Live_Stock_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f91eb4e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#yahoo finance as data source\n",
    "#pip install yfinance\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ee00f18c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#See the yahoo finance ticker for your stock symbol\n",
    "stock_symbol = 'GAIL.NS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d365868a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "#last 5 years data with interval of 1 day\n",
    "data = yf.download(tickers=stock_symbol,period='5y',interval='1d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f82e205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbbe3e6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>105.989113</td>\n",
       "      <td>107.648491</td>\n",
       "      <td>105.215675</td>\n",
       "      <td>106.860992</td>\n",
       "      <td>90.179657</td>\n",
       "      <td>3554636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
       "      <td>106.973488</td>\n",
       "      <td>107.634430</td>\n",
       "      <td>105.567238</td>\n",
       "      <td>106.003181</td>\n",
       "      <td>89.455750</td>\n",
       "      <td>3153665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>105.750053</td>\n",
       "      <td>108.829742</td>\n",
       "      <td>105.342239</td>\n",
       "      <td>108.351616</td>\n",
       "      <td>91.437592</td>\n",
       "      <td>4824690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>109.476616</td>\n",
       "      <td>109.701614</td>\n",
       "      <td>107.381302</td>\n",
       "      <td>108.323494</td>\n",
       "      <td>91.413857</td>\n",
       "      <td>7515022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>109.125053</td>\n",
       "      <td>111.614120</td>\n",
       "      <td>108.562553</td>\n",
       "      <td>111.192245</td>\n",
       "      <td>93.834793</td>\n",
       "      <td>8320938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close  Adj Close   Volume\n",
       "Date                                                                          \n",
       "2016-06-27  105.989113  107.648491  105.215675  106.860992  90.179657  3554636\n",
       "2016-06-28  106.973488  107.634430  105.567238  106.003181  89.455750  3153665\n",
       "2016-06-29  105.750053  108.829742  105.342239  108.351616  91.437592  4824690\n",
       "2016-06-30  109.476616  109.701614  107.381302  108.323494  91.413857  7515022\n",
       "2016-07-01  109.125053  111.614120  108.562553  111.192245  93.834793  8320938"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d445949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1232"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf283ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\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>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-21</th>\n",
       "      <td>153.000000</td>\n",
       "      <td>156.149994</td>\n",
       "      <td>151.250000</td>\n",
       "      <td>155.250000</td>\n",
       "      <td>155.250000</td>\n",
       "      <td>13488792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>158.699997</td>\n",
       "      <td>154.899994</td>\n",
       "      <td>155.300003</td>\n",
       "      <td>155.300003</td>\n",
       "      <td>7862363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-23</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>152.699997</td>\n",
       "      <td>153.350006</td>\n",
       "      <td>153.350006</td>\n",
       "      <td>8190583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-24</th>\n",
       "      <td>153.899994</td>\n",
       "      <td>154.949997</td>\n",
       "      <td>151.750000</td>\n",
       "      <td>152.250000</td>\n",
       "      <td>152.250000</td>\n",
       "      <td>8171227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-25</th>\n",
       "      <td>153.000000</td>\n",
       "      <td>154.699997</td>\n",
       "      <td>152.250000</td>\n",
       "      <td>153.100006</td>\n",
       "      <td>153.100006</td>\n",
       "      <td>6551074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2021-06-21  153.000000  156.149994  151.250000  155.250000  155.250000   \n",
       "2021-06-22  157.000000  158.699997  154.899994  155.300003  155.300003   \n",
       "2021-06-23  157.000000  157.000000  152.699997  153.350006  153.350006   \n",
       "2021-06-24  153.899994  154.949997  151.750000  152.250000  152.250000   \n",
       "2021-06-25  153.000000  154.699997  152.250000  153.100006  153.100006   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2021-06-21  13488792  \n",
       "2021-06-22   7862363  \n",
       "2021-06-23   8190583  \n",
       "2021-06-24   8171227  \n",
       "2021-06-25   6551074  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6292e20a",
   "metadata": {},
   "outputs": [],
   "source": [
    "opn = data[['Open']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "21e132d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 10,
     "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": [
    "opn.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5194440",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8038cb6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = opn.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69b08672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[105.98911285],\n",
       "       [106.97348785],\n",
       "       [105.75005341],\n",
       "       ...,\n",
       "       [157.        ],\n",
       "       [153.8999939 ],\n",
       "       [153.        ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af022e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcbb40731d0>]"
      ]
     },
     "execution_count": 14,
     "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(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0e73b789",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e6c1c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf6a3e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using MinMaxScaler for normalizing data between 0 & 1\n",
    "normalizer = MinMaxScaler(feature_range=(0,1))\n",
    "ds_scaled = normalizer.fit_transform(np.array(ds).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7dbea1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1232, 1232)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ds_scaled), len(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a20c5654",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining test and train data sizes\n",
    "train_size = int(len(ds_scaled)*0.70)\n",
    "test_size = len(ds_scaled) - train_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b964aa4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(862, 370)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_size,test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "25ca615e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting data between train and test\n",
    "ds_train, ds_test = ds_scaled[0:train_size,:], ds_scaled[train_size:len(ds_scaled),:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "95913f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(862, 370)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ds_train),len(ds_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5dd0b45e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating dataset in time series for LSTM model \n",
    "#X[100,120,140,160,180] : Y[200]\n",
    "def create_ds(dataset,step):\n",
    "    Xtrain, Ytrain = [], []\n",
    "    for i in range(len(dataset)-step-1):\n",
    "        a = dataset[i:(i+step), 0]\n",
    "        Xtrain.append(a)\n",
    "        Ytrain.append(dataset[i + step, 0])\n",
    "    return np.array(Xtrain), np.array(Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d28b7d1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking 100 days price as one record for training\n",
    "time_stamp = 100\n",
    "X_train, y_train = create_ds(ds_train,time_stamp)\n",
    "X_test, y_test = create_ds(ds_test,time_stamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "03ce9096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((761, 100), (761,))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "84acdd72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((269, 100), (269,))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c657d193",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshaping data to fit into LSTM model\n",
    "X_train = X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
    "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "82844671",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b5250913",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 100, 50)           10400     \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 100, 50)           20200     \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Creating LSTM model using keras\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=50,return_sequences=True,input_shape=(X_train.shape[1],1)))\n",
    "model.add(LSTM(units=50,return_sequences=True))\n",
    "model.add(LSTM(units=50))\n",
    "model.add(Dense(units=1,activation='linear'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7e8ff062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 761 samples, validate on 269 samples\n",
      "Epoch 1/100\n",
      "761/761 [==============================] - 13s 17ms/step - loss: 0.2048 - val_loss: 0.0253\n",
      "Epoch 2/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0197 - val_loss: 0.0112\n",
      "Epoch 3/100\n",
      "761/761 [==============================] - 10s 14ms/step - loss: 0.0088 - val_loss: 0.0101\n",
      "Epoch 4/100\n",
      "761/761 [==============================] - 11s 14ms/step - loss: 0.0065 - val_loss: 0.0134\n",
      "Epoch 5/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0051 - val_loss: 0.0083\n",
      "Epoch 6/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0044 - val_loss: 0.0071\n",
      "Epoch 7/100\n",
      "761/761 [==============================] - 10s 14ms/step - loss: 0.0039 - val_loss: 0.0065\n",
      "Epoch 8/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0034 - val_loss: 0.0053\n",
      "Epoch 9/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0031 - val_loss: 0.0047\n",
      "Epoch 10/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0030 - val_loss: 0.0044\n",
      "Epoch 11/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0029 - val_loss: 0.0042\n",
      "Epoch 12/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0028 - val_loss: 0.0042\n",
      "Epoch 13/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0028 - val_loss: 0.0043\n",
      "Epoch 14/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0028 - val_loss: 0.0041\n",
      "Epoch 15/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0027 - val_loss: 0.0043\n",
      "Epoch 16/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0028 - val_loss: 0.0042\n",
      "Epoch 17/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0026 - val_loss: 0.0041\n",
      "Epoch 18/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0027 - val_loss: 0.0041\n",
      "Epoch 19/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0027 - val_loss: 0.0039\n",
      "Epoch 20/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0026 - val_loss: 0.0038\n",
      "Epoch 21/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0026 - val_loss: 0.0038\n",
      "Epoch 22/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0025 - val_loss: 0.0037\n",
      "Epoch 23/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0024 - val_loss: 0.0037\n",
      "Epoch 24/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0024 - val_loss: 0.0036\n",
      "Epoch 25/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0024 - val_loss: 0.0037\n",
      "Epoch 26/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0023 - val_loss: 0.0036\n",
      "Epoch 27/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0024 - val_loss: 0.0035\n",
      "Epoch 28/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0024 - val_loss: 0.0036\n",
      "Epoch 29/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0023 - val_loss: 0.0035\n",
      "Epoch 30/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0022 - val_loss: 0.0034\n",
      "Epoch 31/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0022 - val_loss: 0.0034\n",
      "Epoch 32/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0021 - val_loss: 0.0033\n",
      "Epoch 33/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0021 - val_loss: 0.0033\n",
      "Epoch 34/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0020 - val_loss: 0.0033\n",
      "Epoch 35/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0021 - val_loss: 0.0032\n",
      "Epoch 36/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0022 - val_loss: 0.0034\n",
      "Epoch 37/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0023 - val_loss: 0.0032\n",
      "Epoch 38/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0020 - val_loss: 0.0032\n",
      "Epoch 39/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0020 - val_loss: 0.0031\n",
      "Epoch 40/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0020 - val_loss: 0.0031\n",
      "Epoch 41/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0019 - val_loss: 0.0030\n",
      "Epoch 42/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0019 - val_loss: 0.0030\n",
      "Epoch 43/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0019 - val_loss: 0.0030\n",
      "Epoch 44/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0022 - val_loss: 0.0031\n",
      "Epoch 45/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0023 - val_loss: 0.0030\n",
      "Epoch 46/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0021 - val_loss: 0.0030\n",
      "Epoch 47/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0021 - val_loss: 0.0030\n",
      "Epoch 48/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0019 - val_loss: 0.0029\n",
      "Epoch 49/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0019 - val_loss: 0.0028\n",
      "Epoch 50/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0018 - val_loss: 0.0028\n",
      "Epoch 51/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0018 - val_loss: 0.0028\n",
      "Epoch 52/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0018 - val_loss: 0.0028\n",
      "Epoch 53/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0018 - val_loss: 0.0027\n",
      "Epoch 54/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0018 - val_loss: 0.0027\n",
      "Epoch 55/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0017 - val_loss: 0.0027\n",
      "Epoch 56/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0019 - val_loss: 0.0027\n",
      "Epoch 57/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0018 - val_loss: 0.0026\n",
      "Epoch 58/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0017 - val_loss: 0.0027\n",
      "Epoch 59/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0017 - val_loss: 0.0026\n",
      "Epoch 60/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0019 - val_loss: 0.0027\n",
      "Epoch 61/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0018 - val_loss: 0.0028\n",
      "Epoch 62/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0019 - val_loss: 0.0027\n",
      "Epoch 63/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0018 - val_loss: 0.0025\n",
      "Epoch 64/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0016 - val_loss: 0.0024\n",
      "Epoch 65/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0015 - val_loss: 0.0024\n",
      "Epoch 66/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0016 - val_loss: 0.0024\n",
      "Epoch 67/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0016 - val_loss: 0.0023\n",
      "Epoch 68/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0015 - val_loss: 0.0023\n",
      "Epoch 69/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0015 - val_loss: 0.0023\n",
      "Epoch 70/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0015 - val_loss: 0.0022\n",
      "Epoch 71/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0015 - val_loss: 0.0022\n",
      "Epoch 72/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0015 - val_loss: 0.0022\n",
      "Epoch 73/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0017 - val_loss: 0.0023\n",
      "Epoch 74/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0015 - val_loss: 0.0021\n",
      "Epoch 75/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0017 - val_loss: 0.0021\n",
      "Epoch 76/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0014 - val_loss: 0.0021\n",
      "Epoch 77/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0014 - val_loss: 0.0020\n",
      "Epoch 78/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0014 - val_loss: 0.0020\n",
      "Epoch 79/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0014 - val_loss: 0.0020\n",
      "Epoch 80/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0013 - val_loss: 0.0019\n",
      "Epoch 81/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0015 - val_loss: 0.0019\n",
      "Epoch 82/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0014 - val_loss: 0.0019\n",
      "Epoch 83/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0014 - val_loss: 0.0019\n",
      "Epoch 84/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0014 - val_loss: 0.0018\n",
      "Epoch 85/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0012 - val_loss: 0.0018\n",
      "Epoch 86/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0012 - val_loss: 0.0018\n",
      "Epoch 87/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0013 - val_loss: 0.0018\n",
      "Epoch 88/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0013 - val_loss: 0.0017\n",
      "Epoch 89/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0013 - val_loss: 0.0017\n",
      "Epoch 90/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0013 - val_loss: 0.0017\n",
      "Epoch 91/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0014 - val_loss: 0.0017\n",
      "Epoch 92/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0013 - val_loss: 0.0017\n",
      "Epoch 93/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0012 - val_loss: 0.0016\n",
      "Epoch 94/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0011 - val_loss: 0.0016\n",
      "Epoch 95/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0013 - val_loss: 0.0016\n",
      "Epoch 96/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0012 - val_loss: 0.0016\n",
      "Epoch 97/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0011 - val_loss: 0.0016\n",
      "Epoch 98/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0012 - val_loss: 0.0017\n",
      "Epoch 99/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0012 - val_loss: 0.0017\n",
      "Epoch 100/100\n",
      "761/761 [==============================] - 10s 13ms/step - loss: 0.0012 - val_loss: 0.0016\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7fcbc4faa690>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training model with adam optimizer and mean squared error loss function\n",
    "model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=100,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "18b1ba6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcb509cf790>]"
      ]
     },
     "execution_count": 38,
     "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": [
    "#PLotting loss, it shows that loss has decreased significantly and model trained well\n",
    "loss = model.history.history['loss']\n",
    "plt.plot(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3e7f2fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicitng on train and test data\n",
    "train_predict = model.predict(X_train)\n",
    "test_predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c2d6c2d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Inverse transform to get actual value\n",
    "train_predict = normalizer.inverse_transform(train_predict)\n",
    "test_predict = normalizer.inverse_transform(test_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9d0543ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcbc5653b50>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lvKibVV3K1Es17H/tQDuliaPZGKOu/lS5aGx2eOPW6PrJx3S/78TD1as7J1pAG2DfaihbAMCjy9XxX9tYgz8YxuWw8c9llZTmevinITwJLXeLWybTZafNF6SEZmoooDgQiomR7xVnXCiizxBB03JHgq+ZzKbNXGd/Crd3TOz2R34TXvpRt/lsrGMGB4mtbLarpxq3w06ru5SizkoAAnUqTcMWSyK0UtEEgEf4Ew6WWuku8sW8ST6zZh93vbyZF751NLPL1IDvK592dRNpNANFJw5LBcyIGBlSrpVwnE+3O7eMFbcROZJZpPbftxoOuQQAfyg62Le3qZOCTCc/eTo29C+R5V6YHbW2czOc1LT6KBVNbJHjKe8M9k3c3TmJ2yPiDjx5JadveZnTndC+xRKENXZBNOSyfltCcTejfbLoZKqtitc4Tp3WYaPDIu7Tm97hZoeHTU03RPYtNSx3D35aewiZhO4rOpnFxTdXq5vG6l1NzC7L7X4SmEYzQHT6gVTA26wKSAPsXanyxQCMO1i9xsezJ8IUz9zxar+d70VcGC2d0ZvF5/+yLKFAJRpQPXF2NBonx+NAEGaMaKBG5vPYR7u77tATzozu+21O0NrycqTZVbNWLZx+F1z8NOQa0ylaEw/xmE8eM4XqV03mdHV4h41OT2xU0cWOV5i57+nI+4lCpT7w4I+5ESaiyRDx286LrWUbP46a5bbT7guy+OevxrTrmHfNYKHFPRXwNkPxdMiboMS9sRLsLph3fnR9b5gCmVcOs86Eus2R6Jv9lvwwdW0+9jR0dNk90ZR+q2XustuYKvaRKzpZJyfzm1c309DuT/oSrTGOwYKp0XZPXsInE3udMX6w4Asqnj/HFPfEKRZMcZ9tU7l2djkrAHA77bgL1FNAiKibaXrrcgBy6KBQKGvbgx9/sGfxrW1VlnhhL7Vc69v8CUMmu8lIrNH0GS3uIx0plXh78mD8Ilj/JKz4u4qAmWFkhUiUeiAeM0dLRoEqSA2wU80ojU/+9YV7Pui6fzik/gB8bbDx+ZiYcKfdRrlQ6Q22h5XQnv3Hd5O6xHgaMq01V+2QG814ucj7F4LShq11nyoEYj6RmDeAbvLXm26Z2WInLTKTl/cq8XXZbWRlq2NUy+jAdLlfTXSaaETWhDwFZAg/gVDPPvfzjMHUvAwnXzx8In/9SpehKQBufvZTggmUPBju+fgaTbJocR/pBL2qgIUnD8YvVm2+FlWqrngaXPYCnHRL78eZYAysli1U/umsUqh8D1DifuLsMT3sDPz9NHjo82p57WPw6EWw5uHIahshSoyBx9JxKomXWSkpGUx3BkBrTjRlwesbq/FnjUO6cujInUIDuTRixJtnl0YtfptdTXjytSQ8vs0GNsIca1vLyvB0QO3ndtoIzf8CVbKQ6/zf5InQ0Twa/AzjZDUuAkwW6kkgUDTLsNy7F1+rSyU/08lt583nlLljucDIfxOPNb7ddHt1V0xEo+krWtxHOqbLxZ0Lh18N5cZMzExj0HDSkeDJTbyvleknwRWvwaFXKEGccBjsW00wFMYbCLOgPI8LD43NvX75UmVBF9ACuz+Eba+pJwkzR83uD7kv68/c4/wV9+05m5NtKwE49pBYf3My/PCJTyLLLfmzon14YAUzfvISJwV+xZvHPQFAozSs9ey4G5I7F7yJxd1hszFP7GCCrZanQ9GZq26HjczSySzx/ZFVcgbfC3yd98PzsAvJKUW1/NH1BwBCxTNx07Pl7rMIf15GdAJXdwPLpgsHojlzespxo9H0BS3uIx1T3D154HDBFx5SPvMTf9r3Y5UvVhYuQNFUaNqJP6CsR5fDRklONPrF7bBFXASLbFuix/C1QMCwyFc9yAmhdznZvhInAY6zraZZZnLeoVGfeTIDhCt3NvDi+qivvD5zGi+FFnND4KuRtq2d2TT6Vd8bMcU9Lr2CJzcaQhmHzQZzbCqsc4WcEWlXNWFjxXeVVIOtV4XUk0kgfypkleAWQZ5c0X1M+to90XOPzY1OykpU1xVgxc6oCynT2EZb7prBQov7SMcq7gA5Y+DCh2L80P2iYDKE/Ij3f8da91cp9u+NGTQNhMIRF0SZsCQFbd0fzWcTh0OEqZX5Mcm9NlW38tTqPT125cf/jQ27/LDOzVWB7/Jw6ISYdjPB1pyphk8+Xtx7sNztQjBe1BGUNvbJaKhkrsfZJW/OHlnCrnAJ87yrQNhwXv0mLk8WAOt21XR7Hc8bE59+d+FBMcd02BL/zH7/WvSmad5gtM9dM1hocR/pRMQ9f3CPW6gE0v3B78gVnVQ0vBcT7hiWcMrcsQCU2S3WcOv+aIEQk1ln0iyUayinJNa//H9PfMJ3HlvD+n3dR/TMGqss8Sv832PL7GvZ05HY0r3zpU0AZOcYbqicuGzSntxuM1/abYJxoo79FBK2fO3js0yavBNWE7yQYfDk4nSrSVaHlseGbHb6Q5GnkxyPEugzF8TeeM9Y0HvW6wIjukZb7prBQov7SOXjh+EPi6HNsBQTZX0cAHd+pKxgm1+F+RW2b+PyoyYzLi/qTjhuVimVt5/BtYdafPqtVbGW+8FfgQsfokmo/tmNePN7L1aDv2v3qG339jC4avqxXw0fwuopX4/xXSdCGIOhFE2NXeHOVW6joD96AzKEVwjBeFHPPlnE6fPH9nh8gD8Gz1ULdiOk0YjDD3ijYaI1LV5m3/Qi/3i/ElBPFgWZzi6zeePDIn9/0cFdzucykpwFtc897TnklldintqGCi3uI5X/fl1NVtqrBimTGjTtA3ev9uKXUas1t3MXuR4nj121JLpR634V7thWA3lGGbv22ljLPU8NwrYLI31AjhrkzM9UA4qmy70pLieLlJKHPtzJb1/djD8YjtxUWrwBfMGeJwpx9PfgkEthaqzbBo/hlnnj53DHJNizAv5fvgrbBMZRR5UsYu64PJ775lE88rUjuhz6wcsP4/xDyqmikJfHXglf+Jda4VDiHvRFxb3KKA7+mFE4pKHDT0Fm1/h26+AqwKTCrlWfTI9YovBITfoQDIWpb/fz61c2D/m5tLiPdCLiPriWexgbdUSPmd2mBgonFGYiCJNLO/xqJjx9rRL3oqlgc6pSfd4mFVI54zSY9zkAnFKJt8yfBEB+nKA1xxWkeOjDXdz41Dp+++oWAqEw+YYotvmCeANdLfcYY7hkBpz1u67x/Wbe9/d+p96/fZd6XfMIhMOMFQ3sk0UEQmHmjstjydRo5sqT5oxh0cR8jplRYhTVFnww/jKYcYrawKluPjIQFXevUV2p068mI+1r6kxYscppt3HtcVMjAl5e0HU27hFTVF9C2uee1vRpYt8A0eI+ErHOOK36WLkG4lPiArtbd3PhsxeybF/fam+afl1TLzeFy/F07ldJyV68gbdc32G+zahWtOZhZa1nlaicLR11ynIfvxi++KiKtQfqnMrVIYtVGGNeZqy4722KdctYi0EHQhKP00aWy06rN5jQci8y8th8+8Tp3V9YTpkqym2y/Q316nBDWzUuEWKfLEro9rn34sU8eY0KkTRdJErkDQzL3RHyEg5LQmEZqR/b7g8RDIXZ3dDB2NzEaRR+cMosVv34JB698ogubprlN5zA9FIVu68t9/SmwZjP4bIPvfRqcR+JNFbGvvfkdS1BBKyuWc36+vX8cfUf+3R408f9cPB4AB4LHWecdwd88Ccm2mo5y2a5YbTsU8KeWQzt9ermE2c1T7v4j3w063pK5ytXSbwr4tMqFcWyp7GD+97dgdW1bGaizPE4afMG8SWw3CcargxHoiQ3JvERRGbpvM4mlW4B2CHLes3saCZJc1vF3fC5O8J+DrvtNabe8DwfbFfx/p3+EJ//6zLq2vxkdxP2CGrQ9IgpRQghuPTIiki722GP+Om1zz29MZ9KuwmgGlR0VsiRSLtR0MGZBYH2aEbHOGo61GDr5sbNSCkT5n9JhCnufwqdy3+dp5MXNqonVUbTBZxvfzu6QzigsklmFkLTTpWdMs5NVDJ+KiUX3hh573bEityOunYAfvr0el7bWBMTB97uD1KY5SLb46DVF8AbDOFy2PAHw5w0ZwxnLRzHu1tqWbmzEXtPvwozrQIoSztoPC14myK57DeFy6noxadvPjnkWm9QhlvGKX2RTI73vavSAwfDYVbvalLr+2GRuZ22yH46Wia98RmuvAORH05b7iORDsNlMckY3OzG376/XU388Ya87Gvfl/ThTeswjI3dXg+V0ogeWft4ZBuHCEOuJawxs0hZ72aFpD6GZta2+mj1BthnDEJak5W1+YK47DZyPA7llgmEOWfhOH5+7jzuOn8hZy8cF4mQcdp7uIF5cuGz98LCi2DqcdH2zibYt5pGmU0t+b1a7ub/x53ALZNBV5+pNZwyxpXTA9Z93A5b1HLXPve0xt9LbqLBRIv7SMSc3j/FEChXVsLNqtujRR42NmxMuE0i4qfQt5FJOLMY9q5Qg6ZmKuGpn4lulFmkImNChrglk6wsjvk/e5kNVV0nGe2s78Bpt5HtNsQ9GCbTZefLR0yK+O5Ngz1R0ZAYFlwA5/0Fjv8xHHYlzDxDWe7b3+K98DxA9BpqmeVWD7QxWRsNy92TQNxDFldKsuJ+3fHR/DlCiIi7Sbtl0hvTsOjtOzgYaHEfiXTUq5JxCy9Uroal30q4WXVHNYeNPQybsPVJ3BNZD9KstzruYCiZrZaNyBdAifv4RdH3fbDcS3PcvW7jctjI9TiNaJlQgslFSvx6FXeTMXPh9DtVquTOJmivYZdUM1p7s9zPOUj57o+abin6YVSK8oiu4m79fybrlsl2O/joxhP599Xq6cy8Lu2WSW+sov6RJahgKNDiPhLpqIeMQuUGufhplfQrAdUd1UzKncTEnIlsaUx+UoTpK7ZiyzeShk08ApZ+E8Yfotwbx1wPwq5SBk+xuDqSsNzvv1RNZDprYe+pErLcdrLdDrbWtOELhmNdIkTHk5MdV4jpZzgA4SCfOXgO0HtyroUT8qm8/QzmjrO4wxym5R5bOWlheV6/H7VLctwcWlGoDm9OYtLintZYDYt/fTC0tXP1gOpIpKNeDV72gDfopcHbwJjMMYzJGkNdZ13Sh//7e5Vd2sTss6B6ncoaWTAJvva6WnHcDXDM97sWsM4s7nKMeI6bWcqjVx7BYRWFCW8oVjKcDsKWUSZ3nOUekfS+jkRZnjBmTJ3CF20Tue64ad1v3x1GtIyH2Hj9HI8zpkvhfoqzQ/vcRwWmuOd4HENeYlFb7iORzsZe66LuaFZiWZFXQaGnkPrO+j6f5qpjpkTfzD0Xrv1QCbsVIWKF3WWW6+vdGhdCcMSUoi6JuUwWlOeR5VIinuW2U2+Z4BFvuZvi1+eUuJYnDEdOKbedN59x+d2U9OsJu4swti5umWx3rH3k7W12bXeH1z73tGT1rkaeXRsNdli7twmA2WNzIzOchwot7iMRb3OvPu29bXsBmJgzkSJPEQ3e5P13ppAcO7OEd64/jrd+8Jnk+/aNFXD1uwnj7nviF59VOd5ddhu3njcPUPniv2bcYMJSxgxgxlvuHuMm0GfxtP4fs0r6tq8VIQja3F0GVM1kYSb7m/tnjTns2ueejpz35/e57uHVAKzc2ci/PlBlHicUZlLXOsyWuxDifiFEjRBiXVz7N4QQG4UQ64UQv7S0/0gIsVUIsUkIccpQdHrY+PAeWH7vkB3+2Dvf4LbnN6gBwF582qaYF2cUU5RRREewg85gcpWPppZkccrcMRw5tZgJhZlMKkocjZOQnLEwtu/FODxO9VU7flYpXzp8EpW3n8G5B49nQoEaqAyGJDON7JDQ1XLPMMTem6B4d49Y/48DEXcgaHOTQc8/yG8c3w+XD9G0wNrnnp40dfjZVtMWeT+pKJMWb5DGIUxHkIzl/gBwqrVBCHEccA6wUEo5F7jLaJ8DXAjMNfb5sxCi+yl7qYSU8MIP4PnvR4tVDDI76zu45+3tSLNmag/Ue5UbJt+TT6FH+eeTtd43V7d1mWQ01Ljs6nzxBv85B43jeyfN4JIjK/juSTPIMdwc8dEyprh3BgZguWcWdbtZUofKzOLgcR5+eNosPr7pJG48fTaHTo6Ojfz5S4uoKO7DjdKCQ0fLpDWt3iA+y8D7oonK7bpqV+Kav4NBr+IupXwbiFeNrwO3Syl9xjZmBYNzgEellD4p5Q5gK3DYIPZ3+Oiw+LR3vj/ohzdjzwXhpNwyDZ0N5LnzcNqclGaqEL+qtqpez2PmdHlmTfKTngaDNl+04pMVh93GN06Yzrj8DJx2GwsmqJtavOU+pUTlXinL66O/3Do2YB9Y/IDNmcncEhdXHzuV/EwXXztmSozPPd7/3hfskTEFPaCajgRCYYKWz9bMLzSUn3d/fe4zgKOFEB8KId4SQhiFPRkP7LZst8do64IQ4kohxAohxIra2tp+duMA0mCJ9jATUg0iLUZK3Bw6EcheLfcGb0PEYp+Sp/zW25u393qe4TIMj5pewoLyPL570owetzODReLF/aQ5Y3joisNjcrIkhcOtqk5NWtr7tr3hygJfW0xTlkXQswYg7qbP/eHlu/p9DM3IJRCSESE/aEJ+ZKb1UNbM7e+30QEUAkcAhwKPCyGm9LxLLFLKe4B7ABYvXjzyn0XNZF7OTNi/rsdN+0OzIe65wkgpm8Dn/s6ed3hrz1vcePiNMeJellXGVQuuYm7R3B7P8fV/rSTXo2Z8/uysOYPX+SQYn5/BM9cd1et2EvVVSJQ1b+m03sMvE3LtcjUpbKBkFqmsmLuXqyerkhmDbrmbOWo06UUgFI4I+UNXHE6NMZg6lKGv/f027gGelKq+2HIhRBgoBvYCEyzblRttqU+jYblPP0kVgRhkzDDAXFSCrUSW+zWvXQPAVQuuosHbwNR8NatUCMF1B1/X6zleWBctQj19TE4PWw4fkSeLPs5V6hFH1wIa/SKrRH32950ENgfcVB8j6AVxaY77dGjXwFxGmpGNtSZxpsve/9DePtBfc+a/wHEAQogZgAuoA54BLhRCuIUQk4HpwPJB6Ofw01gJOeNg7AJo2RtN7jVI1LSoO3nEcu/B517ZUhljuSeDjJv8k/Q0/gOM2U9bX2eiHghyy8Bn5NoPB6FhO1mWFL+luV1z7idLltvBIZMKOKwi+c9UkzoEwxJ/KIzTLhBCRNJUDOW8hmRCIR8BlgEzhRB7hBBfBe4HphjhkY8Cl0jFeuBx4FPgReBaKWX/ZnWMNBp2QEEFTD9ZvV9x/6Aevralk+87HuNMI496wBmb5tcqzvvb99Pka6LIk3z0R3yioh7zog8j5mWOSHEfZ+TWyTWGkT55ghx3/631eLLdDnyhMJ3+EPVtPlq8gUi1J01qEwiGCQTDEVE3x1hueOqTmIHWwaTXZ0Ep5UXdrPpyN9vfCtw6kE6NSBorVRrZsgUw9XhY+YCq5TlIIrR69XJ+53g68n5DIyywZNz1hqKz2bY2qbS7fbHc40VipFruS6YWsWJnI2Nye082dsCZfRZc+AiUHwqPXwxv/Jzs0sEbu3A7bPgCIa59eBWvb1QBaAvL83g6ibEKzcgmEFYDqma0mNNSl6DVG6Qga5Bchxb0DNVkCHRC6z5luQPMPB2ad3etmNRPttW20Vy1Labtyw+pLI91bT6qW7y0B9oj68wkYYUZyYt7R9zkH8eBKAXTD7594gze/P5n+jax6kAhBMw6HbJL4AxVn9X+3m8H7fBup52Gdn9E2AHW7GnuYQ9NqhAIhvGHZBfLHVQ9g6FAj+IkQ7MxJpw/Ub1OPEK97lul/rzNsPjyfh/eHwxTJmJzw7ShZm4efttrhMKSt2+Izgrd1LgJ6JvlHj/5Z6Ra7nab6PdEoAPKmLlw8q3w8o1MFNXskmMGfMhOfzASRWElcQpkzXDy8Ie7cDlsnH9Iee8bA1c8qIIwigwLXYv7SKHVmByUU2a8GhNj2uvghevV8sFfAXv//K+JxD2MjVZvIDJjsc0ftdzN8noTcyYmdfxwWPL712JTAjt6qmikSY7JRwNw40Fe7PMXD/hwdW2Jp6Lf/sJGfnZ2z2GumgPLDU99ApC0uJuYUXFWt0z7EIn7yHw2H2m0GRWPcoxydBkFKm7a6pZp6H0CUXf4Q2HGi64pe6stpejqOlUFI7uRzSHHmUNxRnJx3xv2t/D0x7EzUkfkgGWqUTQdgFPK2jlxzsAt99lliWvlfrqva/UqTWqQ0c0TlzVTamNHIOE2A0WLezJELHdD3G02VUyjak10m7rN/T68PxhmutiLNzvWEremBG3qVDMjy7LU00NFXkXShSsS5SsZqdEyKYUrE7LHDtrYy01nzuHo6V1v2Nke/YCdqhTGDZSOy+saLrulpnVIzq3FPRla96sCyW6LZZVVAvtWR98PQNxbva2Mte+ldeLxMe1WcW/0qi/AuGzlEppR0PM0fiuJ6jWOVJ97ylFQAQ2Vg3KoDJedf371cJbfeAIAv/r8QgBKskdg5JAmKeLHusqNLKgAt5wzl1ljcxjfn/oCSaBNgmRo3a+sdqulnFUMtRui72v7L+63rrqStkljeK54Lhx+Nf7yJfAQ/OXNaARNs1f53N129UM3RT4ZfIGu4q597oNEQQVUvjuohyzN8VB5+xkA3PvOdho6hi4trGboCIclDe1+Ml32SLTanZ9fEFn/lSUVfGVJxZCdX1vuydBWHXXJmJjpY20OqDga6pOvYRpPk9xPUAhaMnPhtDtwzT8XgO110UHU59ZVAnDx3IuxCRsnTjox6eP7EhS40Jb7IFFQoWYsB4em8EJ+ppMmLe4pyd1vKePMtN5LctwHNMRXi3sytFZBdtyAmVn4IXcc5E2A1up+HbrNH80yuIfoqPnp88eS6YoOxuyoV9E0h4w5hDUXr4lkgkyGRG6ZkRrnnnIUTgYkNA1NNsfCLNeQDbhp+k4oLOnwR3+nu+o7ut32/W0qSMKcdX2gc/XrX3hvhMPQtDsa426SZQx8lc6B7FJor+l78WZgW+OeyHLIclO322yxE49sflw2F05b38MtE1nuTu2WGRzMiW2DNKgaT36ma0ir9Wj6xo1PfcKcm16KvF+zp6nbbQ+rUE/3VxqlJLW4H2ik7FmU26oh5OtaONqMea84Sln1IT94m/p8+n8s/ziy3CCiFoEzzm0ibD48jv4NvJg+91ljc5haksVXjphEjmfwcqKMaoZY3AszXTR1BgjrCk0jgkc/2h3zPr74jBWnQ/2Gl0xRIn+gxV0PqD50PrTVwJVvgi1BTKr5ozV/xCYHf1kVb5h1Jmx8VrW11agY+CT50Ts/4pWGZ6OnsqQYMAc8CzKdXHH0FP6w1keGPbPLMZLBdMs8/LUjuoRmaQZI9hgVSWUt5jKI5Gc6CYUlL39azanzxva+g+aAYnXRxGOm+M3NUIaUttwPJB0NsPVV2L8WqrspwBER98mx7TY7zD8fnB7lloHoZCeDVm+AO1/aSHWLlx89+QlVzdHaqx2BDp7dHhX2rLCkwRetp2g3fOJHTiumvCADbH7c9v5Z7mbSsPjqRppBQAiVimD3h0Ny+PxMdTO++l8rh+T4moHR5us+a6cvGMZhE5GxMy3uB5LaTdHlvx6jKuzE01gJCMjrYZqxOdjaVhPT/OamWv70xjYOv+01Hlm+iyW/eD2ybn97tHCGTUIxNpp8TZE20yee5bLjdtiVW2aAlrsW9yFiximwd6VKRzHIDKS6k2ZwSeQa6yl1wN1vbiMYluQZlrv/ANfHHd2/9ta4ItHL/tR1m8ZKlb/b0cNEkm4s956K3zYaVvqR+Zdzxy43BcJFo9dquStxz3Q5cDtsiAFY7r5gCIdN4EhQuk4zCFQcBUjY89GgH9rliI69xBdc0RxYvAkCEzqSyAszdgBFXAbC6P61txmFuc+7R6UTqHwHwpYPcNmfYO2jXQdT4/Hkg90NLbE3i56yvZlCXuKcw/RgG4WODBq80epOZmrQbLcSd2w+nKKruG+ubo3J1f7tR1fzpze2xmzjC4S11T6UlMxSr0Pkdzf539qqIT2+pmc6/V3FvSe3jInNJrj9s/N56pojh6Jb3Z/3gJ5tpNFWrSYhzf88nPEr6KiP+k6lhJduiC73hBBQMgNqN8Y0t3pjxd2aV2J3rUo09tGy1YwVDRQ4c2Isd7P8VqbbjttpR9h9OG2x4t7hD3Lyb97mu49/HGn778f7uPOlTbT7gvxvzT6klPiCYdw6ZezQkVGgbu7xT4KDwOTi7Mjymt1Ng358TfLssEwqNOkto2OZ8Zu/8LCJHDwx+WCLwWCUi3sNZJWqRGDTTgS7CzY+p9btXxvd7pSf936sMfOgen1MU4s3gMvZCTaVI2ZfcyfVz90KTbv4ZOULADzj+AVZwkdh1hiafE2EpXLlmLHpWS4HOR4HQvgRMjbSxbQknv9kP/Hc+dImvvGIsuK31LRqy30oEULVV20ZfMt6cnFWJNdMjk4gdkB4Y2MNj8eFPAKc/5dlXdraeoiWyXE7OG1e2aD2rS+M7l98e03UX+7JhcnHRMX902dUWt8fbIPxh/R+rHEHq5msW16JNLV0+vBMvZmpU35GKfXMEHsY89Ev8T1+Bc7gLjLDYQg76ZQuCsoOJiRDtPpVgjBzEDTDaSfX4wS7DxGO9d3Fzzy1jsa3dKpZjXe9vJkPtjfEJCHTDAE547q45QaL0hwPeRlO6rvJ964ZPKqaO7nsgY+4/om1vW6b43HwXA+uskA4PKyTBUe3uLdVx6YVmH4KNO6A5ffCO3fBpKXRmai9seACNYv19aiVX+etRgpJjRPuzfoxP3Q8AkDD3i1I2YQj5OZk/x0c7/sV+fkq1NJ0zZh+dJfDhscpESLEmxtbYgZp/RZx31rTxhOrorNdn1y9t2//C83AyB03JG4ZkwmFGexs6H6qu2ZwWL2rqddtTphVyo5fnM6hFaoSWncD3cGQHNYcTqNc3GtUPUyTqcep1+e/r14XXJD8sTIKYO55yjUTUlZzi3dnZHWzy8fx9o8BKBMNtNgF4WA2O+VYqiiKlMwzo2gajCnn4wsykEJZ3TLsYvqNL0RCsqyW+4m/fovr/9O9tXHLufOSvxZN3zHdMkMU0TKxMJO9jVrch5qetLgkR0XM3XLuPIQQLK5QPvREuZuklATDclgj1EavuIdD0F4ba7kXTYsuX/SoKp3XF4pnQDigimcD+KN+uxdsM2M2bbTbaA9Fa6AWeNQXxYyY+elZc7n0yAoWTSygI6R+1DKsvlwtXnXzSJQzpjsWTczv27Vo+kbOOJWmorOx9237gcdhTygimqEj3iIvynJx0pwxjDPyr5tVlhJF0Zgu0vg0IgeSXsVdCHG/EKJGCNFlCqcQ4ntCCCmEKDbeCyHE74UQW4UQa4UQi4ai04NC0y4IB2PTCggBF/wTln4bZp4Wm789GfKNkElzVmuwNrLqCdtBnO27hWN8vwFgny0DfzAvsj5iuRtumZljc/jZ2XOx2wQdASXuU4vUNmYdxsj05m4G2o6aFnUpmRMpNENErpFfv2Vo3GEuh63HeRMm4bDkK/d9yDtbanvdVtOVdktoY/ykI38oHJNLxpx52mG4UJfvaGBrTRsN7X6Chrjbh9Hnnszw+wPAH4EHrY1CiAnAyYA11+lpwHTj73DgbuN15FFvFMKwWusAc85Wf/3BkkRKSonXVwM5kO/Io9bRzFo5FYCF3nsI2e+AcBb/vnoJJdlu8t3qi2INhzRpN3LOnLtwCndsVy6bqSXRx8HCLBct3q6j9v936iweKMthe117TAUYzRAQEfcqGDt/0A/vtNtixli6o6kzwDtb6li9q4l1/++UQe9HumPNFdPmDeLOjoYQ+wJh3BY3i8diude2+rjgryqaxmW3seqmk4DYQtgHml7PLKV8G2hIsOo3wPWA9dnlHOBBqfgAyBdCDF8sUE+YxTWMIseDQu44sDmhcSfPrNmH095KTijMxLxyjpntZsWPVYGNZuFC2AKMzy3m0IpCKoqz8Dg8ZDgyIj536yOhKe4FGTlAdHKUGWN73KzShN0pyHLisNuYMSZn8K5Rk5i+WO7tdSqvUR9wOZITdzNKStdi6R9W19cdL8bOW4m33E23zK9e3hTjIvWHwgQNqz/lBlSFEOcAe6WUa+JWjQesAaJ7jLZEx7hSCLFCCLGitnYYHiH3fKT87clGwySDza4iZhoraWz3I+wd5IQl+RlFNPmaKDZqYQq7EuvPLYz1wxd6Cmn0NvLqzlc57vHj2NOqol9aAyo8sjBD1XA1fXyfVrVgE/Ddk2aw/IYTYo517kHjtLV+IMkeo0JnW3uJdZcSfr8I/pBEeK0Fl8NGuz/Enl4GVZsMcdeVtvpHIBQ1qv67Ojb6yR+MnemdYbhlXli3n51xRTvM46RUKKQQIhO4AbhpICeWUt4jpVwspVxcUlLS+w6DSSgIO95R5fH66lfvjYIKZGMlv3plM9LuJQ8H+e78mKRgwqGqL80ZE3vfK3AX0Oht5MktT1LvrefN3W8C0WpNRRnKR28W8ahr81GQ6SLH46Q0Ln/FsTMP8P90tGN3KoHvzXJvrwVfM3Q29CnRmDkwd8bve67XalruYrC/16ME0+I+bd5YlY3Vgj+Y2OcO8KW/xWYFDYbVcVItWmYqMBlYI4SoBMqBVUKIscBeYIJl23KjbWSx/Q01gWnOOYN/7IIKaKyk1RskaPeR58ggz50XEfeT54yJiHuRpyh2V08BDd4GajpUdsk7PrqDvW17I+JenKnEvd0X5IanPuFfH+yKGWR79MojmF6qpqvb9I/7wJNT1vtEpnpL3p+4Gc09YY6pNHf2XHLPdCvE5zUKhsJ87/E1bKluTfqco5FAKIwQyiqPj06Kd8t4ekjpsa1GPZ27UkncpZSfSClLpZQVUsoKlOtlkZRyP/AMcLERNXME0CylHHnZjirfVb7xackXmU6aggqEt4nXXN/DZw+Sm1FCgbuAzmAnvpCPP3zxYG4+T0XVFGYUxuxanFHM/vb9bG/eHmm7ffnttAZasQkbRZnKd769to2HP1Tj2NaB1COmFDFjrNpGW27DQG4Ss1RbLakiaj6NXbf5ZbjvZPA2d9ltf5IzjM2bfbx/fkddO0+s2sNJv3m7V9fOaMYfkjjtNtxxoact3gChsMRljwp6Rg/i/lGlGlMZzii1ZEIhHwGWATOFEHuEEF/tYfPnge3AVuBe4JpB6eVgsutDWPUglB8KriHwSRsRM1NtVTTZHOSWziPPrSzuJm8TboedzrD68cZb7mVZZTT6GgmEA/x86c9ZOn4pG+o30OZvI8uZRabLgd0mWLu3648/guEy1C7XYSB3fO/i3lEfXa7bHLvumetU4roEFn2rL7ki2dYnuVZvdB9r1MPrG2PrDmiiBEJhXHYbbocNv2WQdMHPXgZiy+pluLoX904jPDJ3JIu7lPIiKWWZlNIppSyXUt4Xt75CSllnLEsp5bVSyqlSyvlSyhVD1fF+0VYDD54NQR+ccuugHz4UlrzYNIEO6ebF0CLa7IKCvHLy3fkAEdfMR/s/wmFz4HHE+snHZkXLqM0snMmi0kVUd1RT21lLjjMHm01QmuOOmSIdH+MeNqJsBFrdDzi548DXAt6W7rdprwUEjFsEdVti15npphPcIG49V4VXjs/vOad/basvsryhKuqCsRZbtw4aamIJhFQ+GLfDFrHcrZEwVnF39uByMSPZRrTlnlasexKCXrj8BRg/+POrXly3n6uf2cfRvt9ybfhKAIo8BZHZp02+JrY3bef9fe8zu3B2l/2t4j4lbwqTcpX7Zn3derJdypcenwDs9xcdHPNeast9+Mg1Bsh7iphpr4PMQpUDvn5r4m0SDMpWFGdx8ZJJtPeQhRDg589tiCxbhd4av92Xmc2jiQ5/kAeX7aSxI4DLEHcpZczEJmu0TEFm9/WIGzvURMPcjOHL5Dm6xL1hG7hzoWzhkBy+tlUJbz15hO3qh5XnyYu6ZXxNvL/vfQBuOPyGLvtX5FZEll12F5PzVDKxfe37KHAnzgVthleaRCx3Le4HnmRi3dtrIasEiqaqm4DPsK6lVFY/QHPi/XM9Tlo6AwnLvSWiuiVqCHRYBMoX0GkMErF2T9TduaFKfRbPf7KfNsu4ltVyt9sEPzlzTsJjmWm4cz3DZ7mPrgTRzXt7roU6QHY1RAtgC7satMp350fdMt4mNjRsoMhTxLzirom8yrLL+NaibzG3aC4AE3MmIhBIJOU5sf0uy/Pw4OWHMT1ugpL5u9cDqsNARNx78Lu31ShxLzYmz9VtUU+RHQ0QMlL6dpNdMi/DSVhCqy/Y6+O+y2Hj06qoe6jTUq1L56hJjFW4392qwlQ/qmxgcnFWpD0+bt3j7Nk+zuzBLz/UjC7LvWVP9NF5CFi3L3rn//ISNTkqz5UXsbobvA2sr1vP7KKuLhmTK+ZfwZJxSwDwODw4bepHbFrxOUbB5CevObKLsCuUuutQyGEgx5iM3ZO4N+1UOYjGGDf3aiNlU7tlkLO9vut+EIm73lXfe7TLoon5bNof9blbo2e0WyYxXmNcYuGEfO743AIAppVm09QZzaPfFpfmw+OIivd9lyyOVF4yGU4ja3SJe/NeyBsacZdSsqIyOqV88VT1Iee783HaneS789nUuIltzds4ZEzysxOPm6jSEC8dtxRQov6zs+ZQlpd4YO2nZ83ljAVlHD19EGfeapLD6YHM4u7dMoFO5YopqICCyeDKgSojTbM5oSmjEDoST26aUqLGXXbUdy33BrEpK2aOyWF7bVukzRpFk6zlvqGqhYofPse6nqKz0gjz6eaWc+ay1Ei6F5Yyxr0VP8/AGut+wuwxPH3t0sj74uzuffIHgtEj7gGv+tHkDo1bxhcMY3WFNvvUDyLXrVIGFGcU89qu1wA4fGzyudRuPvJmnj73aaYVqARn08fkcOnSyd1uP6Ewkz99cVGPEyw0Q0hPse5NRo69ggpV2nHM3GjYY7uRgqN0dnQ5jrHGLOSalq4x71LKmIiYqaXZtPtDfGzUXTXF3e2wJe1zf2Gd8hu/uqE6qe1THVPcM5z2yO9nT2Mn33ksmmVlQXl+zD7ZcdFqRZYxsG+fOGOIepoco0fczQgG0y86yJg/rFljc7jl3Hk0+ZqwCRs5LuU6KclQ6QCynFk9umXiyXRmMiVvyuB3WDM05I7vdkA0kgrazB5aPD2awM603EtmqZzwoa5RMbkZDtwOW4wlafKtRz9m7k9fAuAHp8zkkEnKFfiNR1YDanIOqNJwybplfIbYjRZDwczZ5HHa8Rj+9wfer4ysX3PTyRwzIzatR2lObECDNadPfubwptkePeJuPioPkbiblekvW1rBV46YRLOvmVxXLjah/sUlmepLsah0EQ7b6BrHHlXkT4DmPV3bW6rgnV+p5Yi4z1BWekdDNP69xEgm19k1a6QQgsIsF00dyjXQ5gvS4g3w4/9+wjNrok8LY3I9zB2nIrT2NKpBftNyz3Y7knbLmKUeR0txda/lZuaw23DYRMxYRV4CsY73sYP6H8Pw11AYPSpjPioP0YDqZQ98BER9cs2+5kiUDBCJdjmo9KAhOb9mhJA3wUgM1gQZ+dH2F/9PzT7NKIxmIi02Hts/+Q/sWqbi382C7e210WULuR5n5Ds2z7DU44mfFt/U4Y+E9mW4HKzelVy1qHbDkvUFwzR1+MnPdOENhHhi1R5OnjM2UnYuXYi4ZYwIF4/T3iVHTzz5CWLdS3LctPmCZLqGV15HkbiblvvQppc3Jzw0+5oj/naAL8/+Mhn2DC6Y2Ye6rJrUI3+iem3aFSvu+5R7hM8/EJ2EYIZDvvAD9VoySw3IQrcZI/MylLh39DCZqaHdF/P+hF+9FaneZYr85urWXvP8m1E5t7+wkdtf2Ejl7Wfwt3e2c9fLm2nzBrnq2Kk97p9qdPqVlW66ZDxOG23Gv/Lshd0/8f/pi4tiXDA3nD6brz24goqi4U25PTqet0BZ7u48cA9N4YovHa5+1JccWQGoCUtWyz3HlcOl8y4l06lzrKc1prg3W8oabH9Lif1pd8KUYy3bTordN+hTMfDQbcRMUbaLHXXt1LT4Eq4HOG2+MmB+dpaaYGMKu5X6tq5tXbaJu0lIKak2zptMyb9UozMQwmW3RdL01hn/oxNnl/LrC7qf+HjGgrJIdA3ASXPGUHn7GTGDq8PB6BH3pt1D5m8H9QiXXbSGD2tUREyLv4U8V14ve2nSDlOwzcgYgFd+ol5nnR67rd0BZ/42Wuox5I+6bLqx3I+bVUpNq4/lO7qv5GTOWu4psZXLoZ4emjsCXQpBm4TiZsK2+0OEIqGV6ZefxhsIJZyUlJfhGta87P0l9XrcH6SEfaugbEHSu6zc2ZB07uv6Nh//W7cRUfoI1799PaAsdzPtgGYUkVkIziwl7k9fByvuh6o1MP/ziWdHL74MrvkQFl0MFz0CGQWqolM34j53nHL1fbAj8UQnK71FuVTWtbPw5pd5ePmuhOvjBfyhD3ZGilmko+XuDYQS3hCHs5rSQBgd4r7tdWirhgmHJb3L5+5exkm/eTupba97eDUNoWiGP2/QS3ugPcYtoxklCKEiZvauhNX/hGe/o9oXXdL9PnYHnP0HlfPIZo+dyBSX290cxNychOHR04CeLxBmV4PyqT+3NnGiM7OakMkvXtgYiR4JJpnfZiSzubqV8/78Ho2G26rDH0qrsM/RIe7v/FpZRPPOH5LDVzV3YnNFp4+vrVWzDq1ZHjWjiPyJKjLGSh8MC/LGQ8N22Poq3D4JVj4QWVVoRGdsrm7r9TCFWV0jOa49Tg2C+oyKQ9D9jNVOf2w8/NHTiyPWfDpY7o8s38XqXU1c+vflgCrIkSh8MVUzeaS/uK/4O+x8F476Tmz0QgL8wXDkLp4s3kCIgiwXNndU3JdVLQNgXPbQ+fg1IxhzUNWkaDo4+jC4NuEI2PUBfHA3IGHNY5FVDruNHI8jJv66u6iMkgQDemfMV9/Jdl+Q376qnjYTCXUgFI6Z8WruY94Igmngc59mlKQ0B4mbOhKLe6qS3uLevAee/wFMPR4Ou7LXzS+5fzkH3/JK0off3dDBrJ+8yOpdTdicjYT9qmzeqztfBWBa/rT+9VuT2ljFfd7n4JTb+rb/oV9VdQe2qu8RdZtiVifKNFh5+xk8ftUS/nP1kkjb2DwPc8py+f7J0WnwbmPA8LqHV7Nyp4p3jzdMW7wBTvjVWwTDkrs+v5AfnjaLWWNzaOoIRG4E8S6bVMS8Qe1v8bK1po2Wzlhx/+lZidP5pgrpLe47l0E4ACfdDM6eK9gALNuuBqms+bJ7ysC3wZJSVTiaCXVOwGFzUNlSSUVuRaRIh2aUYRX38++HGSf3bf+SmUbxdqFeO+pV0jGD6m7CIA+bXMjiimhdXpfDxvPfOppzDopO3Es02zQvbiLOmt1NEX/84ZMLufrYqRwyqYDGDj9vbVZ5b1I9WiYYCrPKMpnr7je3UdXsjamP0FOlpVQgtXvfG3tXgjMTSnrP5bK7ISribZYJImZe50Rc+c+VxpJEOFuZkFuGy6Z+KMeWH9vtfpo0p8BI7Da9j6Ju5dy74TvrYcZp6n1vtVl7wBwkzHLZE/rh394cm6jMWsHJTDOcl+GksSOaETGY4j73v727g6c/jv5Pn1i1h85AiOljsiNtp88vY9bYHK48JjUna6W2uO/7GJ79rsrNAVC7OVqHEmDvCig7SEUj9II1N4c1Z7NZCzF+cMmKsLcjRIhLDz+II8qOAOALs76Q/HVo0ouyhXDhw/C5v/X/GK4sNbBqpqi2ToqKozcbujjbxeVLJ3PLufO6jaDZbynfWNcWFXczH3m8FRtI8WiZXQ2Jn8hLc6K5YgqzXLz47WNiinWkEqkt7q1VsOI+FVnw4T3wp0OVjx3UbL+qNVC+OKlDPfxhNNb3T29Ea1ve8eJGqlu8zL7pRR5cVhlpt7puhEO5Z0oySrjr2Lt48XMvMiFnwgAuTJPSCAGzzgDPIMxzMHMhtfRQl7XX7ghuOmsOn12k4uwvW1rRZZsGSyCBabnf85Vo3QFrrHdxtptAildzGpcg4ReorJnpQq/iLoS4XwhRI4RYZ2m7UwixUQixVgjxlBAi37LuR0KIrUKITUKIU4ao3wozu15jJSz7o1pe8wgE/SpKJuSHiUu62ztCqzfA3qaoT/Mhi9AHwzJS0eamp9dHYoLNqIETZ5fy54vVwGlpZilOu5Px2UNX7UkzyrAmEovDFNy+Rq789Ky5keXPGYLf1BEV9z2NnZQXZHDy3Ggor90WlYribFfKh0J2N2bgSqMMmMlcyQPAqXFtrwDzpJQLgM3AjwCEEHOAC4G5xj5/FkIM3awAc+Bq93JVvmzC4RDogHd/o7LwZY+BaSf2epjeBocuvn95ZPknT6/jxXVVfOexjwE4enoJ7SHlFirN7JrFT6MZEK5ssLtjxP2JryuDxSzO3NjRt/BdgNU/OYlnv3EUXztmsnGMqD992fb6iK/dxGq5q5zwqS3u3SVeS6f0xr1eiZTybaAhru1lKaX53/kAMOdVnwM8KqX0SSl3AFuBPsze6COuLJVoaf2T6v3SbwEC3rxNzfK79Dlw9F7qylq84PcXHdzjtg3tfq7+1ypeXK+q1GQ47TR41b+nKKOof9eh0XSHEOo73h6dsXrIpEK233Y6Xzpc5bExC3P0hYIsF/PG50Vyjz+1eg8zbnyBW5/7lKaOAEumxJZpNItQZLrsSaXCHenEl8sD+MzMEuaU5SbYOjUZjNvU5cALxvJ4wDrys8doGzoKKpRVI+ww5TgoNCIVjvh6NKVqL5gTQq47bhpLpnQv0FkJ4oszXHbaA+04bA7c9vTKb60ZIWQVqwLa7/8B/rAYGndiswnsNsFr3zuWu7+cfE3eLoc2Blhf3VCDPxTm3nd2AFCQFTuZx0ycleV2sKKykbV7mvl0XwupSmVd1wHVrx87dVgLWg82AxJ3IcSNQBB4qB/7XimEWCGEWFFbm7hmZFKYWfjGzAVXJhx3o0rSdMQ1SR/CfMScVZZDSY47kio1nqevW9qlbdbYHNoD7WQ5U3NEXZMCZJWoCU0v/xgmHancjQZTS7Ij1nd/6C5zZPxMTadhuWe7HZGiFm9vGcDvdpjZ19zJKXPHsPLHUbdtQYIw0VSm398KIcSlwJnACTKaM3QvYA0TKTfauiClvAe4B2Dx4sX9j6saOw/W/QfKD1Xv55+v/vqAabm7HeqLfunSyXzx8EkEw2Hm3KSq3Rwzo4SS7OgI+y8/t4B54/OYPiaH9s3tZDm0uGuGCDNVdf4kOP/vqrj2IOF22LDbRJf0vmV5sT530y1j9UknW9FpJNLcGaAsLyMm5/r4/N4nOqYS/RJ3IcSpwPXAsVJK6/PNM8DDQohfA+OA6cDyBIcYPA76sopzX/qtfh/C9LlbR8pdDhsubBRkqskbmU57TJjU5JIs5hjpV9sD7boIh2boMFNVzzpjUIUdVJhkvLAvLM/j0IpYP74Z5+602xib62F/i5d3t3Q/wW8kEwpLWr1BcuOeTrIG8AQ0Eun1aoQQjwCfAYqFEHuAn6KiY9zAK4aP6gMp5dVSyvVCiMeBT1HummullMmVWu8v2SVw8i0DOsRDH6jQx0Qj5QWZLho7AmS5Hdgslc2tj8LtQe2W0QwhB31ZvQ5RVtN4nr7uqC5tpuXusAueuvZIrnloFZ/sae6yXSrQ6lWDqemUJCwRvYq7lPKiBM339bD9rcCtA+nUgcQbCPHkauU5SvRhtxhfhMXdWDIAHYEOcl3pM8quGWE4PXDoFUN2eJuAsIQzF5RxzWcSJ7szQyGdNhtleRkcN7OU1buaCIbCKVelyBtQbthECdjSidT6VIYAM63pOQeNY3aCMChzNKHIGGz599VLWDypgAmFUf+cdstoUhnTPXH+IeURV2M85iQmpyPW9+5PwclM5gSsVE8M1hvpfXVJYE5mWDq1OOF60xtp/gAOrSjkP18/MjL4CuhoGU1KYwp1T7MzHaZbxhB5cx9fIPXE3YyOS9Xyecky6sT9n8sqY2qjeo2wLk83j2ge40uc0UP5rY5AhxZ3TcpiM2K7XT1YsjPH5jBrbA7XfEZlSHQbv4dUnKlqWu49XW86kN5XF8em/a385On13Pzsp5G21buaAMjsRrzv/PxCygsymFKSWLyllLQH28l0aLeMJjWJiHsPlvu4/Axe/PYxHG5M8ou4ZVJY3E23zAWLy7nlnLk97ZKSpFfsTy/sM5KDtVimHv/gP6reqTkxI56l04p59/+O7/aY3pCXsAxry12TspiTMvvigzZvBNbUHalCRNyNa/jl+QuHsztDxqiy3M0ES9ZZebPG5gB0O5DUG+2BdgAt7pqUxdZNzvaeMMecUtEt4w+qkTTtcx/hhMKSnz2zns0WP3p3mJnvsiwFCxaW5zMm183UkuzudusRLe6aVMecvmHrg9a508ByT3efe8q7ZfY1dfLA+5U88H4lZywo47m1VSyamM8TXz+ySxKgRqMggVkkGMAbDEXKkPUHLe6aVMe03PtSXCmVo2V0KGSK0GopiWcW0li1qymm+IaJ6ZYxH8tARct4HFrcNaOXi5eo5HslOclnNU2HaJl0F/eUt9y7yyvd1BGgvKBrG6hHyTW7mwhLSWcg3G0YZDJ0BFRqHS3umlTl0qWTuXTp5D7tY1ru722to7wgg+ljcoaia0OC3yjO43Kkt8895cX9/nd3JGxPlIzftNzf2VLHO0bSo8MmF0Zi2fuDabnrGaqa0YQp7n97dwd/e3cHlbefMcw9Sp5AcHRY7il/dWZFpHie/jg20/DO+nbe31bfZbt2X3BgPveg4ZbRKX81owj3AH4zw81occuk/NWdPn9szPs/GGXyqpq9NHcEIh/khzuilQInFUWt7I37W/E4+/9v0G4ZzWgklWuNanFPEczEX6BCuc5aOI4vLJ7AO1vqWHjzy9z10iYgOvBalufhrR8cx1PXHAmoUMqBWO4t/hZswqbdMppRRU+zWUc6EZ+7FveRS1OHnzc3RUt9maFcx88ujbTdZ/jkbzFSDjx+laocPzYvWlWpp7wxvdHqbyXbmY1NpPS/UqPpE6ksjNEZqnpAdcSyaX/sxCVztukRU4pABHAVvI/Ns49mX7T2qVlKq8RSXmsgj5it/lZyXKkTKaDRDAYpLe5BPYlpxNNgTEoCePYbR0UK3OZlOHGPfRpX/goAHtnwOGV5U1g6rThSTWmwCgy0+lt1oQ7NqMPWl+ms3VDV3ImUKikZcMAKfwRCYYSIVpdKV1L61nXa/DLe/b/jWPWTk5g3Pi9ildd11uHMW4W/4UhC3jLuW/kK9W1+irMTT9Loy8y8eLTlrtH0jyW/eJ0jb38dgAeXVTLtxhd4+uO9HHvnG7y5qWbIzusPSZx2W5cZ7OlGSos7QHlBJoWGxW6yumY1QoQ5d9qZhDom0Sm24w8FKc6O3e64mSXAwO7grQHlc9doNP3nKaPU5bce/Zid9R387Jn1zPzxC7y4LnGo80AIhMJp75KBNBD3RHxS+wlOm5NbTj+FMs8shN2HI+dTcjJip0ovrigEYnPN9BVtuWs0sS7S/hBfz7SuzY8vGOaOFzcO6LiJ2FnfkdLRPsmSlle4tm4tswtn47K7uHbJCQBklP+LJ/b9LGY7lRcjhKufqT+llDT7msl1a5+7ZnRT0+od0P5mjVYT8xeZqBjIql2NkYiX/vDx7kaWTC3q9/6pQtqJezAc5NP6T5lfMh+AE6bNRvrKANjQuIYN9Rsi2y6YFCJr+i9Y5b+zX+dqDbTSGexkTOaYgXdco0lhAsEBDFwRW0DHYRO0Gjmj4lMKP/bRLj775/f5x/uV/TpPc0eAujY/C8vz+t3XVKFXcRdC3C+EqBFCrLO0FQohXhFCbDFeC4x2IYT4vRBiqxBirRBi0VB2PhFbm7bSGexkfrES98JsN+986X+88fm3AHhn7zuRbde3vYzN0cba+uVsa9rW53NVtakslGOytLhrRjf+AVjSAC1eJe4/PWsOXzh0QqS9ri3W3fOflXvU9glyRyXD5f/4CICJhek/6TAZy/0B4NS4th8Cr0kppwOvGe8BTgOmG39XAncPTjd7R0qJlJJV1asAWFCyILKuIMtNcWYhswtn80HVB5H2ZfuWMTFnIjZh48XKF/t8zuqOagDGZo7tZUuNJv047+DxkeWBuEk+++f3aOkMcNFhE7ls6WQKMmMDH8KWcLaaVh8A9f308W+sagFg3nhtuSOlfBtoiGs+B/iHsfwP4FxL+4NS8QGQL4QoG6S+9shtH97G0keXcsdHdzAtfxoTciZ02ebwssNZXbMab9BLo7eRDQ0bOHPKmRw65lBe3PEiUvbt0XJ/uxrJH5ulxV0z+vjhabOYUKjCjwci7qt2NVHX5icvwwlAfqYzZr01w6tpsT/04a4+/15B1USeOSaH8gJtuXfHGClllbG8HzD9EuOB3Zbt9hhtXRBCXCmEWCGEWFFbW5tokz7xUuVLkVQA1x96fcJtDhlzCMFwkHV163hn7zuEZZijy4/m1MmnUtlSycaGvo3M72/fj13YKckoGXD/NZpUY0yuhz9epDyvAxF3k/ICdaPIj7Pc69uVtS6ljMkl9cyafX0+R6s3SG5GSs/dTJoBD6hKdfvs8y1USnmPlHKxlHJxScnAxNEb9NLoa+S6g67jvYveY8m4JQm3W1iiqpy/tus1frXiV1TkVjCnaA4nTjwRh3Dwwo4X+nTe/e37KckswW5L3fSnGs1AMDMr+vs4oNrp71p79ULD114QZ7l/799rCYUl83/2Mr5gOBI2ubO+o8/9bfUFyHZrce+JatPdYrya08n2AlZ/SLnRNqREfN+9uEcKPAVU5Fbwrw3/os3fxq8/82tswka+J59Dxh7C+/ve79N593fspyzrgHidNJoRiVnNqK+W+zcfXR3zPstlj6QeKIiblLhmdxM76tojVdcuN6pGtXoDtHdTia07Ov0hMrW498gzwCXG8iXA05b2i42omSOAZov7Zsgwfd/JCO3pk08H4Euzv8T0gumR9oNKDmJL05ZIfvZkqGqr0oOpmlGNabn3Vdxf+bQ65r1VcIvixB1gS3U0SaA3oKz+e9/ZwaV/X96n8/qC4ZTORd8XkgmFfARYBswUQuwRQnwVuB04SQixBTjReA/wPLAd2ArcC1wzJL2Oo6pd3T+SEferFl7FE2c/wbcWfSumfUHJAsIyzLq6dd3sGUtYhqnuqNaDqZpRjSnuwdDA4tytGUBKc6LpuM2InK8/tCrSVmhJI/JRZWOfzqPEfXS4UXt9PpFSXtTNqhMSbCuBawfaqb5iinsy8eY2YWNGwYwu7aY/fk3tGg4rO6zX4zR4GwiEAzrGXTOqMcXdN8AB1bG5lvoKLjv/d+osygsy8AZCkbwzJlcePYVfvqiK8PS1ipo3ENKWeyqxv30/xRnFuOxdH+eSJc+dx+S8ySzfn9xj3r42NVKvfe6a0YzDMLnDvaRWjZ9pOqU4KyZL61++ckjM+q9/ZipnLRzHWQvHxbS77DYcdht3nq/msZTkJM702n0/wgPKJZVKpMVV7m8fnIHNUypO4cOqD9nb1vsY8I5mVeFpct7kAZ9Xo0lV7EZepmAP4r6zvp2ZP34xpmi9LxiOhCQ67YKyvIyE+1pLYD5w2aFsvvU0AD6/eAJfO3oyta2+pOPdpZT4R5FbJi3EfV/bvkHxfX9u+ucQQvDklid73XZ783YcNkfCyVIazWjBbuRED4W7d8tc/5+1ANz9ZjTFhy8YjkxaSnYuUlFWrJVekuPGGwhHomh6w0wfrN0yKUJYhtnXto/y7PIBH2ts1lgOLj2Y9/a+1+u225u3MylnEg7b6Air0mgSYdZC6Mly/3CHmuBuRtRsrWmlrs3H5rgymd0xvVTVSzAnOZmYN4fmJPLM1LX5IoOyo0XcU16Zajpq8If9lOcMXNxBDaw++OmD+EI+3Pbu/XlbG7cyt3juoJxTo0lVTJ97KIloGVP/V+9qAqA9wUSmRPzzq4cTkrJL/HuOR4n79tr2XtMJWLNIxs+ATVdS/ha2p1VliRsscZ9fPJ9gOMiWxi3dbtMR6GBv216m5U8blHNqNKmKabmHkvCtBA3XTZERymhWQuttz7F5nkgJTSs5HmWbXnz/cn7x/IYu662UWgZezfOmOykv7rtbVSqbCdmD4/s2wyR7EvcdzTuQSKbnT+92G41mNCCEwG4ThOLcMm9uqmH6jc9T3+aLtO1u6GT9vuZIAY7rjh/Y78e03AH++vb2Hn3vZvdW/eQkirqppZxupLy472nbg13YGZs9OJOJxmePx2P3sKWpe3E3100r0Ja7RmMXoovP/e/vVRIISR5boYwv032zrbbdqIAW9ZmfHRfumCzW2HiAjh7E3RT++HJ+6UzKi/un9Z8yMXciTpuz942TwG6zM6NwRiQvfCK2Nm7FbXcPyiCuRpPqJLLcy/KU8O4yknvdfM48ANq8QXwBJe4ZLjsrfnwivzx/Af1hTG6sBd6TD7/DH8RuE6NmMBVSXNzb/G2srF7J4WMPH9TjHj/heNbXr+e7b36X5VVdJzVtbtzMlLwpOhukRoOyyuPTDzz6kbLYO408MIVZyvhq8wUis1ndDhvF2e7ILNe+IoTg7R8cx68vULPLe0oi1u4LkeWyI0T/6iWnIikt7q/vfh1/yM+ZU88c1OOeN/08puVP45Wdr/DNN75Js685sk5KyYaGDcwpmjOo59RoUhW7XRC2DKgGLakIzPzreRkuhDAtd9U2GFb0xKLMSC6als5AzEzZq/+5kmseWmn0I0jWKMkGaZLSV3v21LNZULyAiryKQT1uoaeQp855ik0Nmzj/f+fz363/pS3Qxv+2/Y/vHPIdmnxNzCqcNajn1GhSFYdNRCJhAKqavZHlVqM2qsthI9vtoNUXpKrZi00waDNFM93qOF/824cAfHzTSeRnunhx/f7INu3+0Kjyt0OKW+7AoAu7lZmFM5lTNIdHNj7C/Z/cz962vXz/re8DsHTc0iE7r0aTSthErM993d7ok25zp3KVuOxK3Nu8Qd7fVs9nZpbiGiT/d7xo3/Js17DIxnb/qLPcU17ch5pTK05lb9te/GE/fz3xr5xScQq3H307E3J12gGNBrr63Pe3RC13s+ap0yHIdjt47pMq9jZ1ctS04kE7f5YrVrR31LXFvH9jUw3vb6tna01se7ozum5l/eD8GeezpnYNswtnc+T4Izly/JHD3SWNZkRht8da7taBzYhbxm4j2+OI+OBPnjt4qbLjLfdVu5qobY3G1z+3VqUE70hyRmy6oMW9F3JcOfz2uN8Odzc0mhGLw2aLmaHaahH3Fq9adtptERGeOy6313QBfSGRu+XQW1+NLJsDvHPKcgftnKmAdstoNJoBYROxicMShSS6HDbGGWl942PiB4o16ubkOV2fCP77saq98PfLDh3U8450tLhrNJoB4bDZYhKHNXUEmFQUa5m77DYOrSgEkk/xmyzW2PXsHgZNi0dJ2gETLe4ajWZA2G2x6Qe21bYzuTgrZpscj4MMwy0THmx1t3D+4uis8VljcyLL2W5HJMnZaEGLu0ajGRAOu4gp1lHf5uuS98Vht5FlxKMnk0Gyr1xgiPqiiQUcPlk9IVQURW8wK39y4qCfc6SjB1Q1Gs2AiLfcA6Fwwhj2TCNkcSgM9198dgHfP2UmHqedcUZ6YGvumdFSWs+Kttw1Gs2AaO4I8M6WukhUSjAkcdptfPtEldL3osMmAlBoFNtoaPcPeh/sNhFJQzDNqNw0vzyfWWNzWDypYNDPlwoMyHIXQnwHuAKVb/8T4DKgDHgUKAJWAl+RUg7+p6nRaEYE2+vaAXh1Qw0nzRmDPxQ2xH0G3z5xRmS7qSVKdL961NAWlb986WSOnVHC3HG5nH/I6M3c2m9xF0KMB74JzJFSdgohHgcuBE4HfiOlfFQI8Rfgq8Ddg9JbjUYzYrn6XytZPKmAQCiM09518NJuE+z4xelDnpkxw2Vn3vi8IT1HKjBQt4wDyBBCOIBMoAo4HviPsf4fwLkDPIdGo0kRVuxsJCzpNo3vaEq5O9z0W9yllHuBu4BdKFFvRrlhmqSU5iyGPcD4RPsLIa4UQqwQQqyora3tbzc0Gs0IpL852jWDR78/ASFEAXAOMBkYB2QBpya7v5TyHinlYinl4pKS0VGwVqMZLSRyy2gOLAO5vZ4I7JBS1kopA8CTwFIg33DTAJQDewfYR41Gk2IMVjpfTf8ZyCewCzhCCJEplCPtBOBT4A3gfGObS4CnB9ZFjUaTami3zPAzEJ/7h6iB01WoMEgbcA/wf8B3hRBbUeGQ9w1CPzUaTQqhxX34GVCcu5Typ8BP45q3A4cN5LgajSa1ma9DEYcdfXvVaDSDjnXqv2Z40OKu0WgGnYxRVox6JKLFXaPRDIjnv3l0lzaX9rkPO/oT0Gg0A2LOuFyy4ix1PRN1+NHirtFoBow/FO59I80BRYu7RqMZMIHQ0FVX0vQPLe4ajUaThmhx12g0mjREi7tGo9GkIVrcNRqNJg3R4q7RaAaVOWW5w90FDQPMLaPRaDRW1v7sZD2BaYSgxV2j0QyY/1y9hC01beR6nMPdFY2BFneNRjNgFlcUsriicLi7obGgn580Go0mDdHirtFoNGmIFneNRqNJQ7S4azQaTRqixV2j0WjSEC3uGo1Gk4ZocddoNJo0RIu7RqPRpCFCyuFPsi+EqAV29nP3YqBuELszXKTDdaTDNUB6XEc6XAOkx3UM5TVMklKWJFoxIsR9IAghVkgpFw93PwZKOlxHOlwDpMd1pMM1QHpcx3Bdg3bLaDQaTRqixV2j0WjSkHQQ93uGuwODRDpcRzpcA6THdaTDNUB6XMewXEPK+9w1Go1G05V0sNw1Go1GE4cWd41Go0lDUlrchRCnCiE2CSG2CiF+ONz96Q4hxAQhxBtCiE+FEOuFEN8y2guFEK8IIbYYrwVGuxBC/N64rrVCiEXDewVRhBB2IcRqIcSzxvvJQogPjb4+JoRwGe1u4/1WY33FsHbcghAiXwjxHyHERiHEBiHEklT7LIQQ3zG+S+uEEI8IITyp8FkIIe4XQtQIIdZZ2vr8vxdCXGJsv0UIcckIuY47je/UWiHEU0KIfMu6HxnXsUkIcYqlfeg0TEqZkn+AHdgGTAFcwBpgznD3q5u+lgGLjOUcYDMwB/gl8EOj/YfAHcby6cALgACOAD4c7muwXMt3gYeBZ433jwMXGst/Ab5uLF8D/MVYvhB4bLj7brmGfwBXGMsuID+VPgtgPLADyLB8BpemwmcBHAMsAtZZ2vr0vwcKge3Ga4GxXDACruNkwGEs32G5jjmGPrmByYZu2Ydaw4b1SzrAf+4S4CXL+x8BPxrufiXZ96eBk4BNQJnRVgZsMpb/Clxk2T6y3TD3uxx4DTgeeNb40dVZvtCRzwR4CVhiLDuM7cQIuIY8QxhFXHvKfBaGuO82xM1hfBanpMpnAVTEiWKf/vfARcBfLe0x2w3XdcStOw94yFiO0Sbz8xhqDUtlt4z5BTfZY7SNaIxH4oOBD4ExUsoqY9V+YIyxPFKv7bfA9UDYeF8ENEkpg8Z7az8j12Csbza2H24mA7XA3w330t+EEFmk0GchpdwL3AXsAqpQ/9uVpN5nYdLX//2I+0wScDnqqQOG6TpSWdxTDiFENvAE8G0pZYt1nVS37hEblyqEOBOokVKuHO6+DBAH6nH6binlwUA7yhUQIQU+iwLgHNSNahyQBZw6rJ0aJEb6/z4ZhBA3AkHgoeHsRyqL+15gguV9udE2IhFCOFHC/pCU8kmjuVoIUWasLwNqjPaReG1LgbOFEJXAoyjXzO+AfCGEw9jG2s/INRjr84D6A9nhbtgD7JFSfmi8/w9K7FPpszgR2CGlrJVSBoAnUZ9Pqn0WJn3934/EzwQAIcSlwJnAl4wbFQzTdaSyuH8ETDciBFyogaJnhrlPCRFCCOA+YIOU8teWVc8A5kj/JShfvNl+sREtcATQbHlsHRaklD+SUpZLKStQ/+vXpZRfAt4Azjc2i78G89rON7YfdotMSrkf2C2EmGk0nQB8Sgp9Fih3zBFCiEzju2VeQ0p9Fhb6+r9/CThZCFFgPMWcbLQNK0KIU1Fuy7OllB2WVc8AFxpRS5OB6cByhlrDDvQgxCAPaJyOijzZBtw43P3poZ9HoR411wIfG3+no/yerwFbgFeBQmN7AfzJuK5PgMXDfQ1x1/MZotEyU4wv6lbg34DbaPcY77ca66cMd78t/T8IWGF8Hv9FRVyk1GcB/D9gI7AO+CcqEmPEfxbAI6hxggDqKeqr/fnfo3zaW42/y0bIdWxF+dDN3/hfLNvfaFzHJuA0S/uQaZhOP6DRaDRpSCq7ZTQajUbTDVrcNRqNJg3R4q7RaDRpiBZ3jUajSUO0uGs0Gk0aosVdo9Fo0hAt7hqNRpOG/H+cWUb7u6zVUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Comparing using visuals\n",
    "plt.plot(normalizer.inverse_transform(ds_scaled))\n",
    "plt.plot(train_predict)\n",
    "plt.plot(test_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "55300bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bf6d5613",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.vstack((train_predict,test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "41b272ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcbc5572150>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eWpKenrea4U2v2jJSyovirIpqUy/VtP81Az0oTQStxhz13k9UiMZmhzduCa2feFT8144/VD26ikINtAF2r4LRcwF4bJna/+vr6/H4AjgdNv65ZBsjinP5p2F4YnrulrBMvtNOh9tHFa3UU0al1x+WI98rORGpiG7DCJqeOxLcreS3bORa+9O4ekaGb/+5b8PL18XVs7HOGRwoNrPRru5qXA477a4RVHRvA8DbqGQaNlmE0EaIFgByhSfmZKmVeJkv5kXy2dW7ueOVjbz4nSOZMVpN+L76SXSYSKMZKFo4LBMwM2KkX4VWAhEx3XhhGSsuI3Mkv0K9fvcqOPgSADz+0GTfrpZuyvJz+Nni8NS/WJ57eWHI2y7Oy6G+3c0I0cImOZbqbl/fjLurKPZ40LgDT13JqZte4dQc6NxkScIaNTeUcrnvs5jG3cz2KaCbybY6XudY9bYOG10W4z6l5R1ucuSyoeX64GtHGJ57Lh7aE6RMQvyOTmZz8Y171UVj1Y4WZowujl8EptEMEC0/kAn0tKoG0gC7Vii9GIAx89VjZD57LEzjWTxWvW77e8EQRlt36GJx/l+XxDRQsSZUT5gRysYpynUgCDBSNFEvS3n8w53RL0hETl784zYLtDa9Ehx21q9RC6feARcvhmKjnKI99hSPeecxTajjqs+fonbvsNGdG55VdLHjVabtXhx8Pl4o6YNcPGEXwli0GEb81nPCe9lGzqMWuOx0un0s+NVrYeM6512TKrRxzwR6WqFyCpSMU8a9eRvYnTD7vND63jANZEk1TD8dGjcGs2/2WPRhGjvc1DZ1Rb08Vkm/1TN32m1MFrspFt2slRP5w2sbaer0JH2K1hxHX9nk0HhuScw7E3ujMX8w94sqn7/INO6xJRZM4z7DprR2duTUAODKseMqU3cBfkJhpintywAoootyobztXDx4fImNb0O78sTLe+nluq/DEzNlMo4isUbTZ7RxH+pIqYx3bgmMPQjWPQXLH1AZMFMNVYhY0gORmBoteWWqITXAdlVRGin+9cV7Poh+fcCv/gDcHbD+hbCc8By7jWqh5A22BJShPfOud5M6xUia8q09V+1QHFK8PKjnr/ikDVv7btUIxLwjMS8AcfTrzbDMDLGdNpnPK7uU8XXabRQUqn3slaGJ6WqPKnQab2TW+HPLyBMevP7EMfdzjMnUkrwcvnToeP721aipKQBueu4TfDEsuS+QeP8aTbJo4z7U8fWoBha5JTB2gRpzt6lWdZUHwGUvwok3976fccbE6uh5Kj5dMAK2vQco437CjJEJXgw8cAo8fL5aXvM4PHYRrH4kuNqGnypj4nHEGCXiZXZKSgYznAHQXhSSLPjv+r14CsYgnUV0FU+iiWKaMfLNC0eEPH6bXRU8udti7t9mAxsBjratYUVgCqBe58qx4Z/zRepkOdd6vs2T/iN5zHcMY+RenHiZKNSdgLdiuuG5xze+1pBKaX4Ot54zh5NnjeICQ/8mEmt+uxn2itdMRKPpK9q4D3XMkIurGA69CqqNSsx8Y9Jwwucgtzj2a61MORGueB0WXqEM4rhDYPcqfP4APd4Ac6tLuHBhuPb61w5XHnQZbbBzKXz2urqTMDVqdi7lvoK/cE/O77iv9kxOsq0A4OiDw+PNyfCTJz8OLreVTg8dw4PLmfqzlznR+zvePPZJAJql4a0XRlyQXMXQE9u4O2w2ZoutjLM1sNgfqlx1OWzkj5jIYe67WCmn8gPv1bwfmI1dSE6uaOAu5/8C4K+chovEnrvbYvhL8kIFXPEmls0QDoQ0cxJp3Gg0fUEb96GOadxzS8DhhC8+rGLmJ/y87/uqXqA8XICKydCyHY9XeY9Oh42qolD2i8thC4YIDrJtCu3D3QZewyNf+RDH+9/lJPsKcvByrG0VrTKfcxaGYubJTBCu2N7ES+tCsfJ9+Qfwsn8B13svD45t7i6k2aOOvRnTuEfIK+QWh1IoI7DZYKZNpXUul1OD46onbLjxXSnVZOs3/OrOxFs6GQqqcAkfTy2Pn5O+pjb03qOKQ0VZsfq6AizfHgoh5RvbaM9dkyq0cR/qWI07QNFIuPDhsDh0vyibCH4P4v0/scZ1OZWeXWGTpl5/IBiCGC0soqDte0J6NhE4RIAGWRom7rVhbztPr6pNeCg/fSY87XJpo4tveL/PI/7jw8ZNga2Zk42YfKRxT+C524VgrGjEJ23slqFUyeLcnCjdnFpZxY5AFbN7VoKwkXPVmzhzCwBYu6M+7nm8YBQ+/enCA8P26bDF/pnd+XroomleYHTMXZMqtHEf6gSNe2lq91uuDKTrgz9RLLqpaXovLN0xIOHkWaMAGG23eMPte0INQkymn06rUKGhoqrw+PKPn/yY7z2+mnW742f0TB+lPPErPD9g04xrqO2K7ene/vIGAAqLjDBUUYSadG5xXOVLu00wRjSyh3IClq99pMqkyTsBVeCFDEBuMTkuVWS1sDo8ZbPb4w/enRTlKgN9+tzwC+9pc3tXvS4zsmu0565JFdq4D1U+egT+dwF0GJ5iLNXHAXD7h8oLtnlUml9552d87YiJjCkJhROOnT6CbbedxjULLTH99rpwz33+V+HCh2kR6vjsRr75vReryd81tWrbXQkmV8049muBg1k16eqw2HUshDEZSsXk8BWuYhU28nlCFyDD8AohGCv2sVtWcOqcUQn3D3CX72y1YDdSGo08fG9PKE20vq2HGTe+xD/e3waoO4uy/Jyoat7ItMg7L5of9X5OQ+TMp2PuWc/BN78adteWLrRxH6o8c7UqVtqlJimTmjTtA3ev6sEjQ15rcfcOinNzePwbh4U2at+j0h076qHEaGPX2RDuuZeoSdhOYcgHFKlJztJ8NaFohtxbIjRZpJQ8vHQ7f3xtIx5fIHhRaevx4vYlLhTiyB/AwZfC5PCwDblGWOaNX8FvJkDtcvhlqUrbBMbQSJ2sYNaYEp7/9hE8+vVFUbt+6GuHcN7B1dRRziujroQv/kutcCjj7nOHjHud0Rz8caNxSFOXh7L86Px26+QqwITy6K5PZkQsVnqkJnvw+QPs6/Tw+1c3pv29tHEf6gSNe2o99wA2Ggnts7BDTRSOK89HEKCYTvjdNFh8jTLuFZPBlqNa9fW0qJTKqafA7HMByJHKeMvSCQCURhi01oiGFA8v3cENT6/lj69twusPUGoYxQ63jx5vtOce5gxXTYUz/hSd32/qvr/3J/X87TvU4+pHIRBglGhit6zA6w8wa0wJh00OKVeeOHMkB40v5aipVUZTbcEHYy+DqSerDXLUxUd6Q8a9x+iu1O1RxUi7W7pjdqzKsdu45tjJQQNeXRZdjbtokjoWv465ZzV9KuwbINq4D0WsFad1H6nQQKQk7gAw47qmvdwQqCa3e48SJXvpet5yfo85NqNb0epHlLdeUKU0W7oalec+dgF86TGVaw805qhQh6xUaYwl+eHGfVdLeFjG2gza65fk5tgocNpp7/HF9NwrDB2b754wJf6JFY1WTblNtryhHh0u6NiLU/jZLStihn3uvXgBT31TpUiaIRJl5A0Mz93h7yEQkPgDMtg/ttPjx+cPsLOpi1HFsWUUfnjydFb+9EQeu3JRVJhm2fXHM2WEyt3XnnsCPJ1w/ymw4h+DfST9psmo53Da0296tXEfijRvC3+eWxLdgmgAmDHuR3zHAfC4/1jjfbfCB39mvK2BM2yWZs1tu5Vhz6+Ezn3q4hPhNR9w8V18OP1HjJijQiWRoYhP6lQWS21zF/e9uxVraNlUoizKzaGjx4c7huc+3ghlOGKJ3JhEZhCZrfO6W5TcArBVju5V2dEUSXNZjbsRc3cEPBxy6+tMvv4FPtii8v27PX7O/9sSGjs8FMZJewQ1abpoUgVCCC79XE1w3OWwB+P0OuaegIb1sON9+L9vD/aR9BvzrjROAlVK0aqQQ5FOo6FDTgF4O0OKjinCNO5/9p/NMzmnUhIwuidtC8kFnGd/O/SCgFepSeaXQ8t2pU4ZESaqGjuZqgtvCD53OcKN3NbGTgB+vngdr6+vD8sD7/T4KC9wUpjroN3tpcfnx+mw4fEFOHHmSM6YN4Z3NzWwYnsz9kS/ClNWAZSn7TPuFnpaglr2GwLV1PQS0zfvHIqtFygjLJMj3UElx/veVfLAvkCAVTta1Pp+eGSuHFvwdTpbJgFtu0PLHfXRqbAZgNsI5e0PfTjtuQ9FuoyQxQRjcjPF8XbTOwxgY2dPLtukkT2y5ongNg4RgGJLWmN+hfLezQ5JfUzNbGh3097jZbcxCWkVK+tw+3DabRTlOlRYxhvgrHlj+NXZs7njvHmcOW9MMEMmx57Ac88thi/cC/MugsnHhsa7W2D3KpplIQ2U9uq5m/8fV4ywTB7RMVNrOmVYKCcB1te4HLaQ565j7vGxGve6NYN3HAPA04s2USrRxn0oYpb3TzIMlLMgpbuPLKHvIJ9AfiXsWq4mTU0p4cnHhDbKr1CZMX7DuCUjVhbBnF+8wqd10UVG2/d1kWO3UegyjLsvQL7TzlcWTQjG7k2HPVbTkDDmXgDn/BWO+ykcciVMO0157lve4r3AbED0mmpZ4FI3tGGqjYbnnhvDuPstoZRkjfu1x4X0c4QQwXCTDsskoNVSDLcnQ4278d3r7TuYCrRxH4p07VMt4+ZdqEINh38npbuP5T1Is9/qmPlQNUMtG5kvgDLuYw8KPe+D5z6iyNXrNk6HjeLcHCNbxh+juEgZv16Nu8nIWXDq7UoqubsFOuvZIdVtfG+e+1kHqtj9EVMsTT+MTlG5Itq4W/+fyYZlCl0OPrzhBP59lbo7M89Lh2US0LZbVVaXTshY42416h9akgrSgTbuQ5GufZBXrsIgFy9Wol8pxIwVW7GVGqJh4xfB4d+GsQer8MZRPwJhV5LBkyyhjiQ89/svVYVMZ8zrXSqhwGWn0OVgc30Hbl8gPCRCaD45lq58QvJK1ZxBwMcx82cCvYtzzRtXyrbbTmPWGEs4zGF67uGdk+ZVl/T7VruqyMXCmnK1e7OISRv3+LTtVs1mRs5WLSczEKtj8a8P0ts7V0+oDkW69qnJyzTxwHvbosbEjDNg71qlGlk2Ab7+X7Xi2OvhqP+JbmCdXxm1j0iOnTaCx65cxCE15TEvKFbychwELLNMrgjPPWjS+zoTZbnDmDp5El+yjefaYw+Iv308jGyZXMLz9Ytyc8IOKdBP4+zQMffeaa1VzkduieokloGYxr0o15H2Fovacx+KdDcn1xd1gHzjqEmhJ7POhmuWKsNuRYhww+402/X17o0LIVg0qSJKmMtkbnUJBU5lxAtcdvZZCjwiPXfT+PVZEtdyh+EoGsGt58xhTGmcln6JsDsJYIsKyxS6wv2jnt6qa+PtXsfcE+PphNYdKsxWUKXmUXz7ryCov6za0cxza0ITwWt2tQAwY1RxsMI5XWjjPhTpaU29UJgF05AcPa2Kd350LG/98JjkX/yt5XDVu33Ou//1F5TGu9Nu45ZzZgNKL/7rxgUmIGXYBGak555rXAT6bDyt/8eCqr691ooQ+GyuqAlVUyzMZE9r/7wxh13H3BPSYvTkLZ8Ehcbn2NU4eMeTJOf85X2ufWQVACu2N/OvD1Sbx3Hl+TS2D7LnLoS4XwhRL4RYGzH+LSHEeiHEOiHEby3j1wkhNgshNgghTk7HQQ8aS++BZfembfdH3/4Gt77wqZoA7Ec2SrJMrirg5Fkj+dzkSsaV5zOhog/ZOEWjYFTfm3Hk5qiv2nHTR/DlQyew7bbTOHv+WMaVqYlKn18yzVCHhGjPPc8w9j0xmncnxPp/HIhxB3w2F3kk/kF+67h+hHwIyQLrmHsczNqPwhGhz9EcywBaujx8Vt8RfD6hIp+2Hh/NaZQjSCbm/iBwF/CQOSCEOBY4C5gnpXQLIUYY4zOBC4FZwBjgNSHEVCll/+5VhxJSwos/VMvzvxKMwaaS7fu6uOftLVxX2opIcW67lY17O5g+KrWFUb3htCvjHOnwn3XgGHa3dHPuwdVUFbn415LttLt9UdkypnHv9g7Ac8+viLtZUrvKL2B+eS4/mTWdCxeO49/LaynJz+HfK1SK3l++fBA1lf1LW3XobJnEmIa8oCqk1NmROca9vceH2zLxftB4FXZduaOZ43trcdlPevXcpZRvA5E5O1cDt0kp3cY2ZgeDs4DHpJRuKeVWYDNwSAqPd/Awc88Btr+f8t2bueeCQFrDMqamy7Ord/eyZWrpcIc6Pllx2G186/gpjCnNI8duY+44dVGL9NwnVSntldElfbyoWucG7APLH7Dl5DOryslVR0+mNN/J14+aFBZzj4y/9wV7cE5BT6jGpNMIwRRUhXT8W3cO3vH0Ea8/gM/y2Zr6Qun8vPsbc58KHCmEWCqEeEsIYTT2ZCxg/Y/XGmNRCCGuFEIsF0Isb2jIgCtwkyXbwxSkSiFthiRuEd0IZMqrUk0GyzE8YkoVc6tL+P6JUxNuZyaLRBr3E2eO5OErDg3TZEkKh0vlRk84vPdte8NZAO6OsKECi0EvGIBxN2Pujyzb0e99ZDWdDar2I69MFdPl5Af1gjIBr18GDfmB40qDldbp7Jnb32+jAygHFgELgSeEEJMSvyQcKeU9wD0ACxYsGPr3oqaYV04+7FmbcNP+0GoY92JhSMqmIeZ+9b9WUJyrKj5/ccbMlO8/EWNL83j22iN63U6ivgqxVPMOP6D39MuYXLNMGYaBkl+hJvF2LlN3VlVTU+65mxo1GpTEwPt3whl3KuOeXxHqAXzcT6FqeuLXDyG8/kDQkD98xaHUG5Op6Ux97e+3sRZ4Sqr+YsuEEAGgEtgFjLNsV22MZT7Nhuc+5UTVBCLFmGmAxSiBrXR47i+uDTWhnjKyKMGWg0fwziJ1IpiqsXgqKKhSn/19J4LNATfuCzPoZREyx33atXNgIaOs5OmroH4dHHRxSHba5LBrBu+4+oG1J3G+097/1N4+0F935hngWAAhxFTACTQCzwIXCiFcQoiJwBRgWQqOc/Bp3gZFY2DUXGjbFRL3ShH1bepKHvTcUxxzlxHFP0mX8e9nzOO0pVDiOGUUjwa3obUf8EHTFgosEr8jivuvuV/gcnDwhDIOqUlf8VrGETBSYxs3qph7QT/v3IYAvoDE4w+QYxcIIYIyFemsa0gmFfJRYAkwTQhRK4S4HLgfmGSkRz4GXCIV64AngE+Al4BrsiJTBlTMvawGppykni+/P6W7b2jr5n8cj3O6oaPuzUltNkukUFFCXfRBxLwGDUnjPsbQ1ik2ppE+fpIiV/+99UgKXQ7c/gDdHj/7Oty09XiD3Z6GJdL4znbUR3vuGYbXF8DrCwSNujnHcv3TH4dNtKaSXu8FpZQXxVn1lTjb3wLcMpCDGpI0b1MysqPnwuTjYMWDqpdniozQqlXL+JNjcfD5p80wtzrBC/pIpJEYqp77YZMrWL69mZHFvYuN7XdmnAEXPgrVC+GJi+GNX1E4InVzFy6HDbfXzzWPrOS/61UC2rzqEhYnMVeRlXgNPf6OvYbnnsHGPaAmVM1ssRxLX4L2Hh9lBSkKHVrQFarJ4O2G9t3KcweYdqpKw4rsmNRPPmvooLXus7Cxrzysmks0drjZ2zbwMuWuiOIfx/5oBdMPvnvCVN78n2P6Vli1vxACpp+qKiRPU/1Z7e/9MWW7d+XYaer0BA07wOra1gSvyHI87eqxtVaFwzI4LOP1BfD4ZZTnDqqfQToYmr/woUarMSdcOl49jl+kHnevhLVPDjhE4/EFGC32hY11oCo3D731dQ699fUB7R+ii3+Gqudut4l+FwLtV0bOgpNugdpljBd7U7LLbo8vmEVhZViGZqQMpZ3uXaceh5Dn/sjSHfxnRW3vGxpc8dByHl22Iygstz+Mu56iT4b2OvVoFk8UGYUxnY3w4o/U8vyvgr1/8ddYxj2AjfYeb7BiMRCQcQW4eiMQkNz5+qawMUeijkaa5Jh4JAA3HNiDfc6CAe+usSN2KfptL67nF2fOGvD+MwqfW7VzhNDvr7xP2dZp5fqnPwbgvIP7Fjs1s+KsYZlO7bkPIh2GZ1ZktKPLK1N509awTNOWfu/e4w8wVkSLIFnDMR2e/n8BPt3TxuKPwitSh+SEZaZRMQWAk0d3csLMgZeQzxgdexL9k93R3auyHo/htQuLDEXltME5lgGQF9V0RmF11Jq7vDG3GSjauCdD0HM3jLvNpppp1K0ObTOAajmPL8AUsYuewvFh41ZJ0C53/2/NY+mVDNVsmYzCmQ+Fo1I293Lj6TM5ckp0XLkwdxjeYJvG3Wwi48jLyIbY5RETpWNKotNlN9W3p+W9tXFPhvY96svlsnhWBVWwe1Xo+QCMu9vjYaqopX38cWHjVuPeOQDPPVa/xqEac884ymqgaVtKdpXntPPPyw9l2Q3HA/C78+cBUFU4BDOHBkLAD+29zFOY8XZznqtkbMoy0/YnkXNd1YYKKsDNZ81i+qgixvanv0ASaOOeDO17lNdu/XIVVIK3K/S8of/GffnqNeQKL97KWXDoVXjO/QcAf30zlEEzEM/d7Y027jrmniLKalLmuZuMKMpl222nce7B1UwfVURT19BvStEnFl8Dv5uqGnDEw/Tczd6+rtTWfewPAgFJU6eHfGcoNHP7+XODy189rIaXvnsUZx0YU35rwGjjngwde0MhGRNTPtbmgJojYd+m6NclyZI1Ku2RolFwym9wzjkbgC2NoS//b15a3+/9u2M0uNCee4ooq1EVy770NF4ozc+hJduM++pH1WMijSbTuE89WfWvPfrH6T+uFHP3W8o5M733qiLXfk3x1cY9GdrroDBiwsxMyyoeo1TqervNTECZUDE3ryvUWu/UOaPCrvjvbm6ku6+NKgxihWWGap57xlE+EZDQkh41x/ICZ9om3Aad5q3x11nDMj/dC9M+v3+OqRf8AUmXJUS6Y19X3G3f/0wlSZhV1/tbq1//wnsjEFAtvkrDJzuDBRUjZqqJns76vjdvBtp7vJQbxn3c2FBald1miyo88sQw0skQy3PP0WGZ1GAWtqU4NGNSmu9Ma7ee/Y6172lLAj12M2TjLEzv8fSRG57+mJk3vhx8vrq2Je62h9Sou/srjVaS2rjvb6RMbJQ79oLfHd042sx5rzlCefV+j2ra20fufWcrZSjjbisMZUrkxAibuP399NyNmPv0UUVMrirgq4smUJSbOk2UYU2ajXt5vpOWbm+w+CXjabek5HbWx9/ODMsMMeP+2IfhF6TI5jNWchzqN3zYJGXk97dxH4Y5VhE8fJ4SJrryzZBWtBXzR2v+iE3mf0U1b5h+Oqx/To111Ksc+D6Qm2MjT7Tjlg5cli+yOeFZlp/DFUdO4vaXN8ScGE0GMyzzyNcXRaVmaQZI4UiVSdWUIMQwAErzc/AHJK98spfPzx7V+wuGMm/eBm/+OvS8M7q2I4jbSA90DS3jHklXgiw28067OE85Utpz3590NcHm12DPGtgbZ3InaNwnho/b7DDnPMjJDeXfdoTH3dt7vNz+8nr2tvVw3VMfU9faHbX7vBw7ZbTTYS8Jy8axGzHxzx1QSXWZSpXy9FM9zixfj+xupEkBQigpgp1L07L70nx1Mb7qXyvSsv/9ytt3hJbzK1Xjk3h4OlWygn1oOyMdCbLY3L4ADpsIzp1p474/adgQWv7bUarDTiTN2wABJQnKjM3J1o7w28w3NzTw5zc+49BbX+fRZTs47Nf/jXqp1x+gXLRTWhHulZkx8QKnHZdDfTkG6rlr454mpp4Mu1Yk9kT7yUC6Ow0pAv6QPjsoddVEPRE8HSokM4Ry22OFxhJJB9z95mf4ApISw3Pvr3PWX4b3r709okn0kj9Hb9O8Tel3OxIUksTx3JNpfuv1S8pEO6KgImzcTFXMdzqCRrm/Xw63z4/DJnDEaF2nSQE1RwASaj9M+a6djpBxi2y4klF0NwMSDrsWvrlU/aYSXQw9neAaWt3CemIkJnQloQszagBNXAbC8P61dxiNuc+5R8kJbHtHeRgmS/4Max6LnkyNJLcU7C5oC79YJKP25vEFKCfauJvSoIWukHF3x1AH3Li3PUw18LuPreLPb2wO28btDWivPZ2YvTzTFHc3+b81dWndf1oxDfmY+TBiuso269oXP5nB3a7mtIYQsVKRE4VlTGw2wW1fmMPT3/xcOg4r/vvu13cbanTsVXG9OefDab9TXzYzdiolvHx9aDkRQkDVVGgILzRq7wk37lZdiYa9O3nhl6fz2n9fYZRoQhSGh2XM9lv5LjsuQ3yoJyIVssvj46Q/vM33n/goOPbMR7u5/eUNdLp9/N/q3UgpcfsCwX1o0kBembq4R94JpoCJlaEJxdU7W1K+//1Gl6F6ahb/5VdCwAs9cfTqzbDMEGJrY3RFbW+KjqON3/yFh4xn/vi+JVsMlGFu3OuhYIQSAjvgBDV5s/55tW7PmtB2J/+q932NnB3SnTZo6/HitIRCdrd2s/f5W6BlB0ue+AOnynd43nUDBcIdJWdq5qYXOB0UGcJR7T3hxSymJ/HCx3uI5PaXN/CtR5UXv6m+XXvu6UQI1V+1LfWe9cTKgqDWTFEmCIh5umIX9EUad7NOxByP2k/noHnub6yv54kPo3Pwz/vrkqixRGqtRS4Hp8wendJj6wvD+xffWR+Kl+cWw8SjQsb9k2eVrO8PP4OxB/e+rzHzVSXrpleDQx09Pr7sfIunnTcykiamilpGfvhb3E9cwQHtavLWLXPols6oCjxzEjQvx06xkZPe1u2LuY2JdTa+rVtdCO54ZSMfbGkKEyHTpIGiMVFhuVQxoiiXkrwc9sXRex9S3HeS0o0xK0xNzMwY06jn92Lc3R2DEnOva+3msgc/5EdPrul126JcB88nCJV5A4FBLRYc3sa9Y2+4rMCUk1VJ9LJ74Z07YMLhybf2mnuBqmL9b8jLb+/xcTlPM9+2mRdc1/ETh9LUaNq1icnu9dzjO42TPL/hOPfvoipgzTi602ELemzXP/1x2CSttWJ1c30HT64MdYZ5atWu5I5bkxqKx6QlLGMyrjyP7U3xS92HBAE/7FVNLNj2Tvi6qLBMuXqMN6k6SGGZVTtaet3m+Okj2PrrU1lYo84h3kS3zy8HVcNpmBv3etUP02Tyserxhf9Rj3MvSH5feWUw6xwVmvErr9nd1U61VCGTCtHOcfaPABgtmnAJLysCU9kuR1FHRdTumoyS87FleWEaM1NueDGYkmX13E/4/Vv86D/xvY2bz56d/Llo+o4ZlklTRsv48nx2NQ9x495uCQ/uC5/Up3MfOItCWWfBsEwi477/wzKJbHFVkTr2m8+ejRCCBTUqhh5Lu0lKiS8gBzVDbfga94AfOhvCPfeKA0LLFz2mWuf1hcqpapKoVcXrXN3qy36H93ye9Ed3sF8WiN9Z5udnzOLSz9Vw0PgyhBA8963Q69uM2HsszZh4HDS+NOltNf2gaIySqehuTsvucx32mEZkSNHZEFqODFF17Qt56xAKy8Tz3N0dg16dGumRVxQ4OXHmSMYY+utml6VYWTRmiDSWjMj+olfjLoS4XwhRL4SIKuEUQvxACCGFEJXGcyGEuFMIsVkIsUYIcVA6DjoltOxQRRVWWQEh4IJ/wuHfhWmn9L2AotRImTSqWnO7VVHTCjmVH3i/yZnumznK/QcAXvIvpJn4GtXTRhXxizNnBW/rZo8tYaHhKZh9GIPlzXEm2o44IBRSMgspNGmi2Oir25aecJjTYUuqbiIQkHz1vqW8s6mh121TjvXC1hrRPLqrMTzE6cyHnPzYMXe/V10oByEs02lJbYysK/H4A2FaMuYddZcRQl22tYnN9R00dXrwGcbdPogx92Sm3x8E7gIesg4KIcYBJwFWrdNTgCnG36HA3cbj0GOf0QjD6q0DzDxT/fUHi4iUlBJvcy044acXHc9pD+9hjVSNB+b13IMHB3ab4LErFyXdaefa46Zwyf3LaOr0MLkqdDtYXuCkrSd61v7Hn5/Og6OL2NLYGdYBRpMGgsa9DkbNSfnuc+y2pFRBW7q9vLOpkVU7Wlj7y5NTfhwJ6TYqTotGx/bcI2Wz8ytje+6DKBpm1Yrp6PHhKgyFRN3eAC5LmCXX4rk3tLu54G8qm8Zpt7HyxhOB8EbY+5te31lK+TYQq074D8CPAOu9y1nAQ1LxAVAqhBi8XKBEmM01jCbHKaF4DNhyoHk7z67ezSihPJlZ06az9densvynJwDQSiHd5LKwpoyFNeXUVCYXWzRL0c3iKDPH9tjpsXtLlhXk4LDbmDpyaFX6ZSV98dw7GxOX3sfA6UjOuJtZUoMSDTDPacTM8Pg7qJh7fkRyQn55yHO3hkCComH7/3trDX1FNsiJ9NzNsMzvXtkQFiL1+AP4DK8/4yZUhRBnAbuklKsjVo0FrAmitcZYrH1cKYRYLoRY3tAwCLeQtR8qTyLZbJhksNlV1kvzNpo7PYwUTbTKfHAWIISg0uKhT6os4Oaz+jbJad4GmjG+T+rasAn4/olTWXb98WHbnn3gGO2t708KR6rU2fZect2lhDsPgv9NIr3WgtNho9Pjp7aXSdUWw7gPilExwzIVB4S8eFDn3NUYHnMHo0q1EfZ8DL+dBFuNDJueNvWYu/9b63n9oYvMM6vC7z48vvBK7zzj9/ji2j1sj2jaYe4no1IhhRD5wPXAjQN5YynlPVLKBVLKBVVVVb2/IJX4feqLVHNk6oWJymqQzdv43asbGS2akEVjYm72j68dwpQ+etTBGJ9h3Bs73JTlOynKzWFEhH7F0dP28/90uGPPUQa+N8+9swHcrcr49UFozJyYO+3OdxNuZ3ruYjAEt7qaVEZM0UjVX9hrqKB6OsDXE6opMcmvVB79xpfV/+PDv6txt2HcB6FvqulxnzJ7VFCN1cTjix1zB/jy38NVQX0BtZ9My5aZDEwEVgshtgHVwEohxChgFzDOsm21MTa02PKGKmCaeVbq9200TG7v8TFe7MVVGa5Lc9JMFXeM/OIkQ75ThWU63T6uf/pj/vXBjrBJtseuXMSUESpOaRtCanrDhlix5kisKYIRFc2JMOdUWrsTt9wzwwqRukY+f4AfPLGaTXvbk37PPtPdrFKC8wwP3QzTmGqpBRHG3fTczf/DJ89A7YpB9twDCKG88sjspMiwTG4CSY/P6pVUgTOTjLuU8mMp5QgpZY2UsgYVejlISrkHeBa42MiaWQS0SimHntrRtndVbPyAE1K/77IaRE8Lrzt/wAzbTvxjwm+///dL81l940n98qxMT2FLQwePLFXz2NaJ1EWTKpg6St0NDIrnNtwpTqJK1RqLrv8kfN3GV1SFZwy9lT1JVhibF/vI+PzWxk6eXFnLiX94u9fQTr/pboL8slChkhmaMe9QCiLuJgtHKA/fqoX/70ssnntJeo4zAR6/JMduwxWRetrW48UfkDjtIYOel8C4f7hNnftgZqklkwr5KLAEmCaEqBVCXJ5g8xeALcBm4F7gmyk5ylSyYymsfAiqF6p0rFRjZMxMttVRJ8uRcy8MW+1y2CnJ798Hnpdjx24TrNkVR2wJgtPbgziPM3wpHtu7cbem/jVuDF/37LXK0MXw6NvdyTXJtt7JWbWIrFkP/12foL3dQOhuVl67GVs3z9Vsp1cYYdzNHgltu+CI78Oia1SNiPk/HCTP3Wm34XLY8FgmSef+4hUgvK1enjO+ce820iOLh7Jxl1JeJKUcLaXMkVJWSynvi1hfI6VsNJallPIaKeVkKeUcKeXydB14v+ioh4fOBJ8bTr4l5bv3ByQvtYyjS7p41X8wh7nvwllZk7L922yCEUWusBLpyBz3gJF1INDWfb9TPEZ5nWZYIRadDYCAMQdB46bwdabcdIwLxC1nq/TKsaWJw3kN7e7g8qd1oRCMtdm6ddIwpXQ1xQ7LmDr3kWGZEksEd+QsGG9kTe9eqR4HIebu9Ss9GJfDFvTcrZkwVuOekyDkYmayDabnngEycylk7VNqYucbb8PoeSnf/Utr93DNs7up4I+0oGLfqY65RQqA3XnR/LDnUnvug0exkRjWXhff6+w0skaqpqu5n1jEmJStqSzg4sMm8OzqxHcGv3r+0+Cy1dBb87f7UtncJ7qb1bmZYZmufSqt8b0/qT6zkXnuxZZEupGzQl/eXauUQmvO/m1y0eXx8dCS7YAy4m5fACllWGGTNVumLD9+C8DmLlVoWJw3eCZ2eMkPNH2mvIE0GHaAhnZlePdRgh87v79gXtpj35URBVBBz10b9/1PMrnunQ0q9lwxWV0EzJxuKUOx5tbYry/OzaGt2xuz3Vss9raFHIEui4Hqb7vGhAQC0NNieO6Gbnl3cyiefvKvlLS2lSJLD4OKAwzZawGtO0IXiP3ImtpQuPPTOvVZvPDxHjos81pWz91uE/zs9Jkx92XKcJuKroPB8PLcW3cl7oU6QHY0hTfAPm1u+uq3Rpfk8lCMdErzd68nVAeBoHFP4F131CvjXmkUzzVugrEHqRCG35D0jaMuWZKXQ0BCu9vX6+2+02Hjk7pQeKjb0q0rLRo17laQARWScThVSmTXPqgzxOxmnxv9Gpsdzv6rWrbnqL/S8dCyPVwWZD9hNdzvblaTwB9ua2KipcgwMm89Nyexf5yfIC6fboaX595WG34rmGLW7g5d+X955qxgY+tUUmRUqT71zc/FyZNX1l2nQg4CRcbFPJFxb9muNIhGGgVsew3Jpk7LJGdnbI1zM312x77es10OGl/Khj2hmLs1eyYtYRkzvm567QWV6i5l90p1vuZ4JAdepP5MKpREB5VTU3+MvdBjzEvMG1fKb86dC8ABIwpp6Q7p6HdEyHzkWn7j912yINh5yWQwnazhZdxbd0FJeoy7lJLl20JVeZd8riYt7/PUNz/HL86YyeiS2BNrPz9jFqfNHc2RU1JYeatJjpxcVZgTLyzj7VahmLIaKJuovFvTszXTBfPK48rgTqpS8zhb90W3e4NwFcNpI4vY0tARHLNm0STruX9a10bNT55nbaLsLJPuFvVoZsoUjlRpn9veU30RkqX6EPU4dT/r4hC6u7n5rFkcbojuBaQMC29F1hlYc92PnzGSxdeEzrWyMH5Mfn8wfMIy3h71oylOT1jG7QuQZCh0QEwZWZSwsnVceT5//tLQFePMehLlurcYGntlNSr+PHJWKO3RlMsdMSM6/91glFGFXN8WnfMupQzLiJk8opBOj5+PdrYwf3xZ0Li7HLakY+4vrlVx49c+3cvssb3knJvSA6aHXjQSPlmsliceldT7AXDU/8CM09MivtYbpnHPy7EHjXZtczf3vL0luM3c6tKw1xRGZKtVWObAvnvC/r/7sDJ8PHdT86M4thzAQDF/WNNHFenGGMOZ4rFxJ0RNKehgPLlySkjAzvTcq6YrQ+mPVvksznPgctjCPEmT7zz2EbN+/jIAPzx5GgdPUEb2W4+uAlRxDqjWcMmGZdyGsUtUiRnELFgy0yCtDd8nHpnU+wEq7j4Ihh1Cmk25OXZyjfj7g+9vC65ffeNJHDU1PFd/RFF4QoNV06e0n/UsqWL4GHfzVjlNxt3sTH/Z4TV8ddGExBtrspfScdFa5qCkgN/5nVoOGvepymPvagrlv1cZDVyswlsGQgjKC5y0dKnQQIfbR1uPl58+83FYiuTI4lxmjVGedm2zmuQ3PfdClyPpsIzZ6jGp5uqxPHdQ4Zk0JjGkkh7Lxcxht+GwibC5iljFh5Exdgiptw52D4XhE5Yxb5XTNKF62YOqUKM37Q9NllMyzhAGa4G80tD4Sz9WaYF55SElUnPS8OP/wI4lKl5timt1NkQLbaFS68zv2GzDU48ksiy+pcsTTO3LczpYtSO5blGdhifr9gVo6fJQmu+kx+vnyZW1nDRzVLDtHGCZUDXOudK4SA2SF94fgmEZI8MlN8cepdETSWmMXPeqIhcdbl9QC2qwGIaee3rl5a0FD5phiNno3Iyvm+xW4RHOfzBUhGCmQ774Q9VQuqCq1/ZzJXnKuFuLkiJp6nSHPT/+d2+x+CPl3Hxa10Zjh4eNSQiImVk5t724ngNvehWAv7+zhRueXstTKyPuTrqbIbdEpTeCmhA9+ddw1p97fZ+hQrdHeelmSMaa5njmvPh3/H/+0kE8fEWoJ9H1p84AoKZicCW3h5Fx362EiNLUAODLh6ofdbqyZDQZgmncWy1tDba8pYz9KbfDpKMt20aE73zukLhWnIyZikInWxs7qW9zx1wPcMoc5cD84gxVYGO2ZbSyryN6LGqbiIuElJK9xvtGtfzrbgrF20HFzg/7Znih0hCn2+vHabcFZXobjf/RCTNG8PsL4hc+njZ3dDC7BuDEmSPZdttpYZOrg8HwMe4tO9MWbwd1C1foclBeMLjpT5pBxjTYVs/91Z+px+mnhm9rd8Dpfwy1evR7QiGbOJ77sdNHUN/uZtnW+J2czKrlRMJWToe6e2jt8kY1gjbxR6R/dXr8+IOplRK2vAlL/qJWmnK/GUyP1x+zKKkkzzmouuz9JfOOuD9IqYopRs9N+iUrtjclrX29r8PN82vqBrXrimaIkF8OOQXKuC++FpbfD3WrYc75sScWF1wG31wKB10MFz2qDKSwxTXus8YozZoPtsYudLLSW5bLtsZO5t30Co8s2xFzfaTA2MMfbA82s/D5PPDQWfDydSqzxxQNy2B6vP6YF8RM/V0PD+P+2X+hYy+MOyTpl5x79xJO/MPbSW177SOr2NPW0+vki2YYIITKmNm1Alb9E577nho/6JL4r7E74Mz/VZpHNnt4IVOEtrs5iZlMzDzRhJ7bG2BHk4qpP78mdssFs5uQya9fXB/MHinuslwQ2mqNNnr7Xw9mIGzc2845f3mPZiNs1eXxJ5f2mSEMD+P+zu+VVzH7vLTsvq7VTDfbD1VMmqFP6fjwBhTQJ8eCkrHQtAU2vwa3TYAVDwZXlRvZGRv3dvS6m1ghwmuOVeX9bqPjEMSvWO32hCcHHDmlMvgdL+sMFfawb7NK9UxT9Xe6eHTZDlbtaOHSB5YBqiFHrPTFTFXyyH7jvvwB2P4uHPG98NS0GHh8geBVPFl6vH7KdJxdY8WcVDWpmAKOPkyujVsEOz6AD+4GJKx+PLjKYbdRlOsIy7+Ol5VRFWNC77Q5at6p0+3jj6+pAqqoyVFjrCvCuHe6fcELQXnXttCKbe9BwBuuz54BHGC0pDQniVu6Yhv3TCW789xba+GFH8Lk4+CQK3vd/JL7l7Fkyz623XZaUrvf2dTFkb+No8mtGb5YjfvscyGiG1evLLwclv1Nee4AjRvCVuc77bRHCFhtu+00lm1tCtPxH1WSy8zRxZw6ZxR3vKK6PrmMCcNrH1kV3C7SMW3r8XL6ne/iC0juOH8ejR1unlm1i5YuL0WGhG2Je7dqvuFug0+fVS8cEVv+dqjiM+5C9rT1sLm+g7Zub1hv45+fMZNf/l9sKYhMILs99+1LlEdx4k2Q03tD6iVb1CSVVS87kQLfpxZJVY0miNW4n3c/TD2pb6+vmmY0bxfqsWufEh0z2BsnDfKQieUsqAmlIzodNl74zpGcdWAoXBKr2rQkohBn9c6WYDz+0InlXHX0ZA6eUEZzl4e3NioNnCJvgwrDlE1UYRlHbtr6JKQDnz/ASksx191vfkZda09Yf4REnZYygcw++t7YtQJy8qFqRq+b7mwKGfEOS4GIqesciyv/uSLs+bHTquJsqRlWlE1Uj1P6aNStnH03fG8dTD1FPe+tN2sCzEnCAqc9Zhz+bcNgm1g7OJmebEleDs1doerrYm+jkjg204snHp2ensRp4u/vbg0WdgE8ubKWbq+fKSMLg2OnzhnN9FFFXHnU5ME4xAGT2WGZ3R+pZtfH/VSloDVsVHrQZpXcruUw+kCVjdALVm0Oq2az2Qux2xM7Tcrk9R8czeSqwrjrNcOI0fPgwkeg5oj+78NZoP7MScrWnSGt8wh6m8avLHTytcMnMntscdwMmj2tPYwydFIaO0LG3dQjj/Rig8a9oAo+ex0WXZ3ESQ0ddjTFviMfURTSiikvcPLSd/ugaDnEyGzPvb0Olt+nMguW3gN/Xqhi7KCq/epWQ/WCpHb1yNJQatef39gcXP7NS+vZ29bDjBtf4qEl24Ljka3OItXhNMMYIWD6aaocf6CYWkhtsdMVkzscwY1nzOQLB6k8+8sOr4napsmSSGB67vd89eDgmDXXe0yBoDDQpoz70T+Gb6+Cycf2+/gGgzExBL9AqWZmC70adyHE/UKIeiHEWsvY7UKI9UKINUKIp4UQpZZ11wkhNgshNggh0qu4b6rrNW+DJXep5dWPgs+jsmT8Hhh/WK+7ae/xsqslFNN82GLofQEZ7Ghz4+J1wZxgM2vghBkj+OjGE4MTTRpNSrEKiUVgGlxfH1Nwf37GrODyuYbBb+kKGffa5m6qy/I4aVZIOsBu6X86Jd9IwywerXTpyyf16f2HAvHSlp3JKGBmCMmcyYPA5yPGXgVmSynnAhuB6wCEEDOBC4FZxmv+IoRIX1WAOXG1c5lqXzbuUPB2wbt/UCp8hSPhgBN63U1v+ekX378suPyzxWt5aW0d33v8IwCOnFIVUxlOo0kJzkKwu8KM+5NXK4fFbM7c3NW39F2AVT87kee+dQRfP2qisY9QPH3Jln1hWSMQ7rmPz2lRCxmkGxNJPOG1pOSNM4Rez0RK+TbQFDH2ipTS/O98AJh11WcBj0kp3VLKrcBmoA/VG33EWaBifuueUs8P/w4g4M1bVZXfpc+rZr29YG1ecOdF8xNu29Tp4ap/reSldapLTaS8qkaTUoRQ33GLHMHBE8rZcuupfPnQCcbzvpf9lxU4mT22JKg9/vSqWqbe8CK3PP8JLV1eDpsU3qbRbEKR77RTKYzq2IJoSeJMIZY09zHTqpg5ungQjiY9pOIy9TXgRWN5LGCRw6PWGEsfZTXKqxF2mHQslBuZCouuDkmq9oJZEHLtsQdw2KT4JdQFMSZUE02yajQpoaBSNdB+53fwt6PB58FmE9htgtd/cDR3f+Xg3vcRb9fGBOtrn9bj8Qe4952tAJQVhIcZTeGsApeDugZ1odnUmqGlm8C2xugJ1auPnjyoDa1TzYCMuxDiBsAHPNyP114phFguhFje0BAdT0waU4Vv5CyVinXsDUqkadE3k96FGT+fPrqIqiJXUCo1ksXXRjf6nT4qPRLCGk2QgipV0PT6TVD3kSocMphcVRj0vvtDPOckslIzx/DcC10OnH7VoPu9nfFlh4c6u1u7OXnWSFb8NBS2zbZK834bdyHEpcDpwJdlSDN0F2CtQa42xqKQUt4jpVwgpVxQVTWA/PBRRr/S6oXqcc55cO7fwZV8WqLpubsc6ot+6eET2firU/jkptB88FFTq6gqDM2w//bcubzw7SMTNqvWaFJCpFR1HIne/uBy2ML6fpqMLgmPuZvbuBw2ClHJBx/u7nusf6jQ2u1ldElemOb62NLeCx0ziX5d8oUQnwd+BBwtpbTe3zwLPCKE+D0wBpgCLIuxi9Rx4FeU3Ojh3+n3LsyYu3Wm3Omw4cRGWb4q3sjPsYelSU2sKmDmmOyJz2mGMKZUtd0Ffje9Z7YnjxAiSrd9XnUJC2vC4/hmnnuO3cZIlw+v386bn4UrVmYK/oCkvcdHccTdScEA7oCGIr2ejRDiUeAYoFIIUQv8HJUd4wJeNWJUH0gpr5JSrhNCPAF8ggrXXCOlTG/fucIqOOnmAe3i4Q9U6mOsmfKyfCfNXV4KXA5sFg9nILfCGk2fOPAr6tHdDq/9AmRyDa77y+Jro4uvTM/dYRecN6eEnjV5uN2ZqYLa3qMmU7NJJCwWvVooKeVFMYbvS7D9LcAtAzmo/UmP189Tq1TkKNaH3WZ8ERbE8WQ0mrSTkwsLr1C1G5DSsAyATUBAwulzR/PNYw6IfQhGKmSOzUYBPbQ5i/B1S3z+QMZ1KerxqotjfpYnQ2TWp5IGTFnTsw4cw4wYaVDm76jCmGz591WHsWBCGePKsys+p8kAzEyOFHvuZnjivIOr44YazSKmHIcAdzteRwEAnhhywUMdU+I42x207D67JDCLGQ6fXBlzvekjmT+AhTXl/OfqzwUnXzWa/YcZFkyt526GIxNVZzrMsIzNBp4O/A4lEub2Zp5xN7PjMrV9XrIMO+P+zyXbwnqj9niV554b5xYt1/jC62IlzaAjjJ9rysMyysg5E3iy00YVMX1UEd88ZjK4O/DnqGy0eF2chjKm557ofLOB7D67CDbsaedni9dx03MhAf5VO1oAyI9jvG8/fx7VZXlMqirYH4eo0cQnTWGZoHFP4LmPKc3jpe8exaGTKsDTQSDHCMtksHE3wzIXLKjm5rNmJXpJRjKsUj52G+JgbZbS4x/+Zw0A3d7YST2HH1DJuz8+Lv0Hp9H0SnrCMuY1I+kYtLuDQJ7puac3GS4dBI27cTH77XmZ02SkLwwrz90UWLJW5ZkVpjpnXTPkSXNYJnnj3o50Zm5YxuNT/z8dcx/i+AOSXzy7jo2WOHo8TOW7AkvDgnnVpYwsdulGG5qhT9rCMuGPCZESPO3gUk5RJnvu2R5zz/iwzO6Wbh58fxsPvr+N0+aO5vk1dRw0vpQnr/5clAhQs9GQwGwSDNDj8wfbkGk0Q5v0eJqm5x5I5obA260uLoa8RyZmy+hUyAzB2gXebKSxckdLWPMNEzMsY96WgcqWydVpjZpMIE1hmYsPU+J7Vcl0E/OoRh0i6Llr4z5UyXjPvcMdW3S/pctLdVn0GKhbydU7WwhISbc3EDcNUqMZUqQpLHPp4RO59PCJyW3sVuFPW64y7u9tbqS6LC+jBPQ8RnMepyO7Y+4Zb9zvf3drzPFYYvym5/7Opkbe2aQ0qQ+ZWB7MZddoMoNB1HQxPHe7Ydz//u5W/v7uVrbddtrgHVMf8fqGh+ee8WdndkSKZPFH4UrD2/d18v5n+6K263T7dMxdkxmkKSzTJ9zKuDvyMje7bLiEZTL+7E6dE97H8X+NNnl1rT20dnmDH+TSraFOgRMq8oPL6/e0k5uT8f8GzXAgTWGZPuExjXvmhGEi0cY9QzCFv0Clcp0xbwxfXDCOdzY1Mu+mV7jj5Q1AaOJ1dEkub/3wWJ7+5ucAlUqpPXdNZpCeIqY+0aM03B0FpYN3DAMkGHPXxn3o0tLl4c0NoRZ9ZirXcTNCjXvvM2LyNxuSA098Q3WOH1US6qqkdWM0GcFQCMuYxj2/7025hwqhCtXsnlDNaOO+YU944ZJZbbrI0uTaF5G8a7bSqrK014rVpEOjGXIkCssE/LDiH/D4V2DfZ+k7BsO4O/NL0/ceacacUM12zz2js2WaOkM9HJ/71hHBBreRTTd6vH5Gl+Ry+AGVwW5KmdZgQKMJeu6xwjJL7oJXb1TLziI45+70HENPKzjysDlze9+2F+pau5FSiZIB+63xh9cfQAhi9o7NJjLauJ8yZzTv/vhY8p0OyhN0Lr/gb0vY1+GhsjB2kUZSlXkazaATx3MPBOD9u2DSsZBXBlvfTt8h9LRCbklKdnXYr/8LwLbbTuOhJdu4cfE6/nThgfz+1Y388sxZHDNtRC976B8evyTHbouqYM82Mt59rS7Lj2nYX/j2kVx7rGoZtqa2FY8/QGVh+HbHTqsCsv8KrskSgmGZCG+kcQN01sPcC2D8Imirhdba9BxDTyvkpj4N8mmj1eV3HvuI7fu6+MWz65j20xd5aW3sVOeB4PUHsj4kA1lg3OMxc0wx/3PyNGaPDX0RIz33BTXlQLjWjEYzZIkXlqldrh7HLoBxh6jlT56FzsbUH0Mcz90aIu0Pkf1MGzs8uH0BfvPS+gHtNxbb93Ul1K7PFrL+DL9/4tTgcqSsr6mLoVvmaTKDOJ77ruXK4FYcACNnq7GXr4O/HQU+d/i2AT/4Y0t2JEVPC+SWRg3Xt/f0f5+EerSamPfSsZqBrNzRHMx46Q8f7WzmsMkVvW+Y4WS9cZ8/roxCl4OTZ41kaoT+xfHTVUzvpJkjB+PQNJq+ES8sU7scxh4MNhvYc2DWOWq8bRd8+n+h7Tyd8JfD4C+HgrefxrhtNxSPjhr2+gY2cWVtoOOwCdoNzahISeHHP9zBF/7yPv94f1u/3qe1y0tjh4d51amZNxjK9GrchRD3CyHqhRBrLWPlQohXhRCbjMcyY1wIIe4UQmwWQqwRQhyUzoNPhrICJ+/9+Dj+8uWDo9bNG1fKtttOY/bY7P+gNVmAiFHE5O6A+k9USMbkvAfgxiYoqIJNr4TGP3lWxef3bQ4fTxafGzr2QnF11CrPADxpgLYeZdx/fsZMvrhwXHC8sSM83POfFWouoS2GdlQyfO0fHwIwvjy/ly0zn2Q89weBz0eM/QR4XUo5BXjdeA5wCjDF+LsSSFM+Vt8oyc/Rk6aaLCAiWyYQgNoP1fPqhZbNBNjsMPFo2PJmyNPf8qbKpsmvgHVP9f3t23arx5KxAJwzf2xw1UDCJF/4y3u0dXu56JDxXHb4RMrywxMfApZ0tvp2FWba188Y//q6NoBh4dD1atyllG8DTRHDZwH/MJb/AZxtGX9IKj4ASoUQ0fdwGo2m71jDMkvvgVtHw2NfVnntNYdHbz/paOVpN6xXF4Itb8Dk41TYZsNLQRGwpGkzxPiKlVH/ySnTGVeuctQHYtxX7mihscMTrE8pzQ+vU7EqvJoe+8NLdyD7Ual7+AGVTBtZRHWZ9tzjMVJKWWcs7wHMoPVYYKdlu1pjLAohxJVCiOVCiOUNDQ2xNtFoNFas2TKfPAO+HrA54ORfgbMgevsJhsHf8QHsXqUM/ZSTYPa54OuGjS/17f1bDeNeosIyI4tzuesiFXkdiHE3qS5TF4rSCM99X6fy1qWUYVpSz67e3ef3aO/xUZyX0eU9STPgCVWpLp99voRKKe+RUi6QUi6oqqoa6GFoNMMAS1imdSfMuQCu2wEHXxp78/JJkF+pipqe/Ra4ipVxH7cIisbAx//p29u3Gn5bcchfM5UVPX2cUO32RPdevdCItZdFeO4/+Pca/AHJnF+8gtsXCKZNbt/X1af3BGh3eyl0aeOeiL1muMV4rDfGdwHjLNtVG2MajWagmGGZgF/Fv0ti3hSHbz/uUBVfr18HX7gX8stVVs3002DrW31Li2zbBXnl4AyFNMxuRn313L/92Kqw5wVOe1B6oCyiKHH1zha2NnYGu659zega1d7jpTNOJ7Z4dHv85GvjnpBngUuM5UuAxZbxi42smUVAqyV8o9FoBoIZlunYCwFfMDySkDnnqsepp8A0S17EuEPB26UybZKldVfUBcX03Ptq3F/9ZG/Yc6vBrYhRcb5pb0gksMervP5739nKpQ8s69P7un2BYSMU2OslTAjxKHAMUCmEqAV+DtwGPCGEuBzYDlxgbP4CcCqwGegCLkvDMWs0wxTDc2/Zrh5LxsXf1GT2uaqwqXR8+Hi1kTpZuwxGz03u7dt2Rb2nadx9/oHluVuT2UYUhUTJzpk/lqdX7eLqh1cGx8otMiIfbmvu0/so4z48ihZ7Ne5SyovirDo+xrYSuGagB6XRaGJgeu4tRuw7Gc8doGpa9FhZjcqD3/khLLwiuf207lTaNRZM4+4e4ITqqGJLfwWnnR9/fjrVZXn0eP1B3RmTK4+cxG9fUk14+tpFrcfrHzae+/A4S40mGzBj7q19NO7x9jXuUNj2rkqT7I2eVvUX8Z4Ow+UO9CKtGllpOqmyIEzr6a9fDS8yvPqYyZwxbwxnzBsTNu6023DYbdx+nrrbqCqKrfQa/zgCw0ZLanicpUaTFZhhmZ0q82Wg0ruzzlEKklvf7H3bxk3qsXJq2LDdro4psimOle37Opn205fCmta7fYFgSmKOXTC6JC/ma60tMB+8bCEbbzkFgPMXjOPrR06kod2ddL67lBLPMArLaOOu0WQKwbDMjrB0xH4z4wyV/bLiwd63bVBhECrDQzx2427Cn8D7/9F/1gBw95uhDlFuXyBYtJRsLVJFQbiXXlXkoscbCGbR9IYpH6zDMhqNZmhhhmX8bhUzHygOF8w8Ez57o/fQTOMGsOVEva8p65HIc1+6VRW4mxk1m+vbaexwszGiTWY8powoBEJFTibmxaE1CZ2Zxg53cFJWG3eNRjPEsKSUpMK4A1QfAu422Lcp8Xb1n6qJWXt4DoYZc/cnkS1j2v9VO1oA6IxRyBSLf15+KO/95Lio/PeiXGXctzR09roPq4pkZAVstqKNu0aTKVjbwpVPTM0+xxoTmbtXJd6ufj1UTY8aNj13fxKxFZ9xd1BhpDKandB6e+WoktxgY3srRbnqQnPx/cv49QufJtzHCMvEq/m+2Y427hpNpiDS4LlXHAB2J+xdF3+bnjZo3QEjZsQ4JIHdJvBHhGXe3FDPlBteYF9HqFnIzqZu1u1uDTbguPa4KQM6dNNzB/jb21sSxt7Nw1v5sxOpiNNLOdvQxl2jyRjSYNztDjVJWp/A8zUnU0fMjL0LIaJi7g+8tw2vX/L4cpW2aYZvPmvoDHZAM2PmZ0akOyaLNTceoCuBcTcNf2Q7v2xGG3eNJlMQlp9rqow7wOh5ShfeH2di0pQoiOG5AzE999ElyvDuMMS9bjpLtf/r6PHh9irjnue0s/ynJ/Db85KskI1gZHG4B54oht/l8WG3iWEzmQrauGs0mYM1LONIYWhh+mmqN+o/z4mtFLl3HeQUQOmEmC932ESU/MBjHyqPvdvQgSkvUF56h9sbrGZ1OWxUFrqCVa59RQjB2z88lt9fMA8goYhYp9tPgdOOsP4Ps5zhIY+m0WQDBarnL5+/LbX7PeAE1bVp61uw7R3loY+cFVpftxpGzVFqkjGw2wUBy4SqzyJFYOqvl+Q5EUJ57jbDwKbCix5fkc+OJnV30NbtJRCQ2IwQ0FX/XIHNBn/58sF0eXwUDBM1SJPhdbYaTSZTNFL1RrWlOG7scMIlz0JXE/xhNiz9q2r08erP4ZTbYO9aOPBL8V9uE8FMGIC61lDz7XajN6rTYaPQ5aDd7aOutQebIGWVovkutZ8v/X0pAB/deCKl+U5eWrcnuE2nxz+s4u2gwzIaTWaRasNuJb9chWjWPQOv/RI69sC/LwVPB0w5Of4hifCY+9pdrcHl1m4VKnHalXHv6PHx/mf7OGbaCJwpin9HGu2bn4ueHG7u9GjPXaPRDGNmfwE+fkIVNp1zjwrTVEyGA6JEYINExtz3tIU8d7PnaY5DUOhy8PzHdXR5/Fx+RIry9IECZ7gZ29oY3hv2jQ31vP/ZvmHnuWvjrtFoQkw5CeZ/VfVmnXM+zPtiry+x28M9d+vEZjAsY7dRmOsIxuBPmjWSVBFptFfuaKGhPZRf//wa1S+oK8mK2GxBG3eNRhPCZoez7urTSxw2W1iFarvFuLf1qOUcuy1ohGeNKaa6LJ9UESvcsvCW14LL5gTvzNHFKXvPTEDH3DUazYCwiXDhsFgpiU6HjTGGrG9kTvxAsWbdnDQz+o7gmY92A/DAZQtT+r5DHW3cNRrNgHDYbGHCYS1dXiZUhHvmTruNhTXlQPISv8lizV0vTDBpWjlMZAdMtHHXaDQDwm4Llx/4rKGTiZUFYdsU5TrIM8IygVRbdwvnLQh1ipo+qii4XOhyBEXOhgvauGs0mgHhsIuwZh37OtxRui8Ou40CIx89GQXJvnKBYdQPGl/GoRPVHUJNRegCs+JnJ6T8PYc6ekJVo9EMiEjP3esPxMxhzzdSFtPhuP/6C3P5n5OnkZtjZ4whD2zVnhkurfWsaM9do9EMiNYuL+9sagxmpfj8khy7je+eoCR9LzpkPADlRrONpk5Pyo/BbhOMKFJ3CwcYnZvmVJcyfVQRCyaUpfz9MoEBee5CiO8BV6D09j8GLgNGA48BFcAK4KtSytR/mhqNZkiwpVF1Qnrt03pOnDkSjz9gGPepfPeEUEPtyVXK6KaygCkWXzt8IkdPrWLWmGLOO7i69xdkKf027kKIscC3gZlSym4hxBPAhcCpwB+klI8JIf4KXA7cnZKj1Wg0Q5ar/rWCBRPK8PoD5NijJy/tNsHWX5+admXGPKed2WNL0voemcBAwzIOIE8I4QDygTrgOMDUDf0HcPYA30Oj0WQIy7c3E5DElfEdTpK7g02/jbuUchdwB7ADZdRbUWGYFimlWcVQC4yN9XohxJVCiOVCiOUNDQ39PQyNRjME6a9GuyZ19PsTEEKUAWcBE4ExQAHw+WRfL6W8R0q5QEq5oKpqeDSs1WiGC7HCMpr9y0AurycAW6WUDVJKL/AUcDhQaoRpAKqBXQM8Ro1Gk2GkSs5X038G8gnsABYJIfKFCqQdD3wCvAGcZ2xzCbB4YIeo0WgyDR2WGXwGEnNfipo4XYlKg7QB9wA/Br4vhNiMSoe8LwXHqdFoMght3AefAeW5Syl/Dvw8YngLcMhA9qvRaDKbOToVcdDRl1eNRpNyrKX/msFBG3eNRpNy8oZZS7uhiDbuGo1mQLzw7SOjxpw65j7o6E9Ao9EMiJljiimI8NR1Jergo427RqMZMB5/oPeNNPsVbdw1Gs2A8frT111J0z+0cddoNJosRBt3jUajyUK0cddoNJosRBt3jUajyUK0cddoNCll5ujiwT4EDQPUltFoNBora35xki5gGiJo467RaAbMf646jE31HRTn5gz2oWgMtHHXaDQDZkFNOQtqygf7MDQW9P2TRqPRZCHauGs0Gk0Woo27RqPRZCHauGs0Gk0Woo27RqPRZCHauGs0Gk0Woo27RqPRZCHauGs0Gk0WIqQcfJF9IUQDsL2fL68EGlN4OINFNpxHNpwDZMd5ZMM5QHacRzrPYYKUsirWiiFh3AeCEGK5lHLBYB/HQMmG88iGc4DsOI9sOAfIjvMYrHPQYRmNRqPJQrRx12g0miwkG4z7PYN9ACkiG84jG84BsuM8suEcIDvOY1DOIeNj7hqNRqOJJhs8d41Go9FEoI27RqPRZCEZbdyFEJ8XQmwQQmwWQvxksI8nHkKIcUKIN4QQnwgh1gkhvmOMlwshXhVCbDIey4xxIYS40zivNUKIgwb3DEIIIexCiFVCiOeM5xOFEEuNY31cCOE0xl3G883G+ppBPXALQohSIcR/hBDrhRCfCiEOy7TPQgjxPeO7tFYI8agQIjcTPgshxP1CiHohxFrLWJ//90KIS4ztNwkhLhki53G78Z1aI4R4WghRall3nXEeG4QQJ1vG02fDpJQZ+QfYgc+ASYATWA3MHOzjinOso4GDjOUiYCMwE/gt8BNj/CfAb4zlU4EXAQEsApYO9jlYzuX7wCPAc8bzJ4ALjeW/Alcby98E/mosXwg8PtjHbjmHfwBXGMtOoDSTPgtgLLAVyLN8BpdmwmcBHAUcBKy1jPXpfw+UA1uMxzJjuWwInMdJgMNY/o3lPGYa9skFTDTslj3dNmxQv6QD/OceBrxseX4dcN1gH1eSx74YOBHYAIw2xkYDG4zlvwEXWbYPbjfIx10NvA4cBzxn/OgaLV/o4GcCvAwcZiw7jO3EEDiHEsMwiojxjPksDOO+0zBuDuOzODlTPgugJsIo9ul/D1wE/M0yHrbdYJ1HxLpzgIeN5TDbZH4e6bZhmRyWMb/gJrXG2JDGuCWeDywFRkop64xVe4CRxvJQPbc/Aj8CAsbzCqBFSukznluPM3gOxvpWY/vBZiLQADxghJf+LoQoIIM+CynlLuAOYAdQh/rfriDzPguTvv7vh9xnEoOvoe46YJDOI5ONe8YhhCgEngS+K6Vss66T6tI9ZPNShRCnA/VSyhWDfSwDxIG6nb5bSjkf6ESFAoJkwGdRBpyFulCNAQqAzw/qQaWIof6/TwYhxA2AD3h4MI8jk437LmCc5Xm1MTYkEULkoAz7w1LKp4zhvUKI0cb60UC9MT4Uz+1w4EwhxDbgMVRo5k9AqRDCYWxjPc7gORjrS4B9+/OA41AL1EoplxrP/4My9pn0WZwAbJVSNkgpvcBTqM8n0z4Lk77+74fiZwKAEOJS4HTgy8aFCgbpPDLZuH8ITDEyBJyoiaJnB/mYYiKEEMB9wKdSyt9bVj0LmDP9l6Bi8eb4xUa2wCKg1XLbOihIKa+TUlZLKWtQ/+v/Sim/DLwBnGdsFnkO5rmdZ2w/6B6ZlHIPsFMIMc0YOh74hAz6LFDhmEVCiHzju2WeQ0Z9Fhb6+r9/GThJCFFm3MWcZIwNKkKIz6PClmdKKbssq54FLjSyliYCU4BlpNuG7e9JiBRPaJyKyjz5DLhhsI8nwXEegbrVXAN8ZPydiop7vg5sAl4Dyo3tBfBn47w+BhYM9jlEnM8xhLJlJhlf1M3AvwGXMZ5rPN9srJ802MdtOf4DgeXG5/EMKuMioz4L4JfAemAt8E9UJsaQ/yyAR1HzBF7UXdTl/fnfo2Lam42/y4bIeWxGxdDN3/hfLdvfYJzHBuAUy3jabJiWH9BoNJosJJPDMhqNRqOJgzbuGo1Gk4Vo467RaDRZiDbuGo1Gk4Vo467RaDRZiDbuGo1Gk4Vo467RaDRZyP8DcP7tXvc2THgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Combining the predited data to create uniform data visualization\n",
    "plt.plot(normalizer.inverse_transform(ds_scaled))\n",
    "plt.plot(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "16af9283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "370"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ds_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c13df7b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting the last 100 days records\n",
    "fut_inp = ds_test[270:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "52b30579",
   "metadata": {},
   "outputs": [],
   "source": [
    "fut_inp = fut_inp.reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ac2686a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_inp = list(fut_inp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dad5024f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fut_inp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "98042a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating list of the last 100 data\n",
    "tmp_inp = tmp_inp[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "44799d02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.6947309970855713], [0.6953206062316895], [0.7002814412117004], [0.7073392271995544], [0.715061366558075], [0.7226243615150452], [0.729603111743927], [0.7358121275901794], [0.7412080764770508], [0.7458339333534241], [0.7497867345809937], [0.7531965374946594], [0.7562095522880554], [0.758974552154541], [0.761631965637207], [0.7643031477928162], [0.7670859694480896], [0.7700509428977966], [0.7732412219047546], [0.776674747467041], [0.780346691608429], [0.7842351198196411], [0.7883058786392212], [0.7925163507461548], [0.7968212962150574], [0.8011761903762817], [0.8055402040481567], [0.8098787069320679], [0.8141643404960632], [0.8183774948120117]]\n"
     ]
    }
   ],
   "source": [
    "#Predicting next 30 days price suing the current data\n",
    "#It will predict in sliding window manner (algorithm) with stride 1\n",
    "lst_output=[]\n",
    "n_steps=100\n",
    "i=0\n",
    "while(i<30):\n",
    "    \n",
    "    if(len(tmp_inp)>100):\n",
    "        fut_inp = np.array(tmp_inp[1:])\n",
    "        fut_inp=fut_inp.reshape(1,-1)\n",
    "        fut_inp = fut_inp.reshape((1, n_steps, 1))\n",
    "        yhat = model.predict(fut_inp, verbose=0)\n",
    "        tmp_inp.extend(yhat[0].tolist())\n",
    "        tmp_inp = tmp_inp[1:]\n",
    "        lst_output.extend(yhat.tolist())\n",
    "        i=i+1\n",
    "    else:\n",
    "        fut_inp = fut_inp.reshape((1, n_steps,1))\n",
    "        yhat = model.predict(fut_inp, verbose=0)\n",
    "        tmp_inp.extend(yhat[0].tolist())\n",
    "        lst_output.extend(yhat.tolist())\n",
    "        i=i+1\n",
    "    \n",
    "\n",
    "print(lst_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d49a83e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1232"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ds_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "49450ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a dummy plane to plot graph one after another\n",
    "plot_new=np.arange(1,101)\n",
    "plot_pred=np.arange(101,131)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "29f84e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcb519c4350>]"
      ]
     },
     "execution_count": 82,
     "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(plot_new, normalizer.inverse_transform(ds_scaled[1132:]))\n",
    "plt.plot(plot_pred, normalizer.inverse_transform(lst_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e41c9362",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_new = ds_scaled.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d6c8d3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1232"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ds_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "24e99607",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcbc59a0490>]"
      ]
     },
     "execution_count": 85,
     "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": [
    "#Entends helps us to fill the missing value with approx value\n",
    "ds_new.extend(lst_output)\n",
    "plt.plot(ds_new[1200:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7f04f058",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating final data for plotting\n",
    "final_graph = normalizer.inverse_transform(ds_new).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2dfdef30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcba1f08fd0>"
      ]
     },
     "execution_count": 112,
     "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": [
    "#Plotting final results with predicted value after 30 Days\n",
    "plt.plot(final_graph,)\n",
    "plt.ylabel(\"Price\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.title(\"{0} prediction of next month open\".format(stock_symbol))\n",
    "plt.axhline(y=final_graph[len(final_graph)-1], color = 'red', linestyle = ':', label = 'NEXT 30D: {0}'.format(round(float(*final_graph[len(final_graph)-1]),2)))\n",
    "plt.legend()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}