5y2yuu+5a/Pz8MAFmkwmTyXVuTCdfA6bT2k0m6NQ2hMhW1ob+pkS8piJLREREpIX5dv1SLP+dQWfbqdUn7IaFXCPmZDEVx8GAzpSEdqWqdXfaRoQRGx5ETFggSeGBxIQHEhMWSIjV+z8l7XY7lXsMbrwsEn9/XT8lzZOKLBEREZEWwDAMsrNW4b/qt1xpywbghGFleesxVPW8k8Doy4iJCKV3eCBDQgM1FU/kEqjIEhEREWnGqhxO/pu5noDVs7jJvgaASsPC+jajiBn5FHd07ebjDEWaHxVZIiIiIs1QWWUVH63dhP//5jCqajn+JgcAW9sMo93tv+UHnXr4OEOR5ktFloiIiEgzcrTUxrufbyNg/UvcZywhxGQDE+yNGECbUc+S1KWPr1MUafZUZImIiIg0A3nHynh99df4b3qDR02LaWs6DiY4Ep5I2MhZdOo+0NcpirQYKrJEREREmrgPvshj3b/+wi8s/6CD+QgApa26EHTLDCJ7jXKtfy4iDUZFloiIiEgTtu7bI+z/12943u8DAGxB0QTc/AStrr4fLPpTT8QX9F+eiIiISBP17eFS5r39Pm9bFgNg3PgrrDf8AgKCfZyZSMumIktERESkCTp2opKfvvE//uych5/ZiaPnHVgGP+nrtEQE0F3mRERERJoYW5WDR9/+gh+WvM3l5u9xBrfDMmKur9MSkZNUZImIiIg0IYZh8H8fbMWxdz2PWJYAYB71Jwhp6+PMRKSapguKiIiINCHz/vsNyzZ9y3+sf8VsMuDKVOhxq6/TEpHTaCRLREREpIn49+bvmbt8F7/ye4/OpgIIjYPhs32dloicQUWWiIiISBOQvfcYv/znFpLN23jQ71NX4+3zIKi1T/MSkZpUZImIiIg0cvuOljH2rWz8q04wL/hVV2OfB6H7EN8mJiK10jVZIiIiIo1YcbmdB+dv4NiJSv4a/g8ibQehdUcY9ltfpyYiZ6GRLBEREZFGyu5wMm5hNt8ePsEdrbYz3FY9TfAvYA31bXIiclYqskREREQaIcMweOpfOfzvm6PEBJTzh4BXXDv6/Qy6/MC3yYnIOanIEhEREWmEXvn8OxZtzMNsgsVdP8K/7CC06QY3P+3r1ETkPFRkiYiIiDQyy3IKmP2frwF45boCYnP/DSYz3PlXCAj2cXYicj5a+EJERESkEdmyv4hJ723CMOCn14Yz+Jufu3YMmAjx1/k2ORG5ICqyREREROpQeaWD3YeOs+9YGVUOA4fTwGEYOE9/dho4DNxtDuep/QvX76PC7mTgZZH8qurPmE4chnY9YdATvv5oInKBVGSJiIiIXASn02B/YTlfF5TwdcFx93PukRM4jUs7do+YUP56dS7mj/4NZj+482Xws9ZN4iJS71RkiYiIiJxHcbmdnScLqR35x9lZUMLOguOcqHTUGt8mJIBu7UKw+lkwm01YTGAxmzCbTK5nswmLyYTZhHu7uj0i2J8HewcR9NaPXQf7wVSIu7oBP62IXCoVWSIiItLgVu44yBd7CwEwThv1MfB4UdsmhmG432Oc9n4Dw+NY7tiT207DoMphUOU0qHI4Tz6ffO104nAa2B3Vz4b7dXG5nfziilo/R4DFTPeoVvSIDaVnTBgJMaH0iA2lXSsrJpPJ6+/lZNLw7o+gvBBiesONUy/uOCLiMyqyREREpEHtOXKCsW99cclT6hpa+9ZB9IgJPVlIhdEzJpTOkSH4W+p4sebN78CuZWAJgDv/Bhb/uj2+iNQ7FVkiIiLSoF5e9Q1OA3rFhpHcra27/fRxn+pBoNNHgzzGhUxgOtliMp3aZzqj3f0+k6vV32LCYjaffDbhZzHjZza5HhYTfuaTr6vbLSaCAyx0jwolPMiLYsd2HIrywGEDRxU47eCwn3p2b1d5tjkqYfXvXce4aRpE97rwc4pIo6EiS0RERBrM90XlfPjl9wA8e2ci13SM8HFGl8Aw4MRhOLwTjuyEI7tPbu+Cku8v7dgdrnUt2S4iTZKKLBEREWkwf1/9LVVOgwHd2jadAsvpgKJ9ruLpzIKqoujs7wuKAL8gsPi5pv6Z/V3bZn/XFMDq12fus7aCH0xxvRaRJumS/uudPXs2TzzxBD//+c958cUXAdcFpjNmzODvf/87hYWF9OvXjz//+c9cccUV7vfZbDamTp3Ku+++S3l5OTfffDN/+ctf6NChgzumsLCQiRMn8tFHHwEwatQo5s2bR+vWrd0x+/btY/z48fz3v/8lKCiI1NRU5syZQ0BAgDtm69atPPbYY2zYsIE2bdrw6KOP8tRTT138xagiIiJyUQ4ft7FoYx4Av7zaAZvfBcMJGCdXrzBcr93bpz17bDvBWQWGw1UAOR2nbVe3nxlTdfL55JQ8R9XJ50rXvurt6nbnadP3Kkpc0/5qZYKIThCZAO0uP/mcAJGXuYosEWmRLrrI2rhxI3//+9/p3bu3R/tzzz3H3LlzmT9/PpdffjnPPvssQ4cOZefOnYSGhgIwadIkPv74YxYtWkTbtm2ZMmUKI0aMIDs7G4vFAkBqair79+9n2bJlADzyyCOkpaXx8ccfA+BwOLjtttto164da9eu5ejRozzwwAMYhsG8efMAKCkpYejQoQwaNIiNGzeya9cu0tPTCQkJYcqUKRf70UVEROQivLZ2D7YqJwPbw1WfjgH7CV+ndOEsVlfhFHmZZ0HVtjv4B/o6OxFpZC6qyCotLeW+++7jlVde4dlnn3W3G4bBiy++yJNPPsno0aMBePPNN4mOjuadd97h0Ucfpbi4mNdee423336bIUOGALBgwQLi4+NZsWIFKSkp7Nixg2XLlpGVlUW/fv0AeOWVV0hOTmbnzp0kJCSQkZHB9u3bycvLIy4uDoDnn3+e9PR0Zs6cSVhYGAsXLqSiooL58+djtVpJTExk165dzJ07l8mTJ2s0S0REpIEUl9lZkLUXgN9E/w/T0RMQGgvRiSdXqDCByXzadvWqFWe2nYwz+4HZcsa25eS2+bTt6vaTD0vAyUf1dL2T25aT22a/02JObgeEQHi86/0iIhfgooqs8ePHc9tttzFkyBCPImvPnj0UFBQwbNgwd5vVamXgwIGsW7eORx99lOzsbOx2u0dMXFwciYmJrFu3jpSUFDIzMwkPD3cXWAD9+/cnPDycdevWkZCQQGZmJomJie4CCyAlJQWbzUZ2djaDBg0iMzOTgQMHYrVaPWKmTZtGbm4uXbp0qfHZbDYbNtupKQElJSUA2O127Hb7xXxddab6/L7OQ5oO9RnxlvqMeOtC+8xra7+l1FbFlVF+dNnzDgBVQ2di9BxV7znWCYfT9ZBLpt8z4q3G1GcuNAevi6xFixbx5ZdfsnHjxhr7CgoKAIiOjvZoj46OZu/eve6YgIAAIiIiasRUv7+goICoqKgax4+KivKIOfM8ERERBAQEeMR07ty5xnmq99VWZM2ePZsZM2bUaM/IyCA4OLhGuy8sX77c1ylIE6M+I95SnxFvnavP2Bzw6pcWwMQj5iWYygspDYhi5Xdm2PNJwyUpjYp+z4i3GkOfKSsru6A4r4qsvLw8fv7zn5ORkUFg4NnnH585Dc8wjPNOzTszprb4uogxTt4K/mz5TJs2jcmTJ7tfl5SUEB8fz7BhwwgLCzvnZ6hvdrud5cuXM3ToUPz9dWNCOT/1GfGW+ox460L6zKtrcymr2kW3NlZuMVYBEDR4Krf2GdGAmUpjod8z4q3G1GeqZ7mdj1dFVnZ2NocOHaJPnz7uNofDwZo1a3jppZfYuXMn4Bolio2NdcccOnTIPYIUExNDZWUlhYWFHqNZhw4dYsCAAe6YgwcP1jj/4cOHPY6zfv16j/2FhYXY7XaPmOpRrdPPAzVH26pZrVaP6YXV/P39ff5DrdaYcpGmQX1GvKU+I946W5+psDt4fZ1rNsuzCd9h3rQPgtti6fNjLOpjLZp+z4i3GkOfudDzm7056M0338zWrVvZvHmz+9G3b1/uu+8+Nm/eTNeuXYmJifEYyqusrGT16tXuAqpPnz74+/t7xOTn55OTk+OOSU5Opri4mA0bNrhj1q9fT3FxsUdMTk4O+fn57piMjAysVqu7CExOTmbNmjVUVlZ6xMTFxdWYRigiIiJ17/3s/Rw+biMuzEq/goWuxuseAf8g3yYmIlKPvBrJCg0NJTEx0aMtJCSEtm3butsnTZrErFmzuOyyy7jsssuYNWsWwcHBpKamAhAeHs7DDz/MlClTaNu2LW3atGHq1KkkJSW5Vxvs2bMnw4cPZ+zYsfztb38DXEu4jxgxgoSEBACGDRtGr169SEtL4w9/+APHjh1j6tSpjB071j2tLzU1lRkzZpCens4TTzzB7t27mTVrFk8//bRWFhQREalndoeTv676FoDf9D6G+YvNrpvzXjvWt4mJiNSzOr+V+K9+9SvKy8sZN26c+2bEGRkZ7ntkAbzwwgv4+flx9913u29GPH/+fPc9sgAWLlzIxIkT3asQjho1ipdeesm932KxsHTpUsaNG8f111/vcTPiauHh4Sxfvpzx48fTt29fIiIimDx5ssc1VyIiIlI//r35AN8XlRPZKoAhx15zNV59P4S09W1iIiL17JKLrFWrVnm8NplMTJ8+nenTp5/1PYGBgcybN8990+DatGnThgULFpzz3B07dmTJkiXnjElKSmLNmjXnjBEREZG65XAa/GXVNwD88ioHli9Wuu5plTzex5mJiNQ/r67JEhEREbkQy3IK+O7wCcIC/Rhd8aGrsecoaFPz9ikiIs2NiiwRERGpU4Zh8NJnrlGsx/oG47/9A9eO6yf6MCsRkYajIktERETq1Kqdh9mRX0JwgIUf8wk4q6DTDdC+z/nfLCLSDKjIEhERkTpz+ijWw33bErjlbdeO63/uw6xERBqWiiwRERGpM1nfHSN7byEBfmYeDVkNlcehXU+4bKivUxMRaTAqskRERKTO/PnkKNa910TRavOrrsYBE0D3pxSRFkRFloiIiNSJzXlFrP3mCBaziZ9HfQXH8yE0FpLG+Do1EZEGpSJLRERE6sRL/3WNYt15ZSxtvvqbq7H/z8AvwIdZiYg0PBVZIiIicsl2FhxnxY6DmEwwucteOPw1BIRCn3RfpyYi0uBUZImIiMgl++uaPQDckhhD3LZXXI190yEw3HdJiYj4iIosERERuSSHy+GTnAIApvQqhb1rwewH/X7m48xERHxDRZaIiIhckhUHzDgNGJTQjm67X3M1Jo2B8Pa+TUxExEdUZImIiMhFyy+uYONh1/Lsk/v6w46PXTsGTPBhViIivqUiS0RERC6Kw2nwtzV7cBgm+nWJIGnfAjCc0H0oRF/h6/RERHzGz9cJiIiISONkGAZFZXb2HSsjr7CMvGPlJ59dj++LyrE7DAAmXNcali50vfH6ib5LWkSkEVCRJSIiImzaV8hXeUXsO62Q2l9YTqmt6pzv8zOb6B3hIPnoh1BVDrFXQecfNEzSIiKNlIosERGRFi7n+2JGv7wOw6h9f1SolY5tgolvE0x8RBAd2gQTHxFMfJsgIoP9WPHJvzBnn1zw4vqfg8nUcMmLiDRCKrJERERauHc27MMw4PLoVtyUEOVRSHWICCLQ33LW99rtduKPfo6p/Bi07gQ9RzVg5iIijZOKLBERkRasrLKKjzYfAGD6qCsY0C3SuwM4HXQ/tMy1nfwYWPSnhYiIVhcUERFpwZZuyafUVkWntsH079LW6/ebdi4hpPIQRlAEXH1fPWQoItL06H83iYiItGDvbcwD4O6+8ZjNF3gtVXkRHNkFh3diWTcPAGefh7AEhNRTliIiTYuKLBERkRbqm0PH+WJvIRaziTF9OnjuNAw4nn+ymNoFR3bC4Z2u16UH3WEmwGHyx9n3J5z9yi0RkZZFRZaIiEgLVT2KNbhHFFFFm2FL5qmC6shusJWc/c2hcdDuchxtLiOrqC3XhbRrmKRFRJoAFVkiIiItkK3KwQdffg/AT7ofh9fvqBlkskCbLhCZAJGXQbuEU9uBYQA47XaOfPJJA2YuItL4qcgSERFpgVZsP8SxE5VEh1m5tvBkkRSdCL1uh8jLXQVVm67gZ/VtoiIiTZCKLBERkRZo0cZ9ANxzdQzmLf90NQ6ZAZcN8WFWIiLNg5ZwFxERaWHyjpWx9psjAKRF7oLyY9AqBrre5NvERESaCRVZIiIiLcz72fsxDLiheyTtvv3A1dh7jG4kLCJSR1RkiYiItCAOp8H7X7hWFby/dyvY9alrx5WpPsxKRKR5UZElIiLSgqzZfZj84gpaB/tzs3MtOO0Q0xuie/k6NRGRZkNFloiISAuyaINrwYvRV3fAf+t7rsYr7/VhRiIizY+KLBERkRbi0PEKVu44BEBadxt8n+26F1bSGB9nJiLSvKjIEhERaSE+/PJ7qpwGV3dsTZfvP3I1XjYUWrXzbWIiIs2MiiwREZEWwDAM3tvoWvDiR33bwxZNFRQRqS8qskRERFqADXuOsefICUICLIwK/xZKvofAcLh8uK9TExFpdlRkiYiItADVo1ijroojaPv7rsbEH4J/oA+zEhFpnlRkiYiINHPFZXaWbs0H4N6r2sL2k9djaaqgiEi9UJElIiLSzP37q++xVTnpERNKUslqsJ+ANt2gw7W+Tk1EpFlSkSUiItKMGYbBuxtcUwXvuTYe01fvunZceS+YTD7MTESk+VKRJSIi0ozlfF/CjvwSAvzMjO5mwJ7PXTt63+3bxEREmjEVWSIiIs3Yoo37ALglMYbwXR8CBnT+AUR08m1iIiLNmFdF1ssvv0zv3r0JCwsjLCyM5ORk/vOf/7j3p6enYzKZPB79+/f3OIbNZmPChAlERkYSEhLCqFGj2L9/v0dMYWEhaWlphIeHEx4eTlpaGkVFRR4x+/btY+TIkYSEhBAZGcnEiROprKz0iNm6dSsDBw4kKCiI9u3b88wzz2AYhjcfWUREpMkqq6zio80HALinbwf4apFrx5U/8mFWIiLNn1dFVocOHfjd737HF198wRdffMHgwYO5/fbb2bZtmztm+PDh5Ofnux+ffPKJxzEmTZrE4sWLWbRoEWvXrqW0tJQRI0bgcDjcMampqWzevJlly5axbNkyNm/eTFpamnu/w+Hgtttu48SJE6xdu5ZFixbxwQcfMGXKFHdMSUkJQ4cOJS4ujo0bNzJv3jzmzJnD3Llzvf6SREREmqKlW/I5bquiU9tg+gfkwtHd4BcEvW73dWoiIs2anzfBI0eO9Hg9c+ZMXn75ZbKysrjiiisAsFqtxMTE1Pr+4uJiXnvtNd5++22GDBkCwIIFC4iPj2fFihWkpKSwY8cOli1bRlZWFv369QPglVdeITk5mZ07d5KQkEBGRgbbt28nLy+PuLg4AJ5//nnS09OZOXMmYWFhLFy4kIqKCubPn4/VaiUxMZFdu3Yxd+5cJk+ejEkX+4qISDNXfW+su/vGY976squx50iwhvowKxGR5s+rIut0DoeD999/nxMnTpCcnOxuX7VqFVFRUbRu3ZqBAwcyc+ZMoqKiAMjOzsZutzNs2DB3fFxcHImJiaxbt46UlBQyMzMJDw93F1gA/fv3Jzw8nHXr1pGQkEBmZiaJiYnuAgsgJSUFm81GdnY2gwYNIjMzk4EDB2K1Wj1ipk2bRm5uLl26dKn1c9lsNmw2m/t1SUkJAHa7HbvdfrFfV52oPr+v85CmQ31GvKU+03x8c6iUL/YWYjGbuP2K1hiv/xMTUJV4N0Yd/nzVZ8Rb6jPircbUZy40B6+LrK1bt5KcnExFRQWtWrVi8eLF9OrVC4BbbrmFMWPG0KlTJ/bs2cNTTz3F4MGDyc7Oxmq1UlBQQEBAABERER7HjI6OpqCgAICCggJ3UXa6qKgoj5jo6GiP/REREQQEBHjEdO7cucZ5qvedrciaPXs2M2bMqNGekZFBcHDw+b6eBrF8+XJfpyBNjPqMeEt9pun7V64ZMNMz3MGB/7xIh4oiyv0jyNhRCl9/ct73e0t9RrylPiPeagx9pqys7ILivC6yEhIS2Lx5M0VFRXzwwQc88MADrF69ml69enHPPfe44xITE+nbty+dOnVi6dKljB49+qzHNAzDY/pebVP56iKmetGLc00VnDZtGpMnT3a/LikpIT4+nmHDhhEWFnbW9zUEu93O8uXLGTp0KP7+/j7NRZoG9RnxlvpM81BZ5WT6H1YDdh679Rr6bl0IQEDf+7l18Ig6PZf6jHhLfUa81Zj6TPUst/PxusgKCAige/fuAPTt25eNGzfyxz/+kb/97W81YmNjY+nUqRO7d+8GICYmhsrKSgoLCz1Gsw4dOsSAAQPcMQcPHqxxrMOHD7tHomJiYli/fr3H/sLCQux2u0dM9ajW6ecBaoyCnc5qtXpMMazm7+/v8x9qtcaUizQN6jPiLfWZpm351/kUltmJDrMypJM/5sWu//trufo+LPX0c1WfEW+pz4i3GkOfudDzX/J9sgzD8LiG6XRHjx4lLy+P2NhYAPr06YO/v7/HUF9+fj45OTnuIis5OZni4mI2bNjgjlm/fj3FxcUeMTk5OeTn57tjMjIysFqt9OnTxx2zZs0aj2XdMzIyiIuLqzGNUEREpDl5d4Pr3lhj+sTjt2MxOKsg9iqI6unbxEREWgiviqwnnniCzz//nNzcXLZu3cqTTz7JqlWruO+++ygtLWXq1KlkZmaSm5vLqlWrGDlyJJGRkdx5550AhIeH8/DDDzNlyhRWrlzJpk2buP/++0lKSnKvNtizZ0+GDx/O2LFjycrKIisri7FjxzJixAgSEhIAGDZsGL169SItLY1NmzaxcuVKpk6dytixY91T+lJTU7FaraSnp5OTk8PixYuZNWuWVhYUEZFmLe9YGWu/OQK4VhVk8zuuHVel+jArEZGWxavpggcPHiQtLY38/HzCw8Pp3bs3y5YtY+jQoZSXl7N161beeustioqKiI2NZdCgQbz33nuEhp5aKvaFF17Az8+Pu+++m/Lycm6++Wbmz5+PxWJxxyxcuJCJEye6VyEcNWoUL730knu/xWJh6dKljBs3juuvv56goCBSU1OZM2eOOyY8PJzly5czfvx4+vbtS0REBJMnT/a43kpERKS5eT97P4YB13dvS0fHXsjfDGY/SPyhr1MTEWkxvCqyXnvttbPuCwoK4tNPPz3vMQIDA5k3bx7z5s07a0ybNm1YsGDBOY/TsWNHlixZcs6YpKQk1qxZc96cREREmgOH0+D9L1z3xrrn2o7w1V9dOy5LgZBIH2YmItKyXPI1WSIiIuJ7hmHw4opd5BdX0DrYn2E9ImHLP1w7r/yRb5MTEWlhVGSJiIg0cYZhMHf5Lub99xsApgxLIDDvczieD0ERcHmKjzMUEWlZVGSJiIg0YYZhMCdjp7vA+vVtPUnr3wm+WuQKSPwh+NW8NYmIiNQfr++TJSIiIo2DYRg89+lOXl71LQBPj+jFQzd0gYoS2PGxK+jKe32YoYhIy6QiS0REpAkyDIPfLfuav63+DoDpI3uRfn0X184dH0FVObS9DNr38WGWIiItk4osERGRJsYwDGb/52v+vsZVYD1z+xX8OLnzqYDN77qer/wR6N6QIiINTkWWiIhIE2IYBs8u3cFra/cA8NvbryDt9AKrcC/sXQuYoPc9PslRRKSlU5ElIiLSRBiGwTNLtvPG/3IBePaORO7v38kzqHrZ9i4/gNbxDZugiIgAKrJERESaBMMwmPHxduavywVg1p1JpPbreGYQfFU9VTC1YRMUERE3FVkiIiKNnGEY/OajbbyVuReA341O4kfXdawZuP3fcOxb8A+BniMbOEsREammIktERKQRczoNnv4ohwVZ+zCZ4Peje3P3tbVMA/w+Gxb/1LXd90GwtmrYREVExE1FloiISCPldBo89e8cFq4/WWD9sDd3962lwCrMhXfucS3b3n0IDJnR4LmKiMgpKrJEREQaIafT4Ml/5fDuBleB9Ye7ruSuPh1qBpYXwsIxcOIwRCfBmPlg0T/vIiK+pN/CIiIijYytysFv/r2NRRvzMJng+TFXMvqaWgqsKhu8lwZHdkFYe7jvH2ANbfiERUTEg4osERGRRuLwcRsL1+9lQdY+jpTaMJtg7t1XccfV7WsGGwZ8NAFyP4eAUEj9B4TFNXzSIiJSg4osERERH8v5vpjX/7eHJV/lU+lwAhAdZuU3I6/g1qTY2t/02SzY8h6YLHD3mxCT2IAZi4jIuajIEhER8YEqh5NPtx1k/ro9bMwtdLdf3bE1D17fhVsSY/C3mGt/85dvw5rnXNsjX4TuN9d/wiIicsFUZImIiDSgorJK3t2Qx9uZuRworgDAz2zitt6xPHh9F66Kb33uA3z7X1gyybX9g6lwzY/rNV8REfGeiiwREZEGsOvgcd74Xy6LN+2nwu6aEtg2JIDUfh25v38nosMCz3+Qg9vgHw+AswqSxsDgX9dz1iIicjFUZImISItRVFbJd0dOYBhGg53z8HEbC7L2sfabI+62nrFhPHh9Z0ZdGUegv+XCDlSSDwvvBlsJdLoebv8zmEz1lLWIiFwKFVkiItIsVdgdbDtQwld5RXy1v4iv8orIPVrms3zMJhjWK4YHr+/MdV3aYPKmQLIdh3fGQMl+aHsZ3LMA/Kz1l6yIiFwSFVkiItLkOZwG3x4uZXNekbuo+jr/OFXOmiNWseGBWP3OsqBEPfCzmBncI4q0/p2IbxPs/QEcVfDPh6BgKwRHwn3vQ3Cbuk9URETqjIosERFpcsoqq1i98zCbT45Qbd1fzIlKR424yFYBXBXfmis7tKZ3fGuu7BBO6+AAH2R8kQwD/vNL2J0BfkGue2G16eLrrERE5DxUZImISJOSd6yMB97YwHeHT3i0BwdYSGof7iqqTj7iwgO9m5bX2Kz7E3zxOmCCH74CHfr4OiMREbkAKrJERKTJ2LK/iIfmb+RIaSXtQq0M6xXtKqg6tKZ7VCss5iZcUJ0p50NY/rRrO2UW9Bzp23xEROSCqcgSEZEmYeWOgzz2zibK7Q56xYbxxoPXXtiy501NeRFs+DusmeN63e+nkDzOpymJiIh3VGSJiEijtyBrL0//OwenATde3o6/3HcNrazN7J+wE0cg88+w4RWoPO5q6zHCNYolIiJNSjP7F0pERJoTp9PguU938tfV3wJwd98OzLwzCX9Lw60OWO9KDsC6efDFG1BV7mqL6gU/mAJX3AnmC7yPloiINBoqskREpFGyVTn45ftb+OirAwBMHno5EwZ3b9oLWZyucC/870XYtAAcla62uKvhxl/C5beAuRkVkiIiLYyKLBERaXSKy+w88vYXrN9zDD+zid/9sDd39eng67TqxpHd8Plc2PIeGCeXne84AG6cAt1uhuZSRIqItGAqskREpFHZX1hG+hsb+eZQKa2sfvz1/j7ccFmkr9O6dAU58Pkc2PYv4ORNkrsNhh9Mhc7X+zIzERGpYyqyRESk0cj5vpgH52/k8HEbMWGBvPHgtfSMDau7EzjscPQbcDpcI0YmM2A6OXp0xnNtbRej+HvX/a52fnKqLeE218hVe933SkSkOVKRJSIijcJnOw8xfuGXlFU66BETyhsPXktseNClH9gwIG89bH0fti2GsqOXfsyLYnItZPGDKRCT6KMcRESkIajIEhERn3t3wz5+/a8cHE6DG7pH8pf7ryEs0P/SDnpoB2z5B+T8E4r2nWoPaAX+Qa7iC6P257Pt4yJGsyz+rhsJ3/ALiLzs0j6TiIg0CSqyRETEZwzD4PmMXbz02TcA/PCaDswenUSA30WurFeU5yqqtv4TDuacag9o5Sp0ku6CLjeBRf/8iYhI/dG/MiIi4jOzPtnBK5/vAWDizZfxiyGXeb9Ee9kx2P4v2PI+7Ft3qt3sD5cNhaQxcPlwCAiuu8RFRETOQUWWiIj4RIXdwdtZewGYeWci9/XrdOFvrrLBjo9dI1bfrACn/eQOE3S+wTVi1XMUBLep+8RFRETOQ0WWiIj4ROZ3R6mwO4kNDyT1uo4X/saKEnj7Tvj+i1NtMUmQdDckjobwZnI/LRERabJUZImIiE+s+voQAIN6RF34FEFbKSy8y1VgBbaG68ZC4l0Q1aP+EhUREfGSiiwREWlwhmHw2c7DAAxKiLqwN1WegHfudi3HHhgOD3wEsVfWY5YiIiIX5yKXbxIREbl43x4+wb5jZQRYzAzo1vb8b7CXw7v3wt7/gTUM0harwBIRkUZLRZaIiDS4VTtdUwX7dW1DiPU8kyrsFbDoPtiz2rUU+/0fQPs+DZCliIjIxfGqyHr55Zfp3bs3YWFhhIWFkZyczH/+8x/3fsMwmD59OnFxcQQFBXHTTTexbds2j2PYbDYmTJhAZGQkISEhjBo1iv3793vEFBYWkpaWRnh4OOHh4aSlpVFUVOQRs2/fPkaOHElISAiRkZFMnDiRyspKj5itW7cycOBAgoKCaN++Pc888wyGYXjzkUVEpB78t/p6rPNNFayqhPcfgG9Xgn8w3Pc+xF/XABmKiIhcPK+KrA4dOvC73/2OL774gi+++ILBgwdz++23uwup5557jrlz5/LSSy+xceNGYmJiGDp0KMePH3cfY9KkSSxevJhFixaxdu1aSktLGTFiBA6Hwx2TmprK5s2bWbZsGcuWLWPz5s2kpaW59zscDm677TZOnDjB2rVrWbRoER988AFTpkxxx5SUlDB06FDi4uLYuHEj8+bNY86cOcydO/eivywREbl0xyvsbMw9BrgWvTgrhx3++SDsWgZ+gZD6HnQa0EBZioiIXDyvFr4YOXKkx+uZM2fy8ssvk5WVRa9evXjxxRd58sknGT16NABvvvkm0dHRvPPOOzz66KMUFxfz2muv8fbbbzNkyBAAFixYQHx8PCtWrCAlJYUdO3awbNkysrKy6NevHwCvvPIKycnJ7Ny5k4SEBDIyMti+fTt5eXnExcUB8Pzzz5Oens7MmTMJCwtj4cKFVFRUMH/+fKxWK4mJiezatYu5c+cyefJk7292KSIideJ/3xzF7jDoEhlCl8iQ2oMcVfDhWPh6CVis8KN3oMuNDZuoiIjIRbro1QUdDgfvv/8+J06cIDk5mT179lBQUMCwYcPcMVarlYEDB7Ju3ToeffRRsrOzsdvtHjFxcXEkJiaybt06UlJSyMzMJDw83F1gAfTv35/w8HDWrVtHQkICmZmZJCYmugssgJSUFGw2G9nZ2QwaNIjMzEwGDhyI1Wr1iJk2bRq5ubl06dKl1s9ls9mw2Wzu1yUlJQDY7Xbsdnut72ko1ef3dR7SdKjPiLcaos+s3FEAwI2Xta39PE4Hlo/HY962GMPsj+Ou+RidbgT140ZJv2fEW+oz4q3G1GcuNAevi6ytW7eSnJxMRUUFrVq1YvHixfTq1Yt169YBEB0d7REfHR3N3r17ASgoKCAgIICIiIgaMQUFBe6YqKia00eioqI8Ys48T0REBAEBAR4xnTt3rnGe6n1nK7Jmz57NjBkzarRnZGQQHBxc63sa2vLly32dgjQx6jPirfrqM4YBn26xACaCi/bwySffnRHg5Op9r9Hx2Oc4sbCx0zgKdtlh1yf1ko/UHf2eEW+pz4i3GkOfKSsru6A4r4ushIQENm/eTFFRER988AEPPPAAq1evdu8/cxqeYRjnnZp3Zkxt8XURU73oxbnymTZtGpMnT3a/LikpIT4+nmHDhhEWFnbOz1Hf7HY7y5cvZ+jQofj7+/s0F2ka1GfEW/XdZ7YdKKEkK4sgfzPj7x6C1e+0S4MNJ5ZPpmA+9jmGyYJz9Ktc02Pk2Q8mjYJ+z4i31GfEW42pz1TPcjsfr4usgIAAunfvDkDfvn3ZuHEjf/zjH3n88ccB1yhRbGysO/7QoUPuEaSYmBgqKyspLCz0GM06dOgQAwYMcMccPHiwxnkPHz7scZz169d77C8sLMRut3vEVI9qnX4eqDnadjqr1eoxxbCav7+/z3+o1RpTLtI0qM+It+qrz6z91rXgxfXd29Eq6LTftYYBn/wSNr8NJjOm0X/HL2l0nZ9f6o9+z4i31GfEW42hz1zo+S/5PlmGYWCz2ejSpQsxMTEew3iVlZWsXr3aXUD16dMHf39/j5j8/HxycnLcMcnJyRQXF7NhwwZ3zPr16ykuLvaIycnJIT8/3x2TkZGB1WqlT58+7pg1a9Z4LOuekZFBXFxcjWmEIiLSMD7beRiAwaevKmgY8OkTsPEVwAS3/xmS7vJNgiIiInXAqyLriSee4PPPPyc3N5etW7fy5JNPsmrVKu677z5MJhOTJk1i1qxZLF68mJycHNLT0wkODiY1NRWA8PBwHn74YaZMmcLKlSvZtGkT999/P0lJSe7VBnv27Mnw4cMZO3YsWVlZZGVlMXbsWEaMGEFCQgIAw4YNo1evXqSlpbFp0yZWrlzJ1KlTGTt2rHtKX2pqKlarlfT0dHJycli8eDGzZs3SyoIiIj5SeKKSTfsKAbgpoZ2r0TBgxW8g6y+u1yP/CFel+ihDERGRuuHVdMGDBw+SlpZGfn4+4eHh9O7dm2XLljF06FAAfvWrX1FeXs64ceMoLCykX79+ZGRkEBoa6j7GCy+8gJ+fH3fffTfl5eXcfPPNzJ8/H4vF4o5ZuHAhEydOdK9COGrUKF566SX3fovFwtKlSxk3bhzXX389QUFBpKamMmfOHHdMeHg4y5cvZ/z48fTt25eIiAgmT57scb2ViIg0nDW7D+M0oEdMKHGtg1yN/3sR/vdH1/Ztz0OfB3yWn4iISF3xqsh67bXXzrnfZDIxffp0pk+fftaYwMBA5s2bx7x5884a06ZNGxYsWHDOc3Xs2JElS5acMyYpKYk1a9acM0ZERBrGf792XRfrvgGxowo+f8G1PWwmXPsTH2UmIiJSty75miwREZHzcTgNVu9yXY81KOFkkXXgS7AVQ2Br6P8z3yUnIiJSx1RkiYhIvducV0hRmZ2wQD+u6dja1fjNStdz15vAbDnbW0VERJocFVkiIlLvPvvaNYp14+Xt8LOc/Kfn2/+6nrsN9lFWIiIi9UNFloiI1LvPdrqux3Iv3V5eCN9/4dpWkSUiIs2MiiwREalXB0sq2HagBJPJNZIFwJ41YDgh8nJoHe/bBEVEROqYiiwREalXq06OYvXu0JrIVlZXY/X1WN1u9lFWIiIi9UdFloiI1KvqpdsHV68qaBjw7WeubU0VFBGRZkhFloiI1JvKKidrdx8BYFCPk1MFj34LxfvAEgCdr/dhdiIiIvVDRZaIiNSbL3KPcaLSQWQrK4lx4a7Gb09OFezYHwJCfJeciIhIPVGRJSIi9aZ6quBNCe0wm02uRvfS7boeS0REmicVWSIiUm+ql24fVH09VlUl7Pncta3rsUREpJlSkSUiIvVi39Eyvj18AovZxA8uj3Q15q0H+wkIiYLoRN8mKCIiUk9UZImISL2oHsXq2ymCsEB/V2P19VjdBoFZ/wSJiEjzpH/hRESkXrinCvaIOtWo67FERKQFUJElIiJ1rrzSQea3RwEYXF1klR6G/K9c290G+SgzERGR+qciS0RE6lzmd0ewVTlp3zqIy6JauRq/W+V6jkmCVlFnfa+IiEhTpyJLRETqXPXS7YN6tMNkql66vfp6LK0qKCIizZuKLBERqVOGYfDZ14eB05ZuNwxdjyUiIi2GiiwREalT3xwq5fuicgL8zCR3a+tqPLgNSg+CfzB07O/bBEVEROqZiiwREalT1VMFk7u2JTjAz9VYPYrV+Qbws/ooMxERkYahIktEROqUe+n2hHanGnU9loiItCAqskREpM6UVNj5IrcQgME9ol2NlWWwN9O1reuxRESkBVCRJSIidWbt7iNUOQ26tguhY9tgV+PedeCwQVgHiLzMtwmKiIg0ABVZIiJSZz6rXro94bT7YFVfj9V9MFQv5y4iItKMqcgSEZE64XQafLbTtXT74B6nF1m6HktERFoWFVkiIlInth0o4UipjZAAC9d2buNqLP4eDn8NJjN0GejbBEVERBqIiiwREakT1Uu333BZJAF+J/95qZ4qGHcNBLfxUWYiIiINS0WWiIjUiVNLt9d2PZZWFRQRkZZDRZaIiFyyo6U2vtpfBMBN1UWW0wHffeba1vVYIiLSgqjIEhGRS7Z612EMA3rFhhETHuhqzN8M5YVgDYP2fX2an4iISENSkSUiIpeselXBQT3anWr85uRUwS43gsXPB1mJiIj4hv7VExGRi2arcvDC8t0s2XIAOHPpdl2PJSIiLZOKLBERuSg7C44z6b3N7MgvAeD+/h25pmOEa2dFCezf4NrW9VgiItLCqMgSERGvOJ0Gr/9vD88t20mlw0mbkABm3ZnE8MSYU0G5n4OzCtp0g4jOPstVRETEF1RkiYjIBfu+qJyp//iKzO+OAq7pgb/7YRJRoYGegd+sdD1rFEtERFogFVkiInJehmHw780HeOrfORyvqCLI38KvR/Qk9bqOmEymmm/Q9VgiItKCqcgSEZFzKiqr5Ml/5bB0Sz4AV8W35oV7rqJLZEjtbzj2HRTuAbMfdL6hATMVERFpHFRkiYjIWX2++zBT3/+KgyU2LGYTP7/5Msbd1A0/yznuAFI9ihXfH6yhDZOoiIhII6IiS0REaiivdDDzP7uYvy4XgK7tQnjh7qu4Mr71+d9cfX+sboPqLT8REZHGTEWWiIh4yCuFO17O4rsjJwD4cXInpt3Sk6AAy/nf7LDDnjWubV2PJSIiLZSKLBGRJshW5eB4RRUl5XZKKqo4XmGnpPzk88ntSocTwzAwDHAaYODaNgwDA3Ce3GeAO+6Ezc7SHAtO4wRRoVaeu6s3NyVEnS+dU/ZvhMrjENwWYq6sr48vIiLSqHlVZM2ePZsPP/yQr7/+mqCgIAYMGMDvf/97EhIS3DHp6em8+eabHu/r168fWVlZ7tc2m42pU6fy7rvvUl5ezs0338xf/vIXOnTo4I4pLCxk4sSJfPTRRwCMGjWKefPm0bp1a3fMvn37GD9+PP/9738JCgoiNTWVOXPmEBAQ4I7ZunUrjz32GBs2bKBNmzY8+uijPPXUU7WvhiUicg5HS20s3ZpPcZkdh2HgcJ58GAYOh+HR5jQMqs5oM6oPZJw65mmtGIbnc/X+ssqTBVWF3V1Y2aqc9fhJTQy/IprZo3sTERJw/vDTVV+P1XUQmM9x3ZaIiEgz5lWRtXr1asaPH8+1115LVVUVTz75JMOGDWP79u2EhJxaZWr48OG88cYb7tenFz0AkyZN4uOPP2bRokW0bduWKVOmMGLECLKzs7FYXNNRUlNT2b9/P8uWLQPgkUceIS0tjY8//hgAh8PBbbfdRrt27Vi7di1Hjx7lgQcewDAM5s2bB0BJSQlDhw5l0KBBbNy4kV27dpGenk5ISAhTpky5iK9LRFqifUfLeHXtd/zjizwq7PVZ3Hgv1OpHWJA/oYF+hAX6ExbkR2ig63WgvwUTYDKZMJnAbAITJsyuRkyA+fR9JhNOh4Pj+3cy9Z7eNX53XxDdH0tERMS7Iqu64Kn2xhtvEBUVRXZ2NjfeeKO73Wq1EhMTU+sxiouLee2113j77bcZMmQIAAsWLCA+Pp4VK1aQkpLCjh07WLZsGVlZWfTr1w+AV155heTkZHbu3ElCQgIZGRls376dvLw84uLiAHj++edJT09n5syZhIWFsXDhQioqKpg/fz5Wq5XExER27drF3LlzmTx5skazROSccr4v5m9rvmPplgM4T44uJbUP54q4MCxm06mHyYTF4nr2M5swm089W0yn4sxn/M45/WWN30an7QzytxAWeGYx5U8rqx8Wc93+HrPb7XzyydcX9/ux7Bgc2OTaVpElIiIt2CVdk1VcXAxAmzZtPNpXrVpFVFQUrVu3ZuDAgcycOZOoKNec/uzsbOx2O8OGDXPHx8XFkZiYyLp160hJSSEzM5Pw8HB3gQXQv39/wsPDWbduHQkJCWRmZpKYmOgusABSUlKw2WxkZ2czaNAgMjMzGThwIFar1SNm2rRp5Obm0qVLlxqfyWazYbPZ3K9LSkoA1x8edrv9Ur6uS1Z9fl/nIU2H+oz3DMMg87tjvLI2l7XfHHW3/6B7W8b+oDP9u7RpNP+Dxumowumo22NeSp8x7V6BHwZGu55UBUWC+l2LoN8z4i31GfFWY+ozF5rDRRdZhmEwefJkbrjhBhITE93tt9xyC2PGjKFTp07s2bOHp556isGDB5OdnY3VaqWgoICAgAAiIiI8jhcdHU1BQQEABQUF7qLsdFFRUR4x0dHRHvsjIiIICAjwiOncuXON81Tvq63Imj17NjNmzKjRnpGRQXBw8Pm+lgaxfPlyX6cgTYz6zPk5DfjqqImVB8zknXAVUSYMrm5rcHN7Jx1CDlL49UH+87WPE20gF9Nnrtr7Np2Ab02d2fbJJ3WflDRq+j0j3lKfEW81hj5TVlZ2QXEXXWQ99thjbNmyhbVr13q033PPPe7txMRE+vbtS6dOnVi6dCmjR48+6/EMw/D4v8O1/Z/iuogxTl5Rfrb/Ez1t2jQmT57sfl1SUkJ8fDzDhg0jLCzsrPk3BLvdzvLlyxk6dCj+/v4+zUWaBvWZ86uwO/hw0wFe+18u+46VAxDob2bMNe158PpOxEc0jv+50lAuus8YBn7zHgeg880P0amr7pHVUuj3jHhLfUa81Zj6TPUst/O5qCJrwoQJfPTRR6xZs8ZjRcDaxMbG0qlTJ3bv3g1ATEwMlZWVFBYWeoxmHTp0iAEDBrhjDh48WONYhw8fdo9ExcTEsH79eo/9hYWF2O12j5jqUa3TzwPUGAWrZrVaPaYXVvP39/f5D7VaY8pFmgb1mZqKy+y8nZXL/HW5HCmtBKB1sD8/Tu7MA8mdaNuq5u+BlsTrPnPoazieD36B+HX9Aai/tTj6PSPeUp8RbzWGPnOh5/eqyDIMgwkTJrB48WJWrVpV63S7Mx09epS8vDxiY2MB6NOnD/7+/ixfvpy7774bgPz8fHJycnjuuecASE5Opri4mA0bNnDdddcBsH79eoqLi92FWHJyMjNnziQ/P9997IyMDKxWK3369HHHPPHEE1RWVrpXycrIyCAuLq7GNEIRaVocToOVOw6yYsdByiodVDkM7A4ndqeBvcpJldNJpcOgyuF0tVfvdzipchgcr3DdRwqgfesgfvKDLtxzbTzBAbp94EX59uSqgp0GgH+Qb3MRERHxMa/+mhg/fjzvvPMO//73vwkNDXWPEoWHhxMUFERpaSnTp0/nhz/8IbGxseTm5vLEE08QGRnJnXfe6Y59+OGHmTJlCm3btqVNmzZMnTqVpKQk92qDPXv2ZPjw4YwdO5a//e1vgGsJ9xEjRrjvyTVs2DB69epFWloaf/jDHzh27BhTp05l7Nix7ml9qampzJgxg/T0dJ544gl2797NrFmzePrppxvNhesi4p3CE5W890Ueb2fu5fui8ks6Vo+YUB4d2JURvePwt+ieTpdk98l58t1u9m0eIiIijYBXRdbLL78MwE033eTR/sYbb5Ceno7FYmHr1q289dZbFBUVERsby6BBg3jvvfcIDQ11x7/wwgv4+flx9913u29GPH/+fPc9sgAWLlzIxIkT3asQjho1ipdeesm932KxsHTpUsaNG8f111/vcTPiauHh4Sxfvpzx48fTt29fIiIimDx5ssc1VyLSNGw7UMxb6/byr83fu2/E2zrYn7uu6UD7iCD8LGYCLCb8zGb8/cz4m034W07b9jPjV91mMRPkbyG+TZD+h8ulcthh2f/Bd5+5Xncf4tt8REREGgGvpwueS1BQEJ9++ul5jxMYGMi8efPcNw2uTZs2bViwYME5j9OxY0eWLFlyzpikpCTWrFlz3pxEpPGxO5x8uq2AN9flsjG30N1+RVwYDwzozKgr4wj0t5zjCFKvyo7B++mwZzVggqEzIKqHr7MSERHxOV18ICKNzuHjNhZt2MfC9fsoKKkAwM9sYnhiDOkDOtOnU4RGoHzt8C549x449h34h8APX4Eet/k6KxERkUZBRZaINBqb84p4c10uS7fkuxeliGwVQOp1HbmvfyeiwwJ9nKEA8M1KeP9BsBVDeDzcuwhiEs//PhERkRZCRZaINBin06CwrJLDpTYOldg4dNzGoeMVHCqxsWlfIV/tL3bHXhXfmvQBnbklKQarn6YENgqGAev/Bp9OA8MJ8f3hngXQqp2vMxMREWlUVGSJCABVDie2quqHg8oqJ07DtVS60zDcz04nOIzq7ep23DEVdodHEXX4eAWHj1dv26hynv3azgCLmRG9Y3lgQGeujG/dcB9ezq+qEj6ZCl++6Xp91X0w4gXwa9n3ExMREamNiiyRJuDNdbl8sbcQwzAwAE7WKQYGhuEaYDi55+T2yWfDSX6BmUUHv6DSYWCrcmCzuwqpypPFVHVh5ThH8VPX2oQEEBVqpd3JR1RoIO0jgrglMYbIFn4T4Eap7Bi8lwZ71wImGPZbSH4MdF2ciIhIrVRkiTRyK3cc5DcfbbuEI5ih6JhX7/C3uJY6t5hMmExgMZuwmE2YTa6HxWzCbAbLyddms8m1bTYR4GemXavq4slKVJiriKouqiJbWQnw0z2pmoxDX7sWuCjMhYBQuOs1uDzF11mJiIg0aiqyRBqxUlsVv/5XDgC3JcXSt3ME1WMHppMFkMn14rR2MOHa53Q4yMnZyrXXXEWwNQCrvxmrnxmrnwWrn5lAfzMBFotHe4CfGYtZIxQC7MqAfz4ElcehdSdIfQ+ievo6KxERkUZPRZZII/aHZV+TX1xBfJsg/jCmN8EB3v0na7fbaXVoC7f2jsXf37+espRmxzAwr/8LrJzuWuCi0/Vw99sQ0tbXmYmIiDQJKrJEGqnsvcd4K2svALPv9L7AErkoVTau2vcqls2fu15f82O49XnwC/BtXiIiIk2I/moTaYRsVQ4e/2ArhgF39enADZdF+jolqVZZBpUnwFkFhsP17Dzz+eS2cfrrKnA6a77PcHq+13CcI85xjrbTns88t6MKnHZw2E9rs7vanI5T244q/CpL6VR+DMNkxpQyC/r9VAtciIiIeElFlkgj9PKqb/nmUCmRrQJ48lZdA9MofP8lZL4E2/7lKmCaKRNgtwRjGvMGfj2G+zodERGRJklFlkgjs/vgcf782TcA/GbkFUSEaJqWzzid8M1yWDcPcj/33GeygNkCZr+Tj5PbJovna/fDXHO/yVzLe88WZ/E855lt7mdz7ee3+IPZHyzVbf4n2zz32w3I2LibYd1u9s13LiIi0gyoyBJpRJxOg8c/2ILdYXBzjyhG9I71dUotk70CtrwHmX+GIztdbWY/SPwhJI+HmN7Ndwqd3U6VZb+vsxAREWnSVGSJNCIL1u/ly31FhARY+O0diZia6x/yjVXZMdj4Gmz4G5w47GoLCIW+6a5rk8I7+DQ9ERERaRpUZIk0EgeKyvn9f74G4PFbehDXOsjHGbUgx76DzL/ApgVQVe5qC+sA/X/mWl0vMMy3+YmIiEiToiJLpBEwDIOn/pXDiUoH13Rszf39Ovk6pZYhbyOs+xPs+BgwXG0xSTDg53DFHa7rlERERES8pCJLpBFYsiWflV8fwt9i4vc/7I3ZrGmC51W4F7Ythty1ruXHOfmdmUw1t93TLk2n2koL4MCmU8frPhQGTIAuNzbf661ERESkQajIEvGxwhOVTP9oGwDjB3XnsuhQH2fUiJUccC2hvu1D2L/x0o9n9ofe97gWs4judenHExEREUFFlojPzfxkB0dPVHJZVCt+dlM3X6fT+JQehu3/co1a7V2He1ofJuh8A/QcCUFtXO2GcWp/9bZRHX/GttkPug2G0JiG+ywiIiLSIqjIEvGhtbuP8M/s/ZhM8Lsf9sbqZ/F1So1D2TH4egnkfAB71oDhPLUvvj8kjoZet6tAEhERkUZJRZaIj5RXOpi2eAsAP+7fiT6dInyckY9VlMDOT1yF1bf/BWfVqX1x17gKqyvu1DLqIiIi0uipyBLxkRdW7CLvWDlx4YH8cngPX6fTcJxOKNoLh3fC4a9PPR/cBg7bqbjoxFOFVZuuvstXRERExEsqskR8YOv+Yl79/DsAnr0zkVbWZvifoqMKCnNPFlKnFVNHdp+6F9WZIi+HK0a7iqt2CQ2aroiIiEhdaYZ/2Yk0bnaHk8c/2ILTgFFXxjG4R/T532QYrulzzipw2E9te7Q5XEuZu9uqMNkriDy+HdN3QWAyznif44xjOU4+V7c5wXC4roeqfjjPeF3bPlsJHN4FR3eDo7L2z2OxQuRlrkKqXQ/Xc9QV0Lablk8XERGRJk9FlkgDe/XzPWzPL6F1sD9Pj7yAZcMry+DNEfB9ttfn8gOuB/jG67fWDb8gaHf5qUKqXQ/Xo3UnsOjXj4iIiDRP+itHpAHtOXKCF1fsAuCp23oR2cp6/jetmH72AsvsV/Nh8T+5bcEw+3H8RDmhYRGYLKfvO/NhOWOfBUyWU88m86mH2ez5+vT9ZrOrsKoepQrv6GoTERERaUFUZIlcJKfToMzuoMxWRamtihM2Bycqqzhx8nVZpcO9fcJWxYlKBxv3HMNW5eQHl0Uy+pr25z/Jt5/Bhr+5tu9d5LovlNn/tELo3FPrqux2PvvkE2699Vb8/f3r4FOLiIiIyPmoyBLx0pFSGz/6exbfHCq9qPcH+VuYdWcSpvNde1ReBP8e79q+9ieQcMtFnU9EREREGpaKLBEv/XHFbo8Cy2SCVgF+BFsthFj9aGX1IyTAj5CTr6vbggMstLL60a9LW+LbBJ//RP/5FZR8D226wdBn6vETiYiIiEhdUpEl4oXvDpfy7oZ9ALye3pf+XdsS5G85/6iUt7b9C7a857rO6c6/QUBI3R5fREREROqNiiwRL/zh051UOQ0GJbS7sKXXL8bxAljyC9f2DZMh/tr6OY+IiIiI1Ast+yVygb7cV8h/cgowm+D/bulZPycxDPhoIpQfg5jeMPDx+jmPiIiIiNQbFVkiF8AwDH73ydcA/PCaDiTEhNbPib58E3Z/6rpZ7+i/g19A/ZxHREREROqNiiyRC7BixyE25B7D6mdm8rDL6+ckx/bAsidc2zc/BVH1NFomIiIiIvVKRZbIeVQ5nPx+mWsU66EbuhAbHlT3J3E64F8/A/sJ6HQD9B9f9+cQERERkQahIkvkPN7P3s83h0ppHezPTwd2q5+TrJsH+zIhoBXc8Rcw6z9NERERkaZKf8mJnENZZRUvLN8FwITBlxEe5F/3JynIgc9muraH/w4iOtX9OURERESkwajIEjmH19fu4dBxGx0igri/f8e6P0GVDRY/Co5KSLgVrr6/7s8hIiIiIg1KRZbIWRwttfHX1d8B8MuUBKx+lro/yarZcDAHgtvCyD9CXd/UWEREREQanIoskbOY999vKLVVkdg+jJG94+r+BPuy4H9/dG2P/CO0iqr7c4iIiIhIg1ORJVKL3CMnWJC1F4Bpt/TEbK7jESZbqWuaoOGEK1Oh58i6Pb6IiIiI+IyKLJFa/CFjJ1VOg4GXt+P67pF1f4KMX0NhLoR1gFt+V/fHFxERERGf8arImj17Ntdeey2hoaFERUVxxx13sHPnTo8YwzCYPn06cXFxBAUFcdNNN7Ft2zaPGJvNxoQJE4iMjCQkJIRRo0axf/9+j5jCwkLS0tIIDw8nPDyctLQ0ioqKPGL27dvHyJEjCQkJITIykokTJ1JZWekRs3XrVgYOHEhQUBDt27fnmWeewTAMbz62tDCb84pYuiUfkwn+75YedX+CXRmQ/YZr+46/QGB43Z9DRERERHzGqyJr9erVjB8/nqysLJYvX05VVRXDhg3jxIkT7pjnnnuOuXPn8tJLL7Fx40ZiYmIYOnQox48fd8dMmjSJxYsXs2jRItauXUtpaSkjRozA4XC4Y1JTU9m8eTPLli1j2bJlbN68mbS0NPd+h8PBbbfdxokTJ1i7di2LFi3igw8+YMqUKe6YkpIShg4dSlxcHBs3bmTevHnMmTOHuXPnXtSXJc2fYRjM/mQHAHde3Z6esWF1e4KyY/DRY67t/uOg68C6Pb6IiIiI+JyfN8HLli3zeP3GG28QFRVFdnY2N954I4Zh8OKLL/Lkk08yevRoAN58802io6N55513ePTRRykuLua1117j7bffZsiQIQAsWLCA+Ph4VqxYQUpKCjt27GDZsmVkZWXRr18/AF555RWSk5PZuXMnCQkJZGRksH37dvLy8oiLcy1K8Pzzz5Oens7MmTMJCwtj4cKFVFRUMH/+fKxWK4mJiezatYu5c+cyefJkTFrJTc7w2c5DrN9zjAA/M1OGJVzawSrLoPSg63G8wPW842PXc+TlcPPTdZO0iIiIiDQqXhVZZyouLgagTZs2AOzZs4eCggKGDRvmjrFarQwcOJB169bx6KOPkp2djd1u94iJi4sjMTGRdevWkZKSQmZmJuHh4e4CC6B///6Eh4ezbt06EhISyMzMJDEx0V1gAaSkpGCz2cjOzmbQoEFkZmYycOBArFarR8y0adPIzc2lS5cuNT6TzWbDZrO5X5eUlABgt9ux2+2X8nVdsurz+zqP5srhPDWK9eP+HYkK8Tv7d116CNPhr6G0AFPpQThxyPVcevDUs+14rW81zH44Rv4ZAz+o55+l+ox4S31GvKU+I95SnxFvNaY+c6E5XHSRZRgGkydP5oYbbiAxMRGAgoICAKKjoz1io6Oj2bt3rzsmICCAiIiIGjHV7y8oKCAqquZy1lFRUR4xZ54nIiKCgIAAj5jOnTvXOE/1vtqKrNmzZzNjxowa7RkZGQQHB9fyTTS85cuX+zqFZinrkIndhywEWwy62b7hk0++qTUuqmQL1333RyzG+f8jqzIFYPNvTYV/uOvZL5wDra/j6OZ82Jxf1x/hrNRnxFvqM+It9RnxlvqMeKsx9JmysrILirvoIuuxxx5jy5YtrF27tsa+M6fhGYZx3ql5Z8bUFl8XMdWLXpwtn2n/3969R0dV3vsf/+xkJpOLyZgQyQUSiAhRSqSaKAbKTSRABaw36IlNyaqLHhdRZEVOT6W/VuxSsNbqaqGlte2RKnjiOUdRj1Ca1Esih4uARBO0EDVcrAnhEjIht0km+/fHJEPGhMvoyMzI+7WcNXs/+2HmO/BdkQ9772cefFDFxcWefYfDobS0NOXl5Skuzs/35/ios7NTZWVlmj59uqxWa0Br+bppc7q04tdbJHVo8fRM3TFh+IDzjE93Kvz538kwO2Xa02XGD5MuSZJ5SZL7OWZwz36ydEmSZItVhGEoos9rpF2ID9SDnoGv6Bn4ip6Br+gZ+CqYeqb3Krdz+UIh67777tOrr76qiooKDR061DOenJwsyX2WKCUlxTPe0NDgOYOUnJwsp9OpxsZGr7NZDQ0NGj9+vGfOkSNH+r3v0aNHvV5nx44dXscbGxvV2dnpNaf3rFbf95H6n23rZbPZvC4v7GW1WgP+h9ormGr5unh6y0EdcXRoyKVRKpxwuazW8P6TGj6UXvgXqbNVGjFNxr+UyLBE9J8XhOgZ+Iqega/oGfiKnoGvgqFnzvf9fVpd0DRN3XvvvXrppZf0xhtv9LvcLiMjQ8nJyV6n8pxOp8rLyz0BKjs7W1ar1WtOXV2dqqurPXNyc3PV1NSkd955xzNnx44dampq8ppTXV2turrTl1yVlpbKZrMpOzvbM6eiosJrWffS0lKlpqb2u4wQF68TLU79/q2PJUlLZ4xS5EAB6+Qh6bnbpPaT0pAcaf5zUogELAAAAFxYPoWsoqIirVu3Ts8//7xiY2NVX1+v+vp6tbW1SXJfgrdkyRKtWLFCGzZsUHV1tQoLCxUdHa38/HxJkt1u1913360HHnhAr7/+uvbs2aPvfe97ysrK8qw2eNVVV2nmzJlauHChtm/fru3bt2vhwoWaPXu2MjPdK77l5eVp9OjRKigo0J49e/T6669r6dKlWrhwoeeyvvz8fNlsNhUWFqq6ulobNmzQihUrWFkQXla9UaPmji6NTonTLWOH9J/Qckx67lap+TMpMVO667+liJgLXygAAABCgk+XC65Zs0aSNGXKFK/xZ555RoWFhZKkH/3oR2pra9OiRYvU2NiocePGqbS0VLGxsZ75Tz31lCwWi+bNm6e2tjZNmzZNa9euVXj46TMI69ev1+LFiz2rEM6dO1erV6/2HA8PD9fGjRu1aNEiTZgwQVFRUcrPz9cTTzzhmWO321VWVqaioiLl5OQoPj5excXFXvdc4eJ26Hir1m13L8ry41lXKizsc+G7o1laf4d0/CPJniYVbJCiEwJQKQAAAEKFTyGrd9GIszEMQ8uXL9fy5cvPOCcyMlKrVq3SqlWrzjgnISFB69atO+t7paen67XXXjvrnKysLFVUVJx1DkJbY4tTy/93r9qcLkVHhCvaZlG01f0cExHuHouweI7FRIQrKiJcMREW/bJ0nzpdpiaOTNSkUZd5v3BXh1Ryl/TZHil6kDtg2Qc40wUAAAD08aW+JwsIBut3HNQrlZ99qdf495lXeg90u6SXFkq15ZI1xn2JYOLIL/UeAAAAuDgQshDyKmqOSZLuyB6qzKRYtTi71OZ0qcXZpVanS60drj5jLrV6xrvU0dWt7+cO15gh9tMvaJrSpqXSB69IYVbpu+ulIdkB+nQAAAAINYQshLRTHV1692CjJGnxjSOVPsi3L4we8Dvc3lop7foPSYZ0+x+lEVP9VC0AAAAuBj6tLggEm20fH1dXt6lhg6J9DljSAF9KveMPUvkv3Ns3/0r6xq1+qBIAAAAXE0IWQtrbNUclSRNHJn75F6v6H+mvP3JvT/2JdN3dX/41AQAAcNEhZCGkvd1zP9akkZedY+Y5fPR3acO/urev/6E06d++ZGUAAAC4WBGyELIOn2hV7bEWhYcZyh0x6Eu80E7phQKpu0sac4c08xcSX1YNAACAL4iFLxCyKnouFcxOi1PsJ5ukzjbJGuVect0aJUVE99nuebZEegeohn9Iz98pdbZKI6ZJ31kjhfFvDwAAAPjiCFkIWW/vd18q+K9x26T/euT8fpERJlmje8JYtNTWKHU4pCE50vznJEvEV1gxAAAALgaELISkLle3/u9jd8jKbXzVPZiU5T5j1dnqfjhbT2+7nO45ZrfkPOV+9LrsSveXDUfEXOBPAQAAgK8jQhZC0nufnlRze5eui/pM0cfed39p8PdfkWLOcG+Wq6sncLVJnS09AazNHb6G5kgW24X9AAAAAPjaImQhJFX0XCq4yL5VOinpym+fOWBJUrhFCo+TIuMuSH0AAAC4eHGHP0JSRc1RRahT41tedw9c8/3AFgQAAAD0IGQh5DS1duq9wyeVF7ZLts4mKW6oNGJqoMsCAAAAJBGyEIK2fnxM3aZUGPW2e+Cb+VJYeGCLAgAAAHoQshByKmqOaoiOKtv1nnvgmrsCWxAAAADQByELIcU0TVXsP6Y7LeUyZEoZk6X44YEuCwAAAPAgZCGk1B5rUd3JFt0ZXuEeuJYFLwAAABBcCFkIKRX7j2pCWLWGGMekyEulK2cHuiQAAADACyELIeXtmmOaH/6We+fqeZI1MpDlAAAAAP0QshAynF3d+uCTA5oetss9cM33AlsQAAAAMABCFkLG7oONmuGqkM3okpl8tZQyNtAlAQAAAP0QshAy3t7f4LlU0GDBCwAAAAQpQhZCRt0/tuuqsENyhUVIWXcEuhwAAABgQIQshITjpzqUfeJ/JUmdo2ZLUfEBrggAAAAYGCELIWHbPw5rbthWSVLk9YWBLQYAAAA4C0IWQoJjz0uKM9rUaEuVhk8MdDkAAADAGRGyEPRM01TmZy9LkhyZ86Uw2hYAAADBi7+tIujV7q9StrlXLtNQ0uQfBLocAAAA4KwIWQh6jq3PSJKqo3IUOSg9wNUAAAAAZ0fIQnBzdWnY4ZclSUdHzgtsLQAAAMB5IGQhqDn3lSq++4SOm7FKz7090OUAAAAA50TIQlBzbP0PSVJp+BSNTEkIcDUAAADAuRGyELxONSj+n29Ikv6ZcYcMwwhwQQAAAMC5EbIQvN77T4WbLu3pvkKjrr4+0NUAAAAA54WQheBkmura/Zwk6b9cU/StKxIDXBAAAABwfghZCE6H35HlRI1aTZs+SZqhhJiIQFcEAAAAnBdCFoLTnmclSRtd45STyXdjAQAAIHQQshB8OpplVm+QJL3gmqJJIy8LcEEAAADA+SNkIfjs3SCjs0Ufd6foQ+toXZMeH+iKAAAAgPNGyELweff0ghe5IxIVYaFNAQAAEDp8/ttrRUWF5syZo9TUVBmGoZdfftnreGFhoQzD8HrccMMNXnM6Ojp03333KTExUTExMZo7d64+/fRTrzmNjY0qKCiQ3W6X3W5XQUGBTp486TXn0KFDmjNnjmJiYpSYmKjFixfL6XR6zamqqtLkyZMVFRWlIUOG6Oc//7lM0/T1Y+NCObpP+vQduRSml1wTNWkUlwoCAAAgtPgcslpaWjR27FitXr36jHNmzpypuro6z2PTpk1ex5csWaINGzaopKREW7Zs0alTpzR79my5XC7PnPz8fFVWVmrz5s3avHmzKisrVVBQ4Dnucrl08803q6WlRVu2bFFJSYlefPFFPfDAA545DodD06dPV2pqqnbu3KlVq1bpiSee0JNPPunrx8aF8q57wYs3u6/RUV2qidyPBQAAgBBj8fUXzJo1S7NmzTrrHJvNpuTk5AGPNTU16c9//rOee+453XTTTZKkdevWKS0tTX//+981Y8YMffjhh9q8ebO2b9+ucePGSZL++Mc/Kjc3V/v27VNmZqZKS0v1wQcf6PDhw0pNTZUk/epXv1JhYaEeffRRxcXFaf369Wpvb9fatWtls9k0ZswY7d+/X08++aSKi4tlGIavHx9fpS6n9F6JJKmka4qGxkdp+KDoABcFAAAA+MbnkHU+3nrrLQ0ePFiXXnqpJk+erEcffVSDBw+WJO3evVudnZ3Ky8vzzE9NTdWYMWO0detWzZgxQ9u2bZPdbvcELEm64YYbZLfbtXXrVmVmZmrbtm0aM2aMJ2BJ0owZM9TR0aHdu3dr6tSp2rZtmyZPniybzeY158EHH9SBAweUkZHRr/aOjg51dHR49h0OhySps7NTnZ2d/vtN+gJ63z/QdZyTaUrtJ3seTTI6HFK7Q+pwyGhvknr23eNNUkeTjHaH1HZCRusxNVsG6c32b+rOKwapq6sr0J8mpIVMzyBo0DPwFT0DX9Ez8FUw9cz51uD3kDVr1izdeeedGjZsmGpra/XTn/5UN954o3bv3i2bzab6+npFREQoPt57xbikpCTV19dLkurr6z2hrK/Bgwd7zUlKSvI6Hh8fr4iICK85w4cP7/c+vccGClkrV67Uww8/3G+8tLRU0dHBcValrKws0CXI6O5StPOYop0NiuloUIzzqGI6GhTds23pbv/Cr72u60a5FK7opoPatOmA/4q+iAVDzyC00DPwFT0DX9Ez8FUw9Exra+t5zfN7yJo/f75ne8yYMcrJydGwYcO0ceNG3XbbbWf8daZpel2+N9ClfP6Y07voxZkuFXzwwQdVXFzs2Xc4HEpLS1NeXp7i4uLOWP+F0Ok4ooo339CkSZNktVrcZ4wkSabXU5+NPsdM93bfZ7P7c2PqN8foapNOHpJx8qCMxlrp5EEZjQckxz9lmN1nrde0xki2WCnSLtMWJ0Xapci409u2OMkWJ7N3O9Ku+q4Y/eLPhxUeZqjojpsUF2X1w+/cxauzs1NlZWWaPn26rFZ+L3Fu9Ax8Rc/AV/QMfBVMPdN7ldu5fCWXC/aVkpKiYcOGqaamRpKUnJwsp9OpxsZGr7NZDQ0NGj9+vGfOkSNH+r3W0aNHPWeikpOTtWPHDq/jjY2N6uzs9JrTe1ar7/tI6ncWrJfNZvO6vLCX1WoN+B+q5flbNPP4R1J1QMs4zRIlxQ+XEjLcz/HDpfie7UvTZVgjPVPP9+63Le8ckvSpvpl2qQbFBceZw6+DYOhfhBZ6Br6iZ+Arega+CoaeOd/3/8pD1vHjx3X48GGlpKRIkrKzs2W1WlVWVqZ58+ZJkurq6lRdXa3HH39ckpSbm6umpia98847uv766yVJO3bsUFNTkyeI5ebm6tFHH1VdXZ3ntUtLS2Wz2ZSdne2Zs2zZMjmdTkVERHjmpKam9ruMMPT0xBbPGbkz7H9+zAjr2TcGfv78WHiEdGmad4DqDVWXJPV5bf+oqDkqSZo4MtGvrwsAAABcKD6HrFOnTumjjz7y7NfW1qqyslIJCQlKSEjQ8uXLdfvttyslJUUHDhzQsmXLlJiYqFtvvVWSZLfbdffdd+uBBx7QoEGDlJCQoKVLlyorK8uz2uBVV12lmTNnauHChfrDH/4gSfrhD3+o2bNnKzMzU5KUl5en0aNHq6CgQL/85S914sQJLV26VAsXLvRc1pefn6+HH35YhYWFWrZsmWpqarRixQr97Gc/C8mVBbvu2a5Nmzbp29/+dsBT/FfB1W1qS80xSWLpdgAAAIQsn0PWrl27NHXqVM9+7/1LCxYs0Jo1a1RVVaVnn31WJ0+eVEpKiqZOnaoXXnhBsbGxnl/z1FNPyWKxaN68eWpra9O0adO0du1ahYeHe+asX79eixcv9qxCOHfuXK/v5goPD9fGjRu1aNEiTZgwQVFRUcrPz9cTTzzhmWO321VWVqaioiLl5OQoPj5excXFXvdcwb+cXd2qPdaiA8db1OUy1W26H6apnm317J/e7jbd98o1ODrkaO9SXKRFY4faA/1RAAAAgC/E55A1ZcoUz+IRA/nb3/52zteIjIzUqlWrtGrVqjPOSUhI0Lp16876Ounp6XrttdfOOicrK0sVFRXnrAm+cXWbOnyiVfuONGt/fbP7+UizPjnaoq7uM/fH+ZhwRaIs4T5/TzYAAAAQFL7ye7IQ2kzTVF1Tu1eYqjlySjUNzWrvHHh1wVibRZdfFiObNVxhhhRmGAozDBmebfXsn94OC3Ov+BhlDdc9k0dc4E8JAAAA+A8hK4QU//f7qq4N19pPd8gwDJk6vYq7e/V107N4u2lKpszTxz2X6bnHu/vum977fZ9bnS6d6hj4C4FtljCNTLpEo5JilZkUq1HJ7ucUe2RI3vMGAAAA+AMhK4Ts/cyh2mZDam66oO9rCTN0+WUxXmFqVFKs0hOiFR5GmAIAAAD6ImSFkP9385Xasm2nsrOvlcXi/qMzdPqLld3bp1dVN2So5z9JUniYIUPuS/Q8l+qFnd431P/SvghLmNITohVh4R4pAAAA4HwQskLIxCsS1bzfVN7opK/lEu4AAADA1wGnJwAAAADAjwhZAAAAAOBHhCwAAAAA8CNCFgAAAAD4ESELAAAAAPyIkAUAAAAAfkTIAgAAAAA/ImQBAAAAgB8RsgAAAADAjwhZAAAAAOBHhCwAAAAA8CNCFgAAAAD4ESELAAAAAPyIkAUAAAAAfkTIAgAAAAA/ImQBAAAAgB8RsgAAAADAjyyBLiDYmaYpSXI4HAGuROrs7FRra6scDoesVmugy0EIoGfgK3oGvqJn4Ct6Br4Kpp7pzQS9GeFMCFnn0NzcLElKS0sLcCUAAAAAgkFzc7PsdvsZjxvmuWLYRa67u1ufffaZYmNjZRhGQGtxOBxKS0vT4cOHFRcXF9BaEBroGfiKnoGv6Bn4ip6Br4KpZ0zTVHNzs1JTUxUWduY7rziTdQ5hYWEaOnRooMvwEhcXF/AGQ2ihZ+Arega+omfgK3oGvgqWnjnbGaxeLHwBAAAAAH5EyAIAAAAAPyJkhRCbzaaHHnpINpst0KUgRNAz8BU9A1/RM/AVPQNfhWLPsPAFAAAAAPgRZ7IAAAAAwI8IWQAAAADgR4QsAAAAAPAjQhYAAAAA+BEhCwAAAAD8iJAVQn73u98pIyNDkZGRys7O1ttvvx3okhAkKioqNGfOHKWmpsowDL388stex03T1PLly5WamqqoqChNmTJFe/fuDUyxCLiVK1fquuuuU2xsrAYPHqzvfOc72rdvn9ccegZ9rVmzRldffbXi4uIUFxen3Nxc/fWvf/Ucp19wLitXrpRhGFqyZIlnjL5BX8uXL5dhGF6P5ORkz/FQ6xdCVoh44YUXtGTJEv3kJz/Rnj17NHHiRM2aNUuHDh0KdGkIAi0tLRo7dqxWr1494PHHH39cTz75pFavXq2dO3cqOTlZ06dPV3Nz8wWuFMGgvLxcRUVF2r59u8rKytTV1aW8vDy1tLR45tAz6Gvo0KF67LHHtGvXLu3atUs33nijbrnlFs9fcOgXnM3OnTv19NNP6+qrr/Yap2/wed/4xjdUV1fneVRVVXmOhVy/mAgJ119/vXnPPfd4jV155ZXmj3/84wBVhGAlydywYYNnv7u720xOTjYfe+wxz1h7e7tpt9vN3//+9wGoEMGmoaHBlGSWl5ebpknP4PzEx8ebf/rTn+gXnFVzc7M5cuRIs6yszJw8ebJ5//33m6bJzxn099BDD5ljx44d8Fgo9gtnskKA0+nU7t27lZeX5zWel5enrVu3BqgqhIra2lrV19d79Y/NZtPkyZPpH0iSmpqaJEkJCQmS6BmcncvlUklJiVpaWpSbm0u/4KyKiop0880366abbvIap28wkJqaGqWmpiojI0Pf/e539cknn0gKzX6xBLoAnNuxY8fkcrmUlJTkNZ6UlKT6+voAVYVQ0dsjA/XPwYMHA1ESgohpmiouLta3vvUtjRkzRhI9g4FVVVUpNzdX7e3tuuSSS7RhwwaNHj3a8xcc+gWfV1JSonfffVc7d+7sd4yfM/i8cePG6dlnn9WoUaN05MgRPfLIIxo/frz27t0bkv1CyAohhmF47Zum2W8MOBP6BwO599579f7772vLli39jtEz6CszM1OVlZU6efKkXnzxRS1YsEDl5eWe4/QL+jp8+LDuv/9+lZaWKjIy8ozz6Bv0mjVrlmc7KytLubm5GjFihP7yl7/ohhtukBRa/cLlgiEgMTFR4eHh/c5aNTQ09Ev0wOf1rsxD/+Dz7rvvPr366qt68803NXToUM84PYOBRERE6IorrlBOTo5WrlypsWPH6te//jX9ggHt3r1bDQ0Nys7OlsVikcViUXl5uX7zm9/IYrF4eoO+wZnExMQoKytLNTU1IflzhpAVAiIiIpSdna2ysjKv8bKyMo0fPz5AVSFUZGRkKDk52at/nE6nysvL6Z+LlGmauvfee/XSSy/pjTfeUEZGhtdxegbnwzRNdXR00C8Y0LRp01RVVaXKykrPIycnR3fddZcqKyt1+eWX0zc4q46ODn344YdKSUkJyZ8zXC4YIoqLi1VQUKCcnBzl5ubq6aef1qFDh3TPPfcEujQEgVOnTumjjz7y7NfW1qqyslIJCQlKT0/XkiVLtGLFCo0cOVIjR47UihUrFB0drfz8/ABWjUApKirS888/r1deeUWxsbGefxm02+2KioryfJcNPYNey5Yt06xZs5SWlqbm5maVlJTorbfe0ubNm+kXDCg2NtZzn2evmJgYDRo0yDNO36CvpUuXas6cOUpPT1dDQ4MeeeQRORwOLViwIDR/zgRsXUP47Le//a05bNgwMyIiwrz22ms9yy0Db775pimp32PBggWmabqXPn3ooYfM5ORk02azmZMmTTKrqqoCWzQCZqBekWQ+88wznjn0DPr6wQ9+4Pn/z2WXXWZOmzbNLC0t9RynX3A++i7hbpr0DbzNnz/fTElJMa1Wq5mammredttt5t69ez3HQ61fDNM0zQDlOwAAAAD42uGeLAAAAADwI0IWAAAAAPgRIQsAAAAA/IiQBQAAAAB+RMgCAAAAAD8iZAEAAACAHxGyAAAAAMCPCFkAAAAA4EeELAAAAADwI0IWAAAAAPgRIQsAAAAA/Oj/Awc/YBZfhLdgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "Trr=[]; Tss=[]; n=3\n",
    "order=['ord-'+str(i) for i in range(2,n)]\n",
    "#Trd = pd.DataFrame(np.zeros((10,n-2)), columns=order)\n",
    "#Tsd = pd.DataFrame(np.zeros((10,n-2)), columns=order)\n",
    "\n",
    "DROP=[];b=[]\n",
    "\n",
    "for i in range(len(Train_X_std.columns)):\n",
    "    vif = pd.DataFrame()\n",
    "    X = Train_X_std.drop(DROP,axis=1)\n",
    "    vif['Features'] = X.columns\n",
    "    vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "    vif['VIF'] = round(vif['VIF'], 2)\n",
    "    vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "    vif.reset_index(drop=True, inplace=True)\n",
    "    if vif.loc[0][1]>1:\n",
    "        DROP.append(vif.loc[0][0])\n",
    "        LR = LinearRegression()\n",
    "        LR.fit(Train_X_std.drop(DROP,axis=1), Train_Y)\n",
    "\n",
    "        pred1 = LR.predict(Train_X_std.drop(DROP,axis=1))\n",
    "        pred2 = LR.predict(Test_X_std.drop(DROP,axis=1))\n",
    "        \n",
    "        Trr.append(np.sqrt(mean_squared_error(Train_Y, pred1)))\n",
    "        Tss.append(np.sqrt(mean_squared_error(Test_Y, pred2)))\n",
    "\n",
    "        #Trd.loc[i,'ord-'+str(k)] = round(np.sqrt(mean_squared_error(Train_Y, pred1)),2)\n",
    "        #Tsd.loc[i,'ord-'+str(k)] = round(np.sqrt(mean_squared_error(Test_Y, pred2)),2)\n",
    "        \n",
    "print('Dropped Features --> ',DROP)\n",
    "plt.plot(Trr, label='Train RMSE')\n",
    "plt.plot(Tss, label='Test RMSE')\n",
    "#plt.ylim([19.75,20.75])\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9aa857e5-42c2-40dc-b466-e647a13c60a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150459.66166466865\n",
      "155774.0243125204\n"
     ]
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "rfe = RFE(lm,n_features_to_select=Train_X_std.shape[1]-28)             # running RFE\n",
    "rfe = rfe.fit(Train_X_std, Train_Y)\n",
    "\n",
    "LR = LinearRegression()\n",
    "LR.fit(Train_X_std.loc[:,rfe.support_], Train_Y)\n",
    "\n",
    "#print(Train_X_std.loc[:,rfe.support_].columns)\n",
    "\n",
    "pred1 = LR.predict(Train_X_std.loc[:,rfe.support_])\n",
    "pred2 = LR.predict(Test_X_std.loc[:,rfe.support_])\n",
    "\n",
    "print(np.sqrt(mean_squared_error(Train_Y, pred1)))\n",
    "print(np.sqrt(mean_squared_error(Test_Y, pred2)))\n",
    "\n",
    "Train_X_std = Train_X_std.loc[:,rfe.support_]\n",
    "Test_X_std = Test_X_std.loc[:,rfe.support_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "73b6eba7-8439-4e31-a622-29a471d28ac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Model_Evaluation_Comparison_Matrix = pd.DataFrame(np.zeros([5,8]), columns=['Train-R2','Test-R2','Train-RSS','Test-RSS',\n",
    "                                                                            'Train-MSE','Test-MSE','Train-RMSE','Test-RMSE'])\n",
    "rc=np.random.choice(Train_X_std.loc[:,Train_X_std.nunique()>=50].columns.values,2,replace=False)\n",
    "def Evaluate(n, pred1, pred2):\n",
    "    # Plotting predicted values alongside the actual datapoints \n",
    "    plt.figure(figsize=[15,6])\n",
    "    for e, i in enumerate(rc):\n",
    "        plt.subplot(2, 3, e + 1)\n",
    "        plt.scatter(y=np.maximum(Train_Y, 0), x=np.maximum(Train_X_std[i], 0), label='Actual')\n",
    "        plt.scatter(y=np.maximum(pred1, 0), x=np.maximum(Train_X_std[i], 0), label='Prediction')\n",
    "        plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    #Evaluating the Multiple Linear Regression Model\n",
    "\n",
    "    print('\\n\\n{}Training Set Metrics{}'.format('-'*20, '-'*20))\n",
    "    print('\\nR2-Score on Training set --->',round(r2_score(Train_Y, pred1),20))\n",
    "    print('Residual Sum of Squares (RSS) on Training set  --->',round(np.sum(np.square(Train_Y-pred1)),20))\n",
    "    print('Mean Squared Error (MSE) on Training set       --->',round(mean_squared_error(Train_Y, pred1),20))\n",
    "    print('Root Mean Squared Error (RMSE) on Training set --->',round(np.sqrt(mean_squared_error(Train_Y, pred1)),20))\n",
    "\n",
    "    print('\\n{}Testing Set Metrics{}'.format('-'*20, '-'*20))\n",
    "    print('\\nR2-Score on Testing set --->',round(r2_score(Test_Y, pred2),20))\n",
    "    print('Residual Sum of Squares (RSS) on Training set  --->',round(np.sum(np.square(Test_Y-pred2)),20))\n",
    "    print('Mean Squared Error (MSE) on Training set       --->',round(mean_squared_error(Test_Y, pred2),20))\n",
    "    print('Root Mean Squared Error (RMSE) on Training set --->',round(np.sqrt(mean_squared_error(Test_Y, pred2)),20))\n",
    "    print('\\n{}Residual Plots{}'.format('-'*20, '-'*20))\n",
    "    \n",
    "    Model_Evaluation_Comparison_Matrix.loc[n,'Train-R2']  = round(r2_score(Train_Y, pred1),20)\n",
    "    Model_Evaluation_Comparison_Matrix.loc[n,'Test-R2']   = round(r2_score(Test_Y, pred2),20)\n",
    "    Model_Evaluation_Comparison_Matrix.loc[n,'Train-RSS'] = round(np.sum(np.square(Train_Y-pred1)),20)\n",
    "    Model_Evaluation_Comparison_Matrix.loc[n,'Test-RSS']  = round(np.sum(np.square(Test_Y-pred2)),20)\n",
    "    Model_Evaluation_Comparison_Matrix.loc[n,'Train-MSE'] = round(mean_squared_error(Train_Y, pred1),20)\n",
    "    Model_Evaluation_Comparison_Matrix.loc[n,'Test-MSE']  = round(mean_squared_error(Test_Y, pred2),20)\n",
    "    Model_Evaluation_Comparison_Matrix.loc[n,'Train-RMSE']= round(np.sqrt(mean_squared_error(Train_Y, pred1)),20)\n",
    "    Model_Evaluation_Comparison_Matrix.loc[n,'Test-RMSE'] = round(np.sqrt(mean_squared_error(Test_Y, pred2)),20)\n",
    "\n",
    "    # Plotting y_test and y_pred to understand the spread.\n",
    "    plt.figure(figsize=[15,4])\n",
    "\n",
    "    plt.subplot(1,2,1)\n",
    "    sns.distplot((Train_Y - pred1))\n",
    "    plt.title('Error Terms')          \n",
    "    plt.xlabel('Errors') \n",
    "\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.scatter(Train_Y,pred1)\n",
    "    plt.plot([Train_Y.min(),Train_Y.max()],[Train_Y.min(),Train_Y.max()], 'r--')\n",
    "    plt.title('Test vs Prediction')     \n",
    "    plt.xlabel('y_test')                       \n",
    "    plt.ylabel('y_pred')                       \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89adda9e-b565-474f-a08a-405db0ae0e37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<<-----------------------------------\u001b[1m Evaluating Multiple Linear Regression Model \u001b[0m----------------------------------->>>\n",
      "\n",
      "The Intercept of the Regresion Model was found to be  1047603.298112138\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "--------------------Training Set Metrics--------------------\n",
      "\n",
      "R2-Score on Training set ---> 0.9300498690131108\n",
      "Residual Sum of Squares (RSS) on Training set  ---> 107802678811630.14\n",
      "Mean Squared Error (MSE) on Training set       ---> 22638109788.246563\n",
      "Root Mean Squared Error (RMSE) on Training set ---> 150459.66166466865\n",
      "\n",
      "--------------------Testing Set Metrics--------------------\n",
      "\n",
      "R2-Score on Testing set ---> 0.9290580851277542\n",
      "Residual Sum of Squares (RSS) on Training set  ---> 28900266060766.57\n",
      "Mean Squared Error (MSE) on Training set       ---> 24265546650.517693\n",
      "Root Mean Squared Error (RMSE) on Training set ---> 155774.0243125204\n",
      "\n",
      "--------------------Residual Plots--------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MLR = LinearRegression().fit(Train_X_std,Train_Y)\n",
    "pred1 = MLR.predict(Train_X_std)\n",
    "pred2 = MLR.predict(Test_X_std)\n",
    "\n",
    "print('{}{}\\033[1m Evaluating Multiple Linear Regression Model \\033[0m{}{}\\n'.format('<'*3,'-'*35 ,'-'*35,'>'*3))\n",
    "#print('The Coeffecient of the Regresion Model was found to be ',MLR.coef_)\n",
    "print('The Intercept of the Regresion Model was found to be ',MLR.intercept_)\n",
    "\n",
    "Evaluate(0, pred1, pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3bd852d2-36f5-4140-950f-f2faca91e5dc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<<-----------------------------------\u001b[1m Evaluating Ridge Regression Model \u001b[0m----------------------------------->>>\n",
      "\n",
      "The Intercept of the Regresion Model was found to be  1047603.298112138\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "--------------------Training Set Metrics--------------------\n",
      "\n",
      "R2-Score on Training set ---> 0.9300496637936124\n",
      "Residual Sum of Squares (RSS) on Training set  ---> 107802995082827.47\n",
      "Mean Squared Error (MSE) on Training set       ---> 22638176203.86969\n",
      "Root Mean Squared Error (RMSE) on Training set ---> 150459.8823735739\n",
      "\n",
      "--------------------Testing Set Metrics--------------------\n",
      "\n",
      "R2-Score on Testing set ---> 0.9290681487441195\n",
      "Residual Sum of Squares (RSS) on Training set  ---> 28896166351997.594\n",
      "Mean Squared Error (MSE) on Training set       ---> 24262104409.737694\n",
      "Root Mean Squared Error (RMSE) on Training set ---> 155762.97509272766\n",
      "\n",
      "--------------------Residual Plots--------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a Ridge Regression model\n",
    "\n",
    "RLR = Ridge().fit(Train_X_std,Train_Y)\n",
    "pred1 = RLR.predict(Train_X_std)\n",
    "pred2 = RLR.predict(Test_X_std)\n",
    "\n",
    "print('{}{}\\033[1m Evaluating Ridge Regression Model \\033[0m{}{}\\n'.format('<'*3,'-'*35 ,'-'*35,'>'*3))\n",
    "#print('The Coeffecient of the Regresion Model was found to be ',MLR.coef_)\n",
    "print('The Intercept of the Regresion Model was found to be ',MLR.intercept_)\n",
    "\n",
    "Evaluate(1, pred1, pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f83dce16-1021-47c5-a3d3-e429ac1a7c85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<<-----------------------------------\u001b[1m Evaluating Lasso Regression Model \u001b[0m----------------------------------->>>\n",
      "\n",
      "The Intercept of the Regresion Model was found to be  1047603.298112138\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "--------------------Training Set Metrics--------------------\n",
      "\n",
      "R2-Score on Training set ---> 0.9300498687633085\n",
      "Residual Sum of Squares (RSS) on Training set  ---> 107802679196609.3\n",
      "Mean Squared Error (MSE) on Training set       ---> 22638109869.090572\n",
      "Root Mean Squared Error (RMSE) on Training set ---> 150459.6619333254\n",
      "\n",
      "--------------------Testing Set Metrics--------------------\n",
      "\n",
      "R2-Score on Testing set ---> 0.9290584288786742\n",
      "Residual Sum of Squares (RSS) on Training set  ---> 28900126023764.98\n",
      "Mean Squared Error (MSE) on Training set       ---> 24265429071.171272\n",
      "Root Mean Squared Error (RMSE) on Training set ---> 155773.64690849115\n",
      "\n",
      "--------------------Residual Plots--------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a Ridge Regression model\n",
    "\n",
    "LLR = Lasso().fit(Train_X_std,Train_Y)\n",
    "pred1 = LLR.predict(Train_X_std)\n",
    "pred2 = LLR.predict(Test_X_std)\n",
    "\n",
    "print('{}{}\\033[1m Evaluating Lasso Regression Model \\033[0m{}{}\\n'.format('<'*3,'-'*35 ,'-'*35,'>'*3))\n",
    "#print('The Coeffecient of the Regresion Model was found to be ',MLR.coef_)\n",
    "print('The Intercept of the Regresion Model was found to be ',MLR.intercept_)\n",
    "\n",
    "Evaluate(2, pred1, pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8a9ab330-75ec-404f-9da2-d1f70c2715ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<<-----------------------------------\u001b[1m Evaluating Elastic-Net Regression Model \u001b[0m----------------------------------->>>\n",
      "\n",
      "The Intercept of the Regresion Model was found to be  1047603.298112138\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "--------------------Training Set Metrics--------------------\n",
      "\n",
      "R2-Score on Training set ---> 0.7871424160759006\n",
      "Residual Sum of Squares (RSS) on Training set  ---> 328042527278328.9\n",
      "Mean Squared Error (MSE) on Training set       ---> 68887552977.38951\n",
      "Root Mean Squared Error (RMSE) on Training set ---> 262464.38420743775\n",
      "\n",
      "--------------------Testing Set Metrics--------------------\n",
      "\n",
      "R2-Score on Testing set ---> 0.7923376537640883\n",
      "Residual Sum of Squares (RSS) on Training set  ---> 84597336677879.95\n",
      "Mean Squared Error (MSE) on Training set       ---> 71030509385.28963\n",
      "Root Mean Squared Error (RMSE) on Training set ---> 266515.49558194476\n",
      "\n",
      "--------------------Residual Plots--------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a ElasticNet Regression model\n",
    "\n",
    "ENR = ElasticNet().fit(Train_X_std,Train_Y)\n",
    "pred1 = ENR.predict(Train_X_std)\n",
    "pred2 = ENR.predict(Test_X_std)\n",
    "\n",
    "print('{}{}\\033[1m Evaluating Elastic-Net Regression Model \\033[0m{}{}\\n'.format('<'*3,'-'*35 ,'-'*35,'>'*3))\n",
    "#print('The Coeffecient of the Regresion Model was found to be ',MLR.coef_)\n",
    "print('The Intercept of the Regresion Model was found to be ',MLR.intercept_)\n",
    "\n",
    "Evaluate(3, pred1, pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "64d60ca7-b2e3-4dae-ac36-9f5f6b4acccb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<<-----------------------------------\u001b[1m Evaluating Polynomial Regression Model \u001b[0m----------------------------------->>>\n",
      "\n",
      "The Coeffecient of the Regresion Model was found to be  [  20994.84559677  227390.04858906   31804.06199226   74824.11542794\n",
      "   67047.30306019 -167295.37787281  158994.71000533 -169344.72504638\n",
      " -115145.31430541  -91321.19625995 -137612.2399299   138101.61019103\n",
      "  -23162.67597162  158091.22199972  109994.6575779   -57057.1944685\n",
      " -124597.14379525   67904.21264383  103609.47722692 -109537.59789928\n",
      "   52722.33661041   54708.75713097 -114216.82932918  118627.30850624\n",
      "  -72092.52083921 -166183.27103103  -26703.18677157 -110473.39901656\n",
      " -168992.17776092 -148340.20789707  -23327.50120846  -64521.60327083\n",
      " -126739.21866619  -98607.46178895  -77491.02524189]\n",
      "The Intercept of the Regresion Model was found to be  1047603.298112138\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "--------------------Training Set Metrics--------------------\n",
      "\n",
      "R2-Score on Training set ---> 0.9456453952746297\n",
      "Residual Sum of Squares (RSS) on Training set  ---> 83767848787023.56\n",
      "Mean Squared Error (MSE) on Training set       ---> 17590896427.3464\n",
      "Root Mean Squared Error (RMSE) on Training set ---> 132630.67679592984\n",
      "\n",
      "--------------------Testing Set Metrics--------------------\n",
      "\n",
      "R2-Score on Testing set ---> 0.9320847769101225\n",
      "Residual Sum of Squares (RSS) on Training set  ---> 27667254547729.414\n",
      "Mean Squared Error (MSE) on Training set       ---> 23230272500.19262\n",
      "Root Mean Squared Error (RMSE) on Training set ---> 152414.80407162756\n",
      "\n",
      "--------------------Residual Plots--------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using the 2nd Order Polynomial Regression model (degree=2)\n",
    "\n",
    "poly_reg = PolynomialFeatures(degree=2)\n",
    "X_poly = poly_reg.fit_transform(Train_X_std)\n",
    "X_poly1 = poly_reg.fit_transform(Test_X_std)\n",
    "PR = LinearRegression()\n",
    "PR.fit(X_poly, Train_Y)\n",
    "\n",
    "pred1 = PR.predict(X_poly)\n",
    "pred2 = PR.predict(X_poly1)\n",
    "\n",
    "print('{}{}\\033[1m Evaluating Polynomial Regression Model \\033[0m{}{}\\n'.format('<'*3,'-'*35 ,'-'*35,'>'*3))\n",
    "print('The Coeffecient of the Regresion Model was found to be ',MLR.coef_)\n",
    "print('The Intercept of the Regresion Model was found to be ',MLR.intercept_)\n",
    "\n",
    "Evaluate(4, pred1, pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8b4060ef-5152-4892-87f2-56a0212191e6",
   "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>Train-R2</th>\n",
       "      <th>Test-R2</th>\n",
       "      <th>Train-RSS</th>\n",
       "      <th>Test-RSS</th>\n",
       "      <th>Train-MSE</th>\n",
       "      <th>Test-MSE</th>\n",
       "      <th>Train-RMSE</th>\n",
       "      <th>Test-RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Multiple Linear Regression (MLR)</th>\n",
       "      <td>0.930050</td>\n",
       "      <td>0.929058</td>\n",
       "      <td>1.078027e+14</td>\n",
       "      <td>2.890027e+13</td>\n",
       "      <td>2.263811e+10</td>\n",
       "      <td>2.426555e+10</td>\n",
       "      <td>150459.661665</td>\n",
       "      <td>155774.024313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge Linear Regression (RLR)</th>\n",
       "      <td>0.930050</td>\n",
       "      <td>0.929068</td>\n",
       "      <td>1.078030e+14</td>\n",
       "      <td>2.889617e+13</td>\n",
       "      <td>2.263818e+10</td>\n",
       "      <td>2.426210e+10</td>\n",
       "      <td>150459.882374</td>\n",
       "      <td>155762.975093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso Linear Regression (LLR)</th>\n",
       "      <td>0.930050</td>\n",
       "      <td>0.929058</td>\n",
       "      <td>1.078027e+14</td>\n",
       "      <td>2.890013e+13</td>\n",
       "      <td>2.263811e+10</td>\n",
       "      <td>2.426543e+10</td>\n",
       "      <td>150459.661933</td>\n",
       "      <td>155773.646908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elastic-Net Regression (ENR)</th>\n",
       "      <td>0.787142</td>\n",
       "      <td>0.792338</td>\n",
       "      <td>3.280425e+14</td>\n",
       "      <td>8.459734e+13</td>\n",
       "      <td>6.888755e+10</td>\n",
       "      <td>7.103051e+10</td>\n",
       "      <td>262464.384207</td>\n",
       "      <td>266515.495582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polynomial Regression (PNR)</th>\n",
       "      <td>0.945645</td>\n",
       "      <td>0.932085</td>\n",
       "      <td>8.376785e+13</td>\n",
       "      <td>2.766725e+13</td>\n",
       "      <td>1.759090e+10</td>\n",
       "      <td>2.323027e+10</td>\n",
       "      <td>132630.676796</td>\n",
       "      <td>152414.804072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Train-R2   Test-R2     Train-RSS  \\\n",
       "Multiple Linear Regression (MLR)  0.930050  0.929058  1.078027e+14   \n",
       "Ridge Linear Regression (RLR)     0.930050  0.929068  1.078030e+14   \n",
       "Lasso Linear Regression (LLR)     0.930050  0.929058  1.078027e+14   \n",
       "Elastic-Net Regression (ENR)      0.787142  0.792338  3.280425e+14   \n",
       "Polynomial Regression (PNR)       0.945645  0.932085  8.376785e+13   \n",
       "\n",
       "                                      Test-RSS     Train-MSE      Test-MSE  \\\n",
       "Multiple Linear Regression (MLR)  2.890027e+13  2.263811e+10  2.426555e+10   \n",
       "Ridge Linear Regression (RLR)     2.889617e+13  2.263818e+10  2.426210e+10   \n",
       "Lasso Linear Regression (LLR)     2.890013e+13  2.263811e+10  2.426543e+10   \n",
       "Elastic-Net Regression (ENR)      8.459734e+13  6.888755e+10  7.103051e+10   \n",
       "Polynomial Regression (PNR)       2.766725e+13  1.759090e+10  2.323027e+10   \n",
       "\n",
       "                                     Train-RMSE      Test-RMSE  \n",
       "Multiple Linear Regression (MLR)  150459.661665  155774.024313  \n",
       "Ridge Linear Regression (RLR)     150459.882374  155762.975093  \n",
       "Lasso Linear Regression (LLR)     150459.661933  155773.646908  \n",
       "Elastic-Net Regression (ENR)      262464.384207  266515.495582  \n",
       "Polynomial Regression (PNR)       132630.676796  152414.804072  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regression Models Results Evaluation\n",
    "\n",
    "EMC = Model_Evaluation_Comparison_Matrix.copy()\n",
    "EMC.index = ['Multiple Linear Regression (MLR)','Ridge Linear Regression (RLR)','Lasso Linear Regression (LLR)','Elastic-Net Regression (ENR)','Polynomial Regression (PNR)']\n",
    "EMC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f3f15fd2-2154-4580-a04d-e395831915ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# R2-Scores Comparison for different Regression Models\n",
    "\n",
    "R2 = round(EMC['Train-R2'].sort_values(ascending=True),4)\n",
    "plt.hlines(y=R2.index, xmin=0, xmax=R2.values)\n",
    "plt.plot(R2.values, R2.index,'o')\n",
    "plt.title('R2-Scores Comparison for various Regression Models')\n",
    "plt.xlabel('R2-Score')\n",
    "#plt.ylabel('Regression Models')\n",
    "for i, v in enumerate(R2):\n",
    "    plt.text(v+0.02, i-0.05, str(v*100), color='blue')\n",
    "plt.xlim([0,1.1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b6fc15e-2181-4a5e-b4c9-b0c023906cb0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}