Python/ml/Laptop_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LAPTOP PRICE PREDICTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Link to the Dataset: [Laptop Pricing](https://www.kaggle.com/andrewgeorgeissac/laptop-price-predictor?select=laptop_pricing.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "from sklearn import preprocessing\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ï»¿Manufacturer</th>\n",
       "      <th>IntelCore(i-)</th>\n",
       "      <th>IntelCoreGen</th>\n",
       "      <th>processing speed(GHz)</th>\n",
       "      <th>Ram(gb)</th>\n",
       "      <th>HDD(gb)</th>\n",
       "      <th>SSD(gb)</th>\n",
       "      <th>Graphics(gb)</th>\n",
       "      <th>ScreenSize(inch)</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.29</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>39057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>45141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.30</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>35999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASUS</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>39990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HP</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.40</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>36263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HP</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>26403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>38290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HP</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1.60</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>53554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.80</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>29990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>38090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HP</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.30</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>42192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>34890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HP</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>33905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2.10</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>42590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2.10</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>45390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HP</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.30</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>40338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ASUS</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1.60</td>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>56990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.80</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>30690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HP</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.40</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>35634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HP</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2.40</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>15.6</td>\n",
       "      <td>74931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ASUS</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2.10</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>512</td>\n",
       "      <td>4</td>\n",
       "      <td>15.6</td>\n",
       "      <td>97990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HP</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2.40</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>15.6</td>\n",
       "      <td>69071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ASUS</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>15.6</td>\n",
       "      <td>109999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2.60</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>15.6</td>\n",
       "      <td>118890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2.60</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>15.6</td>\n",
       "      <td>128690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2.60</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>512</td>\n",
       "      <td>4</td>\n",
       "      <td>15.6</td>\n",
       "      <td>118890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2.40</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>92990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ASUS</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2.60</td>\n",
       "      <td>32</td>\n",
       "      <td>1000</td>\n",
       "      <td>512</td>\n",
       "      <td>8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>402990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>HP</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2.60</td>\n",
       "      <td>8</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>15.6</td>\n",
       "      <td>99555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ï»¿Manufacturer  IntelCore(i-)  IntelCoreGen  processing speed(GHz)  \\\n",
       "0             Dell              3             7                   2.29   \n",
       "1             Dell              3             7                   2.30   \n",
       "2               HP              3             7                   2.30   \n",
       "3             ASUS              3             7                   2.30   \n",
       "4               HP              3             7                   2.40   \n",
       "5               HP              2             4                   2.30   \n",
       "6           Lenovo              3             7                   2.30   \n",
       "7               HP              5             8                   1.60   \n",
       "8           Lenovo              1             3                   1.80   \n",
       "9             Dell              3             8                   3.90   \n",
       "10              HP              3             7                   2.30   \n",
       "11          Lenovo              2             5                   2.30   \n",
       "12              HP              3             7                   2.30   \n",
       "13          Lenovo              3             8                   2.10   \n",
       "14          Lenovo              3             8                   2.10   \n",
       "15              HP              3             7                   2.30   \n",
       "16            ASUS              5             8                   1.60   \n",
       "17          Lenovo              1             3                   1.80   \n",
       "18              HP              3             7                   2.40   \n",
       "19              HP              5             9                   2.40   \n",
       "20            ASUS              5             9                   2.10   \n",
       "21              HP              5             9                   2.40   \n",
       "22            ASUS              7             8                   2.20   \n",
       "23          Lenovo              7             9                   2.60   \n",
       "24          Lenovo              7             9                   2.60   \n",
       "25          Lenovo              7             9                   2.60   \n",
       "26          Lenovo              5             9                   2.40   \n",
       "27            ASUS              7             9                   2.60   \n",
       "28              HP              7             9                   2.60   \n",
       "\n",
       "    Ram(gb)  HDD(gb)  SSD(gb)  Graphics(gb)  ScreenSize(inch)   Price  \n",
       "0         4     1000        0             0              15.6   39057  \n",
       "1         4     1000        0             0              15.6   45141  \n",
       "2         8      256      256             0              14.0   35999  \n",
       "3         4     1000        0             0              14.0   39990  \n",
       "4         4     1000        0             0              15.6   36263  \n",
       "5         4      500      256             0              14.0   26403  \n",
       "6         4     1000        0             0              15.6   38290  \n",
       "7         8     1000        0             0              14.0   53554  \n",
       "8         4     1000        0             0              15.6   29990  \n",
       "9         4     1000        0             0              14.0   38090  \n",
       "10        8     1000        0             0              15.6   42192  \n",
       "11        4     1000        0             0              15.6   34890  \n",
       "12        4     1000        0             0              14.0   33905  \n",
       "13        4     1000        0             0              15.6   42590  \n",
       "14        8     1000        0             0              15.6   45390  \n",
       "15        8     1000        0             0              15.6   40338  \n",
       "16        8      512      512             0              15.6   56990  \n",
       "17        4     1000        0             0              15.6   30690  \n",
       "18        4     1000        0             0              15.6   35634  \n",
       "19        8     1000      256             4              15.6   74931  \n",
       "20        8     1000      512             4              15.6   97990  \n",
       "21        8     1000        0             4              15.6   69071  \n",
       "22        8     1000       16             4              15.6  109999  \n",
       "23        8     1000      256             4              15.6  118890  \n",
       "24       16     1000      256             4              15.6  128690  \n",
       "25        8     1000      512             4              15.6  118890  \n",
       "26        8     1000      256             3              15.6   92990  \n",
       "27       32     1000      512             8              17.3  402990  \n",
       "28        8     1000      256             4              15.6   99555  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'laptoppricing.csv', encoding = 'unicode_escape')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ï»¿Manufacturer          False\n",
       "IntelCore(i-)            False\n",
       "IntelCoreGen             False\n",
       "processing speed(GHz)    False\n",
       "Ram(gb)                  False\n",
       "HDD(gb)                  False\n",
       "SSD(gb)                  False\n",
       "Graphics(gb)             False\n",
       "ScreenSize(inch)         False\n",
       "Price                    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ï»¿Manufacturer', 'IntelCore(i-)', 'IntelCoreGen',\n",
       "       'processing speed(GHz)', 'Ram(gb)', 'HDD(gb)', 'SSD(gb)',\n",
       "       'Graphics(gb)', 'ScreenSize(inch)', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()\n",
    "df['ï»¿Manufacturer'] = df['ï»¿Manufacturer'].astype('|S')\n",
    "df['ï»¿Manufacturer'] = label_encoder.fit_transform(df['ï»¿Manufacturer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.830255</td>\n",
       "      <td>ï»¿Manufacturer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.647852</td>\n",
       "      <td>IntelCore(i-)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.128093</td>\n",
       "      <td>IntelCoreGen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.604507</td>\n",
       "      <td>processing speed(GHz)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.636092</td>\n",
       "      <td>Ram(gb)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>65.612904</td>\n",
       "      <td>HDD(gb)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.828394</td>\n",
       "      <td>SSD(gb)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.230363</td>\n",
       "      <td>Graphics(gb)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>110.209476</td>\n",
       "      <td>ScreenSize(inch)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          VIF               Features\n",
       "0    4.830255        ï»¿Manufacturer\n",
       "1   40.647852          IntelCore(i-)\n",
       "2   74.128093           IntelCoreGen\n",
       "3   37.604507  processing speed(GHz)\n",
       "4    7.636092                Ram(gb)\n",
       "5   65.612904                HDD(gb)\n",
       "6    4.828394                SSD(gb)\n",
       "7    9.230363           Graphics(gb)\n",
       "8  110.209476       ScreenSize(inch)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking vif\n",
    "variables = df[['ï»¿Manufacturer', 'IntelCore(i-)', 'IntelCoreGen',\n",
    "       'processing speed(GHz)', 'Ram(gb)', 'HDD(gb)', 'SSD(gb)',\n",
    "       'Graphics(gb)', 'ScreenSize(inch)']]\n",
    "vif = pd.DataFrame()\n",
    "vif['VIF'] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "vif['Features'] = variables.columns\n",
    "\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>ï»¿Manufacturer</th>\n",
       "      <th>Ram(gb)</th>\n",
       "      <th>SSD(gb)</th>\n",
       "      <th>Graphics(gb)</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>35999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>26403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>56990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>74931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>4</td>\n",
       "      <td>97990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>69071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>109999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>118890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>128690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>4</td>\n",
       "      <td>118890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "      <td>92990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>512</td>\n",
       "      <td>8</td>\n",
       "      <td>402990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>99555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ï»¿Manufacturer  Ram(gb)  SSD(gb)  Graphics(gb)   Price\n",
       "0                 1        4        0             0   39057\n",
       "1                 1        4        0             0   45141\n",
       "2                 2        8      256             0   35999\n",
       "3                 0        4        0             0   39990\n",
       "4                 2        4        0             0   36263\n",
       "5                 2        4      256             0   26403\n",
       "6                 3        4        0             0   38290\n",
       "7                 2        8        0             0   53554\n",
       "8                 3        4        0             0   29990\n",
       "9                 1        4        0             0   38090\n",
       "10                2        8        0             0   42192\n",
       "11                3        4        0             0   34890\n",
       "12                2        4        0             0   33905\n",
       "13                3        4        0             0   42590\n",
       "14                3        8        0             0   45390\n",
       "15                2        8        0             0   40338\n",
       "16                0        8      512             0   56990\n",
       "17                3        4        0             0   30690\n",
       "18                2        4        0             0   35634\n",
       "19                2        8      256             4   74931\n",
       "20                0        8      512             4   97990\n",
       "21                2        8        0             4   69071\n",
       "22                0        8       16             4  109999\n",
       "23                3        8      256             4  118890\n",
       "24                3       16      256             4  128690\n",
       "25                3        8      512             4  118890\n",
       "26                3        8      256             3   92990\n",
       "27                0       32      512             8  402990\n",
       "28                2        8      256             4   99555"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now, we'll drop columns which have vif>10\n",
    "df = df.drop(['IntelCore(i-)','IntelCoreGen','processing speed(GHz)','HDD(gb)','ScreenSize(inch)'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x20d360a3460>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xblmvuw89rT07N6mptirne2UvI3saYFScvsm9XtJM7oH8OUm/IemwmR3MjPvj0Xnd/bOS7pH0ZklHJT0v6d35DhYohmRykbZtaNSFy5eM9fY2n971qioX6y0tK9S0fDO9GGLGyJvc8n18YmpLK5N6c0vduDyk90upoqJMOzau1LrlVWM9JG6k98uCSSRMW5sb9NJrX6sTp1JqqB7p/bJlZbVOpYZVtXiRKsvLVJvp/bJ5ZY1OnEppxbIXeq4cnX+k98syDZ1Na2tLwzk9W058r+xlABNF+Z26Uxr/PbcuSR+abj53/45yf2cuexqX9N55BQjERDK5SK+6sDZvy6uqXKzNFy7sD+OYPfImt3wfn5ja0sqkNl+4sIu4XCoqytTadEHUYSwYiYRpXd3Scd9re/GKZTmnnTjdZPPP5r2AXCIr6tw9d/YDAAAAAGYssu/Umdn9MxkHAAAAAJhc0a/UmVlS0hJJy83sfL1wK2W1pJXFjgcAAAAAQhbF7ZfXSHq/Rgq4H+qFoq5P0qcjiAcAAAAAglX0os7dPynpk2b2B+7+t8V+fwAAAAAoJVH++HjazM4bfWJm55vZ70cYDwAAAAAEJ8qi7nfc/bnRJ+7+U0m/E104AAAAABCeKIu6hJmN/d6cmZVJqogwHgAAAAAITmS/Uyfp65L2mtlnNfIj5L8r6d4I4wEAAACA4ERZ1H1IIz1h/p5GesD8hqR/iDAeAAAAAAhOZEWdu6clfSYzAAAAAADmILKizszWS/p/JV0sKTk63t3XRRUTAAAAAIQmyo5SvqCRq3TDkn5R0u2S/inCeAAAAAAgOFEWdZXufr8kc/efuPufSNoSYTwAAAAAEJwoO0pJmVlC0hEze5+kpyWtiDAeAAAAAAhOlFfq3i9piaRrJb1S0q9LuirCeAAAAAAgOEUv6sxs9Htzl7r7aXc/7u7vdve3ufv3ZriMz5vZCTNrm+T115tZr5kdzAw35G0FAAAAACBGorj98pVm9iJJv2Vmt2vkN+rGuPuzM1jGP0r6lEY6V5nMf7r75XOOEgAAAAACEEVR91lJ90paJ+mHGl/UeWb8lNz922bWVJDoAAAAACAgRb/90t1vdveXSfq8u69z9wuzhnz+Rt3PmtkjZvY1M2vONYGZXW1mB8zsQE9PTx7fGpg58hBxQB4iDshDxAF5iBBF2VHK/2tmaycOeVr2w5Je5O6XSPpbSXfmmsjdb3X3Vndvraury9NbA7NDHiIOyEPEAXmIOCAPEaIof9Lgqxq53dIkJSVdKOlxSTmvqs2Gu/dlPb7HzP7OzJa7+zPzXTYAAAAAxElkRZ27b8h+bmavkHRNPpZtZg2Sut3dzWyzRq5InszHsgEAAAAgTqK8UjeOuz9sZq+aybRm9i+SXi9puZkdl/RRSeWZ5XxW0tsl/Z6ZDUs6I+lKd/eCBA4AAAAAEYqsqDOzXVlPE5JeIWlG30Z193dO8/qnNPKTBwAAAABQ0qK8Urcs6/GwRr5j9+WIYgEAAACAIEX5nbqPRfXeAAAAAFAqorz98iJJfyipKTsOd98SVUwAAAAAEJoob7/8V0mflfQPks5GGAcAAAAABCvKom7Y3T8T4fsDAAAAQPASEb73XWb2+2bWaGYXjA4RxjOpVWvWyswKNqxaszbqVQQAAAAQqCiv1F2V+fvBrHEuaV0EsUyp4/hTuuKWBwu2/DuuubRgywYAAABQ2qLs/fLCqN4bAAAAAEpFlFfqZGYtki6WlBwd5+63RxcRAAAAAIQlyp80+Kik12ukqLtH0i9L+o4kijoAAAAAmKEoO0p5u6TLJHW5+7slXSJpcYTxAAAAAEBwoizqzrh7WtKwmVVLOqEYdpICAAAAAHEW5XfqDpjZeZL+XtIPJZ2W9P0I4wEAAACA4ETZ++XvZx5+1szulVTt7oeiigcAAAAAQlT0os7MXjHVa+7+cDHjAQAAAICQRXGl7oCkdkk9meeW9ZpL2jLdAszs85Iul3TC3VtyvG6SPinpzZKel/SbFIsAAAAASlEURd0HJL1N0hlJX5L07+5+epbL+EdJn9LkP3/wy5LWZ4ZXS/pM5i8QnMHBszrU0auuvpQaq5PasLJGFRVlUYeFBYY8RByQh4ijdNr142f69ZNn+7V08SItqSjTqdSw6quTaqqtUiJh0y8EmKeiF3Xu/teS/trMLpT0Tkn3m9lPJP2Zux+c4TK+bWZNU0yyXdLt7u6Svmdm55lZo7t3zjN8oKgGB8/qzkMdumFfm1JDaSXLE9q9vUU7Nq7kgwyKhjxEHJCHiKN02nVve5d27T04lpfXXbZet3/3J/rp84Pas3OTtjY3UNih4CL7SQN3/7GkfZK+IWmzpIvyuPhVkp7Ken48Mw4IyqGO3rEPMJKUGkrrhn1tOtTRG3FkWEjIQ8QBeYg4Onayf6ygk0by8pP3H9GvvGK1UkNp7dp7UMdO9kccJRaCohd1ZrbOzP7YzB6S9DFJj0h6qbvvzefb5BjnOWK52swOmNmBnp6eHLMAhTdVHnb1pcZOFKNSQ2l196WKGSIWAPIQcUAeIg5m8/mwe5K8NHvh8YlT5CgKL4ordUcl7ZR0r6TvSlor6ffNbJeZ7crTexyXtCbr+WpJHRMncvdb3b3V3Vvr6ury9NbA7EyVh43VSSXLxx+myfKE6quTxQwRCwB5iDggDxEHs/l8WD9JXrq/8HjFMnIUhRdFUbdb0r9LSktaKmnZhCEf9kt6l414jaRevk+HEG1YWaPd21vGThij3yHZuLIm4siwkJCHiAPyEHHUVFulPTs3jcvL6y5br688fFzJ8oT27NykptqqiKPEQhBFRyl/Mt9lmNm/SHq9pOVmdlzSRyWVZ5b/WUn3aOTnDI5q5CcN3j3f9wSiUFFRph0bV2rd8ip196VUX53URnp7Q5GRh4gD8hBxlEiYtjY36CV/8Fo9men9srKiTJvWnEfvlyiqKH7SYEpm9lZJ6919z2TTuPs7p1pGptfL9+Y7NiAKFRVlam26IOowsMCRh4gD8hBxlEiYXrxiqV68YmnUoWABi6z3SzP7IzPbZGafMLPfzPxguCR9XdLvRhUXAAAAAIQksqJO0mOSnpPUppEfC99nZlWSKiUtjjAuAAAAAAhGZLdfuvtdmYdfkPQFM/tDSf+TGXdTNFEBAAAAQFhi8506d/8rM/uUpDJ351caAQAAAGAGorz9UpJkZheZ2XmS5O4pCjoAAAAAmLlIizoz2yjpc5L+NfN9OgAAAADALER2+6WZvUTSzZKulrRa0l4z+xV3H4gqJgAAAAAITZQdpTxuZu+S9K+Snpa0zd3PRhUPAAAAAIQoyt+pq5f0T5J+TdIdkm6LKhYAAAAACFWUvV+e0cjVuV5JR83s/5pZubsPRRhTyVm1Zq06jj9VkGWvXL1GTz/1ZEGWDQAAAGBmil7UmdlX3f0t7t6XPd7du4ody0LQcfwpXXHLgwVZ9h3XXFqQ5QIAAACYuSiu1P2KJJnZKUmeNd4kubtXRxATAAAAAAQpiqLufkk/7+7LInhvAAAAACgpRe8oxd1/vtjvCQAAAAClKtIfHwcAAAAAzA9FHQAAAAAELMiizsy2mtnjZnbUzK7P8frrzazXzA5mhhuiiBMAAAAACi3K36mbEzMrk/RpSW+QdFzSD8xsv7s/OmHS/3T3y4seIAAAAAAUUYhX6jZLOuruT7j7oKQvSdoecUwAAAAAEIkQi7pVkp7Ken48M26inzWzR8zsa2bWnGtBZna1mR0wswM9PT2FiBWYFnmIOCAPEQfkIeKAPESIQizqLMc4n/D8YUkvcvdLJP2tpDtzLcjdb3X3Vndvraury2+UwAyRh4gD8hBxQB4iDshDhCjEou64pDVZz1dL6siewN373P105vE9ksrNbHnxQgQAAACA4gixqPuBpPVmdqGZVUi6UtL+7AnMrMHMLPN4s0bW82TRIwUAAACAAguu90t3Hzaz90n6uqQySZ9393Yz+93M65+V9HZJv2dmw5LOSLrS3SfeogkAAAAAwQuuqJPGbqm8Z8K4z2Y9/pSkTxU7LgAAAAAothBvvwQAAAAAZFDUAQAAAEDAKOoAAAAAIGAUdQAAAAAQMIo6AAAAAAgYRR0AAAAABIyiDgAAAAACRlEHAAAAAAGjqAMAAACAgFHUAQAAAEDAKOoAAAAAIGAUdQAAAAAQMIo6AAAAAAgYRR0AAAAABIyiDgAAAAACFmRRZ2ZbzexxMztqZtfneN3M7ObM64fM7BVRxAkAAAAAhbYo6gBmy8zKJH1a0hskHZf0AzPb7+6PZk32y5LWZ4ZXS/pM5i8QnHTadexkv7r7UqqvTqqptkqJhM15eYODZ3Woo1ddfSk1Vie1YWWNKirK8hgxopTvfJlquZIK8l4IX6HameHhtNo7e9XZm1JjTaWaG6u1aFGQ/5/OK7ZLboVqD2fzXvONYeL8a89foid/+jztbkCKlYfBFXWSNks66u5PSJKZfUnSdknZRd12Sbe7u0v6npmdZ2aN7t5Z/HCBuUunXfe2d2nX3oNKDaWVLE9oz85N2trcMKcGYXDwrO481KEb9rWNLW/39hbt2LiSwq4E5Dtfplrup3715Roc9ry/F8JXqHZmeDitOx95Wh+584Xl3rijRTsuWbWgCxi2S26Fag9n815vfFm9vvFY95xjmLjcF9VW6g+2rB+3r2l3462YeRji0b5K0lNZz49nxs12GiD2jp3sH2sIJCk1lNauvQd17GT/nJZ3qKN37IPW6PJu2NemQx29eYsZ0cl3vky13EPHewvyXghfodqZ9s7esQ+zo8v9yJ1tau9c2O0X2yW3QrWHs3mv9s75tZMTl3v5xlXn7Gva3XgrZh6GWNTlKmt9DtPIzK42swNmdqCnpycvwQGzNVUedvelxhqCUamhtE6cSs3pvbomWV5339yWh3iZT77MNg/TrrzmJkrHfNqZqfKwszf3crt6F3bOsV1yK1R7OJv3mmzfzLSdnLhcM9rd0OT7c9xUQizqjktak/V8taSOOUwjd7/V3VvdvbWuri7vgQIzMVUe1lcnlSwff5gmyxNasSw5p/dqnGR59dVzWx7iZT75Mts8LDPlNTdROubTzkyVh401lTmX21CzsHOO7ZJbodrD2bzXZPtmpu3kZMud6/JQfPn+HDeVEIu6H0hab2YXmlmFpCsl7Z8wzX5J78r0gvkaSb18nw4haqqt0p6dm8YahNF7sUc7qZitDStrtHt7y7jl7d7eoo0ra/IWM6KT73yZarkbVtcU5L0QvkK1M82N1bpxx/jl3rijRc2NC7v9YrvkVqj2cDbv1dxYPa8YJi73rkeePmdf0+7GWzHzMLiOUtx92MzeJ+nrksokfd7d283sdzOvf1bSPZLeLOmopOclvTuqeIH5SCRMW5sb9NJrX6sTp1JasWx+vSZVVJRpx8aVWre8aqwXpo30flky8p0v0y1XUt7fC+ErVDuzaFFCOy5ZpfUrlqqrN6WGmqSaG2sWdGcgEttlMoVqD2f7XvOJIdf8a89folesPZ92NxDFzMPgijpJcvd7NFK4ZY/7bNZjl/TeYscFFEIiYVpXt1Tr6pbmZXkVFWVqbbogL8tC/OQ7X6ZbbiHeC+ErVDuzaFFCl6w5X5esmX7ahYTtkluh2sPZvNd8Y8g1P+1uWIqVhwv73zgAAAAAEDiKOgAAAAAImI3cqQgz65H0k0leXi7pmSKGk0+hxh5q3NLUsT/j7lsnm3GaPIxKyPsiG+vxgvnkYalsx1xKdd3iul6lkofEklsosYRyXo7T9hxFTDM3XVxT5uFMUNTNgJkdcPfWqOOYi1BjDzVuKezYcymV9WE9SuP9C6lU160U1ytO60QsuRFLfsVxHYhp5ooRF7dfAgAAAEDAKOoAAAAAIGAUdTNza9QBzEOosYcatxR27LmUyvqwHqXx/oVUqutWiusVp3UiltyIJb/iuA7ENHMFj4vv1AEAAABAwLhSBwAAAAABo6gDAAAAgIBR1AEAAABAwCjqMrZu3eqSGBgKPUyJPGQo0jAl8pChSMOUyEOGIg1TIg8ZijTMG0VdxjPPxPHH57HQkIeIA/IQcUAeIg7IQ4SCog4AAAAAAkZRBwAAAAABWxR1AHGWTruOnexXd19K9dVJNdVWKZGwqMMCACDWOH8iDshDxEGx8pCibhLptOve9i7t2ntQqaG0kuUJ7dm5SVubG2gQAACYBOdPxAF5iDgoZh5y++Ukjp3sH9sBkpQaSmvX3oM6drI/4sgAAIgvzp+IA/IQcVDMPKSom0R3X2psB4xKDaV14lQqooiA2Vu1Zq3MbFbDqjVrow4bQMA4fyIOyEPEQTHzkNsvJ1FfnVSyPDFuRyTLE1qxLBlhVMDsdBx/Slfc8uCs5rnjmksLFA2AhYDzJ+KAPEQcFDMPuVI3iabaKu3ZuUnJ8pFNNHoPbFNtVcSRAQAQX5w/EQfkIeKgmHnIlbpJJBKmrc0Neum1r9WJUymtWEavSQAATIfzJ+KAPEQcFDMPKeqmkEiY1tUt1bq6pVGHAgBAMDh/Ig7IQ8RBsfKQ2y8BAAAAIGAUdQAAAAAQsKCLOjNLmtn3zewRM2s3s49lxl9gZveZ2ZHM3/OjjhUAAAAACiHook7SgKQt7n6JpE2StprZayRdL+l+d18v6f7McwAAAAAoOUEXdT7idOZpeWZwSdsl3ZYZf5ukHcWPDgAAAAAKL+iiTpLMrMzMDko6Iek+d39IUr27d0pS5u+KCEMEAAAAgIIJvqhz97PuvknSakmbzaxlpvOa2dVmdsDMDvT09BQsRmAqM83DVWvWysxmNQAzRXuIOCAPEQfkIUJUMr9T5+7Pmdm3JG2V1G1mje7eaWaNGrmKl2ueWyXdKkmtra1etGCBLDPNw47jT+mKWx6c1bLvuObS+QWHBYP2EHFAHiIOyEOEKOgrdWZWZ2bnZR5XSvolSf8tab+kqzKTXSVpXyQBAgAAAECBhX6lrlHSbWZWppECda+7321m35W018zeI+lJSe+IMkgAAAAAKJSgizp3PyTp5TnGn5R0WfEjAgAAAIDiCvr2SwAAAABY6CjqAAAAACBgFHUAAAAAEDCKOgAAAAAIGEUdAAAAAASMog4AAAAAAkZRBwAAAAABo6gDAAAAgIBR1AEAAABAwCjqAAAAACBgFHUAAAAAEDCKOgAAAAAIGEUdAAAAAASMog4AAAAAAkZRBwAAAAABo6gDAAAAgIBR1AEAAABAwIIu6sxsjZl908weM7N2M7suM/5PzOxpMzuYGd4cdawAAAAAUAiLog5gnoYlfcDdHzazZZJ+aGb3ZV77a3f/qwhjAwAAAICCC7qoc/dOSZ2Zx6fM7DFJq6KNCgAAAACKJ+jbL7OZWZOkl0t6KDPqfWZ2yMw+b2bnRxcZAAAAABROSRR1ZrZU0pclvd/d+yR9RtKLJW3SyJW8T0wy39VmdsDMDvT09BQrXGAc8hBxQB4iDshDxAF5iBAFX9SZWblGCrovuvtXJMndu939rLunJf29pM255nX3W9291d1b6+rqihc0kIU8RByQh4gD8hBxQB4iREEXdWZmkj4n6TF335M1vjFrsv9HUluxYwMAAACAYgi6oxRJPyfpNyQdNrODmXF/LOmdZrZJkks6JumaKIIDAAAAgEILuqhz9+9Ishwv3VPsWAAAAAAgCkHffgkAAAAACx1FHQAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABAwijoAAAAACBhFHQAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICALYo6gFFm9rOSfl3SayU1SjojqU3SVyX9s7v3RhgeAAAAAMRSLK7UmdnXJP22pK9L2qqRou5iSR+RlJS0z8y2RRchAAAAAMRTXK7U/Ya7PzNh3GlJD2eGT5jZ8uKHBQAAAADxFouiLrugM7MGSZsluaQfuHvXxGkAAAAAACNicfvlKDP7bUnfl/Qrkt4u6Xtm9ltTTL/GzL5pZo+ZWbuZXZcZf4GZ3WdmRzJ/zy/OGgAAAABAccWqqJP0QUkvd/ffdPerJL1S0oemmH5Y0gfc/WWSXiPpvWZ2saTrJd3v7usl3Z95DgAAAAAlJ25F3XFJp7Ken5L01GQTu3unuz+ceXxK0mOSVknaLum2zGS3SdpRiGABAAAAIGqx+E6dme3KPHxa0kNmtk8j36nbrpHbMWeyjCZJL5f0kKR6d++URgo/M1uR96ABAAAAIAbicqVuWWb4H0l3aqSgk6R9kjqnm9nMlkr6sqT3u3vfTN/UzK42swNmdqCnp2fWQQP5QB4iDshDxAF5iDggDxGiWFypc/ePzXVeMyvXSEH3RXf/SmZ0t5k1Zq7SNUo6Mcn73irpVklqbW31XNMAhUYeIg7IQ8QBeYg4IA8RolgUdaPM7C69cJVuVK+kA5JucffUhOlN0uckPebue7Je2i/pKkkfz/zdV7CgAQAAACBCcbn9ctQTGvnR8b/PDH2SuiVdlHk+0c9J+g1JW8zsYGZ4s0aKuTeY2RFJb8g8BwAAAICSE6srdRr5OYPXZT2/y8y+7e6vM7P2iRO7+3ck2STLuqwgEQIAAABAjMTtSl2dma0dfZJ5vDzzdDCakAAAAAAgvuJ2pe4Dkr5jZv+jkStwF0r6fTOr0gu/OwcAAAAAyIhVUefu95jZekkv1UhR999ZnaP8TWSBAQAAAEBMxeL2SzP7+dHH7j7g7o+4+8HRgs7Mqs2sJboIAQAAACCe4nKl7m1m9heS7pX0Q0k9kpKSfkbSL0p6kUZuzQQAAAAAZIlFUefu/8vMzpf0dknvkNQo6YykxzTy+3TfiTI+AAAAAIirWBR1kuTuP9ULv08HAAAAAJiB2BR1kmRm50l6l6QmZcXm7tdGFBIAAAAAxFqsijpJ90j6nqTDktIRxwIAAAAAsRe3oi7p7ruiDgIAAAAAQhGLnzTI8k9m9jtm1mhmF4wOUQcFAAAAAHEVtyt1g5L+UtL/luSZcS5pXWQRAQAAAECMxa2o2yXpZ9z9magDAQAAAIAQxK2oa5f0fNRBAHORTruOnexXd19K9dVJNdVWKZGwqMMCUEAc94gD8hASebDQxa2oOyvpoJl9U9LA6Eh+0gBxl0677m3v0q69B5UaSitZntCenZu0tbmBBhUoURz3iAPyEBJ5gPh1lHKnpJskPSjph1kDEGvHTvaPNaSSlBpKa9fegzp2sj/iyAAUCsc94oA8hEQeIGZX6tz9tqhjAOaiuy811pCOSg2ldeJUSuvqlkYUFYBC4rhHHJCHkMgDxOxKnZmtN7N/M7NHzeyJ0SHquIDp1FcnlSwffzglyxNasSwZUUQACo3jHnFAHkIiDxCzok7SFyR9RtKwpF+UdLukf5pqBjP7vJmdMLO2rHF/YmZPm9nBzPDmgkaNBa+ptkp7dm4aa1BH72Vvqq2KODIAhcJxjzggDyGRB4jZ7ZeSKt39fjMzd/+JpD8xs/+U9NEp5vlHSZ/SSAGY7a/d/a8KFCcwTiJh2trcoJde+1qdOJXSimX0OgWUOo57xAF5CIk8QPyKupSZJSQdMbP3SXpa0oqpZnD3b5tZUzGCA6aSSJjW1S3l3nVgAeG4RxyQh5DIg4Uubrdfvl/SEknXSnqlpN+Q9K45Lut9ZnYoc3vm+bkmMLOrzeyAmR3o6emZ49sA80MeIg7IQ8QBeYg4IA8RolgVde7+A3c/7e7H3f3dknZK+pk5LOozkl4saZOkTkmfmOT9bnX3Vndvraurm2vYwLyQh4gD8hBxQB4iDshDhCgWRZ2ZVZvZh83sU2b2RhvxPklHNVLYzYq7d7v7WXdPS/p7SZvzHTMAAAAAxEFcvlP3T5J+Kum7kn5b0gclVUja4e4HZ7swM2t0987M0/9HUttU0wMAAABAqOJS1K1z9w2SZGb/IOkZSWvd/dR0M5rZv0h6vaTlZnZcIz1lvt7MNklyScckXVOYsAEAAAAgWnEp6oZGH7j7WTP78UwKusz078wx+nN5iwwAAAAAYiwuRd0lZtaXeWySKjPPTZK7e3V0oQEAAABAfMWiqHP3sqhjAAAAAIAQxaL3SwAAAADA3FDUAQAAAEDAKOoAAAAAIGAUdQAAAAAQMIo6AAAAAAgYRR0AAAAABIyiDgAAAAACRlEHAAAAAAGjqAMAAACAgFHUAQAAAEDAKOoAAAAAIGAUdQAAAAAQMIo6AAAAAAgYRR0AAAAABIyiDgAAAAACFnxRZ2afN7MTZtaWNe4CM7vPzI5k/p4fZYwAAAAAUCjBF3WS/lHS1gnjrpd0v7uvl3R/5jkAAAAAlJzgizp3/7akZyeM3i7ptszj2yTtKGZMAAAAAFAswRd1k6h3905JyvxdkWsiM7vazA6Y2YGenp6iBgiMIg8RB+Qh4oA8RByQhwhRqRZ1M+Lut7p7q7u31tXVRR0OFijyEHFAHiIOyEPEAXmIEJVqUddtZo2SlPl7IuJ4AAAAAKAgSrWo2y/pqszjqyTtizAWAAAAACiY4Is6M/sXSd+V9BIzO25m75H0cUlvMLMjkt6QeQ4AAAAAJWdR1AHMl7u/c5KXLitqIAAAAAAQgeCv1AEAAADAQkZRBwAAAAABC/72y0IaHDyrQx296upLqbE6qQ0ra1RRURZ1WCWt/8yA2rtOq7tvQPXVi9XcsFRVlYujDmtGUqlhHe7sVVffgBqqF2tDY42SSQ4xlIYzZ4Z0uKtv7Njc0FCtxYsX6djJfnX3pVRfnVRTbZUSCYs61KLKtV0qK8ujDityhTp/Pn9mUG1dp8a2d0vDMi2prMhDxNFJp33ex1HI585SkE67nny2XydODajvzJCqK8s1MHxWK6srtWiRqat3QP2Dw3rRBVW6cHmVJI3t8yUVizR49qxqqxaP2/ejeXGyf0Am07P9A6qvrtTL6pfpeO8ZdfeltPK8pJ7rH1JnX0qNNZVqbqzWokVcr1mo+MQ5icHBs7rzUIdu2Nem1FBayfKEdm9v0Y6NKynsCqT/zIC+2nZCN+zP2ubbWvSWlhWxPzmlUsPaf7jznNi3bWiksEPwzpwZ0l1tXePy+y/etlFlCdMH/vWRsXF7dm7S1uaGBVPY5douu7e16K0tDQu6sCvU+fP5M4O6u637nO19eUt9sIVdOu26t71Lu/YenPNxFPK5sxSk064HHu/WEz392nPfj8b2wbVb1uuT//0jve2Va/Wxu9rHxn/qV1+uwWEft8+v3bJedxx4Uh/a+jJtbW6QJN3b3qU/v/cxXdG6Vjc/cGTcsfTpbx5RTbJc73z1i8Yt+8YdLdpxySoKuwWKvT6JQx29YyckSUoNpXXDvjYd6uiNOLLS1d51euykJGW2+f42tXedjjiy6R3u7M0Z++FO8gXhO9zVd05+H+05PVbQjY7btfegjp3sjzLUosq1XW7Y36bDXX0RRxatQp0/27pO5dzebV2n5h1zVI6d7B/7cC/N7TgK+dxZCo6d7Neh471jBZ00sg9ufuCI3nXpurGia3T8oeO95+zzmx84oss3rhrb96N5cfnGVWMF3ei0N+xr0+UbV+m3X/fic5b9kTvb1M7njgWLom4SXX2psQNlVGoore6+VEQRlb7uvoFJtvlARBHNXFfAsQPTyXVspl05c/7EqYXTRobcZhVSoc6fpbi9uyfZVrM5jkpxu4Skuy81aXt4ZmB4xm2n2Qv7fjQvRsflmjbXslNDaXX1Lpw2GONR1E2isTqpZPn4zZMsT6i+OhlRRKWvvnrxJNs8/rePNAQcOzCdXMdmmSlnzq9YtnDayJDbrEIq1PmzFLd3/STbajbHUSlul5DUVycnbQ+XLF4047bT/YV9n50Xk02ba9nJ8oQaahZOG4zxKOomsWFljXZvbxl3UO3e3qKNK2sijqx0NTcs1e5tE7b5thY1NyyNOLLpbWisyRn7hkbyBeHb0FB9Tn6/uG6pPvGOS8aN27Nzk5pqq6IMtahybZfd21q0oaE64siiVajzZ0vDspzbu6Vh2bxjjkpTbZX27Nw0r+Mo5HNnKWiqrdKG1TXa9YaLxu2Da7es120PPqGPvrV53PgNq2vO2efXblmvuw89PbbvR/Pirkee1rVb1p9zLN196Gn9/bf/55xl37ijRc187liwzN2jjiEWWltb/cCBA+PGjfbeNdoj1UZ6vyy4kHvwGu39cqwXvNy9X075zfdceTg2o5muuOXBWcV0xzWXzmke2oWSN+s8nKr3yxOnUlqxjN4v6f3yBTM8f846D0u598v5HEchnztjYM7n5VGjvV/2nBpQ75khVSfLNXj2rBqqK1We6f3y+cFhrc3Z+2WZhs6mdcG0vV8Oqr56sV5WX63jvWd04lRKjTUjvV929aXUUJNUc2MNnaSEa94nT7rlm0JFRZlamy6IOowFpapysTZfGOaJKJlcpFddWBt1GEBBVFaWa3OO/F5Xt1Tr6hbuFYHJtstCV6jz55LKipLb3omEzfs4CvncWQoSCVPT8qVqWp57H76o9tzx0+3zqfIie/yLaqVL5hg3SgvlPAAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABCwkv6dOjM7JumUpLOSht29NdqIAAAAACC/Srqoy/hFd38m6iAAAAAAoBC4/RIAAAAAAlbqRZ1L+oaZ/dDMro46GAAAAADIt1Iv6n7O3V8h6ZclvdfMXpf9opldbWYHzOxAT09PNBFiwSMPEQfkIeKAPEQckIcIUUkXde7ekfl7QtK/S9o84fVb3b3V3Vvr6uqiCBFYkHm4as1amdmshlVr1kYddmSKsb0WYh4ifshDxAF5iBCVbEcpZlYlKeHupzKP3yhp92yWkU67jp3sV3dfSvXVSTXVVimRsILEm2+Dg2d1qKNXXX0pNVYntWFljSoqyqIOa1qp1LAOd/aqq29ADdWLtaGxRslkGGl65syQDnf1qbtvQPXVi7WhoVqVleVRhxVLHcef0hW3PDiree645tICRRN/cdhez51J6Udd/WP5fVFDlc6rTOb1PUIU8nkiROQh4iCddj35bL9Onh7UcDqtM0NntXhRmU6nhlWzpFxnBodVtXiRli9drLUX0CagOML4tDw39ZL+3cykkfX8P+5+70xnTqdd97Z3adfeg0oNpZUsT2jPzk3a2twQ+4NzcPCs7jzUoRv2tY3Fvnt7i3ZsXBnrwi6VGtb+w526YX9W3NtatG1DY+wLuzNnhnRXW9c5sb+1pYHCDsF77kxK32jrOSe/39hSt6A/UId8nggReYg4SKddDzzerY6fntFQ2nX7d4/pita1uvmBI2N5ee2W9brjwJN618826UW1S7TlJfW0CSi4kr390t2fcPdLMkOzu980m/mPnewfO1FLUmoorV17D+rYyf6CxJtPhzp6xwo6aST2G/a16VBHb8SRTe1wZ+/YyVrKxL2/TYc74x23JB3u6ssde1dfxJEB8/ejrv6c+f2jrvi3h4UU8nkiROQh4uDYyX4dOt6rZ/oH9Zdff1yXb1w1VtBJI3l58wNHdPnGVfrLrz+uQ8d7aRNQFCVb1M1Xd19q7AAdlRpK68SpVEQRzVzXJLF398U79q6+gUniHogoopnrDjh2YDrkd24hnydCRB4iDrr7Ukq7lPaR/DNTzrwcHZ920SagKCjqJlFfnVSyfPzmSZYntGJZ/G/xaJwk9vrqeMfeUL14krgXRxTRzNUHHDswHfI7t5DPEyEiDxEH9dVJlZlUZhrLx1x56T7yN2GiTUBRUNRNoqm2Snt2bhp3wO7ZuUlNtVURRza9DStrtHt7y7jYd29v0caVNRFHNrUNjTXavW1C3NtatKEx3nFL0oaG6tyxN1RHHBkwfxc1VOXM74sa4t8eFlLI54kQkYeIg6baKm1YXaPaqgp98E0v0V2PPK1rt6wfl5fXblmvuw89rQ++6SXauLqGNgFFEe/eJyKUSJi2Njfopde+VidOpbRiWTi9mlVUlGnHxpVat7xqrEe2jQH0fplMLtK2DY26cPmSF3qQDKT3y8rKcr21pUFN2bHT+yVKxHmVSb2xpU5NyzfT62CWkM8TISIPEQeJhGnLS+rHer/c8LaNen7orL7wm68a1/vlJ95xCb1foqji/2k5QomEaV3dUq2rWxp1KLNWUVGm1qYLog5j1pLJRXrVhbVRhzEnlZXl2hxo7MB0zqtMavOFfHieKOTzRIjIQ8RBImFqWr5UTcujjgR4AbdfAgAAAEDAKOoAAAAAIGDm7lHHEAtm1iPpJ5O8vFzSM0UMJ59CjT3UuKWpY3/G3bdONuM0eRiVkPdFNtbjBfPJw1LZjrmU6rrFdb1KJQ+JJbdQYgnlvByn7TmKmGZuurimzMOZoKibATM74O6tUccxF6HGHmrcUtix51Iq68N6lMb7F1Kprlsprlec1olYciOW/IrjOhDTzBUjLm6/BAAAAICAUdQBAAAAQMAo6mbm1qgDmIdQYw81bins2HMplfVhPUrj/QupVNetFNcrTutELLkRS37FcR2IaeYKHhffqQMAAACAgHGlDgAAAAACRlEHAAAAAAGjqAMAAACAgFHUZWzdutUlMTAUepgSechQpGFK5CFDkYYpkYcMRRqmRB4yFGmYN4q6jGeeieOPz2OhIQ8RB+Qh4oA8RByQhwgFRR0AAAAABIyiDgAAAAACtijqAOJscPCsDnX0qqsvpcbqpDasrFFFRVnUYSGm0mnXsZP96u5Lqb46qabaKiUSFnVY5yCvS1soeQjMBfmd2/BwWu2dversTamxplLNjdVatIj/28chX+Ybw1Tzx2H9ML1i7SeKukkMDp7VnYc6dMO+NqWG0kqWJ7R7e4t2bFzJB2CcI5123dvepV17D47ly56dm7S1uSFWDSx5XdpCyUNgLsjv3IaH07rzkaf1kTtfaNdv3NGiHZesWtCFXRzyZb4xTDW/pMjXD9MrZh4u3KN9Goc6esc++EpSaiitG/a16VBHb8SRIY6OnewfO2ClkXzZtfegjp3sjziy8cjr0hZKHgJzQX7n1t7ZO1bQSSPb5SN3tqm9c2G363HIl/nGMNX8cVg/TK+Y+ymYos7MjpnZYTM7aGYHMuMuMLP7zOxI5u/5WdN/2MyOmtnjZvam2b5fV19qbAeMSg2l1d2Xmve6oPR0T5IvJ07FK1/I69JWjDxctWatzKxgw6o1a/MWK0pLKO1ssXX25t4uXb0Le7vEIV/mG8NU88dh/TC9Yu6n0G6//EV3z+5b9npJ97v7x83s+szzD5nZxZKulNQsaaWk/zCzi9z97EzfqLE6qWR5YtyOSJYnVF+dzMuKoLTUT5IvK5bFK1/I69JWjDzsOP6Urrjlwbwtb6I7rrm0YMtG2EJpZ4utsaYy53ZpqFnY2yUO+TLfGKabP+r1w/SKmYfBXKmbxHZJt2Ue3yZpR9b4L7n7gLv/WNJRSZtns+ANK2u0e3uLkuUjm2j0u0cbV9bkJXCUlqbaKu3ZuWlcvuzZuUlNtVURRzYeeV3aQslDYC7I79yaG6t1447x7fqNO1rU3Liw2/U45Mt8Y5hq/jisH6ZXzP1k7nn5EfOCM7MfS/qpRn51/RZ3v9XMnnP387Km+am7n29mn5L0PXf/58z4z0n6mrv/24RlXi3paklau3btK3/yk5+Me8/RXgJHe6vZSC+BmMJo70YnTqW0YtmkvRudO2KaPMw38rq0FToPzazgV+pCOS9h3madhzPM7wVntPfLrt6UGmqSam6sWdCdpIyKw3l5vjk71fwcD2GYax7OVkhF3Up37zCzFZLuk/QHkvZPUtR9WtJ3JxR197j7lydbfmtrqx84cKCwKwFMc9CShyiSOechRR3yiPYQcUAeIg7mXdQF828cd+/I/D0h6d81cjtlt5k1SlLm74nM5MclrcmafbWkjuJFCwAAAADFEURRZ2ZVZrZs9LGkN0pqk7Rf0lWZya6StC/zeL+kK81ssZldKGm9pO8XN2oAAAAAKLxQer+sl/TvZiaNxPx/3P1eM/uBpL1m9h5JT0p6hyS5e7uZ7ZX0qKRhSe+dTc+XAAAAABCKIIo6d39C0iU5xp+UdNkk89wk6aYChwYAAAAAkQri9ksAAAAAQG4UdQAAAAAQMIo6AAAAAAgYRR0AAAAABIyiDgAAAAACRlEHAAAAAAGjqAMAAACAgFHUAQAAAEDAKOoAAAAAIGAUdQAAAAAQMIo6AAAAAAgYRR0AAAAABIyiDgAAAAACRlEHAAAAAAGjqAMAAACAgFHUAQAAAEDAKOoAAAAAIGAUdQAAAAAQMIo6AAAAAAhYUEWdmZWZ2X+Z2d2Z5xeY2X1mdiTz9/ysaT9sZkfN7HEze1N0UQMAAABA4QRV1Em6TtJjWc+vl3S/u6+XdH/muczsYklXSmqWtFXS35lZWZFjBQAAAICCC6aoM7PVkt4i6R+yRm+XdFvm8W2SdmSN/5K7D7j7jyUdlbS5SKECAAAAQNEEU9RJ+htJfyQpnTWu3t07JSnzd0Vm/CpJT2VNdzwzDgAAAABKShBFnZldLumEu/9wprPkGOc5lnu1mR0wswM9PT3zihGYK/IQcUAeIg7IQ8QBeYgQBVHUSfo5SdvM7JikL0naYmb/LKnbzBolKfP3RGb645LWZM2/WlLHxIW6+63u3ururXV1dYWMH5gUeYg4IA8RB+Qh4oA8RIiCKOrc/cPuvtrdmzTSAcoD7v7rkvZLuioz2VWS9mUe75d0pZktNrMLJa2X9P0ihw0AAAAABbco6gDm6eOS9prZeyQ9KekdkuTu7Wa2V9KjkoYlvdfdz0YXJgAAAAAURnBFnbt/S9K3Mo9PSrpskuluknRT0QIDAAAAgAgEcfslAAAAACA3ijoAAAAACBhFHQAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABAwijoAAAAACBhFHQAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABAwijoAAAAACBhFHQAAAAAEbFEx38zMVku6UtJrJa2UdEZSm6SvSvqau6eLGQ8AAAAAhK5oV+rM7AuSPi9pUNKfS3qnpN+X9B+Stkr6jpm9bpJ5k2b2fTN7xMzazexjmfEXmNl9ZnYk8/f8rHk+bGZHzexxM3tTodcPAAAAAKJQzCt1n3D3thzj2yR9xcwqJK2dZN4BSVvc/bSZlWukAPyapF+RdL+7f9zMrpd0vaQPmdnFGrki2KyRK4L/YWYXufvZfK8UAAAAAESpaFfqsgs6M6sws41mtiFTzMndB9396CTzurufzjwtzwwuabuk2zLjb5O0I/N4u6QvufuAu/9Y0lFJm/O9TgAAAAAQtaJ3lGJmb5H0P5JulvQpSUfN7JdnMF+ZmR2UdELSfe7+kKR6d++UpMzfFZnJV0l6Kmv245lxAAAAAFBSouj98hOSftHdX+/uvyDpFyX99XQzuftZd98kabWkzWbWMsXklmsR50xkdrWZHTCzAz09PTOLHsgz8hBxQB4iDshDxAF5iBBFUdSdmHCb5RMaufo2I+7+nKRvaaRzlW4za5SkzN/R5RyXtCZrttWSOnIs61Z3b3X31rq6utmsA5A35CHigDxEHJCHiAPyECEqZu+Xv2JmvyKp3czuMbPfNLOrJN0l6QfTzFtnZudlHldK+iVJ/y1pv6SrMpNdJWlf5vF+SVea2WIzu1DSeknfz/c6AQAAAEDUitn75VuzHndL+oXM4x5J5587+TiNkm4zszKNFKJ73f1uM/uupL1m9h5JT0p6hyS5e7uZ7ZX0qKRhSe+l50sAAAAApahoRZ27v3se8x6S9PIc409KumySeW6SdNNc3xMAAAAAQlDMK3WSJDO7OcfoXkkH3H1fjtcAAAAAAJOIoqOUpKRNko5kho2SLpD0HjP7mwjiAQAAAIBgFf1KnaSfkbTF3Yclycw+I+kbkt4g6XAE8QAAAABAsKK4UrdKUlXW8ypJKzMdmQxEEA8AAAAABCuKK3V/IemgmX1LIz8S/jpJf2ZmVZL+I4J4ECOp1LAOd/aqq29ADdWLtaGxRslkFGk6e4ODZ3Woo1ddfSk1Vie1YWWNKirK5r3cdNp17GS/uvtSqq9Oqqm2SomEzXl5Z84M6XBXn7r7BlRfvVgbGqpVWVk+7zjzJd/rC6B0DA+n1d7Zq87elBprKtXcWK1Fi6L4/3T+5KPNC/ncGaKJ+2zt+Uv05E+fn/U+nGrfj752sn9AFWUJPT94dtJp5hsHSkPRj3h3/5yZ3SNps0aKuj9299EfBv9gseNBfKRSw9p/uFM37G9TaiitZHlCu7e1aNuGxtifnAYHz+rOQx26YV9W7NtbtGPjynkVdum06972Lu3ae3BsuXt2btLW5oY5NdRnzgzprrauc7bxW1saYlHY5Xt9AZSO4eG07nzkaX3kzhfarxt3tGjHJauCLezy0eaFfO4MUa59duOOFv3tA0f0k5NnZrwPp9r3knRve5f+/N7HdEXrWt38wJFJpxmd/0W1lfqDLevHHR+cPxeWYv74eNPoY3fvdPd97n7naEFnI1YXKx7Ez+HO3rGTkiSlhtK6YX+bDnf2RhzZ9A519I4VdFIm9n1tOtQxv9iPnewfa7BHl7tr70EdO9k/p+Ud7urLvY27+uYVZ77ke30BlI72zt6xD6zSSPvwkTvb1B7AOWIy+WjzQj53hijXPvvInW26fOOqsecz2YdT7fvR1y7fuGqsoJtsmtHXLt+46pzjg/PnwlLMf239pZl92czeZWbNZrbCzNaa2RYz+1NJ/5+klxUxHsRMV9/AWGM0KjWUVndf/L9q2dWXmiT21LyW2z3Jck+cmttyu2O+jfO9vgBKR2dv7vahqzfc9iEfbV7I584QTbbPzMY/n24fTrXvR18z07TTjJpqWiwMxfzx8XeY2cWSfk3Sb0lqlHRG0qOS7pF0k7uTeQtYQ/ViJcsT4xqlZHlC9dWLI4xqZhqrk5PEnpzXcusnWe6KZXNbbn3Mt3G+1xdA6WisqczZPjTUhNs+5KPNC/ncGaLJ9pm7xj2fbh9Ot++T5Ymxv1NNM/E1zp8LV1FvQnf3R939f7v76939Je6+yd1/1d3/mYIOGxprtHtby7iGbPe2Fm1orIk4sultWFmj3dsnxL69RRtXzi/2ptoq7dm5adxy9+zcpKbaqmnmnCTOhurc27ihel5x5ku+1xdA6WhurNaNO8a3XzfuaFFzAOeIyeSjzQv53BmiXPvsxh0tuvvQ02PPZ7IPp9r3o6/d9cjTunbL+imnGX3trkeePuf44Py5sJhn/2uh0G9mVivpVyW9NDPqMUn/4u4nixbEJFpbW/3AgQNRh7HgjfbgNdYzY0A9eI32fjna69TG3L1fTvlt5Vx5ONq71YlTKa1YtnB6v8zX+iKnWefh2IxmuuKWBwsSlCTdcc2lKuZ5CZGadR6O9n7Z1ZtSQ01SzY01wXaSMiofbV7I584YmPd5ebTXydnuw6n2/ehrz/YPqHya3i/nGwdiYd47qmhHvJm9TNIDkr4u6b80EvyrJP2xmW1x9/8uViyIr2RykV51YW3UYcxJRUWZWpsuyPtyEwnTurqlWle3NC/Lq6ws1+YYb+N8ry+A0rFoUUKXrDlfl6yJOpL8yUebF/K5M0S59tlc9uFU+34meZGvOFAaivlvnD+VdJ27780eaWZvk3STpLcVMRYAAAAAKAnFvGdhw8SCTpLc/cuSWooYBwAAAACUjGIWdVP9UAY/ogEAAAAAc1DM2y9XmNmuHONNUl0R4wAAAACAklHMou7vJS2b5LV/KGIcAAAAAFAyivnj4x8r1nsBAAAAwEJRtO/UmdnvmNn6zGMzs8+bWa+ZHTKzlxcrDgAAAAAoJcXsKOU6Sccyj98p6RJJ6yTtknRzEeMAAAAAgJJRzKJu2N2HMo8vl3S7u5909/+QVDXVjGa2xsy+aWaPmVm7mV2XGX+Bmd1nZkcyf8/PmufDZnbUzB43szcVbK0AAAAAIELFLOrSZtZoZklJl0n6j6zXKqeZd1jSB9z9ZZJeI+m9ZnaxpOsl3e/u6yXdn3muzGtXSmqWtFXS35lZWV7XBgAAAABioJhF3Q2SDmjkFsz97t4uSWb2C5KemGpGd+9094czj09JekzSKknbJd2Wmew2STsyj7dL+pK7D7j7jyUdlbQ5nysDAAAAAHFQzJ806Jb0s5JOuftPzexdkt6WGX/1TBdiZk2SXi7pIUn17t4pjRR+ZrYiM9kqSd/Lmu14ZtzEZV09+t5r166d7foAeUEeIg7IQ8QBeYg4IA8RomJeqbtF0ulMQfc6SR+XdLtGirpPzmQBZrZU0pclvd/d+6aaNMc4P2eE+63u3ururXV1/P45okEeIg7IQ8QBeYg4IA8RomJeqStz92czj6+QdKu7f1nSl83s4HQzm1m5Rgq6L7r7VzKju82sMXOVrlHSicz445LWZM2+WlJHPlYCAAAAAOKkmFfqysxstIi8TNIDWa9NWVyamUn6nKTH3H1P1kv7JV2VeXyVpH1Z4680s8VmdqGk9ZK+P8/4AQAAACB2inml7l8k/V8ze0bSGUn/KUlm9jOSeqeZ9+ck/Yakw1lX9f5YI7dw7jWz90h6UtI7JMnd281sr6RHNdJz5nvd/Wx+VwcAAAAAole0os7dbzKz+yU1SvqGu49+xy0h6Q+mmfc7yv09OWnkql/O95N00xzDBQBgxlatWauO408VZNkrV6/R0089WZBlAwBKQzGv1Mndv5dj3I+KGQMAAPnWcfwpXXHLgwVZ9h3XXFqQ5QIASkcxv1MHAAAAAMgzijoAAAAACBhFHQAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABAwijoAAAAACBhFHQAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABAwijoAAAAACFgQRZ2Zfd7MTphZW9a4C8zsPjM7kvl7ftZrHzazo2b2uJm9KZqoAQAAAKDwgijqJP2jpK0Txl0v6X53Xy/p/sxzmdnFkq6U1JyZ5+/MrKx4oQIAAABA8QRR1Ln7tyU9O2H0dkm3ZR7fJmlH1vgvufuAu/9Y0lFJm4sRJwAAAAAUWxBF3STq3b1TkjJ/V2TGr5L0VNZ0xzPjAAAAAKDkhFzUTcZyjPOcE5pdbWYHzOxAT09PgcMCciMPEQfkIeKAPEQckIcIUchFXbeZNUpS5u+JzPjjktZkTbdaUkeuBbj7re7e6u6tdXV1BQ0WmAx5iDggDxEH5CHigDxEiEIu6vZLuirz+CpJ+7LGX2lmi83sQknrJX0/gvgAAIi9VWvWyswKMqxaszbq1QOABWFR1AHMhJn9i6TXS1puZsclfVTSxyXtNbP3SHpS0jskyd3bzWyvpEclDUt6r7ufjSRwAABiruP4U7rilgcLsuw7rrm0IMsFAIwXRFHn7u+c5KXLJpn+Jkk3FS4iAAAAAIiHkG+/BAAAAIAFj6IOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABAwijoAAAAACBhFHQAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABAwijoAAAAACNiiqAOIs3Tadexkv7r7UqqvTqqptkqJhEUdFjAv5DUAlBba9YWDfY3JUNRNIp123dvepV17Dyo1lFayPKE9Ozdpa3MDBw+CRV4DQGmhXV842NeYCrdfTuLYyf6xg0aSUkNp7dp7UMdO9kccGTB35DUAlBba9YWDfY2pUNRNorsvNXbQjEoNpXXiVCqiiID5I68BoLTQri8c7GtMpaSLOjPbamaPm9lRM7t+NvPWVyeVLB+/eZLlCa1YlsxrjEAxkdcAUFpo1xcO9jWmUrLfqTOzMkmflvQGSccl/cDM9rv7ozOZv6m2Snt2bjrnvuWm2qpChp03g4NndaijV119KTVWJ7VhZY0qKsqiDmta/WcG1N51Wt19A6qvXqzmhqWqqlwcdVgz8vyZQbV1nRqLvaVhmZZUVsx7uc+dSelHXf1jy72ooUrnVc6tAW+qrdIn3nGJPvCvj4zl9SfecUkweQ1gYctne1gqQv+8Eqp02vXks/165vSATg8MK7moTKdSw1pSUaaK8oT6B4ZUW5XU0sWL1DWhU5O5dnbCvsZUSraok7RZ0lF3f0KSzOxLkrZLmlFRJ0kVi0xXv26d0i4lbOR5CAYHz+rOQx26YV/b2EG/e3uLdmxcGevCrv/MgL7adkI37M+Ke1uL3tKyIvaF3fNnBnV3W/c5sV/eUj+vwu65Myl9o63nnOW+saVuTh9kBgaGdTbt4/L6bNo1MDCsysryOccJAIWW7/awlIT6eSVU6bTrgce79ZOTz+v27x7TFa1rdfMDR8by8rrL1mtJeZk+uv9Rvetnm/QP//lj/fT5Qe3ZuUlvfFm9vvFY95w6O0kkTFubG/TSa1+rE6dSWrGM3i/xglIu6lZJeirr+XFJr57pzMdO9ut9/+e/xt27nCxP6J5rX6t1dUvzF2UBHOroHSvopJH7rW/Y16Z1y6vU2nRBxNFNrr3r9NjJWsrEvb9NTcs3a/OF8S7q2rpOTRL7Em2+sHbOy/1RV/8U22T2H2IOd/Xpj7586Jy8vv23Ns8rTgAotHy3h6Ui5M8roTp2sl+Hjvfq1m8/off8/Lqxgk4ayctP3n9EV79unS7fuEp/+fXH9Z6fX6dPf/Oodu09qDuufk3Ozk5eOsP9lUiY1tUtZd/iHKX8nbpc/7bwcROYXW1mB8zsQE9Pz7gJQ/4yatcksXf3xTv27r6BSeIeiCiimZtP7FPnYX63ScjbGIU1VR4CxVLM9rBUhPx5Ja6maw+7+1JK+8h2NlPO7Z92jb1m9sL4zl72FwqjlIu645LWZD1fLakjewJ3v9XdW929ta6ubtzMIX8ZtXGS2Our4x17ffXiSeKO91U6aX6xT52H+d0mIW9jFNZUeQgUSzHbw1IR8ueVuJquPayvTqrMNLbdc23/hEnuI4/dXxjfWFPJ/kJBlHJR9wNJ683sQjOrkHSlpP0znXn0y6jZB2woX0bdsLJGu7e3jIt99/YWbVxZE3FkU2tuWKrd2ybEva1FzQ3xv8WgpWFZzthbGpbNa7kXNVTlXO5FDXPLww0N1TmXt6Ghel5xAkCh5bs9LBUhf14JVVNtlTasrtEH3/QS3fXI07p2y/px2/+6y9ardkmF7j70tD74ppfoKw8fH9svzY3V7C8URMl+p87dh83sfZK+LqlM0ufdvX2m84f8ZdSKijLt2LhS65ZXjfWstDGA3i+rKhfrLS0r1LR8c3C9Xy6prNDlLfVqWr4kr71fnleZ1Btb6sZtk/n09lZZWa63tjSMi3NDQzWdpACIvXy3h6Ui5M8roUokTFteUq8nn+3XJatrdHpgWF/4zVfpdGpYlRVlWlyeUP/AsP72nS/XssXlal5ZPW6/sL9QCCVb1EmSu98j6Z65zh/yl1ErKspi3SnKZKoqF8e+U5TJLKmsKEhnI+dVJvPaCUBlZTmdogAIUr7bw1IR8ueVUCUSpqblS9W0fPptfuGE/cL+QiGU8u2XAAAAAFDyKOoAAAAAIGDm7tNPtQCYWY+kn0zy8nJJzxQxnHwKNfZQ45amjv0Zd9862YzT5GFUQt4X2ViPF8wnD0tlO+ZSqusW1/UqlTwkltxCiSWU83KctucoYpq56eKaMg9ngqJuBszsgLu3Rh3HXIQae6hxS2HHnkuprA/rURrvX0ilum6luF5xWidiyY1Y8iuO60BMM1eMuLj9EgAAAAACRlEHAAAAAAGjqJuZW6MOYB5CjT3UuKWwY8+lVNaH9SiN9y+kUl23UlyvOK0TseRGLPkVx3UgppkreFx8pw4AAAAAAsaVOgAAAAAIGEUdAAAAAASMoi7DzO6NOgaAPEQckIeIA/IQcUAeIhSLog4gLl75yle+SRJfMESh2VQvkocoEvIQcUAeIg7IQ8TBlHk4E1ypAwAAAICAUdQBAAAAQMBKuqgzs/9lZu1m1mZm/2JmyahjAgAAAIB8KtmizsxWSbpWUqu7t0gqk3TlbJaRTrue6Dmt7/7PM3qi57TSaW6pBhBvtFuIA/IQAEYUqz0s9Y5SFkmqNLMhSUskdcx0xnTadW97l3btPajUUFrJ8oT27Nykrc0NSiTm/V1GAMg72i3EAXkIACOK2R6W7JU6d39a0l9JelJSp6Red//GTOc/drJ/bAdIUmoorV17D+rYyf6CxAsA80W7hTggDwFgRDHbw5It6szsfEnbJV0oaaWkKjP79QnTXG1mB8zsQE9Pz7j5u/tSYztgVGoorROnUoUNHAvOVHkIzMZ82i3yEPlSjDxctWatzKwgw6o1a+e9DRA22kPkSzHriVK+/fKXJP3Y3Xskycy+IulSSf88OoG73yrpVklqbW0dd4NrfXVSyfLEuB2RLE9oxTL6WkF+TZWHwGzMp90iD5EvxcjDjuNP6YpbHsxDtOe645pLC7JchIP2EPlSzHqiZK/UaeS2y9eY2RIzM0mXSXpspjM31VZpz85NSpaPbKLRe2CbaqsKEy0AzBPtFuKAPASAEcVsD0v2Sp27P2Rm/ybpYUnDkv5Lmf+6zEQiYdra3KCXXvtanTiV0oplSTXVVvElbwCxRbuFOCAPAWBEMdvDki3qJMndPyrpo3OdP5EwratbqnV1S/MYFQAUDu0W4oA8BIARxWoPS/n2SwAAAAAoeRR1AAAAABAwijoAAAAACBhFHQAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABAwijoAAAAACBhFHQAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABAwijoAAAAACBhFHQAAAAAEjKIOAAAAAAJGUQcAAAAAAaOoAwAAAICAUdQBAAAAQMAo6gAAAAAgYBR1AAAAABAwijoAAAAACBhFHQAAAAAErKSLOjM7z8z+zcz+28weM7OfjTomAAAAAMinRVEHUGCflHSvu7/dzCokLYk6IAAAAADIp5It6sysWtLrJP2mJLn7oKTBKGMCAAAAgHwr5dsv10nqkfQFM/svM/sHM6uKOigAAAAAyKdSLuoWSXqFpM+4+8sl9Uu6PnsCM7vazA6Y2YGenp4oYgTIQ8QCeYg4IA8RB+QhQlTKRd1xScfd/aHM83/TSJE3xt1vdfdWd2+tq6sreoCARB4iHshDxAF5iDggDxGiki3q3L1L0lNm9pLMqMskPRphSAAAAACQdyXbUUrGH0j6YqbnyyckvTvieAAAAAAgr0q6qHP3g5Jao44DAAAAAAqlZG+/BAAAAICFgKIOAAAAAAIW+9svzWy1pCslvVbSSklnJLVJ+qqkr7l7OsLwAAAAACBSsS7qzOwLklZJulvSn0s6ISkp6SJJWyX9bzO73t2/HV2UAAAAABCdWBd1kj7h7m05xrdJ+kqmV8u1RY4JAAAAAGIj1kVddkGXKeBeKsklPe7ug+4+KOloVPEBAAAAQNRiXdSNMrO3SPqspP+RZJIuNLNr3P1r0UYGAAAAANEKoqiT9AlJv+juRyXJzF6sTEcpkUYFAAAAABEL5ScNTowWdBlPaKTTFAAAAABY0GJ9pc7MfiXzsN3M7pG0VyPfqXuHpB9EFhgAAAAAxESsizpJb8163C3pFzKPeySdX/xwAAAAACBeYl3Uufu7o44BAAAAAOIs1kXdKDO7OcfoXkkH3H1fseMBAAAAgLgIpaOUpKRNko5kho2SLpD0HjP7m+jCAgAAAIBoBXGlTtLPSNri7sOSZGafkfQNSW+QdDjKwAAAAAAgSqFcqVslqSrreZWkle5+VtJANCEBAAAAQPRCuVL3F5IOmtm3JJmk10n6MzOrkvQfUQYGAAAAAFEKoqhz989lfqdus0aKuj92947Myx+MLjIAAAAAiFasizoza3L3Y5Lk7p2S9k143SStcvfjEYQHAAAAAJGLdVEn6S/NLKGRYu6HGvnR8aRGOk75RUmXSfqoJIo6AAAAAAtSrIs6d3+HmV0s6dck/ZakRklnJD0m6auSbnL3VIQhAgAAAECkYl3USZK7Pyrpf0cdBwAAAADEURA/aWBm7zCzZZnHHzGzr5jZK6KOCwAAAACiFkRRJ+n/5+6nzOznJb1J0m2SPhNxTAAAAAAQuVCKurOZv2+R9Bl33yepIsJ4AAAAACAWQinqnjazWyTtlHSPmS1WOLEDAAAAQMGEUhjtlPR1SVvd/TlJF4gfHQcAAACA+Pd+mbFR0n3ufirz/LSk3gjjAQAAAIBYCKWo+4yk7N4u+3OMy8nMyiQdkPS0u18+mzcdHk6rvbNXnb0pNdZUqrmxWosWhXJxM0yDg2d1qKNXXX0pNVYntWFljSoqyqIOa0ZCib3/zIDau06ru29A9dWL1dywVFWVi6MOa0w67Tp2sl/dfSnVVyfVVFulRMKiDmvBy7VfJC34fZVKDetwZ6+6+gbUUL1YGxprlEyGcmotHM6fwOSy29MlFYs0ePasaqsW52xDJx5LL6tfpuO9Z9Tdl9LK85J6rn9InX3nvrZQ2+SFLJQzj7m7jz5x97SZzTT26zTyY+XVs3nD4eG07nzkaX3kzjalhtJKlid0444W7bhkFSemAhkcPKs7D3Xohn0vbPPd21u0Y+PKWBZH2UKJvf/MgL7adkI37M+Kc1uL3tKyIhaFXTrture9S7v2HhyLb8/OTdra3MCJKUK59sunfvXlGhz2Bb2vUqlh7T/cec7xtG1D44Iu7Dh/ApPL1Z5eu2W97jjwpD609WXj2tBcx9Lu7S369DePqCZZrne++kX62F3t57z2k5NnFmSbvNCF0ro+YWbXmll5ZrhO0hPTzWRmqzXSY+Y/zPYN2zt7xw4iSUoNpfWRO9vU3sldn4VyqKN3rCiSRrb5DfvadKgj/ts8lNjbu06PfQCVMnHub1N71+mIIxtx7GT/2IlOGolv196DOnayP+LIFrZc++XQ8d4Fv68Od/bmPJ4OL/DzBOdPYHK52tObHziiyzeuOqcNzXUs3bCvTZdvXKXfft2Lxwq6ia+NPl9obfJCF0pR97uSLpX0tKTjkl4t6eoZzPc3kv5IUjrXi2Z2tZkdMLMDPT09417r7E2NHSijUkNpdfWmZh08ZqarL/c27+6L/zafT+xT5WG+dfcNTBLnQEHfd6a6J9mOJ07FPwdCN1Ue5tovadeC31ddMT+eojKf82cx20NgMoXMw8nOc2bntqGTHUtm0pmB4Ulfy36+kNrkhS6Ios7dT7j7le6+wt3r3f1X3f3EVPOY2eWSTrj7D6dY7q3u3ururXV1deNea6ypVLJ8/OZJlifUUJOcx5pgKo3VyZzbvL46/tt8PrFPlYf5Vl+9eJI4o7/1UpLqJ9mOK5bFPwdCN1Ue5tovZaYFv68aYn48RWU+589itofAZAqZh5Od59zPbUMnO5bcpSWLF036WvbzhdQmL3SxLurM7I8yf//WzG6eOEwz+89J2mZmxyR9SdIWM/vnmb53c2O1btzRMnbAjH4noLmxZm4rg2ltWFmj3dvHb/Pd21u0cWX8t3kosTc3LNXubRPi3Nai5oalEUc2oqm2Snt2bhoX356dm8Y65UA0cu2XDatrFvy+2tBYk/N42rDAzxOcP4HJ5WpPr92yXncfevqcNjTXsbR7e4vuPvS0/v7b/6OPvrU552ujzxdam7zQmWeX9DFjZm9197vM7Kpcr7v7bTNczusl/eFUvV+2trb6gQMHxo0b7XGoqzelhpqkmhtr+JJ3gY32IDnac9PGmPYgmcsMY5/y28q58jDfQun98sSplFYso/euApl1HubaL5IW/L4a7f1y9Hii98sRMzx/zrk9NDNdccuDeYp2vDuuuVRx/myEvCv6eXl875dlGjqb1gXT9H45eiy9rL5ax3vP6MSplBprRnq/7Oo797WF2iYHbN47KtZnHne/K/N3RsVbvi1alNAla87XJWuiePeFqaKiTK1NF0QdxpyEEntV5WJtvjA+RdxEiYRpXd1SrauLx9VDjJhsvyz0fZVMLtKrLqyNOozY4fwJTG4257lcx1L2vC+qlS5R7tewsMS6qBtlZhdJ+kNJTcqK2d23zGR+d/+WpG8VIDQAAAAAiFQQRZ2kf5X0WY38NMHZiGMBAAAAgNgIpagbdvfPRB0EAAAAAMRNrIs6Mxv9gtJdZvb7kv5d0tgPALn7s5EEBgAAAAAxEeuiTtIPJble6BHmg1mvuaR1RY8IAAAAAGIk1kWdu18YdQwAAAAAEGexLupGmVlS0u9L+nmNXKH7T0mfdfdUpIEBAAAAQMSCKOok3S7plKS/zTx/p6R/kvSOyCICAAAAgBgIpah7ibtn/7biN83skciiAQAAAICYSEQdwAz9l5m9ZvSJmb1a0v8XYTwAAAAAEAuhXKl7taR3mdmTmedrJT1mZoclubtvjC40AAAAAIhOKEXd1qgDAKaTTruOnexXd19K9dVJNdVWKZGw6WcsslDiBELA8YQ4WbVmrTqOP1WQZa9cvUZPP/Xk9BMiL2hbMFtBFHXu/hNJMrMVkpJZ42ldEAvptOve9i7t2ntQqaG0kuUJ7dm5SVubG2LVCIcSJxACjifETcfxp3TFLQ8WZNl3XHNpQZaLc9G2YC6C+E6dmW0zsyOSfizp/0o6JulrkQYFZDl2sn+s8ZWk1FBau/Ye1LGT/RFHNl4ocQIh4HgCUAi0LZiLIIo6SX8q6TWSfpT5QfLLREcpiJHuvtRY4zsqNZTWiVPx+inFUOIEQsDxBKAQaFswF6EUdUPuflJSwswS7v5NSZsijgkYU1+dVLJ8/OGULE9oxbLkJHNEI5Q4gRBwPAEoBNoWzEUoRd1zZrZU0rclfdHMPilpOOKYgDFNtVXas3PTWCM8ev97U21VxJGNF0qcQAg4ngAUAm0L5iKIjlIkbZd0RtL/kvRrkmok7Y40IiBLImHa2tygl177Wp04ldKKZfHsqSqUOIEQcDwBKATaFsxF7Is6MyuTtM/df0lSWtJtEYcE5JRImNbVLdW6uqVRhzKlUOIEQsDxBKAQaFswW7G//dLdz0p63sxqoo4FAAAAAOIm9lfqMlKSDpvZfZLG+nN192ujCwkAAAAAohdKUffVzCBJnvnLjcUAAAAAFrxYF3Vmtl3Sanf/dOb59yXVaaSw+1CUsQEAAABAHMT9O3V/JGl/1vMKSa+U9HpJvxtFQAAAAAAQJ7G+Uiepwt2fynr+HXd/VtKzZsaPdQAAAABY8OJ+pe787Cfu/r6sp3VFjgUAAAAAYifuRd1DZvY7E0ea2TWSvh9BPAAAAAAQK3G//fJ/SbrTzH5V0sOZca+UtFjSjqiCAgAAAIC4iHVR5+4nJF1qZlskNWdGf9XdH5huXjNbI+l2SQ2S0pJudfdPFixYAAAAAIhArIu6UZkibtpCboJhSR9w94fNbJmkH5rZfe7+aP4jBAAAAIBoxP07dXPm7p3u/nDm8SlJj0laFW1UAAAAAJBfJVvUZTOzJkkvl/RQxKEAAAAAQF6VfFFnZkslfVnS+929b8JrV5vZATM70NPTE02AWPDIQ8QBeYg4IA8RB+QhQlTSRZ2ZlWukoPuiu39l4uvufqu7t7p7a10dP3uHaJCHiAPyEHFAHiIOyEOEqGSLOjMzSZ+T9Ji774k6HgAAAAAohJIt6iT9nKTfkLTFzA5mhjdHHRQAAAAA5FMQP2kwF+7+HUkWdRwAAAAAUEilfKUOAAAAAEoeRR0AAAAABIyiDgAAAAACRlEHAAAAAAGjqAMAAACAgFHUAQAAAEDAKOoAAAAAIGAUdQAAAAAQMIo6AAAAAAgYRR0AAAAABIyiDgAAAAACRlEHAAAAAAGjqAMAAACAgFHUAQAAAEDAKOoAAAAAIGAUdQAAAAAQMIo6AAAAAAgYRR0AAAAABIyiDgAAAAACRlEHAAAAAAGjqAMAAACAgFHUAQAAAEDAKOoAAAAAIGAUdQAAAAAQMIo6AAAAAAgYRR0AAAAABIyiDgAAAAACRlEHAAAAAAEr6aLOzLaa2eNmdtTMro86HgAAAADIt0VRB1AoZlYm6dOS3iDpuKQfmNl+d390pssYHk6rvbNXnb0pNdZUqrmxWosWhVEHDw6e1aGOXnX1pdRYndSGlTWqqCiLOqxpnT6T0qNd/eruG1B99WJd3FClpZXJqMOakefPDKqt69RY7C0Ny7SksmLey33uTEo/ytomFzVU6bx5bJNCxYnSlu88LBUhnycKKZ12HTvZr+6+lOqrk2qqrVIiYVGHBeRFOu168tl+nTw9qOF0WmeGziq5qEynUsNaUlGmivKE+geGVFuV1LLkInX3Dah/cFgvuqBKFy7nWEBhlGxRJ2mzpKPu/oQkmdmXJG2XNKOibng4rTsfeVofubNNqaG0kuUJ3bijRTsuWRX7E/bg4FndeahDN+x7Ifbd21u0Y+PKWBd2p8+kdE9bj27YnxX3tha9uaUu9oXd82cGdXdb9zmxX95SP6+C6bkzKX0jxzZ5Y0vdnD5QFypOlLZ852GpCPk8UUjptOve9i7t2ntwbLvs2blJW5sb+DCL4KXTrgce71bHT89oKO26/bvHdEXrWt38wJGxfL/usvVaUl6mTz1wVG9/5Vr9yV3tHAsouFI+66yS9FTW8+OZcTPS3tk7dqKWpNRQWh+5s03tnb35jbIADnX0jhV00kjsN+xr06GOeMf+aFf/2IdGKRP3/jY92tUfcWTTa+s6lTP2tq5T81rujybZJj+a4zYpVJwobfnOw1IR8nmikI6d7B8r6KSR7bJr70EdO7mw82XUqjVrZWYFGUJVyG1iZlq1Zm3eYj12sl+Hjvfqmf5B/eXXH9flG1eNFXTSSL5/8v4jOvn8oN516bqxgm70NY4FFEopX6nL1br5uAnMrpZ0tSStXTv+gO/sTY0dhKNSQ2l19aZ0yZr8BppvXX25Y+/uS0UU0cx09w1MEvdARBHN3HxinyoP871NQt7GKKxi5mGpCPk8UUjdk5yDTpxKaV3d0innnSoPS0XH8ad0xS0PFmTZd1xzaUGWW2iF3CbS7LfL1O1hSunMp8nUUFpmypnvaZfODAzP+VgAZquUr9Qdl5R9Wl0tqSN7Ane/1d1b3b21rq5u3MyNNZVKlo/fPMnyhBpq4n+rUWN1Mmfs9dXxjr2+evEkcS+OKKKZm0/sU+VhvrdJyNsYhVXMPCwVIZ8nCql+knPQimXTb5ep8hAolqnbw6TKTCozjeV5rnxPmLRk8aI5HwvAbJVyUfcDSevN7EIzq5B0paT9M525ubFaN+5oGXfA3rijRc2NNYWJNo82rKzR7u3jY9+9vUUbV8Y79osbqrR724S4t7Xo4oaqiCObXkvDspyxtzQsm9dyL5pkm1w0x21SqDhR2vKdh6Ui5PNEITXVVmnPzk3jtsuenZvUVLuw8wWloam2ShtW16i2qkIffNNLdNcjT+vaLevH5ft1l61X7ZIK3fbgE/qTtzZzLKAoSvb2S3cfNrP3Sfq6pDJJn3f39pnOv2hRQjsuWaX1K5aqqzelhpqkmhtrgvjye0VFmXZsXKl1y6vGeh7bGEDvl0srk3pzS52alm8OrvfLJZUVurylXk3Ll+S1V8nzKpN644RtMp9eBwsVJ0pbvvOwVIR8niikRMK0tblBL732tTpxKqUVy+j9EqUjkTBteUn9WO+XG962Uc8PndUXfvNVOp0aVmVFmRaXJ9Q/MKwb3tqs6mS5bnv3Zj0/OKy19H6JAirZok6S3P0eSffMdf5FixK6ZM35QX43oqKiTK1NF0QdxqwtrUxq84VhflBcUlmhzRfW5n255+V5mxQqTpS2fOdhqQj5PFFIiYRpXd1SvjeEkpRImJqWL1XT8plN37Sc4wCFt7D/nQgAAAAAgaOoAwAAAICAmbtPP9UCYGY9kn4yycvLJT1TxHDyKdTYQ41bmjr2Z9x962QzTpOHUQl5X2RjPV4wnzwsle2YS6muW1zXq1TykFhyCyWWUM7Lcdqeo4hp5qaLa8o8nAmKuhkwswPu3hp1HHMRauyhxi2FHXsupbI+rEdpvH8hleq6leJ6xWmdiCU3YsmvOK4DMc1cMeLi9ksAAAAACBhFHQAAAAAEjKJuZm6NOoB5CDX2UOOWwo49l1JZH9ajNN6/kEp13UpxveK0TsSSG7HkVxzXgZhmruBx8Z06AAAAAAgYV+oAAAAAIGAUdQAAAAAQMIq6jK1bt7okBoZCD1MiDxmKNEyJPGQo0jAl8pChSMOUyEOGIg3zRlGX8cwzcfydQiw05CHigDxEHJCHiAPyEKGgqAMAAACAgFHUAQAAAEDAKOoAAAAAIGAFL+rMrMzM/svM7s48v8DM7jOzI5m/52dN+2EzO2pmj5vZm7LGv9LMDmdeu9nMLDN+sZndkRn/kJk1Zc1zVeY9jpjZVXOJPZ12PdFzWt/9n2f0RM9ppdN5+R4jABQM7RbigDxEHJCHWEgWFeE9rpP0mKTqzPPrJd3v7h83s+szzz9kZhdLulJSs6SVkv7DzC5y97OSPiPpaknfk3SPpK2SvibpPZJ+6u4/Y2ZXSvpzSVeY2QWSPiqpVSM9yvzQzPa7+09nGnQ67bq3vUu79h5UaiitZHlCe3Zu0tbmBiUSNt9tAgB5R7uFOCAPEQfkIRaagl6pM7PVkt4i6R+yRm+XdFvm8W2SdmSN/5K7D7j7jyUdlbTZzBolVbv7d93dJd0+YZ7RZf2bpMsyV/HeJOk+d382U8jdp5FCcMaOnewfawgkKTWU1q69B3XsZP9sFgMARUO7hTggDxEH5CEWmkLffvk3kv5IUjprXL27d0pS5u+KzPhVkp7Kmu54ZtyqzOOJ48fN4+7Dknol1U6xrBnr7kuNNQSjUkNpnTiVms1iAKBoaLcQB+Qh4qAYebhqzVqZ2ayGVWvW5u39gWwFu/3SzC6XdMLdf2hmr5/JLDnG+RTj5zpPdoxXa+S2Tq1dO/4gq69OKlmeGNcgJMsTWrEsmSt2YM6mykNgNubTbpGHyBfyEHFQjDzsOP6UrrjlwVnFdcc1l85qemCmCnml7uckbTOzY5K+JGmLmf2zpO7MLZXK/D2Rmf64pDVZ86+W1JEZvzrH+HHzmNkiSTWSnp1iWeO4+63u3ururXV1deNea6qt0p6dm5QsH9lEo/diN9VWzWYbANOaKg+B2ZhPu0UeIl/IQ8QBeYiFpmBX6tz9w5I+LEmZK3V/6O6/bmZ/KekqSR/P/N2XmWW/pP9jZns00lHKeknfd/ezZnbKzF4j6SFJ75L0t1nzXCXpu5LeLukBd3cz+7qkP8vqWfONo7HMVCJh2trcoJde+1qdOJXSimVJNdVW8eVaALFFu4U4IA8RB+QhFppi9H450ccl7TWz90h6UtI7JMnd281sr6RHJQ1Lem+m50tJ+j1J/yipUiO9Xn4tM/5zkv7JzI5q5ArdlZllPWtmfyrpB5npdrv7s7MNNJEwratbqnV1S2e/lgAQAdotxAF5iDggD7GQFKWoc/dvSfpW5vFJSZdNMt1Nkm7KMf6ApJYc41PKFIU5Xvu8pM/PNWYAAAAACEHBf3wcAAAAAFA4FHUAAAAAEDCKOgAAAAAIGEUdAAAAAASMog4AAAAAAkZRBwAAAAABo6gDAAAAgIBR1AEAAABAwCjqAAAAACBgFHUAAAAAEDCKOgAAAAAIGEUdAAAAAASMog4AAAAAAkZRBwAAAAABo6gDAAAAgIBR1AEAAABAwCjqAAAAACBgFHUAAAAAEDCKOgAAAAAIGEUdAAAAAASMog4AAAAAAkZRBwAAAAABo6gDAAAAgIBR1AEAAABAwCjqAAAAACBgFHUAAAAAEDCKOgAAAAAIGEUdAAAAAASMog4AAAAAAkZRBwAAAAABK1hRZ2ZJM/u+mT1iZu1m9rHM+AvM7D4zO5L5e37WPB82s6Nm9riZvSlr/CvN7HDmtZvNzDLjF5vZHZnxD5lZU9Y8V2Xe44iZXVWo9QQAAACAKBXySt2ApC3ufomkTZK2mtlrJF0v6X53Xy/p/sxzmdnFkq6U1Cxpq6S/M7OyzLI+I+lqSeszw9bM+PdI+qm7/4ykv5b055llXSDpo5JeLWmzpI9mF48AAAAAUCoKVtT5iNOZp+WZwSVtl3RbZvxtknZkHm+X9CV3H3D3H0s6KmmzmTVKqnb377q7S7p9wjyjy/o3SZdlruK9SdJ97v6su/9U0n16oRAEAAAAgJJR0O/UmVmZmR2UdEIjRdZDkurdvVOSMn9XZCZfJemprNmPZ8atyjyeOH7cPO4+LKlXUu0UywIAAACAklLQos7dz7r7JkmrNXLVrWWKyS3XIqYYP9d5XnhDs6vN7ICZHejp6ZkiNKBwyEPEAXmIOCAPEQfkIUJUlN4v3f05Sd/SyC2Q3ZlbKpX5eyIz2XFJa7JmWy2pIzN+dY7x4+Yxs0WSaiQ9O8WyJsZ1q7u3untrXV3d3FcQmAfyEHFAHiIOyEPEAXmIEBWy98s6Mzsv87hS0i9J+m9J+yWN9kZ5laR9mcf7JV2Z6dHyQo10iPL9zC2ap8zsNZnvy71rwjyjy3q7pAcy37v7uqQ3mtn5mQ5S3pgZBwAAAAAlZVEBl90o6bZMD5YJSXvd/W4z+66kvWb2HklPSnqHJLl7u5ntlfSopGFJ73X3s5ll/Z6kf5RUKelrmUGSPifpn8zsqEau0F2ZWdazZvankn6QmW63uz9bwHUFAAAAgEgUrKhz90OSXp5j/ElJl00yz02Sbsox/oCkc76P5+4pZYrCHK99XtLnZxc1AAAAAISlKN+pAwAAAAAUBkUdAAAAAASMog4AAAAAAkZRBwAAAAABo6gDAAAAgIBR1AEAAABAwCjqAAAAACBgFHUAAAAAEDCKOgAAAAAIGEUdAAAAAASMog4AAAAAAkZRBwAAAAABo6gDAAAAgIBR1AEAAABAwCjqAAAAACBgFHUAAAAAEDCKOgAAAAAIGEUdAAAAAASMog4AAAAAAjajos7MLjKz+82sLfN8o5l9pLChAQAAAACmM9MrdX8v6cOShiTJ3Q9JurJQQQEAAAAAZmamRd0Sd//+hHHD+Q4GAAAAADA7My3qnjGzF0tySTKzt0vqLFhUAAAAAIAZWTTD6d4r6VZJLzWzpyX9WNKvFywqAAAAAMCMzKioc/cnJP2SmVVJSrj7qcKGBQAAAACYiZn2fvlnZnaeu/e7+ykzO9/Mbix0cAAAAACAqc30O3W/7O7PjT5x959KenNBIgIAAAAAzNhMi7oyM1s8+sTMKiUtnmJ6AAAAAEARzLSjlH+WdL+ZfUEjPWD+lqTbChYVAAAAAGBGZtpRyl+Y2WFJl0kySX/q7l8vaGQAAAAAgGnN9PZLufvX3P0P3f0DMynozGyNmX3TzB4zs3Yzuy4z/gIzu8/MjmT+np81z4fN7KiZPW5mb8oa/0ozO5x57WYzs8z4xWZ2R2b8Q2bWlDXPVZn3OGJmV810PQEAAAAgJFMWdWb2nczfU2bWlzWcMrO+aZY9LOkD7v4ySa+R9F4zu1jS9ZLud/f1ku7PPFfmtSslNUvaKunvzKwss6zPSLpa0vrMsDUz/j2SfuruPyPpryX9eWZZF0j6qKRXS9os6aPZxSMAAAAAlIopizp3//nM32XuXp01LHP36mnm7XT3hzOPT0l6TNIqSdv1wvfxbpO0I/N4u6QvufuAu/9Y0lFJm82sUVK1u3/X3V3S7RPmGV3Wv0m6LHMV702S7nP3ZzM9dd6nFwpBAAAAACgZ095+aWYJM2ubz5tkbot8uaSHJNW7e6c0UvhJWpGZbJWkp7JmO54ZtyrzeOL4cfO4+7CkXkm1UywLAAAAAErKtEWdu6clPWJma+fyBma2VNKXJb3f3ae6ZdNyvf0U4+c6T3ZsV5vZATM70NPTM0VoQOGQh4gD8hBxQB4iDshDhGimHaU0Smo3s/vNbP/oMN1MZlaukYLui+7+lczo7swtlcr8PZEZf1zSmqzZV0vqyIxfnWP8uHnM7P/f3r3HyVGV+R//PJNJMrnNkIRkEnIhRAJIIuESWe5CQEQWJSoIri6w4uIVENdV/OFPQcEfuPxQUVdhBQEVCKCLoIIggUUlGoIQSLgFcyMQCCSQhEkmt3n2jzo96ZlUz0z3dHdV9Xzfr9e8pru6q+apmqfOqVN1TlU90ASs7WJZHbj7te4+w91njBo1qrvVEakI5aGkgfJQ0kB5KGmgPJQs6ulz6i4pdsFhbNt1wDPuflXeR3cBZwKXh9+/zpt+s5ldBexGdEOUee6+PdyY5RCi7ptnAN/vtKy5wCnAHHd3M/s98K28m6McD3yl2HUQERERERFJuy4bdWbWAHwK2BN4CrgujF3ricOBfwaeMrMnwrT/Q9SYu83MzgZWAKcCuPsiM7sNeJrozpmfdfftYb5PAzcAg4B7wg9EjcafmdkLRFfoTg/LWmtm3wQeDd/7hruv7WHcIiIiIiIimdHdlbobga3AH4H3AvsC5/dkwe7+J+LHtkH0EPO4eS4DLouZPh+YFjO9ldAojPnseuD6nsQqIiIiIiKSVd016vZ193cAmNl1wLzKhyQiIiIiIiI91d2NUrbmXhTR7VJERERERESqpLsrddPNLPcYAgMGhfcGeHcPIBcREREREZHK6rJR5+79qhWIiIiIiIiIFK+nz6kTERERERGRFFKjTkREREREJMPUqBMREREREckwNepEREREREQyTI06ERERERGRDFOjTkREREREJMPUqBMREREREckwNepEREREREQyTI06ERERERGRDFOjTkREREREJMPUqBMREREREckwNepEREREREQyTI06ERERERGRDFOjTkREREREJMPUqBMREREREckwNepEREREREQyrD7pAERqRVubs2xNC6+ub6W5sYFJI4dQV2dJh5U52o6SJcpXSQPloYioUSdSBm1tzr2LXuELtz1B69Y2GvrXcdWH9+eEqWNUsRZB21GyRPkqaaA8rH3jJkzk5ZUv9vj7u42fwEsvrqhgRJJGatSJlMGyNS3tFSpA69Y2vnDbE+xz3pFMHjU04eiyQ9tRskT5KmmgPKx9L698kdOueaTH35/9ycMqGI2klRp1ImXw6vpWhg8ewAcPHI+FE6O/fGwlqze0pq5S3batjUWr1rFqXStjmwYxdWwj9fXpGF776vrW9gOTnNatbancjiLK18LUHbB6slT/iEjlqFEnUgZjGhs449Dd+d4Di9u7v5x/7BSahzUkHVoH27a1ceeCl/jqnQvb47x01jRmTR+XioZdc2MDDf3rOhwoN/SvY3TKtqMIwOhh8fk6amjfzld1B6yurNQ/IlJZyR/FidSADa1b2ytUiM7Wf++BxWxo3ZpwZB0tWrWuvUEHUZxfvXMhi1atSziyyKSRQ7jqw/vT0D8qmnIHg5NGDkk4MpGd9auD84+d0iFfzz92Cv36eM1aqDvgsjUtCUdWm7JS/4hIZelKnUgZvLwuvhvWqnWt7DchoaBirCoQ5yvrWpmegjjr6owTpo5hn/OOZPWGVkYPU7ctSa9V61q5ae5yzj5iMmbgDjfNXc4BE3dh0q59t9ubuqVWV1bqHxGpLDXquqAxAdJTY5sGxXbDGtOUru4vWYizrs6YPGqoDv4yoK+Xkc2NDbyxcQs/fPCF9mnqLqxu1NWWhXJdRCqvj3cSKSw3JuDEq//IR/7rr5x49R+5d9ErtLV50qFJCk0d28ils6Z16IZ16axpTB3blHBkHb29eRjfOLljnN84eRpvb25MODLJGpWR6i5ciLZLdalcFxHQlbqCdItgKUZ9fR2zpo9jyuihvLKulTFNDUwd25SKm4/kW7luE7c9upxvnzKdTVu2MWhAPTc9soQZuw9XXktRVEaqu3Ah2i7VpXJdRKCCjTozux44CVjt7tPCtBHAbGASsAz4sLu/ET77CnA2sB04z91/H6YfBNwADAJ+B5zv7m5mA4GbgIOANcBp7r4szHMm8NUQyqXufmOx8WtMgBSrvr6O6ROGp2JsWiGvrm9l/vJ1zF/+eIfpymsplsrIiLoLx9N2qR6V6yIClb1SdwPwA6KGV86FwAPufrmZXRjef9nM9gVOB6YCuwF/MLO93H078CPgHOAvRI26E4B7iBqAb7j7nmZ2OnAFcFpoOH4dmAE48JiZ3ZVrPPaUxgQko6+P0am05sYGdh85iJP2G9f+PKO7F7ykvJaiqYyMqMySpKlcFxGoYKPO3R82s0mdJp8MHB1e3wg8BHw5TL/V3TcDS83sBeBgM1sGNLr7XAAzuwmYRdSoOxm4OCzrDuAHZmbAe4D73X1tmOd+oobgLcXEnxsT0Pk5OxoTUDl6tlHlTRw+mHNnTtnpOXUThw9OOjTJGJWRKrO6osZu9ahcFxGo/pi6ZndfBeDuq8xsdJg+juhKXM7KMG1reN15em6eF8OytpnZOmBk/vSYeYoyoN4456jJtDnUWfReKkdjdCpv+dqNsc+pO2DCcN42WttYek7jplRmFaLGbnWpXBcRSM+NUuJKee9ieqnzdPyjZucQde1k4sSJHT5btqaFz938+E5di37XxyvrSsr6GJ1Sz0x3lYfltnxtS+w2XrG2RZV/H1dKHvb1cVNZL7MqpTeN3WqWh2nR26uaKtfLry/moWRftW/N96qZjQUIv1eH6SuB/NtLjAdeDtPHx0zvMI+Z1QNNwNoulrUTd7/W3We4+4xRo0Z1DLSLyloqIzdGJ19Wxuj05vbuXeVhuQ0dWB+7jYcOTMv5HUlKNfOwVoweFl9mjRqa/jKrknpTf/a1PCzHo0GGDIgv1wcPULleqr6Wh1Ibqt2ouws4M7w+E/h13vTTzWygme0BTAHmha6aG8zskDBe7oxO8+SWdQowx90d+D1wvJkNN7PhwPFhWlGy3MDIqiw/26jQmella1oSjqyjwQP6cf6xUzps4/OPncKgAf0Sjkwke/rVEbs/9UvXk0yqTvVnz5Wj7mhuHBibh82NAysSs4ikUyUfaXAL0U1RdjWzlUR3pLwcuM3MzgZWAKcCuPsiM7sNeBrYBnw23PkS4NPseKTBPeEH4DrgZ+GmKmuJ7p6Ju681s28Cj4bvfSN305Ri6CYA1ZflMTpZ6Ya1oXUbN81dztlHTMYM3OGmucvZf8IuSYcmkjmr1rXG7k8HTNyFSbumZ7+vNtWfPVeOumPiiCFMaR7a4R4AU5qHMnGEtrdIX1LJu19+pMBHxxb4/mXAZTHT5wPTYqa3EhqFMZ9dD1zf42BjZLmBkWVZHaOTldu7Nzc28MbGLfzwwRfapzX0r6O5MV1ximRBof0pbft9tan+7Lly1B11dcbMvZuZvOtQbW+RPqyPdxLpWq6BccjkXZk8aqgKSCkoK11HsxKnSBZofypM9WfPlCuHtL1FRKNoRcogK2emsxKnSBZof5LeUg6JSLmoUSepkuUH1mat66j3/OZqItIN7U9SqnLUHVmuO0WkPNSok9TQA2srT9tYpHy0P0kaKA9FBNSokxRZtqaFK+59pv1OcgBX3PsM+4wZlpmrX2mnbSyl0pWAnfXmIdsi5aJyXURAjTpJkTUtmzltxkSunrO4/WzjeTOnsLZlsyqmMtE2llLoSkC8rDzKRGqbynURAd39UlJkQL+69koJooOjq+cspn9ff5JvGWkbSynK8YDkWqSHbEsaqFwXEVCjTlJk45btsWe9N27ZXmAOKZa2sZSiqytSfZkeaSBpoHI9Y+rqMbOifkR6Qt0vJTUKPYRVD8YuH21jKUU5HpBci3Q7ekkDlesZ07aN0655pKhZZn/ysAoFI7VEV+q60NbmLHntLeb+/XWWvPYWbW26Z3Ul6ax35WkbSymUN4Xpoc/xKlV/ql7emfZPEQFdqStINwaovro64/i3NzP7nENYta6VsU0NTB3bpO1dZgPqjXOOmkybQ51F70W6on1TilGp+lP1cjxdMRYRUKOuIN2quvra2pz7nnlVFXYFLX29hc/d/PhO3XR+e+6RvG208lriad+UYlSq/lz6evxy91b5VZYHmItItqn7ZQG6MUD16Q57lbd8bUtsXq9Yq20shWnflGJUqv5U+SUiUpiu1BWQ9RsDZPFBwVl/5lOltnk5lztkQH1sXg8eoKJACnt1fSvDBw/ggweOb3+48S8fW5mZfVOqq1L1p8ovEZHCVBIWkBt43Lm7URYGHmd13EFzYwO7jxzESfuNaz9wvHvBS5loSFdyDMmc517lyZXraHPoZ/CO8U3M3Lu5pOWOaRrI1983lUvuXtQe59ffN5UxTQNLjlFq35jGBj5x5B78x++fa8+bf3/P3jRnYN+U6qtU/dncOJCL3zeVi/PKr4vfN5XmRpVfIiJq1BWQ5RsDZHU84MThg/ni8XuzePVb7Q2YLx6/NxOHD046tG5VaqzHirUtLHmthWsfXtJ+EPOFd+/FnqNamLRr8cvdus355WMr+PYp09m0ZRuDB9Rz4yNLeOfuw0uOUWrfhs1buWnuMs4+YnL7CZeb5i7jnZOUN7KzSt24Y33rVu6IKb/23W1YmSIXEckuNeoKyPKNAbLaVWrlmxtZ+camDg2Y84+dwso3N5bUgKmmrsZ69KZRt3rDZq66//kOjcWr7n+eAybsUtI2WbV+EzP3GcOX7ljQvo3PmzmFV9ZvYs9mHRhJvDUtmzltxkSunrO4Q96sbdmSdGiSUpW4cceals2x5ZfyUEREjbqCsnq1C6KuUmccujvfe2Bxh8ZR2rtKvbp+M7c+uqLD1YBbH13BgROHp75RV6mxHhs2bY1toK9v3VrS8gb269d+YA5RXl89ZzE3/cvBvYpTaltDfT2z53fcN2fPX8HlH9wv2cBSIIvjl7NKeSgiUpgadQVk+aYdG1q3tjfoIIr7ew8s5vC3jUw4sq5t2bY99mrAlm3bkw6tW0Mb+nH+sVN2akgPHdivV8sdMWRAbAN9xOABJS2vZcu22Lxu2bKtV3FKbcvyvllJWR2/nFXKQxGRwvRIgwJyd+/Kl5W7X768Lr5Bumpduh/HMGhA/FWkhv69axhVQ+uWNkYO6c+Vp0znig++gytPnc7IIf13+j8Ua2ubxzbQt7Z5ScsbOXRgbF6PHFJaI1H6hkED6mP3zUF9/K6DetRDdSkPRUQKU0lYQJbvfjm2aVBsV8AxTelukL7+1pbYxmgWxkts2rqNNzZu42t3Pd2eLxcctxebtvbuCtjalvht8kaJ22Tztu1ccNxefOcPz3eIc8v23jU+pba9uTE+D9/cWFo34FqR1fHLWaU8FBEpTI26Aip1965qmDq2kUtnTeOrdy5sP3C/dNY0po5tSjq0Lg0bGD8ubcjA9KdpQ//69oYSRAca3/nD8/z87H/o1XJ3Gdw/dps0Dupf0vJGDhnIzfOWt49JcYeb5y3n3fs29ypOqW2Ng+LzcFhD+vfNShrbFD9+eUxjuk+gZZXyUESkMJWEXajE3buqob6+jlnTxzFl9FBeWdfKmPA4hvr6dPe2bRrcP3ZcWlOJDZhqWtuyuSJXGTdu2cZ5M6fsNIak1CuAk0YO4csnvD2TV6AlOZu2xufh5j4+lml7G7Hdo4/fd0zCkdWmcpeHIiK1RI26GlVfX8f0CcOZPiHpSHpu09btDO7fj3OOmkybQ53B4P79MnHg2NwY3+W1tw/FHTawf4e7vblHd3u78pTpJS0vy1egJTkjhzQwe/7TO+Xh908/IOnQErV6Q/z45dfeau3Vo0wkXrnLQxGRUo2bMJGXV75Y1Dy7jZ/ASy+uqFBEatRJiowcMpDrH1nKSfuNwyw6C379I0v56Vnpv91+pbq8jm4cyGeO3pOv37WofbmXvH8qo3vRWMzqFWhJzr5jGjl35pSd8nvflHfprrTcDbU6n8zJwg21sqgS5aGISCleXvkip13zSFHzzP7kYRWKJqJGnaRGlrsGVqrL64ThQ1jW1MKVp0ynZcs2hgyoZ9igfkwYnv5tIrUjq126Ky3LN9TKIpWHIiKFqVEnqZH1roGV6PJaV2ccuedolq1pyeQ2kdqRxS7dlZb1MitrVB6KiBSmRp2kiroG7kzbRCS9tH9Wl7a3iEi8vt13RkREREREJOPUqBMREREREckwNepEREREREQyzNw96RhSwcxeA5YX+HhX4PUqhlNOWY09q3FD17G/7u4nFJqxmzxMSpb/F/m0Hjv0Jg9rZTvGqdV1S+t61UoeKpZ4WYklK/VymrZnjmLque7i6jIPe0KNuh4ws/nuPiPpOEqR1dizGjdkO/Y4tbI+Wo/a+PuVVKvrVovrlaZ1UizxFEt5pXEdFFPPVSMudb8UERERERHJMDXqREREREREMkyNup65NukAeiGrsWc1bsh27HFqZX20HrXx9yupVtetFtcrTeukWOIplvJK4zoopp6reFwaUyciIiIiIpJhulInIiIiIiKSYWrUdcPMTjCz58zsBTO7MOl4esrMrjez1Wa2MOlYimFmE8zsQTN7xswWmdn5ScfUE2bWYGbzzGxBiPuSpGMqRVzemNkIM7vfzBaH38OTjLE7hXIog+sRm1NJrkdWy0MoLS/M7CthXZ8zs/ckF333zKyfmT1uZr8J72tiveKkJQ/TVl91zoEE49jFzO4ws2fDtjk0wVguCP+bhWZ2i5k1JBBDUfVqof3TzA4ys6fCZ1ebmYXpA81sdpj+VzOblDfPmeFvLDazM/Oml608LFdcpdR51dhW4bNel6/ljimWu+unwA/QD/g7MBkYACwA9k06rh7GfhRwILAw6ViKjHsscGB4PQx4PgvbHDBgaHjdH/grcEjScZWwHjvlDfBt4MLw+kLgiqTjLCWHMrgesTmV1HpkuTwsJS/CZwuAgcAeYd37Jb0eXazfF4Cbgd+E9zWxXmnOw7TVV51zIME4bgQ+EV4PAHZJKI5xwFJgUHh/G3BWAnH0uF7tav8E5gGHhrrhHuC9YfpngB+H16cDs8PrEcCS8Ht4eD08fFa28rBccVFknVetbRW3b6UhprgfXanr2sHAC+6+xN23ALcCJyccU4+4+8PA2qTjKJa7r3L3v4XXG4BniArmVPPIW+Ft//CTuQGrBfLmZKJKmvB7VjVjKlYXOZS19SiUU0mtR2bLQygpL04GbnX3ze6+FHiBaBukjpmNB/4R+Ene5MyvVwGpycM01VcFciCJOBqJGjHXAbj7Fnd/M8GQ6oFBZlYPDAZernYARdarsfunmY0FGt19rkdH/Dd1mie3rDuAY8NVoPcA97v7Wnd/A7gfOCHEVJbysJxxlVDnVWVblaN8LXdMhahR17VxwIt571eSgQZGrQiXoA8gOluTeuHy/BPAaqIdMRNx90Czu6+CqCIARiccT491yqHMrUeBnEpqPWqmPOxhXmRpfb8LfAloy5tWC+sVJ5Xxp6C++i4750ASJgOvAT8N3dV+YmZDkgjE3V8CrgRWAKuAde5+XxKxxCh2/xwXXnee3mEed98GrANGdrGsDnpZHpY1riLrvGptq+/S+/K1Yv+/fGrUdc1ipmXu6ksWmdlQ4JfA5919fdLx9IS7b3f3/YHxRGdmpiUcUp+WxRzqLGU5VRPlYRF5kYn1NbOTgNXu/lhPZ4mZlrr16kLq4k+6rCkhByqpnqir4Y/c/QCghah7WtWFcU4nE3WD2w0YYmYfSyKWIhTK767yvpR5ohl7Xx6WNa4i67yKx1TG8rUi/7/O1Kjr2kpgQt778SRw6b6vMbP+RIXML9z9V0nHU6zQ1eQhurlMniGvhq4DhN+rE46nWwVyKHPrkdMpp5Jaj8yXh0XmRVbW93Dg/Wa2jKgr4kwz+znZX69CUhV/SuqrQjmQhJXAyryeKncQNfKScByw1N1fc/etwK+AwxKKpbNi98+V4XXn6R3mCd1Mm4i6e3a5r5SpPCx7XNDjOq8aMZWrfK3IdupMjbquPQpMMbM9zGwA0QDGuxKOqaaFfsTXAc+4+1VJx9NTZjbKzHYJrwcRVSbPJhpU+dwFnBlenwn8OsFYutVFDmVtPQrlVFLrkenysIS8uAs4PdyZbA9gCtFA91Rx96+4+3h3n0T0P5nj7h8j4+vVhdTkYVrqqy5yIIlYXgFeNLO9w6RjgaeTiIWo2+UhZjY4/K+OJRo7lgZF7Z+hi98GMzskrMsZnebJLesUov+/A78Hjjez4eGq5fFhWtnKw3LGVUKdV/GYylW+lvv/V5AncEeiLP0AJxLdFejvwEVJx1NE3LcQ9SHfStTaPzvpmHoY9xFEl5efBJ4IPycmHVcP4t4PeDzEvRD4WtIxlStviPp2PwAsDr9HJB1nKTmUwfWIzakk1yOr5WGpeQFcFNb1OcKdytL8AxzNjruz1cx6xaxnKvIwjfVVfg4kGMP+wPywXe6kmzv2VTiWS4gaBguBnwEDE4ihqHq10P4JzAjr8XfgB4CF6Q3A7UQ35ZgHTM6b5+Nh+gvAv+RNL1t5WK64KKHOq8a2itu30hJT55/cAkVERERERCSD1P1SREREREQkw9SoExERERERyTA16kRERERERDJMjToREREREZEMU6NOREREREQkw9SoSykze8TMBpnZ70qY92IzczPbM2/aBWHajPJGCmZ2npk9Y2a/KGHez5vZ4HLHJOllZtvN7AkzW2hmd+eeS1OmZX/XzI7q5jsXm9kXY6aPMrN7yxWLpJ+ZXWRmi8zsyZCT/2BmJ5nZ42a2wMyeNrNPhu9ebGYvhe8tNrNfmdm+nZZ3h5lN7uZv3mBmp8RMf4eZ3VDWFZREmFmzmd1sZkvM7DEzm2tmHyjTspeZ2a4x099vZheWuEzlrXSrU919e6FjNzN7pNqxSUSNupRy98PcfZO7n1jiIp4ielBizilU7gGgnyF6Ns9HS5j380BRjTozqy/n96TqNrn7/u4+DVgLfLYcCzWzEcAh7v5wKfO7+2vAKjM7vBzxSLqZ2aHAScCB7r4f0YNuXwGuBd7n7tOBA4CH8mb7TsjdKcBsYI6ZjQrLmwr0c/clpcTj7k8B481sYqnrJMkLDxa+E3jY3Se7+0FEdfH4Tt8ra/3k7ne5++XFzqe8lSLk191bgE/lf2hm/SA6fk0iOFGjLtXM7K3wu8nMxoTX+8R8L+4M3Z3AyeHzycA64LW8eX5kZvPDWepL8qYvM7NLzOxvZvZU7u91vroRztRMMrMfA5OBu8LVwIPDVcbHw++9w/f7mdmVYZlPmtm5ZnYesBvwoJk9mL/O4fUpuTOA4SzhVeF7V5jZ28zs3nAW9I95cXb4XinbXapqLjAOoIvcOcvM7gxX9Zaa2efM7Avhe38JjTmITly0X2kzsxPN7Fkz+5OZXW1mv8n7u9PNbE644vKvedPvBEo5OSHZMxZ43d03A7j768AGoB5YE6Ztdvfn4mZ299nAfcA/hUkfBX6d+9zMzjaz583sITP7LzP7Qd7sx4Vy63kzOylv+t10PBkn2TMT2OLuP85NcPfl7v79UJbdbmZ3A/eZ2VAzeyCvvs3V2ZNC2XVjqC/v6HRV5NyYOvqsXI5ZdKXwvy262rzAzA4zsyFm9tvwfqGZnRaWpbyVUvwR2NPMjjazB83sZqKLCZ2P474U8nSBmV0epsUev0nvqVGXDQbcbma7A9eY2f6dPn+K6OAi33rgRTObBnyE6KxyvovcfQawH/AuM9sv77PX3f1A4EfATt3U8rn7p4CXgWPc/TvAs8BR7n4A8DXgW+Gr5wB7AAeEs+K/cPer8+Y9psstENkLOM7d/43obPq54SzoF4H/LPA9SSmLzuodC9wVJhXKHYBpRAfPBwOXARvD9+YCZ4TvHA48FpbdAFwDvNfdjwBGdfrz+wH/CBwKfM3MdgvT5wNHlmsdJdXuAyaEA9T/NLN3uftaonxcbma3mNlHzayrevJvQO6AJD//dgP+L3AI8O687+RMAt5FlIM/DvkKyr9aMJUoLwo5FDjT3WcCrcAHQn17DPD/zczC9/YGrg315XqiHjE53dXRVwP/E642HwgsAk4AXnb36eFKS+4EmPJWimLRVeb3EhpxRPXyRe7euTv6e4FZwD+EXPx2+Kir4zfpBTXqMsDd3wQ+DtxOVLD/oNOZjWOAf46Z9Vais2ezgP/u9NmHzexvwONElVD+zvir8PsxokK8GE1EDdCFwHfCsiHq2vRjd98W1mltkcsFuN3dt5vZUOCw8HeeIDp4H9v5eyUsX6pjUPi/rQFGAPeH6YVyB+BBd98QukiuIzozDFGlMim8HsuOq9H7AEvcfWl4f0unGH4duje/DjxIVCkBrCa6eiw1zt3fAg4iOuH0GjDbzM5y908QnWyYR3TAcX0Xi7G81/n5dzDRQfVad99KVHbnu83d29x9MbCEHQfPyr8aY2Y/DFcpHg2T7s+r/wz4lpk9CfyBqNdCc/jsRXf/c3j9c+CIvMV2V0fPJGrw4e7b3X0dUVl5nJldYWZHhmmgvJWey9Xd84EVwHVh+ry8ujbfccBP3X0jRMd9PTh+k17QmKPsOJDoqtY4YDNRIZpzAnndzvLcDfwHMN/d1+dOAJrZHkQHK+909zcs6uLYkDff5vB7OztyZBsdTwLkfz/fN4kOwD9gZpPYMR7FAO9yDSP53+n8N1rC7zrgTXffv8AyWgpMl3TY5O77m1kT8BuiMXVXUzh3YEdOArTlvW9jR45uYkfO5B9sx+mci7n3DWE50geEkz8PAQ+Z2VPAmcANYZzQU2b2M2ApcFaBRRxAdIADyj+JLAI+lHvj7p+16MYmuTzJr58+StSL4CB332pmy9iRQ4VyBOLr6C65+/NmdhBwIvD/zOw+d/8GylvpuU2dj7vCcWWhY664477ujt+kF3SlLgPMbCzR+LhTgQuAWbkzfRaNcxsM3Nh5PnffBHyZqLtavkainXCdmTUTXUbvzjKihiVmdiBRV8o4TcBL4fVZedPvAz4VLtvnbmoB0RiWYXnfe9XM3h66PMXeLczd1wNLzezUsCwzs+k9WAdJkXCm+Dzgi2bWn8K501PPALk7vj4LTA6NQ4DTOn33ZDNrMLORwNFA7iz6XsDCEv62ZIyZ7W1mU/Im7U9U/hzdadryAvN/CDieHVeB8/NvHlG39uGhzPtQp9lPNbM6M3sb0Zjk3Lg95V/2zQEazOzTedMK3QysCVgdGnTHALvnfTbRopv5QDSE4k9FxPAA8GloH8/eGLpWbnT3nwNXEupzlLdSOfcBH7cwHtTMRuj4rbLUqMuGNcDHQneID7r7BgAzGwT8O/BPhbobuvut7v63TtMWEHW7XETUtejPcfN28ktgRLhc/mng+QLf+zbRWcA/A/3ypv+E6HL9k2a2gB03F7gWuMfCjVKAC4mu3swBVnURz0eBs8OyFhFuCiPZ4u6PAwuIugkXyp2e+i1RAy13QuMzwL1m9ifgVaJumznzwvf/AnzT3V8O048J06X2DQVutOixBU8SdUG/BPiSmT0XyrpL6HiC4QILjzQAPgbMDF2CoWP+vUQ0JvSvRN3qnqZj/j0H/A9wD/Apd28N05V/GefuTjTk4V0W3dhpHtFJ1y/HfP0XwAwzm09Upz2b99kzwJkhN0cQulP20PnAMeHq82NEXdnfAcwLeX0RcGn4rvJWKsLd7yUaozw/5F1u/KeO3yrEovJHsij0TV5CdJZsI3CCu9/V9VwitSs04E5y9zfNbKi7vxVuPPBDYHG4mU9X8z8MnOzub1QjXqkd4STbg8DhubG/If/qicY0X+/uncc2588/kOiA+Yjc2GPpm0IPg9+EG5pU+m8pb0VqhK7UZVgY6H8Z8CTRGbW9ko1IJHH/BuSel/Sv4ezgIqJuTtd0NaNFzxu7Sg06KUW4Ovx1wiM6gItD/i0kGpd3ZzeLmAhcqANjqSblrUjt0JU6ERERERGRDNOVOhERERERkQxTo05ERERERCTD1KgTERERERHJMDXqREREREREMkyNOhERERERkQxTo05ERERERCTD/hcX581wJA/iSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using Pearson's correlation method\n",
    "corr = df.corr(method='pearson')\n",
    "sb.heatmap(corr, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Data for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.values\n",
    "X,y = data[:,:-1], data[:,-1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)  # splitting in the ration 80:20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = RandomForestRegressor(random_state=0)\n",
    "regr.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions and Checking Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9879177200463668"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions are 98.79% accurate."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}