LSTM stock pice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import yfinance as yf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>50.050049</td>\n",
       "      <td>52.082081</td>\n",
       "      <td>48.028027</td>\n",
       "      <td>50.220219</td>\n",
       "      <td>44659096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>50.555557</td>\n",
       "      <td>54.594597</td>\n",
       "      <td>50.300301</td>\n",
       "      <td>54.209209</td>\n",
       "      <td>22834343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>55.430431</td>\n",
       "      <td>56.796799</td>\n",
       "      <td>54.579578</td>\n",
       "      <td>54.754753</td>\n",
       "      <td>18256126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>55.675674</td>\n",
       "      <td>55.855858</td>\n",
       "      <td>51.836838</td>\n",
       "      <td>52.487488</td>\n",
       "      <td>15247337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>52.532532</td>\n",
       "      <td>54.054054</td>\n",
       "      <td>51.991993</td>\n",
       "      <td>53.053055</td>\n",
       "      <td>9188602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-24</th>\n",
       "      <td>2115.439941</td>\n",
       "      <td>2129.169922</td>\n",
       "      <td>2037.689941</td>\n",
       "      <td>2119.399902</td>\n",
       "      <td>3839000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25</th>\n",
       "      <td>2099.709961</td>\n",
       "      <td>2130.000000</td>\n",
       "      <td>2077.129883</td>\n",
       "      <td>2116.100098</td>\n",
       "      <td>2012900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-26</th>\n",
       "      <td>2113.540039</td>\n",
       "      <td>2172.989990</td>\n",
       "      <td>2104.500000</td>\n",
       "      <td>2155.850098</td>\n",
       "      <td>1897400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-27</th>\n",
       "      <td>2189.659912</td>\n",
       "      <td>2246.360107</td>\n",
       "      <td>2182.770020</td>\n",
       "      <td>2246.330078</td>\n",
       "      <td>1893200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-31</th>\n",
       "      <td>2254.929932</td>\n",
       "      <td>2306.810059</td>\n",
       "      <td>2241.679932</td>\n",
       "      <td>2288.139893</td>\n",
       "      <td>1049650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4477 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Volume\n",
       "Date                                                                    \n",
       "2004-08-19    50.050049    52.082081    48.028027    50.220219  44659096\n",
       "2004-08-20    50.555557    54.594597    50.300301    54.209209  22834343\n",
       "2004-08-23    55.430431    56.796799    54.579578    54.754753  18256126\n",
       "2004-08-24    55.675674    55.855858    51.836838    52.487488  15247337\n",
       "2004-08-25    52.532532    54.054054    51.991993    53.053055   9188602\n",
       "...                 ...          ...          ...          ...       ...\n",
       "2022-05-24  2115.439941  2129.169922  2037.689941  2119.399902   3839000\n",
       "2022-05-25  2099.709961  2130.000000  2077.129883  2116.100098   2012900\n",
       "2022-05-26  2113.540039  2172.989990  2104.500000  2155.850098   1897400\n",
       "2022-05-27  2189.659912  2246.360107  2182.770020  2246.330078   1893200\n",
       "2022-05-31  2254.929932  2306.810059  2241.679932  2288.139893   1049650\n",
       "\n",
       "[4477 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GOOGL = yf.Ticker('GOOGL')\n",
    "df = GOOGL.history(start='2004-08-19',proxy=\"127.0.0.1:6152\")\n",
    "df.drop(columns=['Dividends','Stock Splits'],inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(data, time_steps):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(data) - time_steps):\n",
    "        a = data[i:(i + time_steps), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(data[i + time_steps, 0])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.60505340e-05],\n",
       "       [1.40975825e-03],\n",
       "       [1.59489447e-03],\n",
       "       ...,\n",
       "       [9.50229352e-01],\n",
       "       [9.46380969e-01],\n",
       "       [9.26894890e-01]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.array(df['Close']).reshape(-1,1)\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "data=scaler.fit_transform(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3992, 1), (444, 1))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_size = int(len(data)*0.9)\n",
    "test_size = len(data)-train_size\n",
    "train, test = data[0:train_size,:], data[train_size:,:]\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_steps = 10\n",
    "X_train, y_train = create_dataset(train, time_steps)\n",
    "X_test, y_test = create_dataset(test, time_steps)\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_train.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_1 (LSTM)               (None, 128)               66560     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 66,689\n",
      "Trainable params: 66,689\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# params = (hidden size × (hidden size + x_dim) + hidden size) × 4\n",
    "# hidden size = 128\n",
    "# x_dim = 1\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(128, input_shape=(time_steps, 1)))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "63/63 [==============================] - 1s 7ms/step - loss: 0.0040 - accuracy: 2.5113e-04\n",
      "Epoch 2/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 5.5761e-05 - accuracy: 2.5113e-04\n",
      "Epoch 3/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 5.2824e-05 - accuracy: 2.5113e-04\n",
      "Epoch 4/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 5.0239e-05 - accuracy: 2.5113e-04\n",
      "Epoch 5/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.7769e-05 - accuracy: 2.5113e-04\n",
      "Epoch 6/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.7420e-05 - accuracy: 2.5113e-04\n",
      "Epoch 7/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.8364e-05 - accuracy: 2.5113e-04\n",
      "Epoch 8/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.9179e-05 - accuracy: 2.5113e-04\n",
      "Epoch 9/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.6941e-05 - accuracy: 2.5113e-04\n",
      "Epoch 10/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.5921e-05 - accuracy: 2.5113e-04\n",
      "Epoch 11/100\n",
      "63/63 [==============================] - 0s 8ms/step - loss: 4.6120e-05 - accuracy: 2.5113e-04\n",
      "Epoch 12/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.4790e-05 - accuracy: 2.5113e-04\n",
      "Epoch 13/100\n",
      "63/63 [==============================] - 0s 8ms/step - loss: 4.3805e-05 - accuracy: 2.5113e-04\n",
      "Epoch 14/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.5351e-05 - accuracy: 2.5113e-04\n",
      "Epoch 15/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.4473e-05 - accuracy: 2.5113e-04\n",
      "Epoch 16/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.3804e-05 - accuracy: 2.5113e-04\n",
      "Epoch 17/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.1298e-05 - accuracy: 2.5113e-04\n",
      "Epoch 18/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.1217e-05 - accuracy: 2.5113e-04\n",
      "Epoch 19/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.1578e-05 - accuracy: 2.5113e-04\n",
      "Epoch 20/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.0274e-05 - accuracy: 2.5113e-04\n",
      "Epoch 21/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.4279e-05 - accuracy: 2.5113e-04\n",
      "Epoch 22/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.9520e-05 - accuracy: 2.5113e-04\n",
      "Epoch 23/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.9697e-05 - accuracy: 2.5113e-04\n",
      "Epoch 24/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.1256e-05 - accuracy: 2.5113e-04\n",
      "Epoch 25/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.9501e-05 - accuracy: 2.5113e-04\n",
      "Epoch 26/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.9544e-05 - accuracy: 2.5113e-04\n",
      "Epoch 27/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.0608e-05 - accuracy: 2.5113e-04\n",
      "Epoch 28/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.3192e-05 - accuracy: 2.5113e-04\n",
      "Epoch 29/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.7389e-05 - accuracy: 2.5113e-04\n",
      "Epoch 30/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.9340e-05 - accuracy: 2.5113e-04\n",
      "Epoch 31/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 4.0369e-05 - accuracy: 2.5113e-04\n",
      "Epoch 32/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.4217e-05 - accuracy: 2.5113e-04\n",
      "Epoch 33/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.4206e-05 - accuracy: 2.5113e-04\n",
      "Epoch 34/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.9412e-05 - accuracy: 2.5113e-04\n",
      "Epoch 35/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.7036e-05 - accuracy: 2.5113e-04\n",
      "Epoch 36/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.4936e-05 - accuracy: 2.5113e-04\n",
      "Epoch 37/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.2004e-05 - accuracy: 2.5113e-04\n",
      "Epoch 38/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.4580e-05 - accuracy: 2.5113e-04\n",
      "Epoch 39/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.1663e-05 - accuracy: 2.5113e-04\n",
      "Epoch 40/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.1167e-05 - accuracy: 2.5113e-04\n",
      "Epoch 41/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.2356e-05 - accuracy: 2.5113e-04\n",
      "Epoch 42/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.1728e-05 - accuracy: 2.5113e-04\n",
      "Epoch 43/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.3945e-05 - accuracy: 2.5113e-04\n",
      "Epoch 44/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.1237e-05 - accuracy: 2.5113e-04\n",
      "Epoch 45/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.2176e-05 - accuracy: 2.5113e-04\n",
      "Epoch 46/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.9927e-05 - accuracy: 2.5113e-04\n",
      "Epoch 47/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.7905e-05 - accuracy: 2.5113e-04\n",
      "Epoch 48/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.0042e-05 - accuracy: 2.5113e-04\n",
      "Epoch 49/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.9287e-05 - accuracy: 2.5113e-04\n",
      "Epoch 50/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.7366e-05 - accuracy: 2.5113e-04\n",
      "Epoch 51/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.8307e-05 - accuracy: 2.5113e-04\n",
      "Epoch 52/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.0258e-05 - accuracy: 2.5113e-04\n",
      "Epoch 53/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.0711e-05 - accuracy: 2.5113e-04\n",
      "Epoch 54/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.6395e-05 - accuracy: 2.5113e-04\n",
      "Epoch 55/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.9083e-05 - accuracy: 2.5113e-04\n",
      "Epoch 56/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.5610e-05 - accuracy: 2.5113e-04\n",
      "Epoch 57/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.0851e-05 - accuracy: 2.5113e-04\n",
      "Epoch 58/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.6198e-05 - accuracy: 2.5113e-04\n",
      "Epoch 59/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.5347e-05 - accuracy: 2.5113e-04\n",
      "Epoch 60/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.7554e-05 - accuracy: 2.5113e-04\n",
      "Epoch 61/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.3783e-05 - accuracy: 2.5113e-04\n",
      "Epoch 62/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.1650e-05 - accuracy: 2.5113e-04\n",
      "Epoch 63/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.4872e-05 - accuracy: 2.5113e-04\n",
      "Epoch 64/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.4983e-05 - accuracy: 2.5113e-04\n",
      "Epoch 65/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.3501e-05 - accuracy: 2.5113e-04\n",
      "Epoch 66/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.3841e-05 - accuracy: 2.5113e-04\n",
      "Epoch 67/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.3836e-05 - accuracy: 2.5113e-04\n",
      "Epoch 68/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.1901e-05 - accuracy: 2.5113e-04\n",
      "Epoch 69/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.2278e-05 - accuracy: 2.5113e-04\n",
      "Epoch 70/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.3054e-05 - accuracy: 2.5113e-04\n",
      "Epoch 71/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.3018e-05 - accuracy: 2.5113e-04\n",
      "Epoch 72/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 3.0488e-05 - accuracy: 2.5113e-04\n",
      "Epoch 73/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.1127e-05 - accuracy: 2.5113e-04\n",
      "Epoch 74/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.2313e-05 - accuracy: 2.5113e-04\n",
      "Epoch 75/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.1136e-05 - accuracy: 2.5113e-04\n",
      "Epoch 76/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.0287e-05 - accuracy: 2.5113e-04\n",
      "Epoch 77/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.1376e-05 - accuracy: 2.5113e-04\n",
      "Epoch 78/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.4736e-05 - accuracy: 2.5113e-04\n",
      "Epoch 79/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.1514e-05 - accuracy: 2.5113e-04\n",
      "Epoch 80/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.9995e-05 - accuracy: 2.5113e-04\n",
      "Epoch 81/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.9505e-05 - accuracy: 2.5113e-04\n",
      "Epoch 82/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.0198e-05 - accuracy: 2.5113e-04\n",
      "Epoch 83/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.6859e-05 - accuracy: 2.5113e-04\n",
      "Epoch 84/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.9034e-05 - accuracy: 2.5113e-04\n",
      "Epoch 85/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.9200e-05 - accuracy: 2.5113e-04\n",
      "Epoch 86/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.1025e-05 - accuracy: 2.5113e-04\n",
      "Epoch 87/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.0535e-05 - accuracy: 2.5113e-04\n",
      "Epoch 88/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.0518e-05 - accuracy: 2.5113e-04\n",
      "Epoch 89/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.8610e-05 - accuracy: 2.5113e-04\n",
      "Epoch 90/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.9628e-05 - accuracy: 2.5113e-04\n",
      "Epoch 91/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.0725e-05 - accuracy: 2.5113e-04\n",
      "Epoch 92/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.8026e-05 - accuracy: 2.5113e-04\n",
      "Epoch 93/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.8464e-05 - accuracy: 2.5113e-04\n",
      "Epoch 94/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.0613e-05 - accuracy: 2.5113e-04\n",
      "Epoch 95/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 2.0608e-05 - accuracy: 2.5113e-04\n",
      "Epoch 96/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.8899e-05 - accuracy: 2.5113e-04\n",
      "Epoch 97/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.7793e-05 - accuracy: 2.5113e-04\n",
      "Epoch 98/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.8154e-05 - accuracy: 2.5113e-04\n",
      "Epoch 99/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.7454e-05 - accuracy: 2.5113e-04\n",
      "Epoch 100/100\n",
      "63/63 [==============================] - 0s 7ms/step - loss: 1.8480e-05 - accuracy: 2.5113e-04\n",
      "63/63 [==============================] - 1s 3ms/step - loss: 1.6570e-05 - accuracy: 2.5113e-04\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=100, batch_size=64, verbose=1)\n",
    "score = model.evaluate(X_train, y_train, batch_size=64, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_loss(history, title):\n",
    "    loss = history.history[\"loss\"]\n",
    "    epochs = range(len(loss))\n",
    "    plt.figure()\n",
    "    plt.plot(epochs, loss, \"b\", label=\"Training loss\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "visualize_loss(history, \"Training Loss\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainPredict = model.predict(X_train)\n",
    "testPredict = model.predict(X_test)\n",
    "\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY = scaler.inverse_transform([y_train])\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY = scaler.inverse_transform([y_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x286cf6d00>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AEpNJOpWUVzd8iZzuK98AYMbIe+Dm9SQkuJnf66zAfnftNRUjBFyL+l/oFDK6Q8Luz7/ql/8FqVSRGKcjXPHZKxEREamjsrw4UK7x1GDLnXlcPbZ+HbH9og2F5Kx8M7C92NefrPTwpXMSRx4HQGHPA8mkgsqq6kb3q9SmQIozcrbnpcFcXpPcP4e1Ky4qqHPs6ON/3ejrRTPYtYnEOMwyDwq4RERE2ozqkIDL6/VS40+FMNC1JWL7ohcuopMJzrMqM2l15zgddC389keKc/bDZSxVZTvq70RFITV/H0nFqtmBKlfIm4EJCcHHkX3NtkACVOupotNj44Ln6eRPXpDWxDcla0l065GiiIiINEFNRcjyNZUl2FmP1dv+wIqZYdteV4Q3AF1u6DIIk5oNQHVp/W8/1qyZRWLZRpa/9sdA3WmHHxy1fX6p8yZj2fqfAnWfpR8Hv/0Bfv2d8yixGbk1h0tERESaYsjSYIC17+K/kfzTs4HtnYtB12eCnR91nzvZyTpfU1FW7zkq/I8c88s8VJLMh53OJLVTeHoJb0h4sXnzZqd/q4NvWKaW5TnpH7qP2GWfG8vJPhV/FHCJiIi0EW4bzJOVUr4hbF9pAwKu+iT5HwsuefteKmuiT5yvqHL6kEwNKVThS+5Up4017kC5aOMKp7/f3h+oGzNxcpP62hYp4BIREWkLamV1N97wye0VEd4u3Jo8oMGnT0x00kpMSZhJQVn0ifPebbkAHOJeCIArJbNOG7c7OI9re1ERLP+ILM+2QF3GkTc1uF/thQIuERGRtqDaedT3c8ZBACQSnhW+IsKolBcXn5sDGnT65KRgHq9IwdtOvefcHbad1rVfnTbmgGCaipxNM+GlMwPb1X/cAe6G5flqTxRwiYiItAGbFn8DQKXLWQYnifBHiJXVdZflcfuq8biSsD38ecfTo69PHLoGYcGaBQDs+OpxSu/oT40negDWbfShdSuPnArXOFnmuxeGp4aI10zwsdYx71pERKSN6fThtQD4ElMj7q+sqgyUtxcW8vLb03H7qvG6kjAXvA37XQiXfRb1/KEjXOPfOxaWvkfnT28gw1vI96vyAdi2eT1rEwaEHTd4YHh2eQCMgS6D8GGo8QQDwzk9zqrbtoNQwCUiItIGbEobBsDex1wccX91VXAh6S4P9uec+ReQ4inG50qGjBw46d+QvUfU84eOPLmMhVfODWzvzBjvevwX9PesiXpcGJeLKpLpXhOc3J/feb+o12/vFHCJiIi0Bd5qfjIjSE2v+1YgQHlJ3fxZ6aYSnztC7q0I6izDE7rPOqNUXXzBTPHrfDn4zp9W7zl9xk1XCoPnScuK3rid63iz1kRERNqglJoitrq7QUjKhVBm08/YHd2oLtpCaIjlczdwfcIoC0sDVNfUTTnh+c18XDkZ9Z7SZ1yErl+dmJbdsL60QxrhEhERiWc1FSx+/BK6VG2gOiEDXJHHSrxVZdh/7kvys0eH1a/fURGxfR39JsDQYyLuqqqu4tuP3wirG9gtfZentIQHh+7UGI9wXRl5Tcl4oIBLREQkjtml7zNq4xukmSpqEqKPKHmrK3FFeCw4wGxu2IWSM+C81yLueumb5Rz4zSVhdQ3J6N7JVxjex+S6ObuaVc8xsT1/EyjgEhERiVc/vkhFdXAyvDcxA3x10z8AeGqCyUp9NhgMdU1LjNS8UZ4suLDJ5wAYNWRIs5ynLVLAJSIiEo+2LIJpvybtnasCVZWudPDWmk816mQAvJ5gwOUywYlTY0e2fpDz8f5PwQ2r6Jmd1vwnP+2J5j9nDCjgEhERiTfVZfhK8utUb6s04Ar+6Z5z2Itw4r8A8NaEL8ezMXkQjL+MxMl3NGvXCmwm/D63UcckpneG9K7N2o+AMWfuuk0c0FuKIiIicab0oUlkFNcNagYmF0HvYC6rlIzO4HYeGfavWBTWtiy5Oxx/X7P3zZOY6eT1aoSUKKksmkUD5pLFA41wiYiIxBOfL2KwBTDxyNPDAoyUbgPAn/ZhYsUXYW1Xdg9/W7G55Azau9HHpKbFeLJ8Wjc44s+xvUYTaYRLREQkjngK8yL/cf7zDtJc4eMkGdldoqaJcKV1bv7OAa7THm/0MT169IhBT0LcuDK2528GGuESERGJI2sWza5TN63vDWFzt3bKSk2M+kgtKS1Gj/FSGn/enl1jE/y1JRrhEhERiSOdfni0bmV65DlTqYmRs84DlPgamGG+tiFHQu4nu3dsbRdOg5SOu5xPKI1wiYiIxJFKt5PctPj6dYE6T2rkN/zqSz568MjoC1XXa8pLcMOq4HY9C17v0qBDofe+u398O7LLgMsY87QxZqsxZmFIXRdjzMfGmBX+751D9t1ijMk1xiwzxhwTUj/WGLPAv+9fpiEpakVERDoYr8+y0DeAlNTMQPBl0ro0+jyd9xi9ex1ISHZSOPx2PvzqU7jkw8CuwjP+t3vnlAaNcD0LTK5VdzPwqbV2KPCpfxtjzChgCjDaf8wjxgRW2XwUuBwY6v+qfU4REZEObcVPsxi4/Ss8uEh0G5JcTgLTg8cMbfhJBhwCe58TSBex27oMhL7joFPvQJV7yKFNO2cHtsuAy1r7JbC9VvXJwHP+8nPAKSH1r1hrq6y1q4FcYH9jTC+gk7V2lrXWAs+HHCMiItI++XwsXrGKGm/dNQ7rsJahbzljER4SMMbgOvQmALp379nwa178Lpz6n93p7S6l1DNnTOq3u3O4elhrNwH4v3f31/cB1oe0y/PX9fGXa9eLiIi0X3/pzKgX9+XLmcHHcmWF2yI2tVsCM3cY51ruFA66FqYW1R2tOvUxOPmR4PaQo5zv/7egWbodTaJbU793V3P/5CLNy7L11Ec+iTGXG2PmGmPm5ufXXdpAREQk3lWV7giUfTucMYc10+8h/cHB/PjDnDrtN64MBkteu4tpzntPgX3PC26f+yrcvK5pE9wlpnY34Nrif0yI//tWf30e0C+kXV9go7++b4T6iKy1j1trx1lrx+XkNG75ABERkXhQPueFQLnGWvB5GfDD3QBs2ZJXp32vT67Z/Yu53Eq/EOd2N+CaDlzkL18ETAupn2KMSTbGDMSZHP+9/7FjiTFmgv/txAtDjhEREWl3SsorAuXjFt3AygePDe6MMILlcSUHymW9J8a0b9Lydpn41BjzMnAo0M0YkwfcBtwDvGaMuRRYB5wJYK1dZIx5DVgMeICrrbVe/6muwnnjMRX4wP8lIiLSLlUVh8/VGlz8XaBcUVVZp31u2j5QvJGRZ/yZTiNPiHX3Gueid6GmvLV70abtMuCy1p4TZdcRUdrfCdwZoX4usGejeiciItJG2eJNeK3BbepOWa6qrAivyF/GqJJvnRnPY05vmQ42xsBDWrsHbZ5eNxAREWlmtrqcYZums952j7y/sihsuzr3i5bolrQiBVwiIiLNzN7lZD7qZQoi7h+4YxYA1R4f20qrKCUFgPkjb2iZDkqLU8AlIiLSjDy5M3HhJDpdZXvD8OPqtJlQPAOAeQ9dyPd/O4ENG5y3FkuGntJi/ZSWtcs5XCIiItJwpZ8/QLa/bNO7wTkvw9S6KRt+99wX/KPwHXADC78HoFPnbi3WT2lZGuESERFpRluy9w2URwweDMCOwScDUGWD4xz/WH1SnWO7dMqMce+ktSjgEhERaUa+kq1UWzcVY87HNfkuALwjnODKhwsueCvqsd0yU1qkj9LyFHCJiIg0o6SStay0fUg69SHIcN5STMtwHimmmmoYfHjUY1OTtDh0e6U5XCIiIs3IVJdR5srA7Qpmk09NiT5yVX3on0ja/COc9kRLdE9aiQIuERGRZuTyVOB1Z4TVGf86h3bE8RiAk/4N03+DxZB06O9bvpPS4hRwiYiINCO3txKbUOttw55j4KSHMDuX7NnvQhh6NMboEWJHoYBLRESkGSX4qrDJtR4hGgP7XRBel9mz5TolrU6T5kVERJpRoq8Kk6i3DSWcAi4REZFmlGSrMEnprd0NiTMKuERERJpJTeFGOplySO7U2l2ROKOAS0REpJmUb1zsfO86upV7IvFGAZeIiEgT+Lw+1m4rAaC8pAgAk92vNbskcUgBl4iISBP88MSV5Px7MLlbiqkscwKu1Iy6i1VLx6aAS0REpBFKqzxUebzOhs/LuM2vkmaq2LQpj/KSHQCkZ2a3XgclLingEhERaShPNR/ccSrXP/waAEWf3BvYVf7dfxk2/24Acrp2bZXuSfxS4lMREZEGstuWc2bCl+xZsIY571cw/vt7AvuO2fRIoNyjmwIuCacRLhERkQYqK9wCQBI1jP/+dxHbzO10VEt2SdoIBVwiIiINVLJjKwCDXZsCdd69zg1vlDOsJbskbYQCLhERkZ3Kt1NzV382Lfg84u5VucvDtldPehD3qY+E1e176Okx6560XZrDJSIi4le64B0yqgtZ+8af6DXm67B9BT99wEErHwirG3jYxc7C1MffD91HQf8Dcbdgf6XtUMAlIiLil/HBbwHobzeE1VtrcX3197C6r7JP4RBjnI3xv2qR/knb1aRHisaY3xljFhljFhpjXjbGpBhjuhhjPjbGrPB/7xzS/hZjTK4xZpkx5pimd19ERKTpvnj+Dh597oXAdi+znfXbywFY8M6/Mbdns6agIuyYihGntmgfpW3b7YDLGNMH+C0wzlq7J+AGpgA3A59aa4cCn/q3McaM8u8fDUwGHjHGaORVRERa3JzZX3Lns2+Rv30HD/33VX6x6j6uWn11WJv5/zgVr88yZt4fAdjXLuY7xlB23EPUuFM57PDJrdF1aaOa+kgxAUg1xtQAacBG4BbgUP/+54CZwE3AycAr1toqYLUxJhfYH5jVxD6IiIg02Ma1uYyfcSLjgfn/GsY1LI/Y7kT3bEqrPWSE1FV7Len7XwD7X9AifZX2Y7dHuKy1G4D7gHXAJqDIWvsR0MNau8nfZhPQ3X9IH2B9yCny/HUiIiItJuGVMwPlfSIEW8W9DgyUy8tLwvZVdxoQs35J+9aUR4qdcUatBgK9gXRjzPn1HRKhzkY59+XGmLnGmLn5+fm720UREWknVn36JMv/PIqFeYVNPle3ijUR630994Hj7iN5wmWBum3rVwCwPm00AJMGZTb5+tIxNWXS/JHAamttvrW2BngTOBDYYozpBeD/vtXfPg/oF3J8X5xHkHVYax+31o6z1o7LyclpQhdFRKRNK90KNZUM+up6hrk28P3CJfU2X/fgUXz37lP1tlmfMYY5dhSc/0ZYvevKL2D/y0jO6h6oG/XW0QAs63sGDD+exMNv3s0bkY6uKQHXOmCCMSbNGGOAI4AlwHTgIn+bi4Bp/vJ0YIoxJtkYMxAYCnzfhOuLiEg7ll9cCfcNxXdnL7zGmXKc6itn87z3+P75P9Q9oKKQPQq/54C519V7Xpenghp3Kgw5Eq5fBuMuhSu+DDZIr/sPfXfnfnDOS9B5QFNuSTqw3Z40b639zhjzP+AHwAP8CDwOZACvGWMuxQnKzvS3X2SMeQ1Y7G9/tbXW28T+i4hIO7XqyQvJAVz4wPoASPCW0/Od8+kJFJXfTlZaYqB9yeZV7HzgV1rlISM58p+4RG8FHrc/qMrsCSeEJzOl6xAsBhMy66VTZqdmuivpqJqUh8tae5u1doS1dk9r7QXW2iprbYG19ghr7VD/9+0h7e+01g621g631n7Q9O6LiEi75PNyQPGHdao95cWBcnFlTaCcN+MfZD53WGC7rMoT9dSJ3gq8CWnRr+1ys3Xin8KqsoZObEivRaLSWooiIhJ//tIlYnVayapAuazaCaoqCvLoO3tqWLvSaAGXtWT5tuNLzIi83y/R+MK2+3erv73IrijgEhGRuOLbvibqvkM3PhEoV/oDrtR/jw5rU2UTo45w7bhvPxLwkdJjSL196Jzi/Hms7nsgnPU8iW79uZSm0X9BIiISV3asXxoov8Mvwvb96NozUK6qDF9qB8DnSsSN1xnhspaXXnyG9+etDOzvXOaMkI0//tJ6+2CynZfqk/Y7B0ad3PibEKlFAZeIiMSXj24NFI9xz4ExZ8LpT1HlSuVQzzeBfTVVFdi13wKwIPNguH4Z+XtfTYLxUVbpYcfSLzh3xf/RZ9oZ5G0vA+tMgv825RckZfeqvw9jzoQL3oZ9lVFemocCLhERiStdy3ID5cQT74fTn4QxZ5DsCx/RSspfgHnmWACW9TwJMnuSs/BJAIZ+eB7V790CwN6uVTz97BPgqQJge+awXXfCGBh8mPNdpBko4BIRkfhhQxYgOfqvmL2mRG06aMGDgXJSZjcAqoadAMCA4rn0KF0c2H9i4X8pmD8dgM7pyc3YYZGGUcAlIiLxY8dqAN7t+3s48Dfgiv5nqtuO+YFyWlZXABL3vyRi2z3NatI+c1I9dBtxYMQ2IrGkgEtEROJHjfPYsDolQlqI8b+Kelh6dg8AEtI619m3uct4ttCZ3E4TAMgaeUQzdFSkcRRwiYhI3PBWVwLgTkqpu3P0aVGPy+rqX/8wJavOvsqswfQ128je9A3LfX3IydQjRWl5CrhERCRuVFc5I1zuxNS6O/sdEPW4UX2dR4qhAZc3ZySc9Twp/sn2/Vz5DHNtwO3SRHhpebu9lqKIiEhj/bhqE/nlcPSekdMyFBZsIRVITI4wwuVOgNOfcspv+PNo/TEfvNXBNonB49xXzwag25xnmqPrIk2iES4REWkZ1eXs+/wIEl89G6xl5YLZVNd4w5r0+sCZ9B7xkSLAmDNg+HEArOx+NCQkQXKtZXcunA7XBZOnJpxwf6A8/6SPmuFGRBpPI1wiItIyvE4erMPcP8Ht2QwGvul+Lgf9+tE6TROS6llcOikNbt3CYHdS5P2DwrPT03VwoNil36jG9lqkWSjgEhGRlhGaY8svvXR1cHd1GTtnV1XZxPrPlRhlBGwXumfXE8iJxJAeKYqISMuIEHCtSw2OOJV9+XCgXFQTm4ntKYnumJxXZFc0wiUiIi3D+upUbSrYwY5Nq9m4Zhm+r19hjL/+sH2GN++1r5oFPk/znlOkERRwiYhIy4gQcF3Bm/DYm9ROV5rTNULi06booblb0rr0SFFERFpGhICrtmJ3Z5hapEWjpd1RwCUiIi2jAQHXl4N/3wIdEWl5CrhERKRlhARcG8/+MGKThPS6ayGKtAcKuEREpGX4A643+/2B3iMnRGySnKGAS9onBVwiItIy/AGXcUVPzZDSqWtL9UakRSngEhGRlhEIuKL/6UnNVMAl7ZMCLhERaRn+xKeueka4unfv0VK9EWlRCrhERKRlRBnhKr9uFb5+zpyu3l0yW7xbIi2hSQGXMSbbGPM/Y8xSY8wSY8xEY0wXY8zHxpgV/u+dQ9rfYozJNcYsM8Yc0/Tui4h0XItXreWYPz/Hq7NXtnZXGsT6M727agVcqZldcF3wJvxucWt0S6RFNHWE65/ADGvtCGBvYAlwM/CptXYo8Kl/G2PMKGAKMBqYDDxijNGiViIiu8FbXsio5/fiQ9dvOe6Dg5gz9/sGHbf4u4/492vv4/EGUzTYmgrWLfwGG2Gtw+bk8XoBMMb/p+fA30CfcRhjICkdsvrE9PoirWm3l/YxxnQCJgEXA1hrq4FqY8zJwKH+Zs8BM4GbgJOBV6y1VcBqY0wusD8wa3f7ICLSUeW/8Ct6+suZpoLx7x5F5d7b61+cuaaCUR+cyShgzvhV9MxKpWDhJ+zz2QXsAcwo/YLJE/aJWZ99PifIc7n9fTz6rzG7lki8acoI1yAgH3jGGPOjMeZJY0w60MNauwnA/727v30fYH3I8Xn+ujqMMZcbY+YaY+bm5+c3oYsiIu1Tz40f16nL21ERtX1VjYeXn/lnYLvigz9j/jmGfT67IFDX/+ubmreTtXiqygCwCSkxvY5IPGpKwJUA7Ac8aq3dFyjD//gwikgLY0Ucv7bWPm6tHWetHZeTk9OELoqItEPPnhAo2gveDpS3lVZFPWTdf87knI13B7YnbX2BvmZbWJuRpbP5akHs5oNVlWwHwJWq5KbS8TQl4MoD8qy13/m3/4cTgG0xxvQC8H/fGtK+X8jxfYGNTbi+iEiHM/OdF2DNVwCsypqIyd4jsG9bUWnkg6rLGFrwWYPOf9D/xjaqP3PnzWH+8lUNaltZXABAcmaXRl1DpD3Y7YDLWrsZWG+MGe6vOgJYDEwHLvLXXQRM85enA1OMMcnGmIHAUKBhszxFRASqyzh03tWBzaUjroaug6mY6Cz4XLBpbVhzT9kOigsL8JVspaFcpuET5z3lhYx750g6v3AMnvdvwlaV1N/9UmeEKy1LyU2l49ntSfN+vwFeNMYkAauAX+IEca8ZYy4F1gFnAlhrFxljXsMJyjzA1dZabxOvLyLSYax85HQGh2yn5TijW8kDJ8Is2Hv2//HloPeYNKIXAAl/H0AnYK5vGON2/vP6uPvg/d8HT/LLGdBnP6gug3sHAlBR7SUl0eW8PViPorUL6Ar0d22F7//DnK2WvUfvSdJ7v2HTr1fSq3u3sPY1ZTsDrm4RzibSvjUpLYS1dr5/rtVe1tpTrLU7rLUF1tojrLVD/d+3h7S/01o72Fo73Fr7QdO7LyLScQwuDH+p+xf7jgbA1dmZrbGPayWTXhkBvvB/y45zLQdg+ZkzYf/L4Ni/OztOewL6T4SEZEjrQmHGECptIhv+eznm9mze+an+WR9lBRvCtlesWUfNNw8D8PibwV/xPp/ljffepbRwG2U2maz0tMbduEg7oEzzIiJtwdSsOlXG7X9IkRG+HE7V1/+OeIr+w8Y4hQMuh6lFsNdZYfuLexxAJUkMWf8/AAa9MZn128ujdqmicFPY9rl8QHGq8/J5dmkuS5avYHbuVpZ+/gKnzzmPUXmvU0Q62WmJ0e9TpJ1SwCUiEufsthX1N0gJD8ZmzpoDnrpvLCYn1j+LJCE9i2xTFtge7VrLv9/5luLKmjptPZWlVK+dV6f+5wLnz8rhxW8z8qVxTHhhKKO+usa5vqmhyKaTkdzU2SwibY8CLhGROJefF5Kq4cDfON9PfTxYZwz2l8FHeBWl26HMSfng8/+ar0nK3uV10m3dPF73rj6ddz76tE791sdPY0z+O4Ht8k6DADim2skPNsa1JuI1RrrW73JumEh7pH9miIjEscqtq+n+9tkA/Hza5+w1Zl/Y/3IISQcBYEJGuU5xf8sHj1zHscBHe97H5COOIDExfZfXyiwLvuW47oSX2OPdcwE4fN5VfFV+NSvKUjj+kAP49u3HOLX8u0Bbe1shCW9dDT83LD2ESEekES4RkTj242t3BMqDho0BY+oEW0CdumOrZgCQ0a03dB4AGbtOIu067t5Aufvek+HQWwDoZbZzyNI7uGT9rbhev5BTy18PO84YQ9KBv4580rP+C+4kvL3HAWCnvLTLfoi0RxrhEhGJUwXLvmXitjcC2xkp9Uw2T86Ea3+Gf+4VVp3Td1jDL9ipd6CYkuiGQ2+GmXeHNcmqqbXc2jF3Od977gm/WwT/GA1Z/aBoPVy3xDnnqHzc1oLPG5zoL9LB6L98EZE4tea7dwikCP1V3XlUdXTuT0XPcaRunhuoGj54UMMvmBThseOIE2Dpu8EmBCfQV5z4KKljzw22zeoLV34N3YY5qSZCGQMKtqQD0yNFEZE4Yst3sO2evXjl9imMXfUIAGXXr4W+4xp0fOrhNwbKX4572Al0GqPPOJh0Q3D77BeiXys02Nqp55i6wZaIaIRLRCSebP7vpfSqXMsUghPY0zOzG36CYcfAlV9T/cUDTDrmjMZ34LJaI2lRAra1tjv9G392kQ5LAZeISBzptSk84Ck/5WkanZe95xiSzn6m2foUSeYFmvwu0hh6pCgiEqc8XYaQts/prd2NOtaPuZouQ8a3djdE2hQFXCIicWapGQRTi0j4bd1M7q2i/0HO95EnAtDvxD+2YmdE2iY9UhQRiSNF7i7kmiGMaO2OhLroXbA+8FbDERsgSYtPizSWAi4RkThirA/c7tbuRjiXC3A5aR26DW3t3oi0SXqkKCISR1x4Ma44C7hEpMkUcImIxBGX9YJLDx9E2hsFXCIiLeTnxYv560OPsa20Kmobg08jXCLtkAIuEZEYKPjmOd5/5i6KKoJL4fR46yz+uO1G7n3pfT5ZvCWsvbUWAJf14Yq3OVwi0mQatxYRiYGuH/+W44BXvzyI7hlulizP5dc16wG4d+PFvP7SJNb+7hX6d03nm5fvYcDSxym9cj7DqcalES6RdkcBl4hIDCUveJHDSl/lsFr1ZyZ8yRNffsVhfSwHLbsbDNz/0O8YnghH7ngF+E9rdFdEYkQBl4hIM/NVlQfma5xS+mrYvqquI0kuWAJAp3mP4vk5uGbi9Yn/AyC/81h6tEhPRaSlaA6XiEgz2/bJ/VH3JV/xKRz0f5QkdefshJmMsKupMKlhbcpPeizWXRSRFqaAS0SkmXWfcx8AxUNODtTVdOpPxSWfQVI6HHU7KW4b2Pd11zPh1s2B7QEDBrdcZ0WkReiRoohIE+QuX4TJ3oPB3TMDdTWuZBJ9VaSc/QzMfwaGHkNidj8SQ45LvPANeGwSAHv3SITEVKr7/4KK7OFkGdPCdyEisdbkES5jjNsY86Mx5l3/dhdjzMfGmBX+751D2t5ijMk1xiwzxhzT1GuLiLSqHWsZ8tKBDH6kL1tLKgPV213dmJl4CEmJbhj/K8juV/fYXnvD9ctg4CS6n3wHAEm/nE7WqX9vqd6LSAtqjkeK1wJLQrZvBj611g4FPvVvY4wZBUwBRgOTgUeMMXr3WUTarPJt6wLlC+96htwtRezYuJIeng0MTCjY9Qkye8JF7ziPGUWkXWtSwGWM6QscDzwZUn0y8Jy//BxwSkj9K9baKmvtaiAX2L8p1xcRaS01xVvYMv22wPaM5JsZ8ugedH58PwAK+h3dWl0TkTjU1BGuB4EbAV9IXQ9r7SYA//fu/vo+wPqQdnn+OhGRNqf0v+czsGRe1P17HfurFuyNiMS73Q64jDEnAFuttdF/49Q6JEKdjVCHMeZyY8xcY8zc/Pz83e2iiEhM2MpiOud/H6y4rTBs/3rbnYTOEeZtiUiH1ZQRroOAk4wxa4BXgMONMS8AW4wxvQD837f62+cBob+B+gIbI53YWvu4tXactXZcTk5OE7ooItJw7370Ea998eMu25l7nF9lpe4suG4JGAPnvRHYXznwyJj1UUTapt0OuKy1t1hr+1prB+BMhv/MWns+MB24yN/sImCavzwdmGKMSTbGDASGAt8jIhIHVn79Oid8eyZjPz2H/G1bo7YrKC4LlD3XLoROvZ2NgZNgj4kADB2+Z0z7KiJtTywSn94DHGWMWQEc5d/GWrsIeA1YDMwArrbWemNwfWkPfF7welq7F9JRWMvgT5w5V4Ndm0j6994Rm23YXsr3z90S2M7u1Cm4MyEJLpkBl3wIB1wZ0+6KSNvTLIlPrbUzgZn+cgFwRJR2dwJ3Nsc1pX0r+Pt+VJSX0vu2XFwuJYGU2LLl28MmmWaZ8jptvKUF9PnXoMCbPiXXriCzTitgjwkx6KGItHVa2kfiUteKNfQ12yir9vDFwjVMm7e6tbsk7dja5fMB+HZfZ0meBQwN279swRx+uPfYwPYaXw8ysjW/VEQaTgGXxB8bfHm1uKycX/xvb/pPO403nr6XbaVVrdgxaZesZdXHTwAwZMJxrMnanxof2JD/DnOmncd417LA9gDXFoyW3xGRRlDAJXGnZumMQHnjz58BsI9rFaevu5P333imtbol7dSaDx/i8PIP8FgX3Xv0wSamkUI1FTVesJYXH7ubLp4trd1NEWnjFHBJ3MnfHMyPO/6Li8P29dv2dQv3RtozX001A2b/EYAtGSMBMImpjHKt5a+338iqn77kvE33BNsf8ns49TG4cFrE84mIRNMsk+ZFmlP2N3+Nui+rdFUL9kTiTUW1l5REV7M8zisq3E7WgwMD270vfhaAtJRkAO5KfIonphUxyL+//He5pGVp3paI7B6NcEl8qS4jzVMUdXeqt5jKGmUT6YhKt66h+s5+vDDtvahtQudd7UrR9D8EyhVH/Q2TMwyA7of9OlB/mf0fAPlXLlCwJSJNooBL4oqvvLBO3dwJD8HpT7Ghx2GMdK3nqcceaPmOSavb/uN0skwZKfMej7h/y11jmHPXUWz8/q0Gna90cy4ANZPvJ/WgkLxZexwAv/khrG1Ozz12r9MiIn4KuCSubPrudQAKsoKZutOGTYIxZ9Bz2ywArt4W/ZGjxJjPx4+v3cWClXktfum8vHUATHZ9zydfhs/lW//lf+lRvY79a+bQ+/2LWbuttN5zfTXjNUaVz+GnhL1InBBhkensYIC17ZwPmt55EenwFHBJXOkz6zYAagYfHagb1NdJNenqOzZQV+XpeI8VZ33xAff/8z6KK2tifq1la/J46IHbefeHNWH11bkz2Xfx31jyzK+Z/faj5N+2B7MWLm/UuWs8Xr5bEXEZ1Yhmf/cNW27rz4HrndQNmaaCPT89H3AeIb70bS6bZ70SdsyazdsozVvIB4/ewPbaqUTKt3PI7MsAyD7k8sgXdSfCrVvgkg/pNmxig/sqIhKNAi6JGzXrg4sGZx99U6CckuS822HOcFJCLPf1YXNRZct2rpX5SrYy8fMpXL/jDr5ZsDJ2F6qpYPNfRzH82dFcU/wA6z78Z2BXUUUN69+8FYCzEr5gwvybyTFFLHzlNny+hs+dWvbkpRzw4ki+XlZ/0PXetFf45x8vZsIHx9HDFIbtK3V1YvrHn/Dxn4/g3I/GMr7ia35MPziwf/UrN5Dx5EEcu+Vxnnz7w7Bj13znvGFY6s6i/y8uiN6BxBQna7zybYlIM1DAJXFj27olAFQk55CSkgJ/3uF87ZTZg83DzqOrKWZDYUUr9bJ1LJ83M1DOW/pd1HbWWjxe325fxy6bQU/PhsB2v/LFVNZ4+XnJUsw9/RhcubjOMZclvM8Lb7zR4GvsudmZY9Vl+kVR2yz/6VuO//EKrk0IzseyQ46Gq5317rv6tnPSN6dztHteYL8ZNAnfif8G4OKEjwL1N+ZeEBYQDvji/wDIO+bJBvdZRKSpFHBJ3Fi3aikAq6d84VS4XM5XiOSsHnSmlM3bo8/RsSVbYGoWT73yOqu2ljBv7Y6obePd6k35/Oe+PzBi5mWBustW/pY128oitn/v2Xt44rYLGzXiFKoywVkd8NvBv6MwtR8numez9I7x7PXqAXQyTpBbeqIzab1sUHCpm70W38+r097hnnvvIL8k+moANXOfD5RHlX1PtadWcOjzwtQshr11bFj1F0Nvwpz/OuQMpyClP51NSZ1zj5l8Ga5hx0S87s95O6gqKWDpHeMDdSPGRVzyVUQkJpSHS+KDz8sBK53HV4P69ozaLKNLL1zGsmPbJmBAxDZVr/6SFODSpb8CJ4bj6dHPcdZJJ5CR3Hb+k8/btIWBjw1j5/tzVb0PIHmjM7q17H+3M+DKewGY99FLfPT9z/zmhr9ywtp7IAG2lVfTLSO50dcsLy8jFSjpcQDpm14EYB9X8BGm3ed8MsaeDYMnkp7RE4wL7ujKPnYx+/zozKv6599WctEfHic7LanO+SuL8kkM2V6ZX8rIXp2CFYvfjtivbp3SA2WTmAKVUGJTSf7TRpLwQOkW3OldnAbn/Q+K8iC1M753foercjv7PD2QApvJCH+g9kXWyfzC5W70z0dEZHdphEviws8znAnRPgwpidH/ECZ2cnIhHfHdL6O2yc/eq07dJYsu4s7XvmxiL1uQz0vfx4YFt6/4kuTLP4Kj7wQgu3hpYNfYb6/iFs+jvHjHhYG63Z3jVlnuBCSp6RkkXvC/QP0M3/4wtQhzysNORfYekJAE7gRKD7417BzXJrzF4hW5Ec9fTngQ+NFbz4Ztl9mUQNmX0SNQTh9zQqCcnOaMwhVljyYpweX0I7tf8CRDj4Jxv4TRp+C6IPios2vIqFivYeMi9k9EJFYUcEmLevudafzj5ffqJKjMXuBMiC847+P6T5DojHQMYBMLf55XZ7f1eem04Pk69QCdlr7G3JnTG5UcszW88fp/eeyuawLbvqu+g157OxsHXkOpqxNbPM7PoXTd/EC7KxKCCUE/XbBut65dWe48qk1Lz4Ree+Md5gQ6ww8+LeoxGcMPrVNXUlwYsW1VpRMIVqU6wdS1W/+Ez2eprPGyIK+ILTucpLezjp6O6/fL8eWMAmBA/0GBc6Sf8gDelM70ufSFXd/Qzp9bCG/3MQyb/OsIjUVEYqftPF+RNm/hl29zyryLqLKJbCnOo2eWfzTDWrpWrOPTjBM5Yuj4+k+SkhUobshby557jQ3bXTh/Op2NM7+posc4UrfMDey7JfFlmPkybyXP4tSJo5rnpppZ2cpvOX1RMNhaMOU7xvQYEdYmzVfKSdXv89g//8IVO+4P2+fLHoCrcA2dfnwMJj/U6OtXbVjoXCPdGUVyT7gMVrzPwIPOjH5Qr32wexwI+11ABUmkvX0p2z68j0fn7MVhU65lRO/OANiaCvaYcwcA3tSuUOEsCH33Uy/Rs/BHOhcvIan3GAYBvfr0B8B1yQewY034m4K99sZ985qG3ZDLDbcV4v3sLlwrP8Ecfz/uPvs1+OchItJcNMIlLeKn5avY8zPnrbRkU0NegfN4x+v18fJbb5JOOb4ee9Z3Cke//fF1GQrAqNk38uQ7n4ftduU6I2SzkyaSetxfqe42Cjv+srA2oz44s+5k7ThR/nl4Fv3Rw4bXaWNwRuhCg63yCz+E89/AdamTAuGXVf/l4/mNSx8x99FLGbX2vwAM7uefRzfoULhtB6R3i35gQhLmkg8w+5xLakoaAOclfMpVxf8g9T/786cHH2HJpmKKFn0SOKTohOAbgrdu+DWXlj3Bae6vOWHLo2y12fTu7X9EmJoNvfdp1H3UYQzuI27FXP45KNgSkVaigEtiqjh/I88+dh99XvxFWH2PZyfyze2H8eg/buOcny8BYMSeDZhXYwyuU5yRm36ufMbMuTlsd7U7A4DSk56G/hNJumYW5vj7sKcF/8APd+WxrXYyzDiRk+cEjN5+E/Gc+iQuV90cUOaMp8O2tybvQdqgCTDkSMjsiSfTSRR71Nv7UVXjadB1C9YuZtwWZ85WwbApJKek7+KIyEytwKy/ayt3FN7C9KfuYuv6FYH6XgNHkT/+9xHPkUcOSfXM4xMRaYsUcElUlTs28fa9l/DenGAm8Y+/ncObs5c1eAHpLe/dwcWb7qCbKQag5ihn0nc/Vz4H2R+4pvRfgbb99j68YR1LCgYDB7iWUhKSeb2KRLzWkJYS/oac2fM0OPyPge3tpVV89PgtvP56A+YBtQBrLVuWfAvAtpT+uC+dQcLeUR7j7Xka/OpTJyfV+W/S/fK3w3YnXB4c9Xvhn7ewZN4XzFy2ldytwUnjP3z5Dq8+8id2lFUDUP7OjQBUZfSl6zn/2f0b6b0f1QMOo3zCdZARfNv0Js+jbP7JCSa3nf8ZGEN6l94RT7GVekbTRETaKAVcEpG1li3PXsAp5W+QO+1utpdVY6vLOeqjIzltxv6k3NmF9duKd3US+q4NJq6suHAGifucE7nt9cvq5NyKyh0eTM1fsSZQ9niq8eAmrXb6B5cbJt3AluFOZvEFHz7F0Rsf4cxFV1Ne3bBRoFj66rUH6fGqk3tq4d5/2vUBfcdBznAYcgR0HRy+L6N7oHhp6eOMfOckDn15KC8+cR9LVq7m9sdfYb/Pzufsrf/i4TdmAOAq2wpA0nULmpZZ3Z1A0sVvkzb5NrhuMdy4GnvCgwBM8nzLj3Y4XQc7j/VSs5w3Tr/0joGpRVQPOQ6A7KETdv/6IiJxSgGX1DHv+T/w8N3X0b9oDgDXJrzJir9NYs7Tvwtr9+X8JWGjJgDbtxfwwlP/YPoPq6natoZUW8HajL3h8i9IHTQR0rvC1CI489ngQX/eAZnRc2/VEZIuACB/y6ZA2VPjwYub9KTIj6TcXQYAcM76vwTqnnmrdRcn9tZUM2nJ1MB2zuB9m3ZCY/B2HVan+ubqh9jj+QO4beMVgbo/rrqADQVF9KlYxtdJB2MaGvQ2hMsNaV0wYy8OVK3ydcf4AzqT6kymHzPeedycNOFSAA44MXzOnYhIe6C3FNuQ6hoPrz9zP8kjj+OMQ8bE5iI+L2NXPczYWtUHuJbA5iVhdad/dTzvfXEA1Ve9TE5mMjOevYsLCh7kfID1U/FaAwa2H/gn+tee+Dz6VMjuD12HNHxka6fUbJhahG/pDFyvnM2mL5+GIyex8oN/MST3aYpJqzvC5ZdZvbVO3SFL/0qN93gS3S377w9rLY88/RQb8wu4019X2ncSo4cOrve4hnBf8QUlr11JZu60QF2y8ZBM3dG8qkcPBSCzXwNeWtgdxmATUjCeSsYO6BqsH3AwnPMqnYcc6WwPOdLJ9RWbXoiItCoFXG3I2o8f5byNd8HGu7AHFwZGCppVVfiIVdUVs0kuXgsvnw1Aocki+4S/wDvXkmJqON39NRc99BBXp8zgAt9PYce6jfM23b77T4p8rSa+MeZKcHKWX+16i9Xvjmbw3L8C0MmU44sywpXcezT403f5cOHCx152KZ8tWsvhew1sUn8aY/XGrfDYJK42wdE5703ryEjNqueoRkhKI/PQayF3Gr4ug50lb2Y/Erz+Ca/Sy11MyrTLGORZBcDe5/wl2tmazNy4Ct79HQMO+0NIpYHhk2N2TRGReKJHim2IZ9PCQHnjbmYS3yV/wPVD1lFw6xaSe42EoUcHdttJN8LYi/H1PSBQ91zS39g/NNi6alagWH3pZ5DQ+CVmGmTAIYHiQH+wtVN6tCV89rvQmWw+7hJcV8zEM2YKAPkf3E3Bsm9j0s0luavYWuJ8Xl6f5auvPmfg40MZGBJslWQOxt1cwdZOfcfC1CJcv/0BjgoPpgbucxgp/YKPLrfsd13sPidwXnQ47XHoPCB21xARiWMKuOKcz2fx+ixrthRSvu7HQP3M+86LmDG9qqSAhX89iHc+/2a3rrdjRwEAeT0Oh0R/YlKXC27Jg8s+p/NhTlJO16UfwrU/hx27+cQXnPlZPUY5329cTVK/2g8nm1FCEjUZfSLuivp40BhnsvkJ/4Bee5NwiDMv7eyKV+n68rHMX5nXrF2s2rSEkS/sy1P/cN6QXPjUlRzy6SlhbTwTf0vm1TOb9bp1uIMrGC4++1snuOoSfHTZ49gbY3t9EZEObrcDLmNMP2PM58aYJcaYRcaYa/31XYwxHxtjVvi/dw455hZjTK4xZpkx5pjmuIH2zHqqmH3H4Tz35IMMeLQ/Y82ywL7zEj5lY35BnWOK5r3Onp6FJH9W9023z15+gA/+fDQrtxRFvN72DSvo/JwzgTk1Izt8Z3Jm+CNAY6Bzf+wRt2FzRsDIE+k59sTwY9K6NOxGmyDhsJvCtr3X5zqT8Bsqe4+wzdVzZjRHt4Lnm+3Mofqd91ke/OMv2XvDK8GdV82CK78h4Zg7IKVTlDM0n5qDb6AkczAjh/sz17tcTmA8tQgSU2N+fRGRjqwpI1we4Hpr7UhgAnC1MWYUcDPwqbV2KPCpfxv/vinAaGAy8IgxRtkN67F12WwOtD9wycapEfcvX7U6bHvTT5/QfaYTgIx1LefH3OBoTXHhNg5fdjvHur5j1jtPhR1XUVrM9LvPo/DxkwJ1I0c0bOkbc8h1mKu/g7NbJ5+VGXJEcOOaubgzcxo3CT8pLWwzs2RVs/SrqLyK939YiWvFRwCkmBr+L+HNwP6yK+Y4I4E9YzRRPYLEI/9I5vU/YFz6305EpKXtdsBlrd1krf3BXy4BlgB9gJOB5/zNngNO8ZdPBl6x1lZZa1cDucD+u3v99q7G6yPlrUvCK/e7EFKyA5sFW8Mff/V66/RAuaspYY//TqDK46WsykOnB4OPj87Puz0s03r5c2dwUtW7DDIbAbAXTKPvsH2a72ZiKaMHdBsGh/weug3drVN4JgVHyY7c8HCTu2StJeve7hw3fT+Glc/jh4zwlwa+3vc+0nvVTdsgIiLtV7PM4TLGDAD2Bb4DelhrN4ETlAE7szD2AdaHHJbnr4t0vsuNMXONMXPz8/Obo4ttTv7f9iXLsy1Y8csZcNK/nS+/6vw15L58I9u2bKR00Yd1ztHVlPDYm5+QfnfXOvvuv/sPTH/jv/zxhU+p3Bpcc2/7pL9iBh/arPcSU+5EuGYOHNGAZKFRJBz+h7D5aDuzr++ORd+8h7k9O6yuy8GXwuUzA9vjDztlt88vIiJtk4k08bpRJzAmA/gCuNNa+6YxptBamx2yf4e1trMx5mFglrX2BX/9U8D71to36jv/uHHj7Ny5c5vUxzanfDvc66Qo2D7pDroc/tvw/UUb4B/BR37lNpnCEVPovew5vhv1Rw44/pfw97q5nPKPfYKcNBe8cWmdfTtMNp1vXujM1eqICtfBg8HcZusv/oF+A6LnwypY9SOLP3uZ8RfeRUqS80ZktcdH0l87h7XzJGWScNNqJzCsLnOu031kbO5BRERanTFmnrW2zuLATRrhMsYkAm8AL1prd05Q2WKM6eXf3wvYmWkyD+gXcnhfYGNTrt9ebVvyBQBVCZl0OfSaug06ha9Bl2aqWLqljAqbRN+jfg3p3fAdeUf4MUf/lZwDzoIxZ2B71c1k7pp8Z8cNtgCy+lGTHAyW+j27Hx++/K+ozTNePIFD8h7jja+D6TB+fO6GYIPz34Q/bSPhD3nBNwST0hVsiYh0UE15S9EATwFLrLUPhOyaDlzkL18ETAupn2KMSTbGDASGAt/v7vXbs4SPbwVgzbnfRJ4AHiHh6bgd77OWnvTKciaBuw68GgYfAWMvhiu+ggN/Ezz8vNcgsxfeESfBBW/DH/PJOuD8WNxK22EMxb8ID1KPWfaniKk38HlJ9pYCcNTXTh6vHZtWc8D6JwHYPvZaZ43DkFQMIiLSsTUl0/xBwAXAAmPMfH/dH4B7gNeMMZcC64AzAay1i4wxrwGLcd5wvNpa623C9dsna8mu3ADA8EH9ozbzTb4X14wbqSaJJKrpZMpxk4/L5Q/GXG644M3IB2d0h+uXonfVwmV236NO3daSKnp0SglsV9Z4KXr3T+xczbG7bysPPfMc16wNPvbtcvzUGPdURETamt0OuKy1X0PUZc+OiFRprb0TAsvGSST+TO/fZh7DgfU0c+1zDlWL38NOuApec0ZZykaeRXoLdLG9Surct07dys2F9OjUE0/xVlYv+Jqsj36HKyEprE1osOW7chau5lwAWkRE2gWtpdjCisoqee2Tbzh38i8Cy8+UVnn4/PsfKP3wLgo6jeQaYEe3XWRoT+lE8iXTnfKvZ4Mrke7dhsS28+1dVl88mX0hIZmEHc6bm7mLf2B8r/EkPjCUoeD8E8MLXly4r/gCHgsuL7RuzG/Yo2fD8peJiEjHooCrhWzOW8UbH3/FuG1vcVnZ51yz5RWmnns43TKSWfvQSZxY8o3zaZR/DkB2ZkbDT66J2M0jIZmE6xcBYNfOwjwzmQvnn0PBkj2onVhjQer+7NNrL4oO/hNZXztzv/Y4ZWrL9ldERNoMBVwxlrt+I19Pf5qxRR9zdfX8QP1DG6dw993nMObU6zihpO66h3uNbrkM5FKXyQo+Xuxata7O/o2pw9kHyOozHACvScDt1v9OIiISmf5CxNiQp0YS7UHfLYkvw7svA7Bt+Dl0m/Ionm8fxlNTRebwX7RcJ6WuzF5164wL23NvzKYfmTQsx6kb6GSRdx/0m7rtRURE/BRwxZLXE7a59aQX6T7yICex6bf/hnnPAFDTdTjdpjwKxpBw0DX6UOKBO4Gao/9G4kfOsj952ePo+3+fYory4NXzyTjwV067lCy4cXXYkksiIiK16W97DG387nV6A4t6n8HoicfSfcwJzo7UznDig7D3FCjIJXHfDp4DK04ldgnm6e1z7kNOIatv2DI9AKR1ablOiYhIm6SAK4YK5rxBb6Dv8TdDnwgLK+8xwfmS+NR3PABlYy4kXS8miIhIEyjgipE3vpzH6Ts+ZnnKGIZFCrYk/mV0h6lFym0mIiJNpoCruXmqyP3bIZxeswyAlLHntnKHREREpLUpJXYj+XyW7cVlfP74jXz243K8vvC19oqWfMYQf7DlOeh69jjq163RTREREYkjCrgiKNixgydffJkNhRWBusrKSpiahesv2XR5oDeHbXyMw6eN54777qWwvBqA9Vu38/701wBYePIMEo76c6v0X0REROKLsdbuulUrGjdunJ07d27LXdBauD0bgOneicwbeRP9+/TmtC8nk+3dHvGQRb7+PDzwYa5cex17sRyAqlsLSE7UE1sREZGOxBgzz1o7rna9IoLatq8KFE9yz+Kk5afgj6GiGu1ayyNrTwhsbxt5Ad0UbImIiIifooJaPGUF0X8of9gI7iSYcTOMuxR6jAKvB9+9g3BVFeEbcxauEx+kW2JaS3ZZRERE4pwCrloWzvqQfYAVA85l6NYP8XXfE9eEK6HvOEjyJwg4/v7gAe4EXLc4a+1pQpyIiIhEooArxMznpnLo6n8AkHn4dbDHowqiREREpMkUT/h98+m0QLDlSetBz37RlpwWERERaRyNcAFL5nzGQV9dCEDJ+R+QOeTAVu6RiIiItCcdfoTrsWeeZuR7pwJQesB1CrZERESk2XX4Ea4Tyl7HY5LYcdIz5Ox7wq4PEBEREWmkDh9w9blqOlQUkpOR09pdERERkXaqwz9SxJ0ICrZEREQkhhRwiYiIiMSYAi4RERGRGGvxgMsYM9kYs8wYk2uMubmlry8iIiLS0lo04DLGuIGHgWOBUcA5xphRLdkHERERkZbW0iNc+wO51tpV1tpq4BXg5Bbug4iIiEiLaumAqw+wPmQ7z18nIiIi0m61dMBlItTZOo2MudwYM9cYMzc/P78FuiUiIiISOy0dcOUB/UK2+wIbazey1j5urR1nrR2Xk6McWSIiItK2tXTANQcYaowZaIxJAqYA01u4DyIiIiItqkWX9rHWeowx1wAfAm7gaWvtopbsg4iIiEhLM9bWmUIVV4wx+cDaGF+mG7AtxteIZx35/jvyvYPuX/ffce+/I9876P5jef/9rbV15kPFfcDVEowxc62141q7H62lI99/R7530P3r/jvu/Xfkewfdf2vcv5b2EREREYkxBVwiIiIiMaaAy/F4a3eglXXk++/I9w66f91/x9WR7x10/y1+/5rDJSIiIhJjGuESERERibEOH3AZYyYbY5YZY3KNMTe3dn9iwRizxhizwBgz3xgz11/XxRjzsTFmhf9755D2t/h/HsuMMce0Xs93jzHmaWPMVmPMwpC6Rt+vMWas/+eWa4z5lzEm0tJUcSfK/U81xmzw/zcw3xhzXMi+dnP/xph+xpjPjTFLjDGLjDHX+us7xOdfz/23+8/fGJNijPneGPOT/95v99d3lM8+2v23+89+J2OM2xjzozHmXf92fH321toO+4WTfHUlMAhIAn4CRrV2v2Jwn2uAbrXq7gVu9pdvBv7mL4/y/xySgYH+n4+7te+hkfc7CdgPWNiU+wW+BybirAH6AXBsa99bE+5/KvD7CG3b1f0DvYD9/OVMYLn/HjvE51/P/bf7z9/fzwx/ORH4DpjQgT77aPff7j/7kHu6DngJeNe/HVeffUcf4dofyLXWrrLWVgOvACe3cp9aysnAc/7yc8ApIfWvWGurrLWrgVycn1ObYa39Etheq7pR92uM6QV0stbOss7/hc+HHBPXotx/NO3q/q21m6y1P/jLJcASoA8d5POv5/6jaTf3bx2l/s1E/5el43z20e4/mnZ1/8aYvsDxwJMh1XH12Xf0gKsPsD5kO4/6fzm1VRb4yBgzzxhzub+uh7V2Ezi/pIHu/vr2+jNp7P328Zdr17dl1xhjfjbOI8edQ+vt9v6NMQOAfXH+pd/hPv9a9w8d4PP3P1KaD2wFPrbWdqjPPsr9Qwf47IEHgRsBX0hdXH32HT3givRstj2+tnmQtXY/4FjgamPMpHradpSfyU7R7re9/RweBQYD+wCbgPv99e3y/o0xGcAbwP9Za4vraxqhrj3ef4f4/K21XmvtPkBfnBGLPetp3q7uHaLef7v/7I0xJwBbrbXzGnpIhLqY33tHD7jygH4h232Bja3Ul5ix1m70f98KvIXziHCLf/gU//et/ubt9WfS2PvN85dr17dJ1tot/l/GPuAJgo+J2939G2MScYKNF621b/qrO8znH+n+O9LnD2CtLQRmApPpQJ/9TqH330E++4OAk4wxa3CmBh1ujHmBOPvsO3rANQcYaowZaIxJAqYA01u5T83KGJNujMncWQaOBhbi3OdF/mYXAdP85enAFGNMsjFmIDAUZxJhW9eo+/UPP5cYYyb431K5MOSYNmfnLx2/U3H+G4B2dv/+vj4FLLHWPhCyq0N8/tHuvyN8/saYHGNMtr+cChwJLKXjfPYR778jfPbW2lustX2ttQNw/o5/Zq09n3j77Jtr9n1b/QKOw3mTZyVwa2v3Jwb3NwjnbYyfgEU77xHoCnwKrPB/7xJyzK3+n8cy2sjbKbXu+WWcofManH+xXLo79wuMw/nltBJ4CH+i4Hj/inL//wUWAD/j/LLp1R7vHzgY5xHAz8B8/9dxHeXzr+f+2/3nD+wF/Oi/x4XAn/31HeWzj3b/7f6zr/VzOJTgW4px9dkr07yIiIhIjHX0R4oiIiIiMaeAS0RERCTGFHCJiIiIxJgCLhEREZEYU8AlIiIiEmMKuERERERiTAGXiIiISIwp4BIRERGJsf8HlAe7JB3GdUoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "trainY=trainY.reshape(-1,1)\n",
    "plt.plot(trainPredict)\n",
    "plt.plot(trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x286da7df0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "testY=testY.reshape(-1,1)\n",
    "plt.plot(testY)\n",
    "plt.plot(testPredict)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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