Stock_Market_Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author: Ameya Kshirsagar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Stock Market Prediction using Numerical & Textual Analysis \n",
    "- Create a hybrid model for stock price or performance prediction using numerical analysis of historical stock prices, and sentimental analysis of news headlines. The stock to analyze and predict is SENSEX (S&P BSE SENSEX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the Important Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the libraries\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "\n",
    "import nltk\n",
    "import re\n",
    "from textblob import TextBlob\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import xgboost\n",
    "import lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For reading stock data from yahoo\n",
    "from pandas_datareader.data import DataReader\n",
    "# For time stamps\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the first dataset\n",
    "columns=['Date','Category','News']\n",
    "ndf = pd.read_csv(\"india-news-headlines.csv\",names=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing part of the whole dataset:\n"
     ]
    },
    {
     "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>Date</th>\n",
       "      <th>Category</th>\n",
       "      <th>News</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>publish_date</td>\n",
       "      <td>headline_category</td>\n",
       "      <td>headline_text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20010101</td>\n",
       "      <td>sports.wwe</td>\n",
       "      <td>win over cena satisfying but defeating underta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20010102</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Status quo will not be disturbed at Ayodhya; s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20010102</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Fissures in Hurriyat over Pak visit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20010102</td>\n",
       "      <td>unknown</td>\n",
       "      <td>America's unwanted heading for India?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date           Category  \\\n",
       "0  publish_date  headline_category   \n",
       "1      20010101         sports.wwe   \n",
       "2      20010102            unknown   \n",
       "3      20010102            unknown   \n",
       "4      20010102            unknown   \n",
       "\n",
       "                                                News  \n",
       "0                                      headline_text  \n",
       "1  win over cena satisfying but defeating underta...  \n",
       "2  Status quo will not be disturbed at Ayodhya; s...  \n",
       "3                Fissures in Hurriyat over Pak visit  \n",
       "4              America's unwanted heading for India?  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Showing part of the whole dataset:')\n",
    "ndf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing part of the whole dataset:\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>News</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20010101</td>\n",
       "      <td>win over cena satisfying but defeating underta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20010102</td>\n",
       "      <td>Status quo will not be disturbed at Ayodhya; s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20010102</td>\n",
       "      <td>Fissures in Hurriyat over Pak visit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20010102</td>\n",
       "      <td>America's unwanted heading for India?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20010102</td>\n",
       "      <td>For bigwigs; it is destination Goa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3297163</th>\n",
       "      <td>20200630</td>\n",
       "      <td>vehicle of up stf team bringing gangster vikas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3297164</th>\n",
       "      <td>20200630</td>\n",
       "      <td>sushant singh rajputs demise fans trend cbifor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3297165</th>\n",
       "      <td>20200630</td>\n",
       "      <td>amitabh bachchans grandson agastya nanda prepp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3297166</th>\n",
       "      <td>20200630</td>\n",
       "      <td>icse isc result 2020 when where to check class...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3297167</th>\n",
       "      <td>20200630</td>\n",
       "      <td>up govt imposes weekend restrictions from tonight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3297167 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date                                               News\n",
       "1        20010101  win over cena satisfying but defeating underta...\n",
       "2        20010102  Status quo will not be disturbed at Ayodhya; s...\n",
       "3        20010102                Fissures in Hurriyat over Pak visit\n",
       "4        20010102              America's unwanted heading for India?\n",
       "5        20010102                 For bigwigs; it is destination Goa\n",
       "...           ...                                                ...\n",
       "3297163  20200630  vehicle of up stf team bringing gangster vikas...\n",
       "3297164  20200630  sushant singh rajputs demise fans trend cbifor...\n",
       "3297165  20200630  amitabh bachchans grandson agastya nanda prepp...\n",
       "3297166  20200630  icse isc result 2020 when where to check class...\n",
       "3297167  20200630  up govt imposes weekend restrictions from tonight\n",
       "\n",
       "[3297167 rows x 2 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf.drop(0, inplace=True)\n",
    "ndf.drop('Category', axis = 1, inplace=True)\n",
    "print('Showing part of the whole dataset:')\n",
    "ndf.head(-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-14</td>\n",
       "      <td>24935.599609</td>\n",
       "      <td>25194.150391</td>\n",
       "      <td>24867.730469</td>\n",
       "      <td>25150.349609</td>\n",
       "      <td>25150.349609</td>\n",
       "      <td>11600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-15</td>\n",
       "      <td>25186.679688</td>\n",
       "      <td>25342.779297</td>\n",
       "      <td>25075.539063</td>\n",
       "      <td>25320.439453</td>\n",
       "      <td>25320.439453</td>\n",
       "      <td>9700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-16</td>\n",
       "      <td>25402.470703</td>\n",
       "      <td>25572.900391</td>\n",
       "      <td>25372.470703</td>\n",
       "      <td>25494.369141</td>\n",
       "      <td>25494.369141</td>\n",
       "      <td>10800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-17</td>\n",
       "      <td>25596.630859</td>\n",
       "      <td>25831.310547</td>\n",
       "      <td>25448.320313</td>\n",
       "      <td>25803.779297</td>\n",
       "      <td>25803.779297</td>\n",
       "      <td>22700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>25764.669922</td>\n",
       "      <td>25789.509766</td>\n",
       "      <td>25481.509766</td>\n",
       "      <td>25519.220703</td>\n",
       "      <td>25519.220703</td>\n",
       "      <td>10400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>44325.031250</td>\n",
       "      <td>44407.281250</td>\n",
       "      <td>43995.410156</td>\n",
       "      <td>44149.718750</td>\n",
       "      <td>44149.718750</td>\n",
       "      <td>15700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>44435.828125</td>\n",
       "      <td>44730.789063</td>\n",
       "      <td>44118.101563</td>\n",
       "      <td>44655.441406</td>\n",
       "      <td>44655.441406</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>44729.519531</td>\n",
       "      <td>44729.640625</td>\n",
       "      <td>44169.968750</td>\n",
       "      <td>44618.039063</td>\n",
       "      <td>44618.039063</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>2020-12-03</td>\n",
       "      <td>44902.019531</td>\n",
       "      <td>44953.011719</td>\n",
       "      <td>44551.421875</td>\n",
       "      <td>44632.648438</td>\n",
       "      <td>44632.648438</td>\n",
       "      <td>30700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>44665.910156</td>\n",
       "      <td>45148.281250</td>\n",
       "      <td>44665.910156</td>\n",
       "      <td>45079.550781</td>\n",
       "      <td>45079.550781</td>\n",
       "      <td>27600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1229 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date          Open          High           Low         Close  \\\n",
       "0     2015-12-14  24935.599609  25194.150391  24867.730469  25150.349609   \n",
       "1     2015-12-15  25186.679688  25342.779297  25075.539063  25320.439453   \n",
       "2     2015-12-16  25402.470703  25572.900391  25372.470703  25494.369141   \n",
       "3     2015-12-17  25596.630859  25831.310547  25448.320313  25803.779297   \n",
       "4     2015-12-18  25764.669922  25789.509766  25481.509766  25519.220703   \n",
       "...          ...           ...           ...           ...           ...   \n",
       "1224  2020-11-27  44325.031250  44407.281250  43995.410156  44149.718750   \n",
       "1225  2020-12-01  44435.828125  44730.789063  44118.101563  44655.441406   \n",
       "1226  2020-12-02  44729.519531  44729.640625  44169.968750  44618.039063   \n",
       "1227  2020-12-03  44902.019531  44953.011719  44551.421875  44632.648438   \n",
       "1228  2020-12-04  44665.910156  45148.281250  44665.910156  45079.550781   \n",
       "\n",
       "         Adj Close   Volume  \n",
       "0     25150.349609  11600.0  \n",
       "1     25320.439453   9700.0  \n",
       "2     25494.369141  10800.0  \n",
       "3     25803.779297  22700.0  \n",
       "4     25519.220703  10400.0  \n",
       "...            ...      ...  \n",
       "1224  44149.718750  15700.0  \n",
       "1225  44655.441406  16000.0  \n",
       "1226  44618.039063  16000.0  \n",
       "1227  44632.648438  30700.0  \n",
       "1228  45079.550781  27600.0  \n",
       "\n",
       "[1229 rows x 7 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the second dataset\n",
    "hisdf = pd.read_csv(\"^BSESN.csv\")\n",
    "hisdf.head(-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Common Dataset Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3297172 entries, 1 to 3297172\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Dtype         \n",
      "---  ------  -----         \n",
      " 0   Date    datetime64[ns]\n",
      " 1   News    object        \n",
      "dtypes: datetime64[ns](1), object(1)\n",
      "memory usage: 75.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check for common information of the first datast\n",
    "ndf[\"Date\"] = pd.to_datetime(ndf[\"Date\"],format='%Y%m%d')\n",
    "ndf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the headlines for each day\n",
    "ndf['News'] = ndf.groupby(['Date']).transform(lambda x : ' '.join(x)) \n",
    "ndf = ndf.drop_duplicates() \n",
    "ndf.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>News</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>win over cena satisfying but defeating underta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-01-02</td>\n",
       "      <td>Status quo will not be disturbed at Ayodhya; s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-01-03</td>\n",
       "      <td>Powerless north India gropes in the dark Think...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-01-04</td>\n",
       "      <td>The string that pulled Stephen Hawking to Indi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-01-05</td>\n",
       "      <td>Light combat craft takes India into club class...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7075</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>Containment zone residents slam high prices ch...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7076</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>like me i wont let you have a toxic relationsh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7077</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>Atanu Ghosh plans to rewrite old scripts to ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7078</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>6 hot and stylish bikini looks of Katrina Kaif...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7079</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>Detective Byomkesh Bakshy! Edge of Tomorrow Fi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7080 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                                               News\n",
       "0    2001-01-01  win over cena satisfying but defeating underta...\n",
       "1    2001-01-02  Status quo will not be disturbed at Ayodhya; s...\n",
       "2    2001-01-03  Powerless north India gropes in the dark Think...\n",
       "3    2001-01-04  The string that pulled Stephen Hawking to Indi...\n",
       "4    2001-01-05  Light combat craft takes India into club class...\n",
       "...         ...                                                ...\n",
       "7075 2020-06-26  Containment zone residents slam high prices ch...\n",
       "7076 2020-06-27  like me i wont let you have a toxic relationsh...\n",
       "7077 2020-06-28  Atanu Ghosh plans to rewrite old scripts to ma...\n",
       "7078 2020-06-29  6 hot and stylish bikini looks of Katrina Kaif...\n",
       "7079 2020-06-30  Detective Byomkesh Bakshy! Edge of Tomorrow Fi...\n",
       "\n",
       "[7080 rows x 2 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date    0\n",
       "News    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for any duplicated values\n",
    "ndf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7080"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ndf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-14</td>\n",
       "      <td>24935.599609</td>\n",
       "      <td>25194.150391</td>\n",
       "      <td>24867.730469</td>\n",
       "      <td>25150.349609</td>\n",
       "      <td>11600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-15</td>\n",
       "      <td>25186.679688</td>\n",
       "      <td>25342.779297</td>\n",
       "      <td>25075.539063</td>\n",
       "      <td>25320.439453</td>\n",
       "      <td>9700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-16</td>\n",
       "      <td>25402.470703</td>\n",
       "      <td>25572.900391</td>\n",
       "      <td>25372.470703</td>\n",
       "      <td>25494.369141</td>\n",
       "      <td>10800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-17</td>\n",
       "      <td>25596.630859</td>\n",
       "      <td>25831.310547</td>\n",
       "      <td>25448.320313</td>\n",
       "      <td>25803.779297</td>\n",
       "      <td>22700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>25764.669922</td>\n",
       "      <td>25789.509766</td>\n",
       "      <td>25481.509766</td>\n",
       "      <td>25519.220703</td>\n",
       "      <td>10400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>44325.031250</td>\n",
       "      <td>44407.281250</td>\n",
       "      <td>43995.410156</td>\n",
       "      <td>44149.718750</td>\n",
       "      <td>15700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>44435.828125</td>\n",
       "      <td>44730.789063</td>\n",
       "      <td>44118.101563</td>\n",
       "      <td>44655.441406</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>44729.519531</td>\n",
       "      <td>44729.640625</td>\n",
       "      <td>44169.968750</td>\n",
       "      <td>44618.039063</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>2020-12-03</td>\n",
       "      <td>44902.019531</td>\n",
       "      <td>44953.011719</td>\n",
       "      <td>44551.421875</td>\n",
       "      <td>44632.648438</td>\n",
       "      <td>30700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>44665.910156</td>\n",
       "      <td>45148.281250</td>\n",
       "      <td>44665.910156</td>\n",
       "      <td>45079.550781</td>\n",
       "      <td>27600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1229 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date          Open          High           Low         Close  \\\n",
       "0     2015-12-14  24935.599609  25194.150391  24867.730469  25150.349609   \n",
       "1     2015-12-15  25186.679688  25342.779297  25075.539063  25320.439453   \n",
       "2     2015-12-16  25402.470703  25572.900391  25372.470703  25494.369141   \n",
       "3     2015-12-17  25596.630859  25831.310547  25448.320313  25803.779297   \n",
       "4     2015-12-18  25764.669922  25789.509766  25481.509766  25519.220703   \n",
       "...          ...           ...           ...           ...           ...   \n",
       "1224  2020-11-27  44325.031250  44407.281250  43995.410156  44149.718750   \n",
       "1225  2020-12-01  44435.828125  44730.789063  44118.101563  44655.441406   \n",
       "1226  2020-12-02  44729.519531  44729.640625  44169.968750  44618.039063   \n",
       "1227  2020-12-03  44902.019531  44953.011719  44551.421875  44632.648438   \n",
       "1228  2020-12-04  44665.910156  45148.281250  44665.910156  45079.550781   \n",
       "\n",
       "       Volume  \n",
       "0     11600.0  \n",
       "1      9700.0  \n",
       "2     10800.0  \n",
       "3     22700.0  \n",
       "4     10400.0  \n",
       "...       ...  \n",
       "1224  15700.0  \n",
       "1225  16000.0  \n",
       "1226  16000.0  \n",
       "1227  30700.0  \n",
       "1228  27600.0  \n",
       "\n",
       "[1229 rows x 6 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hisdf=hisdf[[\"Date\",\"Open\",\"High\",\"Low\",\"Close\",\"Volume\"]]\n",
    "hisdf.head(-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1234 entries, 0 to 1233\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   Date    1234 non-null   datetime64[ns]\n",
      " 1   Open    1229 non-null   float64       \n",
      " 2   High    1229 non-null   float64       \n",
      " 3   Low     1229 non-null   float64       \n",
      " 4   Close   1229 non-null   float64       \n",
      " 5   Volume  1229 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(5)\n",
      "memory usage: 58.0 KB\n"
     ]
    }
   ],
   "source": [
    "# Check for common information of the second dataset\n",
    "hisdf[\"Date\"]= pd.to_datetime(hisdf[\"Date\"])\n",
    "hisdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1229.000000</td>\n",
       "      <td>1229.000000</td>\n",
       "      <td>1229.000000</td>\n",
       "      <td>1229.000000</td>\n",
       "      <td>1.229000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33612.537529</td>\n",
       "      <td>33770.774116</td>\n",
       "      <td>33378.082519</td>\n",
       "      <td>33568.855966</td>\n",
       "      <td>2.737271e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5145.385399</td>\n",
       "      <td>5155.580751</td>\n",
       "      <td>5113.753852</td>\n",
       "      <td>5134.402915</td>\n",
       "      <td>2.123753e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23060.390625</td>\n",
       "      <td>23142.960938</td>\n",
       "      <td>22494.609375</td>\n",
       "      <td>22951.830078</td>\n",
       "      <td>2.500000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29006.000000</td>\n",
       "      <td>29077.279297</td>\n",
       "      <td>28789.300781</td>\n",
       "      <td>28929.130859</td>\n",
       "      <td>1.050000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34167.531250</td>\n",
       "      <td>34351.339844</td>\n",
       "      <td>33949.460938</td>\n",
       "      <td>34142.148438</td>\n",
       "      <td>1.430000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37840.640625</td>\n",
       "      <td>38022.320313</td>\n",
       "      <td>37586.878906</td>\n",
       "      <td>37752.171875</td>\n",
       "      <td>1.980000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46060.320313</td>\n",
       "      <td>46309.628906</td>\n",
       "      <td>45792.011719</td>\n",
       "      <td>46103.500000</td>\n",
       "      <td>3.181510e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open          High           Low         Close        Volume\n",
       "count   1229.000000   1229.000000   1229.000000   1229.000000  1.229000e+03\n",
       "mean   33612.537529  33770.774116  33378.082519  33568.855966  2.737271e+05\n",
       "std     5145.385399   5155.580751   5113.753852   5134.402915  2.123753e+06\n",
       "min    23060.390625  23142.960938  22494.609375  22951.830078  2.500000e+03\n",
       "25%    29006.000000  29077.279297  28789.300781  28929.130859  1.050000e+04\n",
       "50%    34167.531250  34351.339844  33949.460938  34142.148438  1.430000e+04\n",
       "75%    37840.640625  38022.320313  37586.878906  37752.171875  1.980000e+04\n",
       "max    46060.320313  46309.628906  45792.011719  46103.500000  3.181510e+07"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hisdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date      0\n",
       "Open      5\n",
       "High      5\n",
       "Low       5\n",
       "Close     5\n",
       "Volume    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicated values\n",
    "hisdf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1234"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hisdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'BSESN')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure plot\n",
    "plt.figure(figsize=(20,10))\n",
    "hisdf['Close'].plot()\n",
    "plt.ylabel('BSESN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove Unwanted Characters from the News"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    win over cena satisfying but defeating underta...\n",
       "1    Status quo will not be disturbed at Ayodhya  s...\n",
       "2    Powerless north India gropes in the dark Think...\n",
       "3    The string that pulled Stephen Hawking to Indi...\n",
       "4    Light combat craft takes India into club class...\n",
       "Name: News, dtype: object"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing unwanted characters from the News\n",
    "ndf.replace(\"[^a-zA-Z']\",\" \",regex=True,inplace=True)\n",
    "ndf[\"News\"].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Historical Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the Moving Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11c24c730>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting moving average\n",
    "close = hisdf['Close']\n",
    "\n",
    "ma = close.rolling(window = 50).mean()\n",
    "std = close.rolling(window = 50).std()\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "hisdf['Close'].plot(color='g',label='Close')\n",
    "ma.plot(color = 'r',label='Rolling Mean')\n",
    "std.plot(label = 'Rolling Standard Deviation')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Returns')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting returns\n",
    "returns = close / close.shift(1) - 1\n",
    "\n",
    "plt.figure(figsize = (20,10))\n",
    "returns.plot(label='Return', color = 'g')\n",
    "plt.title(\"Returns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train test split\n",
    "train = hisdf[:1219]\n",
    "test = hisdf[1219:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rolling mean and Standard Deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n3Kne+z1qqO2NYAap3tgQobrY+5JVoCreZ5DT4z70GaRjtV1iifcZARBkcJ0JnA00Aw7GMjaj/SBaHvtM10VZFjIxxjLIxn7kvd/mnJtExuB0MkS+F5kz046LuF0uge+sQyJu1yPi+9l7v9k5NxLL+jk6qJs0NXJl51xrrGYRwIho2YCJyoPvxpxqggXfwTLt4gXkvsaypcCCXB9k0S6nx52ISL5QEElEJDGRv6yXwQrD9sIKXTYDrnHO9QsyPDKrHnE7kSFb87B/2h32z2Iyg0hpsYbJBEJD86KdsB4cXC9P4CTi72z1bO+8gRVt7uKcC/0qf1rEskTVirh9cXBJ1B7//DvnGmEFVY9KcBuJBD3iFYqNHFK511kxzrnOwLvYvpqIvO77wRG3Y2aIee+9c+5vwhknORYMVR0ZXHDOlceCqZ2x4T2HB02bYRkKJ+T0OZ1z12NDJkvFaxuI9xnk5nEfbThlpHhZfKGJCT7G6pAlKl4Q6Z8YyyAb+1E22uS1yKFSmTPwakXcHpjN7UYLWLxFeOjYhVhANlKOh7JBnn035lTk3/R5CbSPbFM9y1Y5P+5ERPKFgkgiIgnw3u+Riu+cexRLPb8WuBL7p/2OzO2wX8UBdkarhRTFxojb5bNslT+iBcWyI5SNksiv0PFONnPDm8D92N+/Xligrjj2Ot/MxnYq5qAPJSPvOOcqAWMIDzVZDHyK1S1ZRXj6drAAWAMSG46e088uLufcUcBnWGAVYC5WRP5P7HjYGtH8ZaxOSwrx5aTv5SJuJ32/C4bafAt865y7H3gK+84Aq0fV3ns/aW+375zrBTwd8dBEbKhiKjabWChL4iAgVGsl3meQ030n9Bnk+P0PapZ9RXj4zmpgNDa71gpsHwv19zosWAfxX2O82mcFaj9KUK2I26syLcu176zAWGxGwIOBC5xzN4ZqmAU11kIzoS3FZizLtjz8bsypyL/LiXzuif5Nz/PvbBGR3KAgkojIXgqyGPpjs7S0BG51zo3y3v+UqemG4Lq4c65kAqnvkScvG7JsVThswn4Z3i+BtlkNf8o13vvFzrlx2PTufSIWjfXeL8nGpiJPCvp477OTxZTZNYRPkt4ALs+ioDTOuTtz8Dx54XbCAaSHgAFZ/ZLunPtfPvUp8rMpEPtdiPd+p3PuBuB47IQXLBNpr4NIWFAUrFZKN+/9F9EaOecaRHs8j2zEgha58f73JBxA+gbokVV2UxBQyy0Fdj+KIbIe0JRMyyJfT62gVs9e896nO+feBW7Cvr9OwgLIYHV/Qt9p72SRoZuIgvrdGPl3OZHPvSj9TRcRUWFtEZGcCIq53hDcLQY8EaXZsojbiaTkh9p4MtZ6KYxCMx9Vc87FOxGrHWd5bhkaXDcOLpGPJSpyKExOT85PDK53AtdndZIUqJnD58ptob6vBO6OEUAqj013nR8iZ9s6MlbDoPhyfu13gAWSCBc1h4zDprLFOVebcP8/ziqAFMjPfSfyuI93kh3zMyK8jwH0jzM8LjdfY8L7UTba5BnnXGUyFm3OXAg8N7+zQiInDLgwi9uRbbKroH437u3fdMi4X4mIFEoKIomI5JD3fiLh4rHtnXNdMjWJ/EX4pFjbcs4dihXFBZiT5KLauSE0i1YxoEOctp3ytiu7jSBjnan1BPVrsmEC4WEU3XM4bXvV4Dot1lTWzrlmZJq2uwAI9X1BnGyDE8m//zkij7fj47RtRf7UUMksMhtxY5TloX0r3gxjVSNuz4/TNj+nBA99BsWIf1zHqwmV0GsMapzlWm0rsrEfBYWf2+Xic++NWwhnBf7OnsXpI4NKPcgFwex2oULQZzrnyjnnyhGelXOW935WDp4iN78bI7+f4h1X8fxCuDZRp2DIZSyRwb3MGWIiIoWOgkgiIrnjkYjb92RaNiLi9rVxpva+mfB38/Dc6FiSjYq4/d+sGjnnSgJ987474L3fAgzCpgP/CXg6eCw721gJfBncrUN45p29Eaq3clCQsZOVu3PwHHkl1PfaWU2p7pxLIXqtsLzyGeEgTR/nXKxZjG7MjSd0zlWN32p32+KEi7mDnfBnFgosxcviiazVc0SM56wBXJJQB3NHZFD2hqwaOefKEP+4T+g1YkMr453MJ8x7n4rNvAnQ0jl3XIzml5LE2bKcc2djfztCHogS1P2ccMH6i3NxeGOoaPZ+WHDqLMLD//a6oHYgN78bI4O1ORp6GNQ2/Cy4W4WMQ6MzCH4Y6hnc3UScGVpFRAoDBZFERHKB9/4bYHpwt3VkNlLwS2xomElt4PUgaJKBc+4i4Org7gZgSN71ON+MAhYEt091zt2auUEQZBhC4rPv5Jj3/h7vfZvgcu9ebuYuYEdw+znn3IWxGjvnDnPOPR4xK1xIaFYjhxWHzbyeCwoyx5uSOxlCfT8QuD7zwuAX+v9hNcPyhfd+FeEi6VWAd4JgRea+XYNNG54bBjnnvnbOnR4EiaIK+vE/4OjgoQ1kDLSGhI6ZutH6HuEPwoV9uwfTqmd+zqrBc+Rnkf5PsSLrYMXD9zjJD/aNV8lYDDqayFm/HoiW9eecuwIrqp3bnoy4/aZzbo8hU865Y4HH8uC543LOVXPOPQl8SDi75lXv/fuZ2wbDAO8L7pYEPnfOxTwunXOtnHPxZnJ7l3CWz4WEh7KlB8tyIje/GxdE3G6ew34BPE74dT/pnNsjEy0IYH9EOGj1QqyMKhGRwkKFtUVEcs8j2D+MAPdiv/yGXAHMwE62LwCaO+fewKaF3h/oBpwe0b6v9z6y7kKh5L3f4Zz7P2x2pRTgUefcadhJzyrgMGya6MbYe3dOsGqBn6XGez/DOdcXCwqUAt5yzt2InbD/hQ132B8bntgOaI2dDD2TaVNDsEyGFOA651xTLHttOXAo8B9sOvjfsRmlsjPNeV57jvAQzaecc52wzzoNCwpeHFx/G1zXyKd+3QqcGjzfacCvzrnXsM/lACxb4iTsxHIdOR8G5YLtnQSsds6Nx06Al2HZFAcEz3E2UC1ivRu896vZ01jsmCgLfBJ8V6wmPMxtivd+jfd+u3PuJSzbpwQwIXidU7EAZ3MsA2l/LLB2cQ5fZ0K897ucc5dhn3sJ4D7n3EnAMKx+Vk0se6M+lrUUa3jVa1gmW9mg3Qzn3FvAEmy401lAR+x4mU2cIcPZfB3vOOd6Yt/NtYBZzrlXsWG6JbAhuhdj31efA5mHMudUXedcZICkJPZZHga0AY4j48xprwD9stqY936wc65V0OfDgCnOuS+x/W0Jth9XARphwwyPwIYQ3hJjm/84574N2kcOTRznvc9p/Z/c/G6ciB0TJYCbnXMemEV4WNoa733CQ8289z865x7DMuDKA985597DZqLbAjQELic8JG8WBTObVEQk+7z3uuiiiy66RLlgJ2zevioTal8Mm3o4tN7pmZbXybQ82mUTcHGM5+gU0fbeLNqMj9XvBLeRGixPTeB1x22LpfNvifG6vwMqR9wflUuf3ZwcbGP3ZxWn3RnYSU2szzV0WQ1UibKNvsCuGOv9jhXujffZ3huxTqc4/U64bZztPBznNU/Cgqcx9xOsuHlonVpxnjNuW+x4WxCjX4uBJvHe0wTfg3sITzee6H5wUYztHYIFW7Jav1NE29LYiWus53sRy4IM3R+aT8d9D2wYUVb9moAFRUL3x2exne7E/v5YgtW3irlfYIGr0PI+CX62ZbEhSFk99xagF7l3PN0b47myukwGuiS4fYdlUSa6v0b9TDJts3eU9bL8OxaxXqeI9vdm0SZXvhsT+K4an6ltzH0pot39WOHvmO8hUDknx9LetNVFF110yauLhrOJiOQSbzUoIoc13JNp+TzsF95QZs5y7JfRf7EspYeBo7z3b1LEeO/fw36ZfRE7sd+GnUhPAq7EfsGOzI5dk9993Fve+0+Aw4GrgNFYcGILVpdnFXaC9xwWbDrYR8k88d6/gGUrfUh4v1gJ/IBlmbT03v+V5y9mL3jv78CyfT7DPtMdWAbOOGxf7+RtiFl+92sets/dCczEghkbgF+BB4Bm3goD58Zz3YdNRX42lmn2HTYb1hbsBHgdNrzrQyw74QjvfZb1Yrz3/2BZRM8E/Q0FYqK13YoV7u2H7WsbsONrIfABcIr3/iqSkN3nvR+JzQQ2GPib8HH/PRYcON4nMLzHez8Kez+GYsfXDizbbTqW3dHEez81yw3kgLdhYKdg2TvfYt9NW7HX8z/s2HwnL547c1ew/WAplnH1LnAT0MB7f6z3/vNYK+/eiHkQ+866G9tXl2PfV1uxgNwY7Bg51nvfKYHNDidj7arNZKwFuNdy87sx+K7qidWzC73mnPbvbixr8DksoBU6/pZg78vZ3vtO3vu0nD6XiEhB4byP+j+JiIhIvnLOnYEFYcCG+TydzP6IiIiIiEhGykQSEZGC4pqI2+OT1QkREREREYlOQSQREclzzrmOMZYVc849ig3JASsa/HNW7UVEREREJDk0nE1ERPKcc24nVgvpS6yexxqsIHA94Fxs5i6wGhVtFEQSERERESl4FEQSEZE8FwSRUuI0WwOc570fmw9dEhERERGRbCq0QaQqVar4WrVqJbsbIiKSgA0bNrB+/Xo2bNjAjh072LlzJ957UlJSKFOmDBUqVODAAw8kJSVenElERERERPLS9OnTV3vvD4y2rHi0BwuDWrVqMW3atGR3Q0RERERERESkyHDOLcxqmQpri4iIiIiIiIhIXAoiiYiIiIiIiIhIXAoiiYiIiIiIiIhIXIW2JlI0O3bsYMmSJWzdujXZXdmnlS5dmho1alCiRIlkd0VEREREREREckmRCiItWbKE8uXLU6tWLZxzye7OPsl7T1paGkuWLOHwww9PdndEREREREREJJcUqeFsW7dupXLlygogJZFzjsqVKysbTERERERERKSIKVJBJEABpAJAn4GIiIiIiIhI0VPkgkgiIiIiIiIiIpL7FERKki5durB27dqYbe6++27GjBmzV9sfP348Xbt23at1RUREREREREQyK1KFtQsD7z3eez7//PO4be+///586JGIiIiIiIiISHzKRMoDTz31FA0bNqRhw4YMGjSI1NRU6tWrR79+/WjevDmLFy+mVq1arF69GoAHHniAunXrctJJJ9GzZ0+eeOIJAPr06cNHH30EQK1atbjnnnto3rw5jRo1Ys6cOQBMmTKFtm3b0qxZM9q2bcvcuXOT86JFREREREREpEgrsplI118PM2fm7jabNoVBg2K3mT59Oq+//jo//fQT3nuOOeYYOnbsyNy5c3n99dcZMmRIhvbTpk1j+PDh/Pzzz+zcuZPmzZvTokWLqNuuUqUKM2bMYMiQITzxxBO88sor1K1blwkTJlC8eHHGjBnDHXfcwfDhw3PrJYuIiIiIiIiIAEU4iJQskyZNokePHpQtWxaAs846i4kTJ1KzZk3atGkTtX337t0pU6YMAGeccUaW2z7rrLMAaNGiBSNGjABg3bp19O7dmz///BPnHDt27MjtlyQiIiIiIiIiUnSDSPEyhvKK9z7q46GgUqLtoylVqhQAKSkp7Ny5E4ABAwbQuXNnRo4cSWpqKp06dcpeh0VEREREREREEqCaSLmsQ4cOfPzxx2zevJlNmzYxcuRIjjvuuCzbt2/fnk8++YStW7eyceNGPvvss2w937p16zjkkEMAGDp0aE66LiIiIiIiIiKSpSKbiZQszZs3p0+fPrRu3RqAyy+/nAMOOCDL9q1ataJbt240adKEmjVr0rJlSypWrJjw891yyy307t2bp556iuOPPz7H/RcRERERERERicZlZzhVQdKyZUs/bdq0DI/98ccf1KtXL0k92nsbN26kXLlybN68mQ4dOvDyyy/TvHnzZHcrRwrrZyEiIiIiIiKyL3POTffet4y2TJlIBcAVV1zB77//ztatW+ndu3ehDyCJiIiIiIiISNGjIFIB8O677ya7CyIiIiIiIiIiMamwtoiIiIiIiIiIxKUgkoiIiIiIiIiIxKUgkoiIiIiIiIiIxKUgkoiIiIiIiIhINm3fDmedBVOmJLsn+UdBpCRITU2lYcOGAIwfP56uXbsCMHr0aB599NFkdk1EREREREREEjBrFowcCZdemuye5B/Nzk5HCugAACAASURBVJZHvPd47ylWLPE4Xbdu3ejWrVse9kpEREREREREcsO8eXZdtmxy+5GflImUi1JTU6lXrx79+vWjefPmLF68mJtvvpmGDRvSqFEjhg0bFnP9oUOHcs011wDQp08frrvuOtq2bUvt2rX56KOPAEhPT6dfv340aNCArl270qVLl93LInXq1In+/fvToUMH6tWrx9SpUznrrLM46qijuOuuu3a3e/vtt2ndujVNmzblyiuvZNeuXQD07duXli1b0qBBA+65557d7WvVqsU999xD8+bNadSoEXPmzMnx+yYiIiIiIiJS2Pz2m11PmQLVq8PChcntT34ouplI118PM2fm7jabNoVBg2I2mTt3Lq+//jpDhgxh+PDhzJw5k19++YXVq1fTqlUrOnTokPDTLVu2jEmTJjFnzhy6devGOeecw4gRI0hNTWX27NmsXLmSevXqcWkWuXMlS5ZkwoQJPPPMM3Tv3p3p06dTqVIljjjiCPr378/KlSsZNmwY33//PSVKlKBfv3688847XHzxxTz00ENUqlSJXbt2ccIJJzBr1iwaN24MQJUqVZgxYwZDhgzhiSee4JVXXkn8PRQREREREREppN56C/r0gX//DQeRANLS4OCDk9atfFN0g0hJUrNmTdq0aQPApEmT6NmzJykpKVStWpWOHTsyderU3cGYeM4880yKFStG/fr1WbFixe5tnnvuuRQrVoxq1arRuXPnLNcPDY1r1KgRDRo0oHr16gDUrl2bxYsXM2nSJKZPn06rVq0A2LJlCwcddBAAH3zwAS+//DI7d+5k2bJl/P7777v7fdZZZwHQokULRowYkd23SERERERERKRQuvhiu54zx2oihdStCyVKJKdP+anoBpHiZAzllbIRgyG99znaVqlSpfbYVna2GVq/WLFiGbZVrFgxdu7cifee3r1788gjj2RYb8GCBTzxxBNMnTqVAw44gD59+rB169Y9tpuSksLOnTuz/8JERERERERECpl168K3hw+HBQvgsMNg0SI44ojk9Ss/qSZSHurQoQPDhg1j165drFq1igkTJtC6descbbN9+/YMHz6c9PR0VqxYwfjx4/d6WyeccAIfffQRK1euBGDNmjUsXLiQ9evXU7ZsWSpWrMiKFSv44osvctRnERERERERkcJu8uTw7YEDoXRpuOIKu3/AAcnpU34ruplIBUCPHj2YPHkyTZo0wTnHwIEDqVatGqmpqXu9zbPPPpuxY8fSsGFD6tSpwzHHHEPFihX3alv169fnwQcf5OSTTyY9PZ0SJUrw/PPP06ZNG5o1a0aDBg2oXbs27dq12+v+ioiIiIiIiBQFodnYQrp0gaOPttstW+Z/f5LB5XTIVbK0bNnST5s2LcNjf/zxB/Xq1UtSj/LPxo0bKVeuHGlpabRu3Zrvv/+eatWqJbtbGewrn4WIiIiIiIjsG669Ft54w25v2AAvvmiZSGPGwIkngnPJ7V9ucc5N995HDYspE6kQ6tq1K2vXrmX79u0MGDCgwAWQRERERERERIqKKVNgxw7LRKpTB6ZPt8dDgaOTTkpu//KTgkiFUE7qIImIiIiIiIhI4o45xq5r1oS2bS0j6e23951i2pFUWFtEREREREREJIq1a8O3Fy6Eo46C3r3hm2+S16dkUhBJRERERERERCSKH37IeP/ss5PTj4JCw9lERERERERERKL44QcoXhyefRYaNIDGjZPdo+RSEElEREREREREJIq//7ZaSH37JrsnBYOGsyVBamoqDRs2BKxIdteuXQEYPXo0jz76aJ497/jx4/khcy5ehHLlyuXZc4uIiIiIiIgUNkuWQI0aye5FwaFMpDzivcd7T7FiicfpunXrRrdu3fKsT+PHj6dcuXK0bds2z55DREREREREpKhYssRmZBOjTKRclJqaSr169ejXrx/Nmzdn8eLF3HzzzTRs2JBGjRoxbNiwmOsPHTqUa665BoA+ffpw3XXX0bZtW2rXrs1HH30EQHp6Ov369aNBgwZ07dqVLl267F4W6dlnn6V+/fo0btyYCy64gNTUVF588UWefvppmjZtysSJE1mwYAHHHnssrVq1YsCAAbn/hoiIiIiIiIgUUt7DP//AIYckuycFR5HNRLr++uuZOXNmrm6zadOmDBo0KGabuXPn8vrrrzNkyBCGDx/OzJkz+eWXX1i9ejWtWrWiQ4cOCT/fsmXLmDRpEnPmzKFbt26cc845jBgxgtTUVGbPns3KlSupV68el1566R7rPvrooyxYsIBSpUqxdu1a9t9/f6666irKlSvHTTfdBFjmU9++fbn44ot5/vnns/dmiIiIiIiIiBRhq1fD9u0azhZJmUi5rGbNmrRp0waASZMm0bNnT1JSUqhatSodO3Zk6tSpCW/rzDPPpFixYtSvX58VK1bs3ua5555LsWLFqFatGp07d466buPGjenVqxdvv/02xYtHjxV+//339OzZE4CLLrooOy9TREREREREpEhbssSuFUQKK7KZSPEyhvJK2bJld9/23udoW6VKldpjW4lu87PPPmPChAmMHj2aBx54gN9++y1qO+dcjvooIiIiIiIiUhSFgkhxh7MtWgSTJ0O3blCmTJ73K5mUiZSHOnTowLBhw9i1axerVq1iwoQJtG7dOkfbbN++PcOHDyc9PZ0VK1Ywfvz4Pdqkp6ezePFiOnfuzMCBA1m7di0bN26kfPnybNiwYXe7du3a8f777wPwzjvv5KhfIiIiIiIiIkVJwplIb74JF1xg49+KOAWR8lCPHj1o3LgxTZo04fjjj2fgwIFUq1YtR9s8++yzqVGjBg0bNuTKK6/kmGOOoWLFihna7Nq1iwsvvJBGjRrRrFkz+vfvz/77788ZZ5zByJEjdxfWfuaZZ3j++edp1aoV69aty1G/RERERERERIqSf/6BlBSoWjVOw/ffh/bt4dBD86VfyeRyOuQqWVq2bOmnTZuW4bE//viDevXqJalH+Wfjxo2UK1eOtLQ0Wrduzffff5/j4FRu21c+CxERERERESma+vSBsWNh8eIYjX75BZo2heefh3798qtreco5N9173zLasiJbE6ko69q1K2vXrmX79u0MGDCgwAWQRERERERERAq7JUsSGMo2aBDstx+cf36+9CnZFEQqhKLVQRIRERERERGR3LNkCTRqFKPB6tXwzjtw5ZVQuXK+9SuZilxNpMI6PK8o0WcgIiIiIiIihZn3FkSKOTPb8OGwYwdcfnm+9SvZilQQqXTp0qSlpSmIkUTee9LS0ihdunSyuyIiIiIiIgXU/PmQng4rV8L69cnujcie1q+HTZviDGd7/32oWxcaN863fiVbkRrOVqNGDZYsWcKqVauS3ZV9WunSpakRd+CoiIiIiIjsi4YOhUsugTp1YN48aNECMs2ZJJJ0S5bYdZantn/8AePHw4MPgnP51a2kK1JBpBIlSnD44YcnuxsiIiIiIiISxaZNcPXVdmL+11/22PTpsGULlCmT3L6JRFq40K4PPTSLBoMGQenSVg9pH1KkhrOJiIiIiIhI/vEeZs2KMwV6hHHjYPNmy0Zatw4++sge//33POuiyF75+2+7PuKIKAuXLrWduHdvqFIlP7uVdAoiiYiIiIiIyF656SZo0gQOP9wSM8BqHQG89x588UXG9l98AeXKQfv2dh0qJTNrll0vXw6jRuVP30VimT8f9tsPqlaNsvCJJ2DXLrj11nzvV7IVqeFsIiIiIiIikj9mzICnn4aLLoJ//oF774WGDeHUU6F1a5g82dpt2WKjfjZtssyjk0+GUqVsWe3iizi+1GLm/1gHLjmQdu0sA2TtWqhYMfbzP/AA/PADjB4NJUrk6UuVfdD8+VC7dpRyR6tWwUsvQa9eFj3dxygTSURERERERLLtf/+z4NDgwXDffTY87corLUEjFEACePddu37hBTv/vukmLCWpdWtSatdk7Lb2PPjyQfjatXn17078SgM2X/Ff+OYb2LYty+e/+2748ksYODBvX6fsm/7+O4uhbC+8YGMyb7st3/tUECiIJCIiIiIiItmybRsMGwY9ekCFCtCuHdSsaSfembOCZs6067FjoXnD7Rz7/n+hSxeLOj36KJ9f/Rk38TjzyrWgHBtZQg0OHPmypSxVqQK33GLj3BYvJv3P+fhd6axfH84QGTwYdu6ErVvDxZBFcsL7LIJIO3bAiy9aul29eknpW7IpiCQiIiIiIiLZMmMG/PsvnHOO3XcOOne226edZqN8UlIssLRsmT2+bf4S3lnaCZ59Fq6/HmbPhltvpc39XXiu5E00/fNDji0+jVP5igF90+DTT6FbN6s/U706HHYYxeocyeb9D2bljY9Rym/hyistvvTNN3D77VCrFixalIx3RIqSlSttGOYeo9VGjrQd+pprktKvgkBBJBEREREREQEsm8f7+O1ChbCbNw8/dswxdp2SAj17wmmnek6uMoND5ozBP/Qww/5qTq0Nsy2F6emnoWRJACpVgtNPt+fu1MkKdf88dz+6v3w6o89/B379FQYOZGyPwVzGK/y4rRlHvnIbizmUQcsv4P/Kvsu7r23lu+/s+TW8TXLqn3/sukaNTAsGD7ZCSaeemu99KihUWFtERERERERYsgQOPdSCMDffHLvtL7/YMLbDDgs/1rUr9O0L1/bdSWe+hbEDYPpPtvAu+Is2zL3hdfqcV3eP7V14oSV5nHwy/PYbvP221VZKT4dun9SH+vW5tj5sqAGvLbmMzinfcdMBr9Jlyhhe3jSMfz/an2kl2rKLXUx/7yR4tj8UU86E7J2lS+364IMjHpw1CyZOhMcft0jpPkpHlYiIiIiIiOweofPSS7HbPfOM1RZu3Dhi5qpt26jx40f4uwbQ+er6Fg1asIAPj3+BLqXH8eu4lbRlMuVb7RlAAhu19vTT8H//ZwGlXbvs8e++s3pH//4Lf/wBV11lGU/f7urI7JvehCVLmDtkLKPoTvUdC6nOMu5ccxO+a1dYvDh33hjZ54SCSIccEvHg889bJflLL01KnwoKZSKJiIiIiIjsw1atgg8/hNGj7f6KFRbEiZZs4b2VMwIb1QPYVGwXXmiViJ2DNm3ggQfgjDNYMHg/vhgHx0+3ppGZS5GKFw9v9/jj4eijYc0a69uUKbBpky075hho1gzOOw/OPRcoVoyj+x5Po/8ez44dcExrT4spQxj83S3QoIENnTvttNx4m2Qf8s8/lshWtWrwwNq1lh73n//Y+Mt9mDKRRERERERE9mFPPglXX20BogcegI0bLaBz220WDwplZYAFmMCCOQ/ftwPuvReOO87GnX32mU19/sMPcP75sN9+VKtm7UPD47IKIkUqVsziUjNmQKlS8N57FkgCaNXKJnZbvz4iiIUNhTvgADj7HMcQriZ19Gw48kg480wr4C37rK1bYfz47K2zdKkFkIqH0m6GDrV9++qrc7l3hY+CSCIiIiIiIvuw1FS7vukm6N3bbg8eDI89Bj/9BNdeG25r8RjPC2d9wyFdmsB991l2xsyZFt0pXTrDtqtXD98+91w48MDE+nTAAVbU+OyzLQHkp58sJlSxoi3PXO7o9NMtc6lpU7v/T6naNmVbuXIWEUukWrgUSd2728yBoWBoejqMGgXbtsG6ddHX+eefiHpIW7ZYpLVdu4yV5PdRCiKJiIiIiIgUUa+/bhlFmXkP/fpZls/8+VbC6PHHrbD2mjWWMVSKrVxRdwI7x4zHf/0NfPklpZ4ZyHRa0OzWky3FY/RoePPNcHQnk1Am0nnnwQcfZL/W9dln20iiceMyFTnOQuj5li8HKleG+++3lUeNyt4TS5Ewfz58/bXdXrnSrt980xLUSpe2oGa0+OLSpRH1kAYNsqrzDz+cL30u6FQTSUREREREpAjavj1cA/iee6BMmfCy8eOtOPYLL9j93aN0vOeA6WOZ0/oNUtZ8Rsk5/9rjp9hVB+D34o1gyMtw0UV7ZB5l1rChDTXb2xnRQyfymzYllsUUqmGzfHnwwJVXwosvWsXu6tVtHJ7sE777Dk45JXx/zRqr9fX44+HHduyw0Zd16oT3r/R0WLAA2rfHinI98ohVfu/QIV/7X1ApE0lERERERKQI+vjj8O3p0zMue+GFjPGfI48E5syBli3hpJMo8+0XlOzehYXPjuIExnAcE5j40AQOLrma5y6fZUGZOAEksDrboayPvbG7sDGJBZEqV7aC4LuDSMWLw/DhUL68pUNt3rx3HZFCxXuLcR5+OHz+uT22Zg089xz8/ruV8goNtWzf3kaqhcybZzW3WrbwNh3gli3w6KP5/hoKKgWRREREREREiqB33rHC1AB33glpaTaB2qOPwogRNpytcWNb3mrpKAsgLVoEr75qw3fefptDr+7GyoYnMInj6PLIcSzbXpn//jf/XsNBB4VvJxJESkmxdSKLgVOnjo3rW7TICj1JkffXX7B4sZXDCu3jaWmWVHTyyXD33VYyK+TPP8O3p0yBkmzjzFGX2IHyyCNQr17+voACTEEkERERERFJqm3bLDNm27Zk96RoWLbMZrcfPdoCRXXrwoQJcMEFNtva7bfbsJ7QSK/TD5pK2+cugPr1rUD2pZfuTh0qVsyKaZ97rs3adtpptr38st9+VhsbEi/K3aaNBdB++CHiwY4doWdPCyItWJDr/ZSCZcIEu+7QASpVsttz5lhdpC5dLEMucna/3bxnw4dfMrlYe/Yf9YalLN14Y351u1BIOIjknEtxzv3snPs0uF/JOfeNc+7P4PqAiLa3O+f+cs7Ndc6dEvF4C+fc7GDZs845Fzxeyjk3LHj8J+dcrdx7iSIiIiIiUpB98IEFO+67L9k9KfzS0+GOO2zIDkCvXjasrXZtGDPGhvTccYcFkurUgWMPWcSnKd1x1arBZ59FVBPOqEcPu+7fP59eSIRQNlKiQaTXXrP6T2++mWnBwIEWPRs8OFf7JwXPxIlQpYoFPMuUsZjopEm2rH59u46sEQaw84cp0L49V396GjWKL4dhw6yYmIUtJJCdTKT/An9E3L8NGOu9PwoYG9zHOVcfuABoAJwKDHHOpQTrvABcARwVXELl1S4D/vXeHwk8DSjHUERERERkHxGaNemddzQTe06sX2+ZF0OHwg03WF2gFi3g6KMtfgLQti089FAw0dRff8Fxx1nV6tGjY0ZpLrjAkpROOilfXkoGobpIVaok1n7//aFZM/j550wLatSwAk1Dh9rMclJkTZhgu3Yo/lOpEkybZrcbNNiz/bl8QEr7NjB/PjeUe4l7L5pvNbRkDwkFkZxzNYDTgVciHu4OvBHcfgM4M+Lx973327z3C4C/gNbOuepABe/9ZO+9B97MtE5oWx8BJ4SylEREREREpGj7+2+7XrTIhmLJ3rnhBvjpJytpNHBgxqLUXbva8rfeCh749ddwAOnbb6FRo5jbdg6aNMm7vseS3UwksCDSrFmwc2emBZdeaulY48fnVvekgFmyxEYsHndc+LHQkLb99w8X1AarmzRywAyG0of1Ddqy5qc/eXrjFRxRr2T+droQSTQTaRBwC5Ae8VhV7/0ygOA6VPLsEGBxRLslwWOHBLczP55hHe/9TmAdUDlzJ5xzVzjnpjnnpq1atSrBrouIiIiISEE2f37G2ytW2PCrzz7L3na8t6Fxqam52r1CYetWe+0XX2xxkpSUjMtLlYInn4SaNbGxbp07W2RowgRo3jwpfU7U3gaRtm6FuXMzLejUycY2ffllbnVPCpiJE+26Q4fwY6EgUqNGGUen1ai8hdNfP5tVHMgX/zeCP5eXB2yop0QXN4jknOsKrPTeT4/XNrRKlMd8jMdjrZPxAe9f9t639N63PDA73yAiIiIiIlIgDRwIX31lJ/1gWUnPP2+ZBE89lfh2fv0VuneH889PTt2eZPviC9iwwYadxTR/PpxwAhQvDt99Fy4QU4AddhiULJn4cDaAVq3s+pNPMi0oU8aiC199lWv9k4Jl4kQrxh6ZObdjh12fckqmxk89RYklqVxX4Q3eG3sQ8+bZwwoiZS2RTKR2QDfnXCrwPnC8c+5tYEUwRI3gOhjJzBLg0Ij1awBLg8drRHk8wzrOueJARWDNXrweEREREREpBP73Pwv23Hqr3W/a1GYC+/VXGDLEMmfGjYPLL7di0bFs3w5nnRWekSk0PG5fMn26vX+dOsVotHmz1QTasQPGjoWjjsqv7uXItddaUeSS2RhhVLcunHoqPP64zSqXwSmn2FRdixblaj+lYJgwAdq1szhpyI8/2vXpp0c0XLYMHnkEevSg0bWd+OQTGDXKsvgOPzxfu1yoxA0iee9v997X8N7Xwgpmj/PeXwiMBnoHzXoDo4Lbo4ELghnXDscKaE8JhrxtcM61CeodXZxpndC2zgmeQyX1RERERESKoPffhyuugEGDwo+deqplnDzxBKSlWZvmza2+z8yZ4Xbbt3mrIB3hrbfgzz/h3XfhtttstNa2bfn0YgqIZcusBlKJEjEaXX01/PabVTAvBBlIIRUrhjOLsuOyy6z8USi7ZLdTg/mdlI1U5KSl2S4eWQ8JLLuxVq1Mdb0ee8wi0AMHcvXVlqQ2fDiceGL2Apb7muLxm2TpUeAD59xlwCLgXADv/W/OuQ+A34GdwNXe+13BOn2BoUAZ4IvgAvAq8JZz7i8sAyleEqaIiIiIiBRS77yT8f7SpVCtGtx5p91v2dKGph1zDNQ6eBubbx8IhywgbdUutn46hkNYij/4YKasO5pNW4uzpVhDHjjgEE4r15oypdL5aecufv/lOJq1jhVRKVqWLctYMHgPr71ms5INGBBlTE/RdMABdr1hQ6YF9erZTG1ffQX/93/53i/JO99/b9eR9ZAA+va1y247dljUuXt3OPJIqmNZkfffDw88kF+9LZxcYU34admypZ8WmqNPREREREQKhc2boXJlO3d/7jl7LHRKcv318MwzlklUr54tGFfhTI7fOBqqVGHTjhJ8vq4dNbs1pdKquayYPJ9SbKMxsyjJjgzPs/KINhz07Qdw6KHsC5o3h4MPhk8/jbJw4UJ7Q489Fr7+es+q20XUlCkWiPzkE5udLoOLL4ZvvtF0gEniPTz9NPTokbtDxx57zLIR16+H8uVjNBw1yoZ2jhoF3brt7tPSpXDIITHW20c456Z771tGW5bo7GwiIiIiIiJ7JS0tPEvW5Mk2a1aXLvDDD3aiH/LYY9a2Xr3ggVde4fiNo7mz9JP4lau4o/dSzuNDhh5yJ//Z+SZXN/me5/tMoxTbGPr4Khg1im0jPqMPr1NxyW9Wrfubb/L99SZDzEyk/v1tSqqhQ/eZABKEgwh7ZCIBNG4My5fbDif5bu5cuPFG6NMnd7ebmmpB6pgBJO/hoYdsqsLTTtv9sHMKICUiJ8PZRERERESkiNuxw2oO9eplxa73RufOMHu2FchesMAeq1cvmG4+QqlSEc+xbh3ccQf/HHEcD8/vz3UrrRYyWJHl336z4W/XXgtlyjjOuqIKVOhGKeDjilC7a1vu/rkHnHyyZRyceCLs3GnTu/fqZdM3FWIjR9qJ+G23wa5dsHKlDQncw3ffWeOHHtpnsrJCYgaRGja0699+23Psk+S56cHc7/GK5gOsXg1XXgnPPhs/yJOaarWPYvr0U5g6FV55JU4RMYlGmUgiIiIiIpKlESOsQPGNN+79NmbPtuvUVBtZlZKSwC/+Dz0EaWksvH4Q4Jg7N5zNFApIHXccHHigzeZWoUJ41erVYfa2Onai+OCDNhPZNdfYeLmrrsIfcYSdQBbS0h5gs9HdfrvVBV650t6PPTKR0tPhhhsseNS/f1L6mUyhfSJTHXbTqJFdh3ZOyVehDMQqVeK3HTnSvodGjIjfNm4QKT0d7r4bjjjChjRKtimIJCIiIiIiWZo1y65ffhk2bQo/npaWeAzmoIPs+sILYdgwCyAVjzUm4u+/rThS794c3LU5YAVzFy2CNm3CzY49Nvrq1arZSCX228/SlVas4M/vV1KJNNryPRsOqWtFmS67zNJ4kmjaNBg4MJyhlYjU1PDtqVPDZX32CCK9+y7MmAEPP2xTT+1jQslmUTORDj4Y9t8ffv01X/skFvQMjTJNS7Okwxkzsm7/9dd2HSqanRXvEwgiDR1q0z3ed5+ykPaSgkgiIiIiIvuwAQOgQYOsYymhuWx27LBMoEMPhU6dLIOgVSsblhIvmBQatvbDD/Dnn3DYYXE6dcstdoL30EO7R2DdcYdNu/3ss/D++/Dkk1mPSNsdRAopU4Y3Pj+Qf6nEZNryaq9v4a674PXX4brrkpqRdNVVNivU44+Hh/jE8/nn4dvt21t9Kcg0Wm3LFnvTWrSA//wn1/pbmBQrBmXLZhFEcs6GtCmIlO969bLvAYAVK+D88203jVa+bNcuGDPGbk+aFPtQXbnS6q1lGUSaOdMy89q122ePidygIJKIiIiIxJWWpkmMiqLNm2HwYJsN7Ysv7LHZs+GKK6x8kPcWRApl/0ydCkuWWJkdgF9+gZYt4xfH3b494/2YpXmGD7fL7bfDwQdnqAP9+ecWuDr/fDsXzMoeQSRsKMzxx0Pt2jDph2I2j/eNN9pYuGefjf0C8tCqVXb9wgv2Xo4eHX+dn3/OOAzowANt9F+LFhGNbr8dFi+2aFuxffe0r3z5LIJIYEPaZs8u1MMaC5sVK2x06R132AjTefPgq69s2YABe7afNg3WroWOHeGff8JB7WjmzbPrPYJIy5fbkx17rO0Q77xjQUTZK/vut4mIiIiIJOycc6z+TJJH/kguGznSTtBKl4annrLPuHFj+N//LFAxcSKsWQMXXWTnXJ98ElrT0/+QYWxufAxbXWnueftI/F0D7AwxivXrbaK0W2+1+2vXZtGhFSusgm6LFpaNFBg/Hr791oJAiaheHTZutMvs2fDGG/DHH1ZHqF07+PHHoOFjj1nR7f79I19cvlqzJuP9Rx+Np0g81gAAIABJREFUH9P4/XeoX98+nylT7DXecUfEefHnn9twwGuvtbPvfViFClnURALLRFq3zqITkue8t93Sezj7bKhaNbysQweYPz98/6efbOK0Nm1sv37r7j95Yr8BLDz7BiusFsW331rbyCGvbNoEp59uNdDOPNM2nLmiv2SLgkgiIiIiEtPff9tJ/Pz5iRU2lcLj22+hUiWLoXz7rQ0XCZk6Fe6917J6LrnEzrs++wwaMptxHM9T/1xAiW2bmHviNfyVXhseeRjq1IkajNmwwZIA+va1+6eeGqUz27fbEJONG+HNNzPUK+nY0YbQJSo0S9lLL1lQLJQpddFFcPTRsHSpZWGRkgJvvw3Nm0PPnhY5y0fbt9vLLVky/NjkyRk/h8y8DweR2re3zKwMJk2y4lONG1uxpX1czEyk0AxtGtKWLz74AB55xGI6jRqFg0ilSsEpp9gsbJs322Ovvgpffuk5jgmMLnYmh554NNdveYQzFj+Pb9TIIuCZfPONHcq7s/TS06149s8/w0cfwXvvWS0syREFkUREREQkpvfft193a9SA886D3r2t7oQUft9/b8GdK67Yc3TH449bYOnWW6HMqkU8WOxuRtCDn2lGY2bx85UvwC+/4J58glP4mlGP/GFBpO7d9xgetn69nczXrGlDI6+5JlNHvLcI07hxFvmpXz9Hr6tOHbu+6aaMr6dCBRvOBhHFqcuWtcBXpUq2g+fjzv3vv3Ydypw48kg7AX788azXWb7cMrkaNIiy8O234YQTbHzbyJGWYraPCwWRVq+OEkxSEClfbN5sWazjx9sxOHq0fd/sv78tb9rU6qSVZSOrPp8KP/9M+V8mMbVcZybQkZP2mwR33cXQB5ZQh3nsPKq+pcdGBKy3bLEMwxNPjHjiu++2Xz6eeAK6ds3X11yUKYgkIiIiIjHNmGEnt9OnWx2aN9+0X5SlcEtLgzlzbHhXrVr2y/9PP1ns4bTTLMhSrRr0rf4xNGlCzwUP0YDfeIG+VF49j2YvXgUpKdSvbwWuxyyqY8WSuneH//53906ybZtl3JQvb89bqVKmEj3e2+xhr71mJ329e+f4tR1zjNVrCpk/PxxQCgWRIofOUL26Fdn+6y8rLpRPQkPZmjSx60aNLInom2+skHk048bZ9e442++/24N33mmpVm3bWjpT6IXu40JBpAMPhLp1My2sVMkyU2bPTkrf9gULF1qc9oYbrJ5Ry5bh4z9Uu+i6i/7ltBe7s54K1Dy3NTRvzpNTjqPutl9g8GBKrVgM999PhTrVWERN5g4ZawHAq6/enbqUmmp13Bo1Cp74vffsWL78cku1lFyjIJKIiIiIxPTrr/b/+kEHWYZExYo2y5YUblOm2PWxx9r1JZdA69ZWNuTmm6HfpVv5pcO1lLqgBxxxBLt+n8c9F8zjn1ufg8qVd28nJcXW+/FHYL/9LHWtbVsbmvb227uzPypUyNSBnTst0NS6tc2U1quXjZ/LBc7ZaK7bbrOhcJHxlCOOsOu//8600gknWBDmscesgFI+CGUinXaaDfN56SULgG3dGj05Zs0aO29u1syCfzz8sKUknXCC3b7kEqtSXKlSvvS/MKhQIRysW7rUijdnqMmlGdoStn69Jetlp4RUqAD+Sy9ZYDdy+GWrVrB8zK/85+lWVJryBQO5hTH9RuCHj6BX6Y944LKFtsOXKQOEh6kuW1/WZgRYvNieID2dRYtsWc2aWLTq0kutyNvzz6uIdi5TEElEREREsrR1qyVnhIbOFCtmQ28mT7b76en2Y+/cucnro8Tnvf0w//DD9plBOFOnadM923c+bD7PzziWgz4YbL/i//ADJeoewXvvWeHnzNq0se1t2YIVOPniCzuBu+gi3LPPAOFMJLZts0DT0UfbNGvr19sZ5uuv5/rJ3iOP2BCaSJUrW1/2CCKBzWRWpoxVqc4HoeBGlSoW8DrwwPBJ9tSpe7YfO9bqQA8eDKVS51rm1jnn2LjDyZMtnSyywJJQvrzFGkIefNBqe+3WsKFlc2nWgLg+/NAud9+ddZv+/eGqq8KzI86cadfbtll2XcuWEY1/+omq3dvApk3s+uZbbudRThrSg8s/68G7W8+m6lEZI8+hGkrLl2PfLzfeaN8dTZqw9Vv7o3Rk2k9WYKlqVauDpOMh1ymIJCIiIiJ7+PFHG/5TpoydW4VKh4BlrsyebVkUI0bA3Xft4vbbNEV2QfbFF5YYdOed4aLNs2ZZHZJQXZLdFiywqZIWLbKaI089FfdErE0bSyyaMSN4oEIFe9IePaj8wPWM4QSOf/0iqwRdoYIVsa5QwXag33+3okwRhbTzknOWmRSaDjyDAw+0E9OPP4YJE/K8L6EgUmTiUO3adj9aEGncOAuKtG6NZW+VLm0RpU6dwtNYSQa7g5eE6+WsXBnRoFEji5ZnGN8o0WzZYtdZza64eDEMGmRxnebN7S1dsMDixSEnnxzcWLgQunWzYM+0aRTv2G53LbPXXrPrww7LuP1QJtLuSSAfewzeegvWreOMx9rxPe2oek7wHTNunKXPSq5TEElEREREMvDeyto8+WT4scaNw7e7d/9/9s47LIqzi+JnABVELKhYsItdLLFX7F2jMSZqrLEldv3sGluMvcYYe2LvvZfYe+8dBOyoKCIiIO58fxyG3YWlwwJ6f8/D8+7OzE5ZYHfmzLnnAunUtzjUYz1su7XFe9hh/Q5rqkvlyzOspU0bWpiEJIHmvLC0BNavpwFo7Vp9Fk8owcFUm/z9mW8UzTDa8uU5XrxoMNHaGtiwAR7dJsART5H53gnaEfr1o3h04QLQogV3ysyULUuhVHNlGTFwIJWczp3ZOi0BMSUiKQo1oZ076dwIDOTFeHAws5KqVweszp6ky2LQIOM+6UI4DHOQNm0CrKyAV68MFpBw7Wjj7s7RSIQzYNMmjsuXA8+fM19fVfl1AFAnTpsWrCts1Ih/3Lt2AY6OAFgmrXVnA9jMwRA7O97Y0FxOsLRkiNitW9hTbDCsU36G0qsXA/wkEyzBEBFJEARBEARBMMLVlRcJM2cykHb3bqBIkZCZOh1KHZqBF5aO+H5ja5T32Y+VaI816XoCKVMi0C4jPuYqxBeVKaNPARYSDVUF9u0DmjZl3tH69fpg9Ny5wyw8dSrVlfnzje1nUeDgAGTIwKBuI6yscLv5SBTBXVzf7kHhaOpUikdWVnE5rDjh4kI3hck85TRpgGXLeMX8v/8l6H68fUvRKF064+kdO9JtsW8fNS0nJ5q13NyAdm11rBnKnp3hVUKktG2rf5wuHc1mRiJIgQIcRfSOkClTKLxqTRdv3uTniiGfPwOLF9OB1K4d49E2buS8Zs2AHj2AC9ueAuPHM9Tr0SM6/kK/XFhqamNDJ5KihA9CVxRqpqEikoadHaZnmoL+Fc7SCiWZYAmKiEiCIAiCIAhfOevWGQdla3lHdevyerpRo5AZ3t60IQ0ahIDq9VARZ5ANL3Cm40J085uFT/8dg/XhvUi9fyuvznPmBBo3Bq5cMfsxCXrGjGH+T7NmQM2a/DUCvBj75ReDBa9eZbD1Dz8ArVvHaBuKwpIVU9lYWrC2YVlRYuPiwnHjxvAXwwCYtzJoELBoEcvFEog3byi+WYS5KmvYkI6NPXv0VXX//gv81EaH1jdG0vI1aRLbXgmRYm1NLVsTNBwcwjiRNGVJRCSTqCowaxbNPcHBnObjY5wz9ewZ3Y137rBk1sKCOXrPnwPlcAHO0zpgwcWyKPldfn7GFCrEL5oaNUxus3NnilLhSm3BkjZDEUlV2Znt5k19tzchYRERSRAEQRAE4Svmzh02xWrfXn+BcPo0L2BDW4gDzK8oVw44cACYOxfpDm1FiW4V0bxVCtSsybIbw6BiXc7cvHKzt6coEVH9g5Cg+PpSa2jdms2KtLIzgNdwWmA6AgP5R5AxI/D337HaVkQikq8vx3Dd2RKRXLmYzfLHH9SJTDJpEpW3vn2B7dsTZD98fMK7kACW/Tg788JYy4HJBzfMuVmbyebdutHuIUSLmjWZPw6YcCIBtHpJJlI4/PyA3r3piqtVi9M0wfPcOf1yK1cCmzcDXbrQZAgAzTOewCHUwnmUh9WeHUyP79mTYt3x41E6HSOK9ypQgPcoNPF36lQgb15+zvTpE4eDFaKNiEiCIAiCIAhfMePH82T94UPebR4yhCU0VasauCPc3WndePuWOTm9ewOKgkWLWBalhabu369f77Nn4C3/jRvZD7pZMypNglk5fJjiYI8e/H2WKMGM7DRpwpSyjR1LxWLpUgpJsaBwYf7eNdFIQ7tgz5AhVqtNMPbs4Z/1qFE0zNWuHcaVZGnJ4Khy5RjqYvgHHk/4+0dsJtJEJK9nnzEAM3EDzrD3vMyaoYULw9uXhGgRzokEAPnzixPJBMuX6zXlhQv5r7B1K0vODN2rFy9SyFmyJET8WbECI/ZVRxHcwcU2M2hb2rePNdJxzCpyceFnilY6q+WwHTmi72woJCzyySMIgiAIgvCV4udHg0WPHiwDGDIEmDaNpiOtixEePmTJga8v+4tXrBhuPZqItHq1flpo56vKlZkvc+6c6d7wQoJx7RrQtSsFo8qVOS1VKl5offONgQbx7BkVxPbtDWoXY472d3D7tvF0NzcgW7akV3llacmMl5QpKSgdPsw4KCNSp2bCtZMTa8x27IjXffD35yZMUbw4kN3nFhbdroKZ+B/el68D5fZt/lKlC1usidCJ9Pgxu7QJoWif43/9xbfo/HneDyhbVl/2DDDqLFTAuXcP6N4dQVVrYtOUhyi9cmC81rJqFXBHj3L08ADq1weqVIm3TQhRICKSIAiCIAjCV8jp0zyv//iRpU6tWhnPr1MHvDPv4kK16dAhKg8myJCBF2bnz+unGbVP/+EH/vzxh769j5DgDB7M/KOff6ZQorFuHbBmjcGCU6bQrjR2bJy2V6UKhak9e4ynu7nR6JEUKVECePKE2USpUzN3KBwODrxiLlOGJWTh0sPJnTvhXVhREaGIpKpoeGMKLuMbOMEVO9usQZaz20O7WAmxx8GBOV1GepGTE21o8vlkxIMHzMDu1ct4uosLHUA3b7LMTat2hk7HBPjUqZFy4xr0GWIT780X8+Xjr2vcOO6fu7tkIZkbEZEEQRAEQRC+EgYPplj08SMr0gBeAFSpwlwkS0tW7dSoARRP+4gPPn6kRaN06UjXrblQKlfmRXG4bJwZM7iBgQPj+7AEE3h5UfcbORKYM8d4Xo4cBlrEs2esU+nYMc5lJg4OzKPevNl4uqsrL/qSKopCIbRxY2DvXtNB22+DbDGx7Bao1tZscRdGLXryhP8i/fvHbNsmRaTAQKB/f+RbOAw70RRFcRsfmrUR91E8kTkzR6OSNk2F8PAw894kbe7f1zevM6RvX96E6NqVZWRAiIlx/nzmHU2frg/zimcUhU3dvLxocvX2ZimdYD5ERBIEQRAEQfgK2LuX5/WbNgENGrBh2j//UGiwsABKlgRev6ZD5cjmN1AaNtA7kEqWjHL9mihRsyazXC5cCLNAjhzAb7/x7H/Xrvg/QMGIXbtoCoiyydqECWyDNHJkvGy3TRuWsy1Zwuf+/tSpkrKIpFG7NsWggQP5v2DIwIHAyAU5cbTXRlqrfvjByMoyaRK1n3XrGJYdXcKJSB8/sjbnzz+Bvn2xq8NGvIIDcuWK27EJerTu72/fGkwUESkcQUF8OwoWDD8vc2aKrlq49vbtQNFTi6ku1avH9moJSLFiLM3VGn+KE8m8iIgkCIIgCILwhaOqwIgRvKPs4qJvGV63rvFy6dODdT0NGvBCefv2aAlIhnzzDQ1M588DHz6EmTlgAOuHunYNcwUnxDc3bzKDKLT7minu32drsu7d4+xC0ujalb//oUP5d6d17EsOIpLWfWr2bOaLG3LqFEc3Rxd8nLMQ2L8fvs6VgevXATCfJV8+akDbtkW8jQsX+LvRMBKRfHz4v3f8OLBqFTBnDhYvUbBjB1CpUrwcogD9+/3xo8HEbNnYEs/TM1H2KSni7k592ZQTCQBy5tQ/Ln1gCj9H6tUDtmwxi2vO3h64fJmPRUQyLyIiCYIgCIIgfOEcPgxcvQoMH85yI4DdlsPFq3h5UQG4do2WJReXaG9j0iReQzRuTDfSp0/6C+9QUqVi/cHLl3TACAnG3bssMYz0Wm7UKMDaGhg9Ot62a2kJtG1LLbJDB8YtAUDRovG2iQTDyYkGI8C427ufn75x14oVgNMfP+NbbMNH16fQlSkLbN6M589pILKzY/RXw4a8ANfQ6YAXL1hO2qmTfnqoiOTmxv+3M2eYUP/TTwCoazRtKpVs8YkmIvn7G0y0sGC7QnEihXLjBseI/ndz5gQsEYypGIyc84YBP/7IGw9mStDPkIFfWQDEqWdmREQSBEEQBEH4wjl8mBf3P/4IlC/PaaVLh7kwffwYqF6dF7O7d/PKNQbkzctonVSpgKpVqU1s3GhiwdKlWeowdy7w9Gmsj0mInLt3gcKFI1lgyxb+ggYNArJkiddtlynDcdUq/jg4ROGISiIoCrB+Pf9+79zRT3/wQJ+TdOIEx1/3fosy1rfhkbkc1B9+wOi3/VHr7WY0KewKV1cV+/YBz5/r1zF8OM0unp7ApUv6i19/fyCz8ppdDx894v9emzbmOeCvFJMiEkA7i4hIoVy9yu+N4sVNz6/wcC2uowQGYzp0PX6l+GmY4J/AZMjAUVH0OVeCeRARSRAEQRAE4Qvn/HlWkaVOrW/DbNRozc2NFqUXL4ADB0Jas8UeW1u6UFauDBNeqzFyJK0ZM2fGaTuCafz9KVZEKCI9e8aWbeXLU92IZ8JedDo60uiRXChaFDh5Ut/B7tEjjtr1cePGrDorUTMjWtruh3+zNuiDufh+3fdYc6EAvJERu9EImPA7sGoVnk/8Fzmm9cM4jEZfzMFQTMbpf+5CVfm7anp2BMs7jx0LX2MqxDsiIkWPq1eBIkV4QyAcu3bhm1ntEISU6J5uPSzmz0O8t2GLAk1EypQJsLIy66a/euTtFgRBEARB+ILR6SgitW3L51mzAlu3GmSs3L5N0SgoiG12jNSl2NOrF+N2tm0DunULMzNfPtYNLVkC/P57BD3OhdiiOWe0jnlGqCrwyy8MhV61itaxeMbQjGBpyaq55ISWAfPTT6ww00Sk7NmpMWhuvgYNgH5702DXhFXovG0R9s+8Db8TV/B06zlUxFk4LtoHLFSRDUBn2MIaAbACa9w+jxyFz097obquOcpeXaLPCxMSnAhFpNy5aRH7+BGwsTH7fiU1rl1jdXM4bt0CWrfGZ+fSqHLtGArlswUSodxSE5Hi2UgpRINkdE9AEARBEARBiCkXL7IbuXbhC7BDeZYsYCpp9eqceOxYvAlIADu0OToCBw9GsMCvv3LHNmyIt21+CYwcSUPEihWxX8e1axxNlqHMmQPs3MmwoogSc+OBJ09YrRgcDHz3XYJtJkHQcsMA5nv17UutTRMfypblqP1PHToEfERq2FQrC5dV3XBn4BI44yaWzXyLt+cfoKjVfYzo+Q66F6/w4c4j1Cr6AgdydYPl33NxBLXwIW02YMwY8x7kV0ykTiRArxp+xfj48H/Y2TnMDFXlP4S1NSz37ITO2hbZsiXKLoZ22RMRyfyIiCQIgiAIgvCF4uEB9OlDu3/z5mFmXrrEK+Q0adgNKp5DaxSFBqdDh4wDhkOpWpX1VosWxet2kzvLlrEUbd262K/j8mVeKIdzInl7A2PHMvW5b9847GXUODrSuZMcqVBBH9f14AHHHDn4u+nUSX9hnTcvx7NnOWbLxvd98mT+/Xv6pMOJ5064E1wAP7SxRMosGWBbOCfS5M+C4ennw3v5bixGV+wcegpIm9ach/hVo5mMjLqzAXoRSUraQoPlw+nMR44wZG/MGCjZs6FhwwjcSmbAsJxNMC8iIgmCIAiCIHyBvHjB/KMrV5hhrZ1wA2Di77ffAunTMyk4gfqv16zJLl3ahbgRisJ2bmfO6NsAfeU8fcq4IiD2b8nHj3SflSplIqJk4kTg/Xtg6lRp9xUFYQUwX1/+P/37r/59dXCgIKH9rhwcOKZIwQvbR4+A06eZ16KFjQPsavXoEfC2YkN0x2LocuVJ8OMR9GgiUoROpK9QRHJ1ZUdNw+cAkD9/mAX/+gvImDG0RnnLFmDwYPPsY1i077QEqMgVokBEJEEQBEEQhC+QQYPYmvzyZaB1a4MZ/v6sL3r7Ftixg1e0CYQW7HzyJN014WjfngE6ixcn2D4kJy5c4Ni8OUtJ3r6N2es9PHgdfOoURSQjPD15AdixY8TtloQIefcu/DRF0esOGTNSPNLIlo2C05QpjAAzjNjJlYu/W61Dm0SCmZcUKfgTTkTKlo0zviIR6cQJNmksUIBfBa1bMy5NcyLly2ewsJsbvzO6do0gbdu8aNlrZmwIJ4QgIpIgCIIgCEIy5vNn6gOGd5H9/HiHuHPnMHqBTsdA6/PnGapcsmSC7pt2F7tbN15sh+vUlikT0LIl27iFu6L7sjh7Vt8e3hRPnwLTptHl0qEDp8XUjfTzz7wAdHICWrQIM/O339gibdy4mK30K+boUeDvvynI7tljehlNRDJ0GgHAzZv6x/XrG8/LlYvjP/9wFBHJ/KRObeIjx9KSSopJxfvL4/NnRuL98AOfe3kB69czN9vNjVlDadIYvGDSJNrq+vVLlP0Ni/adJyKS+RERSRAEQRAEIRkzaRIvZF1c6GDZswfYtYtlTT/+GGbh6dOB3buBP/80oTLEPxkzAnZ2+ucjR7K8p3x5Cl0AWNLm4wNs2pTg+xMbgoOZ/e3rG/t1fP7MbnjVqwOvX4efr9PRlHX1Kn81FSpwemSiU1ju32dcyfDhLB+sU8dg5qlTFA379k1Q59mXhosL89+nTQNq1za9jOZQatXKeLr2v3fzJqsIDdFEpH//5SgikvkxKSIB/DD9SpxIETU9ePyYIpJRKZunJ7B8Oe8IJFaSdhi0zDft81IwHyIiCYIgCIIgJGN27uR45gywfTvQuDGDfXPnZnZ1KOfPU8Vp2RLo2dMs+6Yoxg6pDRt4cX3hAnO9AfBKvUCBJBuw/ddf3OcSJejyiSnBwRQhNGbODL/Mvn0UgGbO5K8me3agXj02UvvwIXrbWbmSRoqOHcPM+PCBE/PkoRtJiFcaNeLYrJnx9GXLqI0WKxbGzYHwgeciIpkfEZF4s0EjQwbm6AEUkR48CCMiTZ5MJ+PQoWbdx8ioV48ibfv2ib0nXx8iIgmCIAiCEC/4+zNEWTAfb98yRLlLF+Pp165RLwgNVn73jmEX2bMzf8iMocqa8DJrFnfjyRM+f/w4ZAEtYPvUKdZRJCFcXYHff+djT88IAsIjQFX52ooV6Q7KlYt3zA8dCr/s6tVsV925c8gEnQ7Ta+7G6Fe94drxd+D6da4wEk6fBkqXDmMSUFWWnri50fYSVs0Q4sywYWx6p4Vqa6RMCaRLZ/o1mTIZCxgiIpmf1KlNdGcDKCI9fx47xTiZcfs2s9NSpmRlc+bMfHztGgP+tS6EePKEtZedO7NNYRKiWDHpEZAYiIgkCIIgCEK80K0bULlylNe6Qjzx4QNLaHQ6th3v2xeYP5+ulpkzDRwpqspfzqNHwNq1Ydq0JTxjx3Ls2ZPlbRpacCsA7myKFGYL2FZV5t389BPg7h7xci1a8AJFy67RBLCIeP8e2LaN63d3B0aPpuNq6VJemLm4sFue4TYDAviaVq2AlBbBVJRKlIDz8CborCyD8+YxvMKrWJH1biHcuqXP3VFVBqgb5fKoKtCnDzc+YgQ3LsQ7lpYUAGOKYdC2iEjmJ1InEsDPyy+cO3coIg0bxrJNCwtqRFr+V4kSIQtOncovmmHDEm1fhaSFVWLvgCAIgiAIyZfPn9ne9/BhXiQDvLANvYMpJBh79tDVMmYMUKWKcemaUZDv4sVsvzNpElU+MzNmDH8A4L//GBp940YYESlzZnaMW76cqlP69Am6Tw0aAAcO8LG7O7OHQl1bITx6xL/l2bP1eThPn0a+3tmzKRz9/bc+9+bUKf3bXqkSr8dK5/PBhoU+qNctNzw9FXzyD0LHTyuAwpP5xhQrBqxejcFHWmHvmre4/8dGpJgygSsaMgSoWRNta6RHUdzGmsFX4X/lLub52KLMjVRAKz8m4t67xzd88GBgwoT4e/OEeEdEJPNjYxOFiOTpCRQsaM5dMis+PixfK1qUHxEaOXMCx47xsbMzuNDixfzg1t4b4atHnEiCIAiCIMSaU6dYpqQJSABzeYSE5/BhhlaPGhWJnf/ECVqU6tWj+JDIlCrFyiwXlzAiEsC73D4+7ImegAQEUFupW5eZQ2fO8Ccsx49zdHFhiZii6J1Inp7MtWnc2CAgHPps8AEDgHXr+LhIkZCZOh1qfNiN/aiHN7BHvR55gRw5kO7XtnBDflT6pxtdYlu38k1q2xaNvk0BD38HHHfuRRdS3brsrlajBq6hFNaiLdTZs/Hp3kOUwwXkdfuPyteKFVzH3Ll8P6XeI0lSvTpHW9vE3Y+vkQidSLlzc/zCc5Hu3OEY+vkUgiZ+Z8wYUho7fDiD3YYPN+v+CUkbEZEEQRAEQYg1Z89ynDePbo0qVWh60C6ghYTj8GFehFpF5Cvfvh1o2BDIm5clUhZJ57Qvf34TIlKpUqwvmz076rqxOHD/PiszunRhFZ2VlT6cXOP9e2DNGmbaODuz0i5rVv1ubdnC9ezZw0BrgBdl16+rWPftWuxGY/RYVRXPLByRIWcaBo1YWiJ9uyaole0OztYaiZ6YB79vqiHt5SNwQ348+3c/w8+bNw/9Xbm4cP8OHgTdRdu3A8+e4cbUvfgR6+CM6xje6z16VLqBShkfAI8ec0d8fdmvu3dvEZCSMDt3UqwUJ5L5iVBEyp6d/3RfuIh08iTH4sWNp5cty7FdO0A5fowJ8YMGAU5OZt0/IWln3KMsAAAgAElEQVSjqMk0uKBs2bLqxYsXE3s3BEEQBOGr5rvvWJqkBQ57e3Pa8eP8qVYtcffvS+XJE5YdzJgBDBwYZuarVwxTXruWScu7dvHCKAkxbhyr1oKCKNCE4uFBi0+HDgmWj7R2LdC2LY06zs5AnTrM0TXM9G7dGli/nuaoSZM4rUvRM2jkvwktXV7j7J43eBtoAx+bbDiXygWz7zfC4PrXUfb0HPwYvAbvshTAZS9HPEEOtP9fFh5kihQsU/vuO1y6ngJly7Jb3Z07LPcLCABSpQq/v9WrM7D+8mVqUXv20AEFsGxx/34+7teP+psgCFHTsSO/o0xmouXLx9rT1avNvl/mQKdjQ0xHR73j0pDgYMBKF0Rh/+NHfjiK0vnVoSjKJVVVy5qaJ5lIgiAIgiDEClWlE0nLiwFogd+4kaaJq1dFRIoKHx+6XWJqFjlyhGOtWgYTVRVYtYq1VL6+VGmGDTOtTCQyWbNyfPmSFzKh5MkD9OjBUKFhw8L0mI45J09SnKlTRz/t9m3mH2lxJ9WqUdQKDORbdfs2xZ3hw4GJE8GLqFGjsPjOLAQpqaA7mgXW3hnwTfqPyOCzB22C/gRsgGkAVEUBxo1D6iEj0TSTJfr2BTAx/H4VL05B6MIF/g04OET8a+rWjZrar78CCxYA/ftzert2bLg2eTL3t2fPOL1VgvBVEaETCWC6dFQBaMmYCxeAhw+B8eNNz7eyAjBxOhXu3btFQBLCkXR8zYIgCIIgJCsePKCDwzDQGWD7aktLVtMIETNrFt+rUaOMpwcGUtTw9g7/GlUFPn1iKZu9vUH3HHd32lI6dKA6cvUq7S1JUEAC9CLSixcmZg4fTtfO77/HeTu9e7MrtaHx/vp1alPaW5MvH+dv2gQ8fkxnj6oCvbsFsi2bszMwcyZOO/+CPDYvMbufB0qrV+Cx9y5O7vRBQ+zBH5aj8avtCvg/eAaMHo0U1pbw9Q0RoUyQKhV/dxcvcpuRdc1u357d9zZvpnj44AGF2pUrebE3ahSP6QvOABaEeMfGhiL233+bmJk9O3vcf6FoGYYR9ll4+JCfvy1bAo0amW2/hOSDiEiCIAiCIMSKQ4c4GjqRAMa5ZM785YpIqspj0+livw4fHxptPn+m0FC5sr6cavdumogWLAj/utmzgTRpKCjUqQNYBAVQjSpenOnQ8+bRflO0aOx3zgxEKiJly0ZbzcqVDB6KJe/eUVx58gRwdeU0X192ZTN0cOXNy7FdO6BcOcDL9T2GpZyBbFXzMTjJzg747z88/+1vePnbYfRo/r4qVABKl7PCPjTEqM/jkGVQe9jmzxq63qgiqCpXZu75vn0sTYyM2rV5PCtW8LmRA00QhBijZcn16mVipqMjnUjJNPYlKm7c4PeIliFuhKryTbGykvpYIUJERBIEQRAEIVb89x87uZiqOHJw4F3eLw1vb6BwYYogJUpEUg4RCUFBdJIEBVEMKlmS+k+9ehQHWrbkcqNGsfudIasW+KF80Al0tFqNfz615xs9cCBfePs2xZckFKAdEZGKSAAwdChgbU1LViw5fVp/DagJnps2sbytY0f9cgUsH2I8fsNRuGCLV2X8tigHJgUNglK4MBWny5eB2rVRuzbf2g8f9DlUGTLo19O9e8z2b8wY/toURd+lKyK0+atXU3Cyt4/ZtgRBMKZ3b44mhZTs2fnh/u6dWffJXNy8yfsOJr8qNm+msv3775FbJIWvmqR/liEIgiAIQpLj0ydemNetazrPJ0uWL9OJtGcPzTE//UTn0JIlMXv9gQMsZerenVVSLVqw8mzjRlZPaFlHCnQog4vYV/V3BPxvJE5XHwbvCo1w6n4mnEB1zH3bDrZHdgGtWlHN27EjajtLEiJLFo4RikgODkCfPkzBvn07Vts4coRllVmz8m9VVYE5c2jSqlABnLBgARzqOGM4JsEGHxGIVPgv3ffoX/FsuD9we3ugYkU6l5o3129nyhQKfjHNLre3Z+mcTmciHD0MuXLpy9VKlozZdgRBCE+uXDQafvpkYqYW1JbMS9pUFRgyhN8xhtNu3AjflQ0ArZr9+rEhg6ayCYIJJFhbEARBEIQYc/Ikb9I2aWJ6fpYswLN774Fzt3nV7eBg3h1MAA4dAqZP58X/ihWApydFiT59Ig7GdndnyVL79lxmyxZOHzKEAcna6+rWZfVA6uB3mO44G53e/4kUvm+ggwJ1tiXK6hS4wgkH8AsazaqHgvXysOVyypRmOfb4xtoaSJ8+EhEJYFvpefNY27dhQ4zW//kzsGYNY6IyZ2aDugMHWN62bFnI+z5lKjBsGJS6dZHn4D94ihywsQFsFeC7EqbXu24d121pqZ82ZEiMdi3WDB3Ki15xIQlC/JAqFR2h4dAU4adPk3xpcGQ8ewZMm8bHpUpxfPWKjtpixUy8YNYsvmjrVn29nyCYQJxIgiAIgiDEmB07qF8Ydr0y5NsXC3H6kSOtG1myMFfGxQWYOzfZdb0JCKAhpkEDihDlyrEMoH175o/euBHxa4cPZ+nUwoV8fvAg0KwZ3SvZsumXS5cO6OV8HHcti6Hb07FIUbMagpcsQ940r5E38wekhj+K4TY2Vp6NAv0a8cImmQpIGlmzRiEiZcpEpW3jRr7xkXDvHrB4sb58bc8e/pn9/DPzhLy9Ge+RIgXw44/gRdKwYUDr1sC+fchThWUbHz8Cr1/TpWCKnDnZQC4x6NABGD2aP4IgxJ2UKdnIIBxfiBPpwQOODx/qp2kxc4UKhVn4/XveFfn2W6B8ebPsn5B8ERFJEARBEIQYERDAzOPGjRnOGY516/D9f7/gLCri46rNvBXauTOv5Pv2Zc5Cw4ZMPE6izJzJzKPZs4E//gDatuVut2pF7QHgubaiUI8wRKdjW/Zjx/TXICNHUuh4+NCE8Hb5MtCkCWZfcUHGbCmBc+eAbdtg1aUjytazx2OvlPgMK4wcybiKiFxPyY2IRCSdjpUUly+DdV7p0gEjRkQactu1K0sEx40D/vyTvy8nJ6BpU30I9b59LCG0fvmIyl6FCuy+ZmGBEyf4N61hMiclkbGy4vGZyiATBCHmpEoVgYhk6ERKxmgikpsbsHMnPxfv3uW0cCLSokXA27e88yEIUSA+NUEQBEEQYsSmTdSDTHa1ef4c6NkTr/JVQKOHe3C3ghWcnAzm373L0qSpU4FKlai05Mtnrl2PFi9esETJ3h4YMIDT8uShGcbOTr9clizA9873kOnf7YDHHdYJ2Nvjw61HGH75ET5tyIShH7NjqdU9WL0JwLtqTliCXGi12xe4mJphHOfOUVnKkAGYNAkp+/QBbG1Dt9Gli74EbsIE870H5iBrVra4D8vVq6xiO3wYuH07A/Dbbyxt27AhxEakR1Upal66xOdTp9JNBABLl9Jp4OhI99iFC0CFkgFsw6bT0V5mYwOAwpxhhmyJCMrZBEH4ckiZkuVsqhpGnLex4WfyFyIiPXzIhg2fPgGPH/O4jYTywEBgxgwq7hUqJMq+CskLcSIJgiAIghAj9u2jgBKuzbiq0hISEIDHE5bjM6xw82aYZQoXZj3OyZPsftO0KW30SYhly5h7c+wYkDYtp7VvbywgISAA6NcP624URS/PodDtPwA8egQcPYo3rz/jLCrisW9a5P10H0qRwriYsjICX/miicUe2Hne5MrPnmW66dy5PMsfNsxIQAJYQgcA339vlkM3KxE5kY4e5RgQEDKhf38GvY4YAQQHhy4XEMC4rW++oXDUtKleQLp+3bjjmfa427XeDKlavJgvNkCrYAFERBKEr4FUqTiaDNfOnv2LKWfz9dUf48mTdDMa5rph+XLeABIXkhBNxIkkCIIgCEK08fYGjh9nvFG4sqqlSxlGM2cOin1XCNbWXNawk1UopUox66ZuXaBTJ9qbkkid1oYNQOXKQJEiLFlbuRKoVs1gAU9PtlW7cgVPmvZChZ0jsWJ5NtSty9ktywKvQE0JAI7PA55cBH4YCNSrw45c0UVrKZ8iRXwdXdIha1bAz48/hmWRmoj08iUNQxaWlsCYMfxDWruWih4YWu7pyWVbtaJrbOdOCn9hs3CHDQNynFiL0ueX8kKpTZtw+5MrF0O4x49PgIMVBCHJocXKBQWZiJhzdEzWTiRVZWNLa2u9IG9hwc9Uo8y3d+9Ys122LAPkBCEaiIgkCIIgCEK0ePBA32Y8XEcqd3dexdesCfTujVQWdMUfPx7JCmvVYv3RoEHA/PlAz54JtevR5tkz4MoVYNIkPp83jwJS6Ln1nTsUvvz8gB07YF+zKV5nYGB23bp8/aVLwMSJzD46cIBVe1Wq0HDVokXM9yl16ng7vCRF1qwcvbx4jA8e8ELuwAEasj584LRChcA08hIl9AFVlpZwd+frDx7kex0URGdBxYph7rKrKjItm4H+V0YAVauy25sJUqWicCUIwteB5kQKDDSR7+foCNy6ZfZ9ii/OnWOI9tChbOQA0KF5926YzmwDBlAsW7cuydzIEZI+Us4mCIIgCEK02LZN/7h+fYMZOh2DsxUF+Pdf3u4Er9evXNGXGJlk4ECubNAgvfc+Edmzh2Pjxhzt7BiSbWEBBvhUq8aSquPHgaZNkSYNtbCtW3nnV3t9kybM4Rk5koHIFhas4nN2TpTDSpJoItKLF9R1ChdmNzVLS2D9es67di1kYUUBRo1iOvmSJQD0HYeKF+eYMiXw119cLJTgYJZYDh4MNGqkbysoCMJXjyYiBQWZmJk9Oz+cPn826z7FF4sX8/tr5Eh2nDx3juJRy5b8rAXAL+hly/g9XKlSYu6ukMwQEUkQBEEQhGixcydQsiRPuAsUMJjx55/M+Jk92yits3Bh6ktayZFJFIVlcNbWbLeeSPlI/v482Z4xg1E5mjARypEjdFnZ2TFUwiA057vvAFdXCkVLlrBUINzrhXAYikibN/PxsWMUH2vV4p/GnTsGL2jZkjMGDQI8PeHuzvzbLFn0i3TtalB6GBjIIO5//qGCt3Urw3IFQRCg15NNdmhzdKSA5OVl1n2KL65coQPWzg7ImBEoXz7MAn5+LA3OmJF5c4IQA0REEgRBEAQhStatYx5xy5Zh8nlu3mTGTJMmdCMZoLUid3OLYuWOjsCKFbSd/PBDotz5XbyYJWh37zIux8jVv3Mn0LAh1aGTJ2Hcbo55PFpnr3PnqDVJVUDUaCLSkyfsLA3QzeXoSHEob15meoRiYUHBEQB+/hkeD3XIkyfMe33hAkONvv2Wv6ctW4A5c4Bx4+SXIgiCEYblbOHInp1jMgzXVlUae41u9oRdoGtXqvRr1gDp05t1/4Tkj4hIgiAIgiBEyJEjjKNp144OD6MsJD8/Kijp0lGFCXORni8fx4cPgVOnaKmPkCZNsLfJPLZ+Gz063o8jLKrKGIgzZ9hBXsuMAIAOHQwW3LSJViNnZ5awGbbwCsHeniHamTLxuVEItxAh2vvVvz8bA2lob3HRomGcSACQJw/tYocPI+u2+WywpqpUOatW5e32sWNpDfvmGwYs9e2b8AcjCEKywzBYOxzaB9GTJ2bbn/ji5Ut+PUcoIs2ezZrhP/5AaEcIQYgBIiIJgiAIgmCShw9p6Ni1izrKrl36O7fQ6RgWdO8e72RqthIDHBwYmHztGlCjBsOq375lTE3YO7/BwUCj7T2wGF1pCdq6NUGPbeRIuodcXIAJE7if586xrK1QoZCFDh9miV358sB//9H2HwGKom8jX7Vqgu76F4NR+LUBmgGgSBH+eQUHh1mgWzecStcQc9APM3y78kqpTRv+cU2ezBaCt24B27fLBZIgCBESqRNJa2GmtdlMYpw9S6H9xo3w87R4wTCmWQruEycC//sfuzwMHZrg+yl8mYiIJAiCIAiCSebPZyi2uzvb3qdNGzJDVRnEuW4d72TWqmXy9YpCN9K6dRQCZs6ka8fR0ShSCADzGwCgN/7CTdvyUDt0MGFDiR9276agVawY98/Dgyfd5cuzjAoAbUqtW7Md3b59dFtFQdeuNGZpHeyEqNm3L7xRSBORSpSgQ+DChTAvUhS0tViPO7nqo/C1DXQnrVzJ1kNDh0rukSAI0SJSJ1LmzLy7oLWBTEIEBgKVK/Mrct48YNUq4/murhyNnEg6HdCpE++gtG7NF0mJrxBLREQSBEEQBCEcwcG8Lm/SxCgrmzMGDGDOTL9+wLBhka4nf362ajfk5Uu2Hn76VD/txAmOQ39LhQYfNuNTitS8U+rrGz8HFMKLFzRQFS8OXLrEDKTcucOcS3/6xEBmf38mPtvZRWvdDRtSbJPz8uhTvz4rK44eBcqU4TRNRPr2W7bdXrDA+DU+PsCjt3bY13s3/z7++4/1lhFZmwRBEEwQqRNJURjM5uFhzl2KFufP814OACxcyHxsQyHMzY0fh0bf3VOmMHtwzBhg9WoKZIIQS0REEgRBEAQBgYGMSNDp+HznTjalMcrK9vEBGjTQC0gzZ0apmHTpEn6a9pJDh/TTTpyg4DR8OKDkyIG+WTZAdXVlQJG2U/FAu3Y8jJUrDUrzwjJsGEOclixhTZWQoCgKywq1CBJNRLKzA376icLcixdAQADw6hX/9AB9cLsgCEJs0L4DatUCFi0ysUCePEnOidS7t750umxZ/XTDXLnHj/k5GtoE4/lzuoZbtKCIJHc6hDgiIpIgCIIgfKW8ewc0bQr07Mlc7NatWV4E0CKfKxfQuHHIwl5ebDt2/Dhbps+ezW5ZUdC0Kc9ZtbDq77+nmSlTJhpIAN5RPXmSgdQ2Nlx+4V0XePSewVybX3/Vt++KA6dOUbiaOBEoVSqChdasoTjWuzffEMFsZM/Oa5ssWfTTmjSheJQtG38dM2bwZjogIpIgCHFDK2cDgD59TCyQNy9FJM32k8jodPxu1tC6ggLGzt4nT8L0gBgzhlalqVNFQBLiBavE3gFBEARBEBKHXr0Ylm3I9u3MCTp0iDcuLS0BeHoyoPjpU1qU6teP0XbGjqVwlC8f9SALCxqadu/m9Js32blN62rWvDlLztY59MXwAZ7A7Nn4vG4Ddlq3QoEWzig6uDGU/PlitA9ubnRFZczIdZtk3TqqXdWrU60QzEqXLszwsDI4O9XuuAP82wwNPYe++58gCEJsMHSjWlubWCBvXpbM+vgkiay169f1j2fPBtKnB7Zt43NDEenpU5ZsA2DN9tKlDJ8Ll7QtCLFDnEiCIAiC8BXh5cUL9W7dGIvQty/jfwCeUC9axAouK6uQUrQdO9gq/dUr4ODBGAtIGlZWvBnq4MDnLVoAb96wSUzp0pymiUiZMgHOzsDRYwpdQVeuwN2pHmq+XI9iC/tCccoPtXFjhitFg3fvgEaNeAhbtgC2tiYWOnKEtVNVqlDdMrxFLZiFsmWZ125I2rTGhjcta/2776IdVSUIgmASw495k+XNmlLt5maW/YmKw4c5PnnCst4OHZgvCAAHDuiFJCMn0rRpPNARI8y+v8KXi4hIgiAIgpAEuHWLF8a7d0dveVWl4NOlC8t9osuWLezcsmQJn//6Kyu4Dh1iKVu1arTI9+r+CVkm9mO6ce7cwLlzbAcTT9Svz9DkP//k8yFDjG+SuriwxC04GEDJklhSdz3S4x2m/eKGkZgA3cFDQMmSLK379CnSbc2cye5rW7caO1tCuXlT34lt927umJBkePBAX8Jx4ABQoQLzzgVBEOJClE4krdXmvXtx3takScDt23Fbx/XrLO3VBCJF4femlRW/0ytVonHKzy9kGR8fdmHr3Jnd5gQhnhARSRAEQRASET8/OoIqVaLIsXBh1K/R6dilt0cPaijt2tGxHh22bOF58cWLzCQqXJhOj1q1gBo1GHn0+IYPZt9rSIWnXz/gzJl471tva6svnTtwgI1jDKMaypVjczStVbGHBzNwBszNh5U5R2JglfMM0dFqoFasMJlbceoU8Ndf1MJMCkhnznCGpSXfHBGQkhz58rH7kIUFA+CNsj4EQRBiSZROpPz5+cGj2X1iia8vjUB168ZpNXB15dedIYoScrMFDNTWbszkyAFgzx5mIWmhhIIQT4iIJAiCIAiJyJw5FIHev2eGwZUrkS//8iVDoSdNon4yahRdGSZDQcNw5w7t8N9/z3bqtWubWOjwYdYVHT8OLF/O4IUI25jFjVq1WCVg6sRay3O4cYOjpycb5VhZUWDa/7wElbBdu5jE3LEj8PPPwMePoesICgLq1eNrxo0zsQP79gF16jAo6dQp6cSWhLGz0/9NiIgkCEJ8YPjVZvJrLlUqfvHE0Yn05g3HoKA4rQYPHoQXkcLy228cHR3BwKSsWYHy5eO2YUEIg4hIgiAIgpCIXLjA8dgxikJPnhi36jVEp2PJm6sr8O+/wIIFwO+jP6Hvd09w/0ZguOU/fGDMj+ZS+u03OoD69zex8jdvKMJoytKhQ4l697JIEd4AvnmTzz08WFUH0BT18CEQ/Flh+7jTp3lwy5YBVatyYVB48veny6lECYOVqyq9/02acGUnTzJAVUjSVKzIMXv2xN0PQRC+DKJ0IgFM84+jiPT6NcfUqWO/Dl9f3kQyJSItXMj8OEMKOvjwJkuLFtHqpCoIMUH+ogRBEAQhEbl8GWjThhVV5cpx2pkzjPnx9dUv9/o1S3pOnQKWzPRFp1RrYdWmFWBvjzlbcuKOVwYEdO2tP1sFsGkT847mzePJ57ZtwC+/AJmt31N4WbaMYUT167NmaMUKYNgw2n+0lOtEwsaGWQ83btCt9eKFXkQqUIDvj6dnyMKWlsD48QwBd3MDypTBjl/3ok0bzs6f6xNPpkeNomOpYEEmi9epQ8eVYU95IcmiiUjiRBIEIT6IMhMJoIh0/77Jcuno4u3NMS4i0oMHHE2JSN278+tb4+1bIOv+5XTmRtiOVBBij1XUiwiCIAiCEN98/MgStMeP2fwMYBVZ1qxAp068Q5oxI11EigKMHQscW/MEZ52GoXy/jfTFZ80K/PQTbqQojfN/ncfPyxYAG1cCLVsCrVphx9KycMEtZF1+Di8v3MfBzw9RZY0HMM1DvyMpUwJFi9Li1K8fw6qTCCVKsGna1q18rp08a/FM9+8zsiKUpk2BixfxsWELNFvQCHaoATfkR+XvtwFvvSk2Zc7MWr6hQykopUhh1mMSYk/9+uzkF4/57oIgfMVYGVwJR9iQs2BBWlqfPg0JGoo5mohkYxOrlwPQN4gz+s4zwHB6+nQqrcoVKujbnwpCPCIikiAIgiCYmYcPgdGjGagNUNMAeFd0yBB9m3Nvb+D+PRUFUnjAccU6uFpOgPUTHRO1f/iBadyWlrDzALr+1QNpRw1Eq4dTGJL0778IbWD1HvA6nwXBtvmRsnoloGgXBisVKsS8hyQqpFSvTjcVQBHt++/52FBEatCAlXgZM7J87/clTnAreA45XBdiIGaiFK5CqVsfaN+OAUkRXikISZ3s2encEwRBiA8MmzlE2OSzUCGO9+7FWUSKixPpxQuOETkx8+Xj2K4dWB9/9y7dxoKQAIiIJAiCIAhm5NkzZlx6e9OC3rAhu6Jp9OnDc9Z8H27g4g9TkLP8QVi8f4nhAB6VaY5cG2eGy+/JlYvBw0deFkOrFSuAwEBcmPQfVo5zQ8ex+dD6z8pwfWOPWROAUqbykJIodepwtLFhZxtN68qcmW2OT59mSduiRWx9PGkSo46A1ChWbABm3+LBqusVk+sXBEEQBICdH02iiUj370fQjSJqtCrzuPSoePmS0Ub29qbnp04NuLuHZMa1nw9kyMCbTYKQAIiIJAiCIAhm5Pff6Zi5eFHvQDLEygpo9G4t0LkzsltY42jKZrD8thIGb6+Cg7tLACbieywsmBdz8mTIhFSpsOFDYyxMCUwZArTVAVOnIjQjKLlQuDDvuhYqZGyWUhSgUSNg6VL9tH792M1Y4+efgf/9T8QjQRAEIWoiFJGyZ2dHijiEa2tOpLh0Z3v5kjdQIsvIzpMHgJcXsGUL70jFpX5OECJBgrUFQRAEwYxcusQGYqYEJAC01fz0E1CxIg7Nf4DG3ivQYPuveJ+nRKT5z9WqMYT61Cnmfx4/zjgEGxs2LnvwIPnlRysKsH07MH9++HlNmugfN2/O3GydDmjWjNPq12eWUqiwJgiCIAgREKGIpCisoY4HEcnfP9arwMuXgINDNBZcuhQIDmYXDUFIIEREEgRBEAQzodMBt24BxYpFsMCMGcw7atgQ2LsXLbpnRtOmnBVVNqbWTK1qVd6AvHJFH0BsZRXrKIdEp0wZfQaSIQ0bstna7dt0JWn88Qc70xQrRnGpShXz7asgCIKQPIlQRAL0HdpiiSYiffwY61VET0T6/BlYuJBld6a+OAUhnhARSRAEQRDMhLs770QWL25i5o4dwKBBzDDYujXUht61K2dH1JFFo0oVYPBgNmabN48hoRUqxO/+JyVSpWJpYJEi7OIG0OZfoACQPn3i7psgCIKQvIhURCpYEPDwiGKhiIkPJ9KrV9EQkfbuBR49EheSkOCIiCQIgiAICcSJE3TLnD/PErObNzk9nIjk4cF28998A6xYYdRBrGlTYM0adnOLjBQpmHtkWPpVvny8HEaSp3hxVhzkzx+34FJBEATh6+LFC6B9+2g4kXQ6wNU1Vtt4/pxjXMvZMmeOYqEFC9h14ttvY78hQYgGIiIJgiAIyYr//gOcnIBVqxJ7T6Kmb1+WV1WoAEycqNeHjMrZgoKAH3/kCeqGDeFUEEVhILadXfS2mTmzvnQtolbAXxq2toCzc9Qlf4IgCIJgSJYsdPhEKSIBscpFCgzUi0ixLWcLCAB8faNwIh07xu4S3boZd6IQhARAurMJgiAIyQY/P6BFC45durCEK0y3+3glMBC4epWij60tBZ3ocv06XzthAnD0KEuvAgOByZPDCEJDh9KqtGlT1DVr0eTGDZ5wfk3s2QNYWyf2XgiCIAjJjVSpohCRChTgGItcpEePOObMCTx9SldyTM4lAA6cvasAACAASURBVJayAZGISAEBQOfOvMM2ZEiM91EQYoo4kQRBEIQkz5EjQMmSFF/8/ICVK2ngWbUKeP064bY7ahRQsSK3mz8/cPZs9F+7bh0DrXv0YNB1YCBFr0GDDBbauhWYPZsLtGwZb/udPj2QK1e8rS5Z4OgIZMyY2HshCIIgJDesrZlJ/flzBAukTcsysbt3Y7xuDw+ORYrQcPzpU8z378EDjtmzR7DA1KkMXZw/n3e8BCGBERFJEARBSNJcvMiO99ev83mKFHzu7MycoFy5gPXr43+7hw4xoDp3bmDSJMDHB/jrr+i/fudOdkzLlIndwyZMANauBSwtQxZwd+edw3LlgGnT4v8ABEEQBEGIEq2KPFI3UtGibAcaQzw9ORYpwjE2uUhbtrDXhouLiZkbNgBjxrAsvnbtmK9cEGKBlLMJgiAISZY7d6ixAMCSJTyHs7amFbx1a5Zt1Up7EZ6/HIR68yOU6tV4EmUR+3skbm48H1u9GsialWJS/vyMG7h1K3rr8PBgiPaMGXxuZQWMHGmwQGAgu7ApChUwSYMWBEEQhETBUERKnTqChYoXBxYvpp0oBucYHh68eaRVxPn7x6yDqE4HbN4MNG4MpEkTZub790C/fuyisXx59FcqCHFERCRBEAQhyeDvDxw/DtSvT31l7VpOnzePph3D87ZhzW6j35ERsP1vOwBA/UMBJqgMMGrTBkiXDihVCqhcOUYnfC1bMjtzxAg6nbSTy6JFmW30+bOBmygCjh7lWL++iZmqypO+ixdZzpaQoU6CIAiCIESK9j0fEBDJQs7OPElxd49RfqGHB5tdaFmIMQ3X9vJiB7kaNUzMnDyZM7dtk5tRglmRcjZBEAQh0VFVoFIllvI3bAgcOMDcgNWrgVq1gJ49w+hAc+bAoqQzbM8fwdtBE5AebzFnoj/DkgCGGfXpw3qyb74B9u7lRqLg6VPg2jVg/Hh2VTM8JytWVIVdwEt4nvcCHj+OdD1nz1LD0uzrRgfavz+wcCEDtZs3j94bJAiCIAhCghCtcrbixTnevBmjdd+8yeZumsMpbDmblxdPB4KDTb/e3Z1juPtNHh60O7drxxawgmBGxIkkCIIgJDqXLlF4SZmSgdkNGujnzZplsODnz8CAAcDcuWzTtngxMmTMiDwHgfXbgf5n2jEw6cMH2rwPHgTGjWMokYsL79pVrAiALXe9vGhW0vjvP45164bZwW3b0Gb8UPyM+0DlkGlOTlxvo0ZAvnyAtTWCXrzBokneOLY1G76rpMDi6DOGOT19Crx7x3ZtFy4AAwcyaEkQBEEQhEQl2plIAFWhb7+N1nqDghij1KBBxCJSz57MPKpTx8S5B/TB3HnyhJkxdCjvrsm5hJAIiBNJEARBSHQ2bWJu0PPnQOHC+ult2gBNm4Y8CQoCWrWigDRwILBxY2g7rtatKUK5uwNQFHy2SYOLT7PBt3kHBivNm8euKpUqUXy6fRvt2tGotH078OwZN7FzJ1voligRss2AAKB3b6BFC6SyS4VBFjOxvc5cYM4coGBBYNEinh0WLAjkyoWU5Uuh99bauIOi+OdMEeYzDRjARO4dOxhusHAhMH16zHv8CoIgCIIQ70RLRLKzY3s0rVVaJDx9ytOTZcvoqi5dmsHYQPhytlevOEbUGc6kiHTqFAO1hwxhrZwgmBkRkQRBEIRE48MHaizTp7OMzd6e3dcAYOlSYM2aEK0lMBD4/ntmCM2eTQu3QTBR69Yct2zhqDU9GzIEtDf17AnV1Q3byk7ApwOHoTo7o+3hLijodwnNm6uoUQN4/Zo6T9u2gMVDV2DiROYrzZsHDBoEiyuXcKbiAEx63xvo2xfYvRvw9qbbaeVKzCi4EJ3SbMLW3ocwt/wKPJ66lvNevOBZ44sXzEHq3l0EJEEQBEFIIkRLRALoQHZ1jXJ9588Db97oO7qWLm3sRNLp9MtqopLPM3+eFIXBw4M3t0IDv3U6lsU7OgKDB0e5L4KQEEg5myAIgpBozJ1LTahjR+DPPzmtSxeeHzVqFLJQQAAdSLt2UdDp2TPcevLkod6zbx/1nc2bOX33bsYQKQqw97gtWlwcCXv8gqlpfsdPfgvQBf/gBopj+YOOaJSjHgp8ssRvT/4ECi3hiVrFinQbhbTNrVmTFXGenkDu3ABSp8bNrHXQsDPw5Am7urUYm9DvmiAIgiAI8UWMRKTdu03OUlU6j3Q64OVLTrtxg1mPTk7Aw4ecNmIE8MsvrELLnh1I5eeNBRiBlr+sBMZkZGcOg+Bud/cwLqRVq3hDauVKrlwQEgFxIgmCIAiJxtmzDJxctgxIm5bT+vZlnFHWrAAePQKqVqWANH++SQFJo0EDdnY7fZp3+urUobBz5w5L1n76icu9QUZ09ZuNG/uf4+OsBShSNg2mYzDOB5bELRSH/bZ/WML2+DFw5kyogAToc7DLltXfMJw8mdsBgB9+iN/3RxAEQRCEhCXaIlKBAgxTfP8+3Kzly4Gffwa6dmWHV42SJRldlD8/kDMnm3c8fgx0bBeMY7XGYsXdcuiMf3E/Tz3Ax4d3q4YM4YmLquLRo5CbVgBPPIYPp9W6bdt4OXZBiA0iIgmCIAiJgqoC584B5csbT1cUIM1rD9a5FSvG/IHt23nrLhLq12dskpYxOX48x337gClTmE/QtSun2dgA5eplgE3/HrC6cAZwc2Pt3PLltKrPmWMyZ6BsWWDFCpa+3brFLIMNGyge7d6tz90UBEEQBCF5ECMnEsBzhjBcvswxXTpGMGqULs1RUVi2T1QsQneMxTi8hANq4TBmVd8GnDxJK9OsWbxr1bIlnJ4dR3ndWZ5wjB/PEMfZs8O0rBUE8yLlbIIgCILZ0ZqsvXgRRkQ6fhwYNYqhkRYWwI8/skasQIEo11mpEmOS9u9ns7RKlYAiRYD16+n8HjGCQtOSJcbh3QD4gnz5orXv2v7evMlGa58+cZednaN37IIgCIIgJB1iLCK5uhq3dgVvLgGAry87smloIhJAR/Tu3cAE6z/Qye1fbCs5Bi2ujQUA2L8EbUuPH/MkaeZMqGPGYHfAVmAz+APwbljlyhCExEREJEEQBCFaBASwo+yFC3RTp0sHBAfzXMbamsv4+QEuLsyyzpuXIs5vv4Vf1+LFzEMCQqrFvLwoFi1cSN/2yJEMoI5B15E0aXh+d++evvtu/fq8YQewdW6FCkCPHhSwYkvevHQyTZrE88jixUVAEgRBEITkSvr0HH18olhQyyoy0aHN25ujqrLarWRJlq6VKaNfpnp14Mm9D1Adp0PXrDlKzxkD52Z8rdalDYrCdrVDhsC7aWe0K3oJvboHo2nem2z7ptmtBSERERFJEARBiJQ3bxgSuWaNPvy6WTP9/MGDgd9/Z6n+sWO0dFcoFQiLtRuRHufw7J4NstcqDBQtimWnCmD6X9Zwe5QC31Z9j82TH8Dy79XAP//wFmD//sCECbEOi8ycmSKSll3UogVFpCpV6ExKkQJYsCBu74elJX+0Bi3/+1/c1icIgiAIQuKRJQvHFy+iWNDOjoGNIScAb97wplmaNHonksbAgeyqFsawBKxbB+XdOyiDBiJ3HgXXr9OhdPZs+M29VjJjPxqggwuAtk1ic2iCkCCIiCQIgiBEyI4dQIcOwLt3fN6yJZ1Hu3axrGvKFGDaNJaMvXvH3KGmtoexPagvFNyCr5IW1msDgdX0iHcK+QEAnARQFfSRf/89MHo0ULBgnPZ3yRKGdFepwufVq1MAy5SJN/fii9q1GdP0/HlIALggCIIgCMkSGxs294hSRAJoeQ4RkTJmZFj2o0d0Ezk46Duz5c5NZ7YRqgr8/TctzFWrhk52cGD3tm3b9DfBAL27KWPG2B+bICQEIiIJgiAIJrlwgdpOqVJsVvb0KdCxI1vSDhzIZVq3BmrVAuxtA9Hgwy5893AR6uMA4JcL2LYN0y80xcQ/VLgfdkfGl7cxqs1D1KsZhAa1PtFtlC0bUK8ekCFDvOxzoULhnd6ZM8fLqo1YsYKimQhIgiAIgpD8yZqVlfVR4uQEHDgQ+vTxY46vXzP/SBORsmc38dpTp2jX/vtvoztbP/5I1/SIESIiCckDEZEEQRAEkyxbRpv2/v0RazylsjzHm3aTgNWrgTdv4G/vCJ9fpiD9b30Ba2t0LwNMnAxM3+aEWrWcMEsFGg0HUMecRxL/pE3LH0EQBEEQkj9ZssTAibRsGWv4wdL7Dx/4U6gQG6wBvEdmhJ8frd05cwLt2hnNqliRMZDbt/P5/fvAzJnMVQLophaEpISISIIgCIJJDh4EatSIQEDS6RiCPWwYE7e/+w7o1Amp69RBakvL0MVy5AA6d2aW0p9/8rk0FREEQRAEISmRNStw/Xo0Fgwpu/906z4Atl67dMloFgDmJBmxaBHg7s7wSDu7cKu1twfevmVjtrJlGc6tVbyJE0lIalgk9g4IgiAISQ9PTzYfqWPKMfTqFUOBevYEypVjr/u1a9kKzUBA0pg0iflJP/wAnD4NpE6d8PsvCIIgCIIQXaJdzlaiBADg47lroZNOnOCYL18ErwkOZr1ajRoMazRBhgxAUBDX9f49p508yYYg4QQpQUhkREQSBEH4ynnyBBg+XB+eDQDnz3OsVi3MwteuUTg6exZYupR2pQIFIl1/pkzAuXMM386ZM373XRAEQRAEIa5kyQL4+NBcHSlOTkDq1NBduho66fhxjhGWne3bx/Ckfv0iXK29PUfN1ZQ/P8fg4PhtDCII8YGUswmCIHzFBAbSNu3lxbtwdnaAlRW7hCgKUKSIwcKbN7OeP316njGVK5do+y0IgiAIghBfaI0yvLzYWS1CLC0BZ2dY3tQ7kbSc7UyZ6LY2aLxGli5lC7bGjSNcrSYiXbnCcfZsoGnThGkOIghxRUQkQRCEr5jjx/X27f799dObNqUtO3VqMP9o/Hhg3DimP27ZYiIxUhAEQRAEIXmi5T/6+EQhIgFAqVKwXr0egAonJwWurhSBChdmwLaqGix79SoTs4cPZ21aBBiKSOnSAU2aAHfvAh8/xuWoBCFhkHI2QRCEr5g9e4BUqYBRo4yn79wJFCsGdhNp1YoCUqdOwJEjIiAJgiAIgvBFoeUOffgQjYVLlUIKPx/kwiPUq8dJhQrRyQ2EKT8bO5YO7sGDI12lJiLdvq0XsQoVAkqViu4RCIL5EBFJEAThK+TVKwY37tgB1KxJEenoUcDfnx3UAKBGhpD8o23b2Gv2n38Aa+tE3W9BEARBEIT4xtaWo59fNBYuWRIAUApX0b49ULQoO9CGw8ODJ1o9e1JIigRNRAKi4YQShEQmShFJURRrRVHOK4pyTVGUW4qijAuZbq8oykFFUR6EjBkMXjNcURRXRVHuKYpS32B6GUVRboTM+1NRqNMqipJKUZT1IdPPKYqSJ/4PVRAEQQCAdetYmp82LbOPunalG8nFBbBJEYwLQzfhSM726LemPNO2Dx4EBgyQZEdBEARBEL5INBEpWk4kZ2eoioKSuIaCBYFbt5gvGY5//+W5U48eUa5SRCQhOREdJ1IggFqqqpYEUApAA0VRKgIYBuCQqqoFABwKeQ5FUYoCaA2gGIAGAP5WFEXr+TwfQHcABUJ+GoRM7wLgraqqTgBmAZgSD8cmCILw1TJgALBqlf750qXA4sV8vGcPR2dnoE4d4LvvQhZ6+BAoXx5Z+7RCDb/dsOjyM7ux1apl1n0XBEEQBEEwJzESkdKkgXeGAiiFq0ibNpLldu0CKleOVmtaGxv94+LFo7EPgpCIRBmsraqqCkAz9qUI+VEBfAugRsj05QCOAhgaMn2dqqqBANwVRXEFUF5RFA8AaVVVPQMAiqKsANAcwN6Q14wNWdcmAH8piqKEbFsQBEEA860toiH9X73Krh4pUwIlStBmPXQob4Z16QKcOEHhaNMmrlNRQGXpp5+4gnXrmIMUnY0JgiAIgiAkc2IiIs2dC2R8Uwa1cBhWFjqY9GW8eAFcvgxMnBit7Ruavdu2jdZLBCHRiNYVgqIoloqiXAXwEsBBVVXPAciiqupzAAgZHUIWdwTw2ODlT0KmOYY8Djvd6DWqqgYDeAcgo4n96K4oykVFUS6+evUqekcoCIKQzPH3p+iTPbveRRQZS5awPC19eqBhQ6B1a8DbG3j9mt0+PDyAatV4wmKJz8CYMWw7mzs3cOkS8OOPIiAJgiAIgvDVoAVr+/mxPO1xyNXsmTNAv37A58/6ZTduBHahCbLCCzh71vQK9+3j2LBhtPdh0CAKVHZ2sTgAQTAj0bpKUFX1s6qqpQDkAF1FkZnsTIVmqJFMj+w1YfdjkaqqZVVVLZs5c+aodlsQBOGLYNEiYOtWCkPdutE9FBGqygzHxo2BZcuAN2+AzZv1edh79zI4u0kTADduAA0aAOPHAx07AqdPA/nymeOQBEEQBEEQkgyGTqTixYFcufh84kSGZi9frl+2VClgNxojCCl4gmaKPXvYzTYkhDs6TJsG9O4dywMQBDMSo1vNqqr6gGVrDQB4KYqSDQBCxpchiz0BYFj4mQPAs5DpOUxMN3qNoihWANIBeBOTfRMEQfhSOXiQbV4nTwaePWOTtDx5gIsXwy/r5sa7Z3Xq8OaXtzdw7+xb3Fp0Cmva78X+IYfwePCfcGpdlrVu584BCxYw/DF1arMfmyAIgiAIQmJjacmbdWHL2RxD6mb++ks/LTAQ8EU6fKpWiyJS2ASW4GDgwAGeiElTEuELJDrd2TIripI+5LENgDoA7gLYAaBjyGIdAWwPebwDQOuQjmt5wQDt8yElb+8VRakY0pWtQ5jXaOv6HsBhyUMSBOFrwMMDOH484vnbt/NmVu3adA9ZW9ON5OlputnHoUMcG9mfBTp1Qmrn/ChY0R75OlRFm5WNUG9qHfqyg4OBWbMYpt2jh5zkCIIgCILwVWNrG15E8gtJBvb21k8LDGQCgO1PLXj37tYt4xfNmcPuts2aJewOC0IiEWWwNoBsAJaHdFizALBBVdVdiqKcAbBBUZQuAB4BaAUAqqreUhRlA4DbAIIB9FJVVasi/RXAMgA2YKD23pDpSwGsDAnhfgN2dxMEQfiiuX5d73K+eRMoVsx4/o4dQPPmfFy7Nmvke/YENs30RAesQM7Lj/GqWTAyl8lNa5KDA1IudMXBVPuRu/VuIF06WpJ69KA3294eCAhgl5D8+c16rIIgCIIgCEkZW1u9aKTh62s8AjyVsrYGRaJff2X72zlzONPDAxg2jGGWIiIJXyjR6c52HUBpE9O9AdSO4DV/APjDxPSLAMLlKamqGoAQEUoQBOFrICCA5WkazZoBa9YAFSrw+fv3QK9eQNastFA3bw7g9WtMfPsbJitLYYVgvIQDrP6zBHY9D7VSdwbwJrUjw7IHD9YX+QuCIAiCIAgRYmtr7DgCeD4GUEQ6fpz35AICWPqGbNmA7t15ota1K+DsDEyaxOYkc+aIy1v4YpH2O4IgCGbm/XuahNauBfr0YQna+/fMt96yheahAQOAp09Zat/y22BYzJ8HFCiAVCsWI8UvXaF4emJCrxew//gUA3sG4PIGV1TFSWTHU+yc9xgYO1YEJEEQBEEQhGiSJg3g5WU8TXMg6XSAiwvw998GTiSAopGNDTBzJtO3Fy2iAzxHDgjCl0p0ytkEQRCEeOTuXSAoiI/79gWcnOg0WryY+UcaHTuoqOi9Byg5BLh9G6hViy1CQurepk/ncrP+SolZ8/IDYIla7TpmPBhBEARBEIQvAFtbVqMZYljGBgCvXjETKVREypCBHW7//pttcV1c2GZNEL5gREQSBEEwM/fucbx1iwISAPyfvTuPs7F84zj+fWasY4tsWZN9iWRQIkmWIlKIFJVUKtSPFGkX0Z4lUijJEiVlX4qUbTCyb8mSNQrZZ+b+/XHNmBnbYxlzhvN5v17zes7cz3Oec58ZxXznuq+7WjULkSQpX54YFdo2R31XvSbV/8kuGjtWatQoUWl02rRWLZ0qla3hf+EF22AtT57kfT8AAACXuwwZTq1EOnDAelLGLWv791+rRMqcOcFFXbpIx49LpUpZL4LUqZNtzkAgECIBQDJbu9aWyyfsbV2tqtONWqxOeUfq3uOjlFZbpI3ZpT59rCz6DP8gCQmxTdYAAABw4TJksIAoof37be+SVavs87gQKWfOBBfly2fL2IAgQU8k4Aq0d6/199u6NdAzgWRNGlu2lGbOtM/XrJEKFYptyihJS5fq2ntv1CKFq9muj5S2Ujnpq6+kjRulZ57hN1oAAACX2MmtJKOipMOHE7c3iguRTvwbDghCVCIBV5iHHrL8QZJy5LB+fwiMRx6RtmyR7rpLGjbMPvr0kRYskEqWjL1oyhTpvvusLnrgQHmNG0vZsgV03gAAAMEmY8bEn8ctYcubN37s339P6okEBCEqkYArSHR0fIAkSdOnB24uwejIEemBB2zt/CefWH/FGTOkjh2lEiVs59d27WzXtQZ3O2nAAKl+faloUWnxYtsmlgAJAAAg2Z1ciRTXVPvkECnR7mxAECJEAq4gGzbYccAA6a23pIgIaceOU6/79lvb5AtJq1MnacQIW3321FOJzz31lBUd/fCDtOfPA3rylxZS27ZSrVrSrFlS7tyBmTQAAACUI0f8Y887cyUSIRKCHSEScAVZscKON94o3XKLPY6MjD//zz92vO8+qUMHacKE5J3fley776R+/aT//c96UdWqJRUsKB08KG3aZJt1XKPtqr/0LWWqXEoaNcqSvh9/PGmLDwAAACS3QoXiH6dNe/pKpAMHpEOH6ImE4EaIBFxBli+3Y8mSUunS9njFCikmRoprtfPyy/HX9+kj6e+/pd9+s61JcUGckzp3lsqVsx5UYWFWdbRqlT0usCtCIQ+1kAoUkLp1k4oXl376Sera1bZXAwAAQEAlDJHSpIkPkbJnjx93jp5IAI21gSvEgQPS7Nm2DWnGjPaRK5eFSFOnSmPH2nXdu0uZtU89Cn6qSjO+lcs5X55zVsNbp47tO9+kSXwKBV+zZ0vr10tffGH/6JCsDDr9wtmW2s2ebY2SnnnGlrAVKxbYCQMAACCRhCFSTEz8crZMmU69lhAJwYxfgQNXgIEDbUXU9OmJe/GULi1NmmTVMbmzR2n70Cn6udjj2pE6v57e1FkuKlr7nn3VGvncdpv15nnjDesA3aqVtHNnwN5TSnLggFSpkjRkyOnP9+snZcli1V6SbE/Y996TataUNm60x1u2SB98QIAEAACQAl11Vfzj48etJYF06q5tEiESghuVSMBlbudO6bnnrJlzu3bWkydO9QIbVXjmNFXYuVgPZRynsId3KnfGjNL992hZrf+pcqvyGlHJVleVH91M+/dL/2u1Ry+l6qVCIz+Sxo2zVKpDh6Bu/DxmjLRwoX0sWWINy1evltKnl155xc536WJL17R+vfTgg9L8+VKDBtKwYfQ8AgAAuIwkDJEyZLAuBMuWSe3b2xghEoIZlUhAgPz9t/Tzz6eOx8RIx46d+33mz5cOH7Z7vfeeFBoq25LtwQf1ytDr9Kme0GMZRyqs5s22Ldvu3dKwYSrSpLwyZ5aaN7dG3GvWSE88IX0+7mpVntVb//wUKdWtK/XqZWvk2raN3/5NVmwTDMaOtX8wZM0qVaxofaQ8T7r3XmnvXunJJ+0fEh06SFqwIP6LOXKkhXAESAAAAJeFfPnsGBMj/fefPQ4Ls4L9qlXjr6OxNoIZIRKQzPbts6Z8HTtKt98urVwZfy46WrrzTqlsWdtCdM0aqVEj6a+/bKXZggWn3i9uR7YyZWTJzkcfWWnRN99Y4+a1axW67x/bPqxRoxO/Okmf3rabj7N4sWUederYaz/dt6TcyFE2iVatpMGDpWLFtOe2ezWx8WCVy71Tzz9/6b5OKcH06VLTplLOnFZQNG+eZXC//ip99pn0+ut2XZs2Us6dyyx0y5HDtsS7/35LmwAAAHBZWLNGeuEFe7xvnx3Tp7fjNdfEX0clEoIZy9mAZHT4sO2QVreuNGOGhUkvvmhFK2FhFkxMnWrZQ9eu1mdn3Dj7iPNh72MqkmmnlkRE66VP8mn58lS6Lv9xZZ4yTnr1VdsSrE4dK5kpWvSs87n1VivVzZjRipSOHJFatpSqVLFbTZok1a1bVCNGDNSedq8p8uEPVXLWMN2l71RboZr2bi0tD3lAZZ6uLuXPf8WFJsOGSVdfLf3+u5UyS4l36Hj8cWnTJunlJqulWrXsXxnTp0sFCwZmwgAAALhgYWH2bz/JQqR06WKr/GW/VIxDiIRgRogEXCILFtgSqKJFrSR2/35p0SJ7PHGiXfPII9asOUcOWzY1bZpUtuA+Nc43T9vGHpSuDlVnrdYNilRRrVOh1Ft1dWdrdl1PUvTIzOrsSqnosRVS0wNSyZJ2o0aNzjnQCQuz1WpjxtjnFSpIzZrZX6DPPGPjX38tDZt+jZ5b1EvS23q54TK1zjBSpUcMV4HeLaXekvM8efnySa1b2373cb+2uYytWWM9xuMCpJNdlcWpX/hQqW47+0L+9FPirT0AAABwWUkV+xPyvn2x/S5jhSRYw0OIhGBGiARcAps22drpTJmsEfOrr9pqsKeftvOPPCI9+qitrW7d2pZMjfpoh2r/+q6+PthXaTYdtQt32SEqX0EdKVRS6YrcqFmb8unv1Ndo0hRPD+VbqJi167W0bAvd/HpdqX79+F+XnIfixW0TsYwZLfQKCbG5hoZaK6TNm6U//rBrBw709PjjZSWVVYN93fXXhCW6WXN1jduhh7NHKu9rr0mjRkndu9t84va8v0wcO2bfm2LFbKlgy5ZnuHDPHmsiNXasfbO//NKqsQAAAHDZSp3ajieHSJL9jtY5eiIhuBEiAUlsyBALIdKls4Z8efPGn+vTR7rpJguU4twS84t+TP2+yk4er1DFaHn4w0rT+iE1aXu10uqo+k0uoop1silud9Hqca9TX7ptwmOSaGm92gAAIABJREFUpGVfSSpz4XO+8UZp8mSrQEr4W5YysfdcsUJat86ue/zx+PPNHghRiwkV1PTtCvryS2lqJmnW5Cm2o9t999mb79TJUrMsWS58gslo7Fhp+PD4z4sXP81FM2ZYurR7tzUe79jxgsI7AAAApCxnC5Fy5JB27aISCcGNxtpAEoqJkXr2tKVgU6dKn35q42XLxl/z3HOxD/75x8KVW29VyX9+VW91Vkmt1J53h6hwm9uVpVo5texTSRXrZDvta33+ue2s9uGH8WHPhXrxRdu2Pm6+cUqXtmPnztKcOVKRIonPN28ubdtmDQhr1rR7RNWsI61eLY0fb72BnnvOnjhlysVNMpn065e4lVSJEglOOmdlZXfcYWVm8+bZF4cACQAA4IqQMEQ6uaVBjhx2pBIJwYxKJCAJ/fKLVex89ZVUrZqN3XST9RyaPVvassWWrunvvy2IWLFC6tJFxzq8rPlPpFfdQtbUOjTUrj+bXLmsV1FSyJTJeiGdLGtWKXfu+B3gTu7T7XnxO1VUrmyVVitWSOXKpdacrHfr0Kt3q3a2CFuzV6+eNGCA9NhjSTPpS2D/fmnuXKlbN2uC/s478UGajh2z0O/rr223uv79T/31FAAAAC5rcSHSv/+e2qkgLkT677/knROQkhAiAUlo0SI71q0bP1a4sB1r1Igd2LPHynbWrpUmTJBq11ZmJd6BLSWZNs22sJ83z4KrM6lc2Y4zZlgBUlyI9txz4WrVb47KvdXUbpQjh9Sw4aWf+AVYsMCqyapUkWrXtn5QefNKioqSHnjA1rq99ZbUpcsVtxMdAAAAElcindzW4LXX7PfACVcZAMGGEAlIQmvW2FK2uK1BT3HwoDWbXrNG+vFH+1sohStTxoKkt96SWrQ483WFC1tw9Mor8WGaJH3wgTRnTiYtmP2dVL263WTuXNv2LIWZO9eyocqV7ViokKToaOnhhy1A+uAD6dlnAz1NAAAAXCJnW85Wvbp0/HjyzwlISeiJBFwk56x6RbLiotM2YpakDRukO++0cpeRIy+LAClOxozW6ynb6dszSbLQZcQI+3p8/bWUM2f8uWzZZB0Iv/tOypzZOngfOXLJ5302Bw/a8rUjR6SJE+17OGeOVKqUdNVVsRc5Z+VIw4fbbnMESAAAAFe0uBBJonMBcDqESMBFeuwx63u0bp31AypW7KQLjh+X3n7bSnoiI6Vhw6R77gnIXC+1vHnjd28bODB+/MRvbPLksa3pVq6UXn892ecn2ffogQesv9N111m/qnr1bFe9WbNsGZskW8L25JPSoEFS167SSy8FZL4AAABIPoRIwNmxnA24CAsWWCYixYdHiXYwW7PGOmn//rvUqJF1ns6bN9nnmZxee0264QapQQNrTN2tm/TXXwkuqFtXevRRqXdvC9PimiklgyNHpMaNbfO4W2+VUqWSjh6Vdu60TdaOHpXuuktWptSsmS057NLFqpAAAABwxUuV4Cfkk5ezAaASCZAkbd4s7d17fs9xzra2z5HDlnq1bGnjJzKRSZOkSpWk7duta/a3317xAZIkZclim5eFhEidOtlqsK1b7et1wvvv29eiZUvp0KFkm9v48RYg/fCDVR3NmGFL2O65x77/mTNL1Yrvkm6/3da49esn9ehBE20AAIAgQSUScHaESAh6H32UeDexM+nTx5pHx/U/mjVL+vlnayT94ovSF19YAcsdd0j67DNroF24sBQRkWJ3I0sO+fLF9x86IUsWaehQayLVuXOyzWXcOAv97rwz8fiDD9pxzNvrlbZGFWnZMgv9nnoq2eYGAACAwCNEAs6OEAlBa/t2afFiC38ka9Ozdu2p1/3wg7RqldS+vfTHH9KOHTb+7bdS+vRS69bx14ald7b0qU0ba64ze7ZUoMClfzMpWFzx1datJ524/Xbpuees2ueNNy7Z6y9ebEFhXAPtBg2k0NDE19x3n3T45/mq9crNthXHzJlBHfwBAAAEq4QhEsvZgFPREwlBaeNG63Mdt5KqbVvpk08sZEjYGHvLFgsd0qSRJKdUitKff6bWNddIEyZYDpI+9Ji0eLlVHI0bZ8vYWra0aqSEfwsFqXz57Lh1q1S69Ekn33lH2rNHevVV6779xhunLB1zTjpwwJaaXYgWLWwJ29Splg81b37SBc5JQ4cq3dNPW+PvSZOs6zYAAACCDpVIwNkRIiGo9O4tffON7TYfFSU9/bSFQS++aL1xBg2ysR9+kD7+OH4TtY6lJqlx5Eu6UUv03z3X6mCJEvr4jxBVPL5DyrRcOnbMLsyWTXrvPauwoY+OJKlQITtu2HCak6Ghti1amjRS9+6KOnRMqd59+8TX7vhxW3r2yy9WsJQli9Skybm/9qFD8a87caJUooQFfyccPmzfq4EDperVpdGjpZw5L+h9AgAA4PJHiAScHSESgsbkydYIO106Kz4ZMEB65BGpb187/8YbtoHa4MG2Idc//1jfo9dz9tMrkc8o6rqi6vFHFzW85k9lXb9GueQp83XZpWYdpPBwqUIF2zOe8CiRPHnsL+B1685wQUiINHCg9h5IrWzv99bGqatUaPogKVcu/fyzNb+WbIWgZCFg48Znf81OnWzp4Xff2eejRlkl1C23JPj2/Pyz9MQTtobxhResgXYIK3wBAACCWcIQKVOmwM0DSKkIkRA0JkywMGPTJqtwueaaxOcbNrQlbl9/Hb+15yMarFd2PSM1bKhUo0frw3xptLGStTrKU1z66afkfx+XG8+z1WFnDJEkKSRE/Uv30x4VVc/lXRRdqoxCP/tUKzc3kmRL4rZuTbyh24MPnj7z2b3bisEk6e67pbJlpXvvTbBd68KF1rdq/Hgrk5o2LbYbOgAAAIJdwhApT57AzQNIqQiREDRmzLAd2LJnP/15z7NKpDfflEIUrYF5uuvxba/J1a4tb9QoKU0aFSxorY4k6bXXkm3ql72iRaWlS89+zaTJnuZ6z2mKq6M5WR5StnvvVYni7ZU96/vq3j1Uw4ZJw4ZJdetKT7c6oExTp6rRdUulP/+0b2qdOlKFCpoyxb7Bi6ftUfmwNdL69dIb6+24bJm0fLn9WqlHD+nZZ607OgAAAKDEIVJcb08A8QiREBR27LAd1h555OzX3XefNPnNBXpXnXTrtl+khx6SN2iQlDatJKlUKeufXbOm1KxZMkz8ClG0qDRmjH0fcudOfG7DBmtL9Ntv1l/7nXdKKf+WuXpLnfXsmo+0LO045V7TQq2erij1jVBk9nk6GjpP6YYfkgsJkZcvn5UfffCBJOm2TCX0Z8hRFay1Mf5FQkKkggWlwoVtCVvLlhfeqRsAAABXrFQJfkLOlStw8wBSKkIkBIVly+xYseJZLtq9W+XeeU4LNFzRGTNL/b+0NVMJehz17Su1ayeVK0fro/NRpowd8+aVZs60HtZx2rWzxtlvvSV17Gg9rnfsSKPn9KFmqbo6px+k3L16STExUmiovHLlFPNIa7We3USj1odrwFvp9WDjI4qZ8ZNWjojU+uHzVahoahVs09a2gytSRLr22rgt9gAAAIAzSliJFBoauHkAKZXnnAv0HC5IeHi4i4iICPQ0ks2BA9bPh/+RXZi48Gf79lMrYSRJ48ZJjz0m7d9vW7U9/zyd9JJQVJT1se7QQdq4UZo6Vapa1c7lzSvVqiUNHWqf9+4tdetmq84qVZI+/VRqeusOe2K5cie2yfjvP6l2bRvetk0aMUJq0cLCva1bWcMOAACA87dvn3TVVfb4Mv1RGbhonuctcs6Fn+4cWxGlYMeP2w/UnTvbypsuXQI9o8CYMUOaNOni7rF6tX0NTylJdU7q08fWsRUqJC1ZYtu0ESAlqVSprHf1zJlS1qxSr14WAo0ZYwFQqVLx1z7/vHTwoFSsmPTvv1LTprLk7+abE+2zmjGjrUzbscMCpF9+sfFFiwiQAAAAcGESViIBOBXL2VKwuIqMOCNG2FgwGTs2fjv3i/lNwOrVUokSJy1BO3pUat1aGj7ctvEaOTJRSIGklyuXdPvttiFatWpSZKSNJwyRPO/c//KuU8eOLVrY8eabpfLlk26+AAAACC6ESMDZUYmUQm3dKr3/vnTbbVKVKjaWIUNAp5Rs/v7b+uLMny99803i8Qu1Zo1UvHiCgagoSx6GD7ft2MaNI0BKJjfeKO3cGR8gSYlDpPORO7f0+OPxn5crd3FzAwAAQHBLlUq66y778QDAqQiRUojVq6VXXrHewZs2SfnzS3v32g/Is2dLL7xgu1gdPx7omV5an31m7/3JJ6WbbpJGjYrfRGvVqlOvd862jt+82Sq1JOuls29f/DWHDlkoV6xY7EBMjNSmjZU5ffihlXuF8J9CcrnxxvjHtWvbsWDBC7/fwIHWxkqScuS48PsAAAAAnidNmCA1bBjomQApEz85pxD9+llBzG23xVdldOli/WBCQ6WSJa145s8/AznLS+v4cctzypWTFiyILwxq3dqOpwuRevaUbrjBQogHHpAmTpSuv96+hrt32zVxX7PrrpMFSE8+aV2cX3vNOj0jWd1wgx2bNpV++MGqki62YXzHjvYXfcKqJAAAAABA0iJESiFmz7bjL79Y5Ywk9egR/8N13FKs6dOTf27J5ccfLVDo1k2qWNFCNUl66CEpfXqrxtq0Kf76I0ekV1+1xzdprj7Rk1K9u/SD6qvHtlZa1P4L6Z9/tHGjXVM413+2hG3QIOmll6z0C8kuSxZp3Trpq6+kNGmknDkv/p7Zs1vJcb58F38vAAAAAMDpESKlAHv3SsuWJR47uaKiXDmpdGnpqafid6HSsWO2ldXIkbY1/WVu9myrPqpb1z5/7jmrIipfXrr1Vtup69ln469fu1bKEPWvfi70iOaqih7Q18qpXaqUf7vuCp2iuiMflnLmVMkuDfWEBqjCU5Wl0aOtfOnNN0/qso3kVKQITQsBAAAA4HLD7mwpwJQp1tundm3LgsaPj+8DFCd9elviVaCAtfGpVk3Sxo1S375WkpMlizRggNSsWUDew/nas0e6+urEY6tXW8VVqtg/lZ4X3ytnwlf/6IcmX2jTuD+1t/1VylYqt/TzXq1SH+XevFtHO3ZVjR+7avGaDDq4WmrfzunPbxZq2hNjlLPvYA3QeLmjBe2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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of dickey fuller test\n",
      "ADF Test Statistic : -1.2371986562284674\n",
      "p-value : 0.6573926747294222\n",
      "#Lags Used : 12\n",
      "Number of Observations Used : 1206\n",
      "Weak evidence against null hypothesis, time series is non-stationary \n"
     ]
    }
   ],
   "source": [
    "#Stationarity test\n",
    "def test_stationarity(timeseries):\n",
    "\n",
    " #Determine the rolling statistics\n",
    " rolmean = timeseries.rolling(20).mean()\n",
    " rolstd = timeseries.rolling(20).std()\n",
    "\n",
    " #Plot rolling statistics:\n",
    " plt.figure(figsize = (20,10))\n",
    " plt.plot(timeseries, color = 'blue', label = 'original')\n",
    " plt.plot(rolmean, color = 'r', label = 'rolling mean')\n",
    " plt.plot(rolstd, color = 'black', label = 'rolling std')\n",
    " plt.xlabel('Date')\n",
    " plt.legend()\n",
    " plt.title('Rolling Mean and Standard Deviation',  fontsize = 30)\n",
    " plt.show(block = False)\n",
    " \n",
    " print('Results of dickey fuller test')\n",
    " result = adfuller(timeseries, autolag = 'AIC')\n",
    " labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
    " for value,label in zip(result, labels):\n",
    "   print(label+' : '+str(value) )\n",
    " if result[1] <= 0.05:\n",
    "   print(\"Strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data is stationary\")\n",
    " else:\n",
    "   print(\"Weak evidence against null hypothesis, time series is non-stationary \")\n",
    "test_stationarity(train['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12f9369d0>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_log = np.log(train['Close']) \n",
    "test_log = np.log(test['Close'])\n",
    "\n",
    "mav = train_log.rolling(24).mean() \n",
    "plt.figure(figsize = (20,10))\n",
    "plt.plot(train_log) \n",
    "plt.plot(mav, color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of dickey fuller test\n",
      "ADF Test Statistic : -1.3255659230909311\n",
      "p-value : 0.6173962367893951\n",
      "#Lags Used : 12\n",
      "Number of Observations Used : 1206\n",
      "Weak evidence against null hypothesis, time series is non-stationary \n"
     ]
    }
   ],
   "source": [
    "train_log.dropna(inplace = True)\n",
    "test_log.dropna(inplace = True)\n",
    "\n",
    "test_stationarity(train_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of dickey fuller test\n",
      "ADF Test Statistic : -7.069643762070366\n",
      "p-value : 4.975591908076066e-10\n",
      "#Lags Used : 12\n",
      "Number of Observations Used : 1183\n",
      "Strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data is stationary\n"
     ]
    }
   ],
   "source": [
    "train_log_diff = train_log - mav\n",
    "train_log_diff.dropna(inplace = True)\n",
    "\n",
    "test_stationarity(train_log_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using auto arima to make predictions using log data\n",
    "from pmdarima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=-7372.030, Time=0.71 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=-7379.211, Time=0.27 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=-7382.510, Time=0.68 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=-7382.013, Time=0.14 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=-7379.290, Time=0.08 sec\n",
      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=-7382.581, Time=0.24 sec\n",
      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=-7383.659, Time=0.58 sec\n",
      " ARIMA(4,1,0)(0,0,0)[0] intercept   : AIC=-7381.766, Time=0.29 sec\n",
      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=-7381.633, Time=1.07 sec\n",
      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=-7380.186, Time=0.51 sec\n",
      " ARIMA(4,1,1)(0,0,0)[0] intercept   : AIC=-7379.695, Time=1.39 sec\n",
      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=-7383.838, Time=0.20 sec\n",
      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=-7382.575, Time=0.22 sec\n",
      " ARIMA(4,1,0)(0,0,0)[0]             : AIC=-7381.915, Time=0.29 sec\n",
      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=-7381.817, Time=0.18 sec\n",
      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=-7380.746, Time=0.48 sec\n",
      " ARIMA(4,1,1)(0,0,0)[0]             : AIC=-7379.857, Time=0.44 sec\n",
      "\n",
      "Best model:  ARIMA(3,1,0)(0,0,0)[0]          \n",
      "Total fit time: 7.762 seconds\n"
     ]
    }
   ],
   "source": [
    "model = auto_arima(train_log, trace = True, error_action = 'ignore', suppress_warnings = True)\n",
    "model.fit(train_log)\n",
    "predictions = model.predict(periods = len(test))\n",
    "predictions = pd.DataFrame(predictions,index = test_log.index,columns=['Prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Actual Stock Price')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_log, label='Train')\n",
    "plt.plot(test_log, label='Test')\n",
    "plt.plot(predictions, label='Prediction')\n",
    "plt.title('BSESN Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Actual Stock Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Error Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE :  0.026837616405067617\n"
     ]
    }
   ],
   "source": [
    "#Calculating error\n",
    "rms = np.sqrt(mean_squared_error(test_log,predictions))\n",
    "print(\"RMSE : \", rms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Textual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Functions to get the subjectivity and polarity\n",
    "def getSubjectivity(text):\n",
    "  return TextBlob(text).sentiment.subjectivity\n",
    "\n",
    "def getPolarity(text):\n",
    "  return  TextBlob(text).sentiment.polarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>News</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>win over cena satisfying but defeating underta...</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-01-02</td>\n",
       "      <td>Status quo will not be disturbed at Ayodhya  s...</td>\n",
       "      <td>0.286859</td>\n",
       "      <td>0.143590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-01-03</td>\n",
       "      <td>Powerless north India gropes in the dark Think...</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.089286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-01-04</td>\n",
       "      <td>The string that pulled Stephen Hawking to Indi...</td>\n",
       "      <td>0.445360</td>\n",
       "      <td>0.093039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-01-05</td>\n",
       "      <td>Light combat craft takes India into club class...</td>\n",
       "      <td>0.480553</td>\n",
       "      <td>0.264024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7075</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>Containment zone residents slam high prices ch...</td>\n",
       "      <td>0.377957</td>\n",
       "      <td>0.060128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7076</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>like me i wont let you have a toxic relationsh...</td>\n",
       "      <td>0.390221</td>\n",
       "      <td>0.080373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7077</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>Atanu Ghosh plans to rewrite old scripts to ma...</td>\n",
       "      <td>0.397919</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7078</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>hot and stylish bikini looks of Katrina Kaif...</td>\n",
       "      <td>0.397084</td>\n",
       "      <td>0.065397</td>\n",
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       "    <tr>\n",
       "      <th>7079</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>Detective Byomkesh Bakshy  Edge of Tomorrow Fi...</td>\n",
       "      <td>0.382257</td>\n",
       "      <td>0.072504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7080 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                                               News  \\\n",
       "0    2001-01-01  win over cena satisfying but defeating underta...   \n",
       "1    2001-01-02  Status quo will not be disturbed at Ayodhya  s...   \n",
       "2    2001-01-03  Powerless north India gropes in the dark Think...   \n",
       "3    2001-01-04  The string that pulled Stephen Hawking to Indi...   \n",
       "4    2001-01-05  Light combat craft takes India into club class...   \n",
       "...         ...                                                ...   \n",
       "7075 2020-06-26  Containment zone residents slam high prices ch...   \n",
       "7076 2020-06-27  like me i wont let you have a toxic relationsh...   \n",
       "7077 2020-06-28  Atanu Ghosh plans to rewrite old scripts to ma...   \n",
       "7078 2020-06-29    hot and stylish bikini looks of Katrina Kaif...   \n",
       "7079 2020-06-30  Detective Byomkesh Bakshy  Edge of Tomorrow Fi...   \n",
       "\n",
       "      Subjectivity  Polarity  \n",
       "0         0.633333  0.433333  \n",
       "1         0.286859  0.143590  \n",
       "2         0.392857  0.089286  \n",
       "3         0.445360  0.093039  \n",
       "4         0.480553  0.264024  \n",
       "...            ...       ...  \n",
       "7075      0.377957  0.060128  \n",
       "7076      0.390221  0.080373  \n",
       "7077      0.397919  0.058824  \n",
       "7078      0.397084  0.065397  \n",
       "7079      0.382257  0.072504  \n",
       "\n",
       "[7080 rows x 4 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding subjectivity and polarity columns\n",
    "ndf['Subjectivity'] = ndf['News'].apply(getSubjectivity)\n",
    "ndf['Polarity'] = ndf['News'].apply(getPolarity)\n",
    "ndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding sentiment score to df_news\n",
    "sia = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Light combat craft takes India into club class...</td>\n",
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       "      <th>7076</th>\n",
       "      <td>2020-06-27</td>\n",
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       "      <td>0.080373</td>\n",
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       "      <th>7077</th>\n",
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       "      <td>2020-06-30</td>\n",
       "      <td>Detective Byomkesh Bakshy  Edge of Tomorrow Fi...</td>\n",
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       "      <td>0.786</td>\n",
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      "text/plain": [
       "           Date                                               News  \\\n",
       "0    2001-01-01  win over cena satisfying but defeating underta...   \n",
       "1    2001-01-02  Status quo will not be disturbed at Ayodhya  s...   \n",
       "2    2001-01-03  Powerless north India gropes in the dark Think...   \n",
       "3    2001-01-04  The string that pulled Stephen Hawking to Indi...   \n",
       "4    2001-01-05  Light combat craft takes India into club class...   \n",
       "...         ...                                                ...   \n",
       "7075 2020-06-26  Containment zone residents slam high prices ch...   \n",
       "7076 2020-06-27  like me i wont let you have a toxic relationsh...   \n",
       "7077 2020-06-28  Atanu Ghosh plans to rewrite old scripts to ma...   \n",
       "7078 2020-06-29    hot and stylish bikini looks of Katrina Kaif...   \n",
       "7079 2020-06-30  Detective Byomkesh Bakshy  Edge of Tomorrow Fi...   \n",
       "\n",
       "      Subjectivity  Polarity  Compound  Negative  Neutral  Positive  \n",
       "0         0.633333  0.433333   -0.0000     0.230    0.473     0.297  \n",
       "1         0.286859  0.143590   -0.9792     0.121    0.809     0.071  \n",
       "2         0.392857  0.089286   -0.8910     0.156    0.735     0.109  \n",
       "3         0.445360  0.093039    0.7543     0.104    0.792     0.104  \n",
       "4         0.480553  0.264024    0.9645     0.142    0.694     0.164  \n",
       "...            ...       ...       ...       ...      ...       ...  \n",
       "7075      0.377957  0.060128   -0.9999     0.170    0.738     0.092  \n",
       "7076      0.390221  0.080373   -0.9999     0.162    0.743     0.095  \n",
       "7077      0.397919  0.058824   -0.9999     0.156    0.767     0.077  \n",
       "7078      0.397084  0.065397   -0.9999     0.151    0.760     0.090  \n",
       "7079      0.382257  0.072504   -0.9999     0.141    0.786     0.073  \n",
       "\n",
       "[7080 rows x 8 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf['Compound'] = [sia.polarity_scores(v)['compound'] for v in ndf['News']]\n",
    "ndf['Negative'] = [sia.polarity_scores(v)['neg'] for v in ndf['News']]\n",
    "ndf['Neutral'] = [sia.polarity_scores(v)['neu'] for v in ndf['News']]\n",
    "ndf['Positive'] = [sia.polarity_scores(v)['pos'] for v in ndf['News']]\n",
    "ndf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge the Historical and Textual Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2015-12-14</td>\n",
       "      <td>24935.599609</td>\n",
       "      <td>25194.150391</td>\n",
       "      <td>24867.730469</td>\n",
       "      <td>25150.349609</td>\n",
       "      <td>11600.0</td>\n",
       "      <td>things men do that make women fall deeper i...</td>\n",
       "      <td>0.472105</td>\n",
       "      <td>0.100389</td>\n",
       "      <td>-0.9996</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.128</td>\n",
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       "      <td>2015-12-15</td>\n",
       "      <td>25186.679688</td>\n",
       "      <td>25342.779297</td>\n",
       "      <td>25075.539063</td>\n",
       "      <td>25320.439453</td>\n",
       "      <td>9700.0</td>\n",
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       "      <td>-0.9999</td>\n",
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       "      <td>-0.9997</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.115</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>25764.669922</td>\n",
       "      <td>25789.509766</td>\n",
       "      <td>25481.509766</td>\n",
       "      <td>25519.220703</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>Stop making THESE diet disasters  How to get s...</td>\n",
       "      <td>0.412045</td>\n",
       "      <td>0.071201</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.113</td>\n",
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       "      <th>1109</th>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>35679.738281</td>\n",
       "      <td>35706.550781</td>\n",
       "      <td>34794.929688</td>\n",
       "      <td>34868.980469</td>\n",
       "      <td>26600.0</td>\n",
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       "      <td>0.139</td>\n",
       "      <td>0.782</td>\n",
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       "      <td>34910.339844</td>\n",
       "      <td>35171.269531</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>Containment zone residents slam high prices ch...</td>\n",
       "      <td>0.377957</td>\n",
       "      <td>0.060128</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>34926.949219</td>\n",
       "      <td>35032.359375</td>\n",
       "      <td>34662.058594</td>\n",
       "      <td>34961.519531</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>hot and stylish bikini looks of Katrina Kaif...</td>\n",
       "      <td>0.397084</td>\n",
       "      <td>0.065397</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>35168.300781</td>\n",
       "      <td>35233.910156</td>\n",
       "      <td>34812.800781</td>\n",
       "      <td>34915.800781</td>\n",
       "      <td>18500.0</td>\n",
       "      <td>Detective Byomkesh Bakshy  Edge of Tomorrow Fi...</td>\n",
       "      <td>0.382257</td>\n",
       "      <td>0.072504</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1114 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date          Open          High           Low         Close  \\\n",
       "0    2015-12-14  24935.599609  25194.150391  24867.730469  25150.349609   \n",
       "1    2015-12-15  25186.679688  25342.779297  25075.539063  25320.439453   \n",
       "2    2015-12-16  25402.470703  25572.900391  25372.470703  25494.369141   \n",
       "3    2015-12-17  25596.630859  25831.310547  25448.320313  25803.779297   \n",
       "4    2015-12-18  25764.669922  25789.509766  25481.509766  25519.220703   \n",
       "...         ...           ...           ...           ...           ...   \n",
       "1109 2020-06-24  35679.738281  35706.550781  34794.929688  34868.980469   \n",
       "1110 2020-06-25  34525.390625  35081.609375  34499.781250  34842.101563   \n",
       "1111 2020-06-26  35144.781250  35254.878906  34910.339844  35171.269531   \n",
       "1112 2020-06-29  34926.949219  35032.359375  34662.058594  34961.519531   \n",
       "1113 2020-06-30  35168.300781  35233.910156  34812.800781  34915.800781   \n",
       "\n",
       "       Volume                                               News  \\\n",
       "0     11600.0     things men do that make women fall deeper i...   \n",
       "1      9700.0  Do looks matter at workplace  Common running m...   \n",
       "2     10800.0  In pics    foods that make you smarter Dos and...   \n",
       "3     22700.0    sex secrets every woman must know You too ca...   \n",
       "4     10400.0  Stop making THESE diet disasters  How to get s...   \n",
       "...       ...                                                ...   \n",
       "1109  26600.0  I never thought I had a voice until today  Vid...   \n",
       "1110  24600.0  Truck firms look for new export markets to sel...   \n",
       "1111  24800.0  Containment zone residents slam high prices ch...   \n",
       "1112  18300.0    hot and stylish bikini looks of Katrina Kaif...   \n",
       "1113  18500.0  Detective Byomkesh Bakshy  Edge of Tomorrow Fi...   \n",
       "\n",
       "      Subjectivity  Polarity  Compound  Negative  Neutral  Positive  \n",
       "0         0.472105  0.100389   -0.9996     0.153    0.719     0.128  \n",
       "1         0.421103  0.074733   -0.9999     0.149    0.745     0.106  \n",
       "2         0.434933  0.111113   -0.9724     0.107    0.786     0.107  \n",
       "3         0.389598  0.058890   -0.9997     0.150    0.735     0.115  \n",
       "4         0.412045  0.071201   -0.9999     0.161    0.726     0.113  \n",
       "...            ...       ...       ...       ...      ...       ...  \n",
       "1109      0.385245  0.087935   -0.9999     0.139    0.782     0.080  \n",
       "1110      0.398419  0.028698   -0.9999     0.145    0.789     0.066  \n",
       "1111      0.377957  0.060128   -0.9999     0.170    0.738     0.092  \n",
       "1112      0.397084  0.065397   -0.9999     0.151    0.760     0.090  \n",
       "1113      0.382257  0.072504   -0.9999     0.141    0.786     0.073  \n",
       "\n",
       "[1114 rows x 13 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge = pd.merge(hisdf, ndf, how='inner', on='Date')\n",
    "df_merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Dataset for Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Compound</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25150.349609</td>\n",
       "      <td>0.472105</td>\n",
       "      <td>0.100389</td>\n",
       "      <td>-0.9996</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25320.439453</td>\n",
       "      <td>0.421103</td>\n",
       "      <td>0.074733</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25494.369141</td>\n",
       "      <td>0.434933</td>\n",
       "      <td>0.111113</td>\n",
       "      <td>-0.9724</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25803.779297</td>\n",
       "      <td>0.389598</td>\n",
       "      <td>0.058890</td>\n",
       "      <td>-0.9997</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25519.220703</td>\n",
       "      <td>0.412045</td>\n",
       "      <td>0.071201</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>34868.980469</td>\n",
       "      <td>0.385245</td>\n",
       "      <td>0.087935</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>34842.101563</td>\n",
       "      <td>0.398419</td>\n",
       "      <td>0.028698</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>35171.269531</td>\n",
       "      <td>0.377957</td>\n",
       "      <td>0.060128</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>34961.519531</td>\n",
       "      <td>0.397084</td>\n",
       "      <td>0.065397</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>34915.800781</td>\n",
       "      <td>0.382257</td>\n",
       "      <td>0.072504</td>\n",
       "      <td>-0.9999</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1114 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Close  Subjectivity  Polarity  Compound  Negative  Neutral  \\\n",
       "0     25150.349609      0.472105  0.100389   -0.9996     0.153    0.719   \n",
       "1     25320.439453      0.421103  0.074733   -0.9999     0.149    0.745   \n",
       "2     25494.369141      0.434933  0.111113   -0.9724     0.107    0.786   \n",
       "3     25803.779297      0.389598  0.058890   -0.9997     0.150    0.735   \n",
       "4     25519.220703      0.412045  0.071201   -0.9999     0.161    0.726   \n",
       "...            ...           ...       ...       ...       ...      ...   \n",
       "1109  34868.980469      0.385245  0.087935   -0.9999     0.139    0.782   \n",
       "1110  34842.101563      0.398419  0.028698   -0.9999     0.145    0.789   \n",
       "1111  35171.269531      0.377957  0.060128   -0.9999     0.170    0.738   \n",
       "1112  34961.519531      0.397084  0.065397   -0.9999     0.151    0.760   \n",
       "1113  34915.800781      0.382257  0.072504   -0.9999     0.141    0.786   \n",
       "\n",
       "      Positive  \n",
       "0        0.128  \n",
       "1        0.106  \n",
       "2        0.107  \n",
       "3        0.115  \n",
       "4        0.113  \n",
       "...        ...  \n",
       "1109     0.080  \n",
       "1110     0.066  \n",
       "1111     0.092  \n",
       "1112     0.090  \n",
       "1113     0.073  \n",
       "\n",
       "[1114 rows x 7 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmerge1 = df_merge[['Close','Subjectivity', 'Polarity', 'Compound', 'Negative', 'Neutral', 'Positive']]\n",
    "dfmerge1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Compound</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.115707</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.804262</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.496063</td>\n",
       "      <td>0.131034</td>\n",
       "      <td>0.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.124658</td>\n",
       "      <td>0.637680</td>\n",
       "      <td>0.618084</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.464567</td>\n",
       "      <td>0.310345</td>\n",
       "      <td>0.581395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.133812</td>\n",
       "      <td>0.735928</td>\n",
       "      <td>0.882084</td>\n",
       "      <td>0.013803</td>\n",
       "      <td>0.133858</td>\n",
       "      <td>0.593103</td>\n",
       "      <td>0.593023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.150096</td>\n",
       "      <td>0.413875</td>\n",
       "      <td>0.503113</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.472441</td>\n",
       "      <td>0.241379</td>\n",
       "      <td>0.686047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.135120</td>\n",
       "      <td>0.573336</td>\n",
       "      <td>0.592454</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.559055</td>\n",
       "      <td>0.179310</td>\n",
       "      <td>0.662791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Close  Subjectivity  Polarity  Compound  Negative   Neutral  Positive\n",
       "0  0.115707      1.000000  0.804262  0.000200  0.496063  0.131034  0.837209\n",
       "1  0.124658      0.637680  0.618084  0.000050  0.464567  0.310345  0.581395\n",
       "2  0.133812      0.735928  0.882084  0.013803  0.133858  0.593103  0.593023\n",
       "3  0.150096      0.413875  0.503113  0.000150  0.472441  0.241379  0.686047\n",
       "4  0.135120      0.573336  0.592454  0.000050  0.559055  0.179310  0.662791"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "df = pd.DataFrame(scaler.fit_transform(dfmerge1))\n",
    "df.columns = dfmerge1.columns\n",
    "df.index = dfmerge1.index\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
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       "      <th>Negative</th>\n",
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       "      <td>0.131034</td>\n",
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       "      <td>0.310345</td>\n",
       "      <td>0.581395</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.735928</td>\n",
       "      <td>0.882084</td>\n",
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       "      <td>0.133858</td>\n",
       "      <td>0.593103</td>\n",
       "      <td>0.593023</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.413875</td>\n",
       "      <td>0.503113</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.472441</td>\n",
       "      <td>0.241379</td>\n",
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       "      <td>0.559055</td>\n",
       "      <td>0.179310</td>\n",
       "      <td>0.662791</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>0.382947</td>\n",
       "      <td>0.713887</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.385827</td>\n",
       "      <td>0.565517</td>\n",
       "      <td>0.279070</td>\n",
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       "      <th>1110</th>\n",
       "      <td>0.476536</td>\n",
       "      <td>0.284023</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.433071</td>\n",
       "      <td>0.613793</td>\n",
       "      <td>0.116279</td>\n",
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       "      <th>1111</th>\n",
       "      <td>0.331174</td>\n",
       "      <td>0.512097</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.629921</td>\n",
       "      <td>0.262069</td>\n",
       "      <td>0.418605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>0.467056</td>\n",
       "      <td>0.550338</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.480315</td>\n",
       "      <td>0.413793</td>\n",
       "      <td>0.395349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>0.361724</td>\n",
       "      <td>0.601906</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.401575</td>\n",
       "      <td>0.593103</td>\n",
       "      <td>0.197674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1114 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Subjectivity  Polarity  Compound  Negative   Neutral  Positive\n",
       "0         1.000000  0.804262  0.000200  0.496063  0.131034  0.837209\n",
       "1         0.637680  0.618084  0.000050  0.464567  0.310345  0.581395\n",
       "2         0.735928  0.882084  0.013803  0.133858  0.593103  0.593023\n",
       "3         0.413875  0.503113  0.000150  0.472441  0.241379  0.686047\n",
       "4         0.573336  0.592454  0.000050  0.559055  0.179310  0.662791\n",
       "...            ...       ...       ...       ...       ...       ...\n",
       "1109      0.382947  0.713887  0.000050  0.385827  0.565517  0.279070\n",
       "1110      0.476536  0.284023  0.000050  0.433071  0.613793  0.116279\n",
       "1111      0.331174  0.512097  0.000050  0.629921  0.262069  0.418605\n",
       "1112      0.467056  0.550338  0.000050  0.480315  0.413793  0.395349\n",
       "1113      0.361724  0.601906  0.000050  0.401575  0.593103  0.197674\n",
       "\n",
       "[1114 rows x 6 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df.drop('Close',axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.115707\n",
       "1       0.124658\n",
       "2       0.133812\n",
       "3       0.150096\n",
       "4       0.135120\n",
       "          ...   \n",
       "1109    0.627192\n",
       "1110    0.625777\n",
       "1111    0.643101\n",
       "1112    0.632062\n",
       "1113    0.629656\n",
       "Name: Close, Length: 1114, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df['Close']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Dataset into Train & Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 6)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state = 0)\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Compound</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>0.484416</td>\n",
       "      <td>0.739807</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>0.590551</td>\n",
       "      <td>0.144828</td>\n",
       "      <td>0.674419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>0.265810</td>\n",
       "      <td>0.484699</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>0.598425</td>\n",
       "      <td>0.241379</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>0.402516</td>\n",
       "      <td>0.605501</td>\n",
       "      <td>0.00015</td>\n",
       "      <td>0.440945</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>0.558140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>0.393804</td>\n",
       "      <td>0.604870</td>\n",
       "      <td>0.00015</td>\n",
       "      <td>0.377953</td>\n",
       "      <td>0.448276</td>\n",
       "      <td>0.476744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>0.484525</td>\n",
       "      <td>0.393420</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>0.307087</td>\n",
       "      <td>0.648276</td>\n",
       "      <td>0.244186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>0.362822</td>\n",
       "      <td>0.174151</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.677165</td>\n",
       "      <td>0.213793</td>\n",
       "      <td>0.430233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>0.421373</td>\n",
       "      <td>0.338042</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>0.566929</td>\n",
       "      <td>0.393103</td>\n",
       "      <td>0.302326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>0.298472</td>\n",
       "      <td>0.603939</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>0.535433</td>\n",
       "      <td>0.420690</td>\n",
       "      <td>0.302326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>0.356953</td>\n",
       "      <td>0.474171</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>0.566929</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.302326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>0.466131</td>\n",
       "      <td>0.428890</td>\n",
       "      <td>0.00010</td>\n",
       "      <td>0.433071</td>\n",
       "      <td>0.448276</td>\n",
       "      <td>0.395349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Subjectivity  Polarity  Compound  Negative   Neutral  Positive\n",
       "927       0.484416  0.739807   0.00005  0.590551  0.144828  0.674419\n",
       "876       0.265810  0.484699   0.00005  0.598425  0.241379  0.511628\n",
       "1050      0.402516  0.605501   0.00015  0.440945  0.344828  0.558140\n",
       "1081      0.393804  0.604870   0.00015  0.377953  0.448276  0.476744\n",
       "658       0.484525  0.393420   0.00005  0.307087  0.648276  0.244186\n",
       "622       0.362822  0.174151   0.00000  0.677165  0.213793  0.430233\n",
       "965       0.421373  0.338042   0.00005  0.566929  0.393103  0.302326\n",
       "818       0.298472  0.603939   0.00005  0.535433  0.420690  0.302326\n",
       "787       0.356953  0.474171   0.00005  0.566929  0.379310  0.302326\n",
       "920       0.466131  0.428890   0.00010  0.433071  0.448276  0.395349"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomForestRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor()\n",
    "rf.fit(x_train, y_train)\n",
    "prediction=rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.59204602 0.77743871 0.46716064 0.73144085 0.25611568 0.33254211\n",
      " 0.63953336 0.683319   0.59969559 0.26583055]\n",
      "1006    0.966906\n",
      "1109    0.627192\n",
      "187     0.299075\n",
      "896     0.723549\n",
      "413     0.465435\n",
      "501     0.578316\n",
      "546     0.591144\n",
      "881     0.839169\n",
      "959     0.903342\n",
      "268     0.228219\n",
      "Name: Close, dtype: float64\n",
      "Mean Squared error:  0.05257968397499098\n"
     ]
    }
   ],
   "source": [
    "print(prediction[:10])\n",
    "print(y_test[:10])\n",
    "print('Mean Squared error: ',mean_squared_error(prediction,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DecisionTreeRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtr = DecisionTreeRegressor()\n",
    "dtr.fit(x_train, y_train)\n",
    "predictions = dtr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.46121848 0.98284344 0.69232194 0.71547783 0.19176137 0.28527224\n",
      " 0.88757586 0.69698498 0.23569794 0.11570669]\n",
      "1006    0.966906\n",
      "1109    0.627192\n",
      "187     0.299075\n",
      "896     0.723549\n",
      "413     0.465435\n",
      "501     0.578316\n",
      "546     0.591144\n",
      "881     0.839169\n",
      "959     0.903342\n",
      "268     0.228219\n",
      "Name: Close, dtype: float64\n",
      "Mean Squared error:  0.10831900809236311\n"
     ]
    }
   ],
   "source": [
    "print(predictions[:10])\n",
    "print(y_test[:10])\n",
    "print('Mean Squared error:',mean_squared_error(predictions,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoostRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostRegressor()"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adb = AdaBoostRegressor()\n",
    "adb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05492347045438241\n"
     ]
    }
   ],
   "source": [
    "predictions = adb.predict(x_test)\n",
    "print(mean_squared_error(predictions, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LGBMRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm = lightgbm.LGBMRegressor()\n",
    "gbm.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0583079056070462\n"
     ]
    }
   ],
   "source": [
    "predictions = gbm.predict(x_test)\n",
    "print(mean_squared_error(predictions, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=4, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = xgboost.XGBRegressor()\n",
    "xgb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05968830860645931\n"
     ]
    }
   ],
   "source": [
    "predictions = xgb.predict(x_test)\n",
    "print(mean_squared_error(predictions, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- RandomForest = 0.05257968397499098\n",
    "- DecisionTree = 0.10831900809236311\n",
    "- AdaBoost = 0.05492347045438241\n",
    "- LightGBM = 0.0583079056070462\n",
    "- XGBoost = 0.05968830860645931\n",
    "\n",
    "\n",
    "From here we can see that RandomForestRegressor shows a better performance than the others"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thank you"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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