Predict Google Stocks - LSTM .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZT1f24vHffuf"
   },
   "outputs": [],
   "source": [
    "# import libraries\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1xiv3pJOgqY3"
   },
   "outputs": [],
   "source": [
    "# Problem : Predict google upward or downward trend of stock for 2017 by learning 5 yrs of past data. \n",
    "\n",
    "train = pd.read_csv('Google_Stock_Price_Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>325.25</td>\n",
       "      <td>332.83</td>\n",
       "      <td>324.97</td>\n",
       "      <td>663.59</td>\n",
       "      <td>7,380,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>331.27</td>\n",
       "      <td>333.87</td>\n",
       "      <td>329.08</td>\n",
       "      <td>666.45</td>\n",
       "      <td>5,749,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>329.83</td>\n",
       "      <td>330.75</td>\n",
       "      <td>326.89</td>\n",
       "      <td>657.21</td>\n",
       "      <td>6,590,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2012</td>\n",
       "      <td>328.34</td>\n",
       "      <td>328.77</td>\n",
       "      <td>323.68</td>\n",
       "      <td>648.24</td>\n",
       "      <td>5,405,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/9/2012</td>\n",
       "      <td>322.04</td>\n",
       "      <td>322.29</td>\n",
       "      <td>309.46</td>\n",
       "      <td>620.76</td>\n",
       "      <td>11,688,800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date    Open    High     Low   Close      Volume\n",
       "0  1/3/2012  325.25  332.83  324.97  663.59   7,380,500\n",
       "1  1/4/2012  331.27  333.87  329.08  666.45   5,749,400\n",
       "2  1/5/2012  329.83  330.75  326.89  657.21   6,590,300\n",
       "3  1/6/2012  328.34  328.77  323.68  648.24   5,405,900\n",
       "4  1/9/2012  322.04  322.29  309.46  620.76  11,688,800"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train set from 2012 Jan to 2016 Dec\n",
    "train.head(5)  ## Open is the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>12/23/2016</td>\n",
       "      <td>790.90</td>\n",
       "      <td>792.74</td>\n",
       "      <td>787.28</td>\n",
       "      <td>789.91</td>\n",
       "      <td>623,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>12/27/2016</td>\n",
       "      <td>790.68</td>\n",
       "      <td>797.86</td>\n",
       "      <td>787.66</td>\n",
       "      <td>791.55</td>\n",
       "      <td>789,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>12/28/2016</td>\n",
       "      <td>793.70</td>\n",
       "      <td>794.23</td>\n",
       "      <td>783.20</td>\n",
       "      <td>785.05</td>\n",
       "      <td>1,153,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>12/29/2016</td>\n",
       "      <td>783.33</td>\n",
       "      <td>785.93</td>\n",
       "      <td>778.92</td>\n",
       "      <td>782.79</td>\n",
       "      <td>744,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>12/30/2016</td>\n",
       "      <td>782.75</td>\n",
       "      <td>782.78</td>\n",
       "      <td>770.41</td>\n",
       "      <td>771.82</td>\n",
       "      <td>1,770,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Open    High     Low   Close     Volume\n",
       "1253  12/23/2016  790.90  792.74  787.28  789.91    623,400\n",
       "1254  12/27/2016  790.68  797.86  787.66  791.55    789,100\n",
       "1255  12/28/2016  793.70  794.23  783.20  785.05  1,153,800\n",
       "1256  12/29/2016  783.33  785.93  778.92  782.79    744,300\n",
       "1257  12/30/2016  782.75  782.78  770.41  771.82  1,770,000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail(5)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1647fbc5190>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the trend of stocks\n",
    "\n",
    "sns.lineplot(pd.to_datetime(train['Date']),train['Open']) # stocks tend to increase yearly with some downward trend inbetween."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = train.iloc[:,1:2].values  ## contains open feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[325.25],\n",
       "       [331.27],\n",
       "       [329.83],\n",
       "       ...,\n",
       "       [793.7 ],\n",
       "       [783.33],\n",
       "       [782.75]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OTrF2kR7gx9x"
   },
   "outputs": [],
   "source": [
    "## Scaling\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler()\n",
    "train_set_scaled = sc.fit_transform(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08581368],\n",
       "       [0.09701243],\n",
       "       [0.09433366],\n",
       "       ...,\n",
       "       [0.95725128],\n",
       "       [0.93796041],\n",
       "       [0.93688146]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iofU21B0i6ST"
   },
   "outputs": [],
   "source": [
    "#Creating a data structure with 60 timesteps and 1 output\n",
    "\n",
    "X_train = []\n",
    "y_train = []\n",
    "# since this is a time series data, for each next day stock prediction we want the RNN to look at 60 previous observations. \n",
    "# We take a 'for' loop from 60, coz we can start predicting only form 60th day. So for this the RNN looks at 0 to 59th stock prices.\n",
    "# X_train contains previous 60 stocks before i, and y_train contains the 'i'th day stock. \n",
    "\n",
    "for i in range(60, 1258):\n",
    "    X_train.append(train_set_scaled[i-60:i, 0])  \n",
    "    y_train.append(train_set_scaled[i, 0])\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08581368, 0.09701243, 0.09433366, ..., 0.07846566, 0.08034452,\n",
       "        0.08497656],\n",
       "       [0.09701243, 0.09433366, 0.09156187, ..., 0.08034452, 0.08497656,\n",
       "        0.08627874],\n",
       "       [0.09433366, 0.09156187, 0.07984225, ..., 0.08497656, 0.08627874,\n",
       "        0.08471612],\n",
       "       ...,\n",
       "       [0.92106928, 0.92438053, 0.93048218, ..., 0.95475854, 0.95204256,\n",
       "        0.95163331],\n",
       "       [0.92438053, 0.93048218, 0.9299055 , ..., 0.95204256, 0.95163331,\n",
       "        0.95725128],\n",
       "       [0.93048218, 0.9299055 , 0.93113327, ..., 0.95163331, 0.95725128,\n",
       "        0.93796041]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train # each list will contain 60 observations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08627874, 0.08471612, 0.07454052, ..., 0.95725128, 0.93796041,\n",
       "       0.93688146])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train #contains 'i'th observations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1198, 60)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FOXqJHmNjBkz"
   },
   "outputs": [],
   "source": [
    "# reshaping as RNN accepts only 3D data\n",
    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1198, 60, 1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape ## here the depth is just 1 as we are only using stocks to predict stocks. it can be more if we want to add more columns other than 'open'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.08581368],\n",
       "        [0.09701243],\n",
       "        [0.09433366],\n",
       "        ...,\n",
       "        [0.07846566],\n",
       "        [0.08034452],\n",
       "        [0.08497656]],\n",
       "\n",
       "       [[0.09701243],\n",
       "        [0.09433366],\n",
       "        [0.09156187],\n",
       "        ...,\n",
       "        [0.08034452],\n",
       "        [0.08497656],\n",
       "        [0.08627874]],\n",
       "\n",
       "       [[0.09433366],\n",
       "        [0.09156187],\n",
       "        [0.07984225],\n",
       "        ...,\n",
       "        [0.08497656],\n",
       "        [0.08627874],\n",
       "        [0.08471612]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.92106928],\n",
       "        [0.92438053],\n",
       "        [0.93048218],\n",
       "        ...,\n",
       "        [0.95475854],\n",
       "        [0.95204256],\n",
       "        [0.95163331]],\n",
       "\n",
       "       [[0.92438053],\n",
       "        [0.93048218],\n",
       "        [0.9299055 ],\n",
       "        ...,\n",
       "        [0.95204256],\n",
       "        [0.95163331],\n",
       "        [0.95725128]],\n",
       "\n",
       "       [[0.93048218],\n",
       "        [0.9299055 ],\n",
       "        [0.93113327],\n",
       "        ...,\n",
       "        [0.95163331],\n",
       "        [0.95725128],\n",
       "        [0.93796041]]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Done with preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "## LSTM Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9JRnqsxEjKsD",
    "outputId": "b3e23e78-18aa-4396-c923-c877b8de60e4"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1338dJ0UjRKH"
   },
   "outputs": [],
   "source": [
    "regressor = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2hIinyXUjbVU"
   },
   "outputs": [],
   "source": [
    "# LSTM Layout and DropOut\n",
    "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1))) ## input shape here is (60,1), return sequences =True as we are using stacked LSTM\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UG7nrVaSjuZ2"
   },
   "outputs": [],
   "source": [
    "# 2nd LSTM\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PuNi6PgFj7jO"
   },
   "outputs": [],
   "source": [
    "# 3rd LSTM\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Jp4Ty8fRkBYV"
   },
   "outputs": [],
   "source": [
    "# 4th LSTM\n",
    "regressor.add(LSTM(units = 50))  ## return_seq = False here beciare there are no LSTM after this\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aM6R1z4WkME8"
   },
   "outputs": [],
   "source": [
    "# Output Layer\n",
    "regressor.add(Dense(units = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking the model\n",
    "\n",
    "regressor.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XTrhVN-tkbko"
   },
   "outputs": [],
   "source": [
    "# compiling the model\n",
    "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "I06Nkrz5kkb-",
    "outputId": "dc9d947a-4d5c-4dbc-ed45-31bf44b98dfb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "38/38 [==============================] - 42s 187ms/step - loss: 0.1072\n",
      "Epoch 2/100\n",
      "38/38 [==============================] - 6s 156ms/step - loss: 0.0078\n",
      "Epoch 3/100\n",
      "38/38 [==============================] - 6s 164ms/step - loss: 0.0064\n",
      "Epoch 4/100\n",
      "38/38 [==============================] - 6s 169ms/step - loss: 0.0053\n",
      "Epoch 5/100\n",
      "38/38 [==============================] - 7s 181ms/step - loss: 0.0053\n",
      "Epoch 6/100\n",
      "38/38 [==============================] - 8s 198ms/step - loss: 0.0055\n",
      "Epoch 7/100\n",
      "38/38 [==============================] - 6s 158ms/step - loss: 0.0049\n",
      "Epoch 8/100\n",
      "38/38 [==============================] - 6s 163ms/step - loss: 0.0045\n",
      "Epoch 9/100\n",
      "38/38 [==============================] - 6s 151ms/step - loss: 0.0043\n",
      "Epoch 10/100\n",
      "38/38 [==============================] - 6s 159ms/step - loss: 0.0046\n",
      "Epoch 11/100\n",
      "38/38 [==============================] - 6s 153ms/step - loss: 0.0041\n",
      "Epoch 12/100\n",
      "38/38 [==============================] - 5s 143ms/step - loss: 0.0044\n",
      "Epoch 13/100\n",
      "38/38 [==============================] - 6s 146ms/step - loss: 0.0048\n",
      "Epoch 14/100\n",
      "38/38 [==============================] - 6s 151ms/step - loss: 0.0043\n",
      "Epoch 15/100\n",
      "38/38 [==============================] - 6s 152ms/step - loss: 0.0039\n",
      "Epoch 16/100\n",
      "38/38 [==============================] - 6s 151ms/step - loss: 0.0034\n",
      "Epoch 17/100\n",
      "38/38 [==============================] - 6s 145ms/step - loss: 0.0037\n",
      "Epoch 18/100\n",
      "38/38 [==============================] - 6s 146ms/step - loss: 0.0040\n",
      "Epoch 19/100\n",
      "38/38 [==============================] - 5s 145ms/step - loss: 0.0042\n",
      "Epoch 20/100\n",
      "38/38 [==============================] - 6s 151ms/step - loss: 0.0036\n",
      "Epoch 21/100\n",
      "38/38 [==============================] - 6s 155ms/step - loss: 0.0037\n",
      "Epoch 22/100\n",
      "38/38 [==============================] - 6s 152ms/step - loss: 0.0036\n",
      "Epoch 23/100\n",
      "38/38 [==============================] - 6s 153ms/step - loss: 0.0035 1s - loss\n",
      "Epoch 24/100\n",
      "38/38 [==============================] - 6s 149ms/step - loss: 0.0032\n",
      "Epoch 25/100\n",
      "38/38 [==============================] - 6s 151ms/step - loss: 0.0035\n",
      "Epoch 26/100\n",
      "38/38 [==============================] - 6s 149ms/step - loss: 0.0032\n",
      "Epoch 27/100\n",
      "38/38 [==============================] - 6s 147ms/step - loss: 0.0033\n",
      "Epoch 28/100\n",
      "38/38 [==============================] - 6s 155ms/step - loss: 0.0031\n",
      "Epoch 29/100\n",
      "38/38 [==============================] - 6s 152ms/step - loss: 0.0028\n",
      "Epoch 30/100\n",
      "38/38 [==============================] - 6s 158ms/step - loss: 0.0029\n",
      "Epoch 31/100\n",
      "38/38 [==============================] - 6s 153ms/step - loss: 0.0030\n",
      "Epoch 32/100\n",
      "38/38 [==============================] - 6s 160ms/step - loss: 0.0025 \n",
      "Epoch 33/100\n",
      "38/38 [==============================] - 6s 169ms/step - loss: 0.0025\n",
      "Epoch 34/100\n",
      "38/38 [==============================] - 6s 157ms/step - loss: 0.0025\n",
      "Epoch 35/100\n",
      "38/38 [==============================] - 5s 145ms/step - loss: 0.0027\n",
      "Epoch 36/100\n",
      "38/38 [==============================] - 6s 145ms/step - loss: 0.0031\n",
      "Epoch 37/100\n",
      "38/38 [==============================] - 6s 145ms/step - loss: 0.0024\n",
      "Epoch 38/100\n",
      "38/38 [==============================] - 6s 151ms/step - loss: 0.0026\n",
      "Epoch 39/100\n",
      "38/38 [==============================] - 6s 163ms/step - loss: 0.0032\n",
      "Epoch 40/100\n",
      "38/38 [==============================] - 6s 146ms/step - loss: 0.0027\n",
      "Epoch 41/100\n",
      "38/38 [==============================] - 6s 160ms/step - loss: 0.0026\n",
      "Epoch 42/100\n",
      "38/38 [==============================] - 6s 151ms/step - loss: 0.0023\n",
      "Epoch 43/100\n",
      "38/38 [==============================] - 6s 156ms/step - loss: 0.0024\n",
      "Epoch 44/100\n",
      "38/38 [==============================] - 6s 151ms/step - loss: 0.0026\n",
      "Epoch 45/100\n",
      "38/38 [==============================] - 7s 172ms/step - loss: 0.0025\n",
      "Epoch 46/100\n",
      "38/38 [==============================] - 6s 156ms/step - loss: 0.0026\n",
      "Epoch 47/100\n",
      "38/38 [==============================] - 6s 163ms/step - loss: 0.0023\n",
      "Epoch 48/100\n",
      "38/38 [==============================] - 6s 171ms/step - loss: 0.0024\n",
      "Epoch 49/100\n",
      "38/38 [==============================] - 6s 163ms/step - loss: 0.0021\n",
      "Epoch 50/100\n",
      "38/38 [==============================] - 6s 158ms/step - loss: 0.0020\n",
      "Epoch 51/100\n",
      "38/38 [==============================] - 6s 161ms/step - loss: 0.0026\n",
      "Epoch 52/100\n",
      "38/38 [==============================] - 6s 162ms/step - loss: 0.0025\n",
      "Epoch 53/100\n",
      "38/38 [==============================] - 7s 176ms/step - loss: 0.0022\n",
      "Epoch 54/100\n",
      "38/38 [==============================] - 7s 189ms/step - loss: 0.0022\n",
      "Epoch 55/100\n",
      "38/38 [==============================] - 7s 190ms/step - loss: 0.0025\n",
      "Epoch 56/100\n",
      "38/38 [==============================] - 8s 224ms/step - loss: 0.0019\n",
      "Epoch 57/100\n",
      "38/38 [==============================] - 8s 206ms/step - loss: 0.0019\n",
      "Epoch 58/100\n",
      "38/38 [==============================] - 11s 298ms/step - loss: 0.0023\n",
      "Epoch 59/100\n",
      "38/38 [==============================] - 10s 271ms/step - loss: 0.0020\n",
      "Epoch 60/100\n",
      "38/38 [==============================] - 10s 273ms/step - loss: 0.0020\n",
      "Epoch 61/100\n",
      "38/38 [==============================] - 10s 272ms/step - loss: 0.0018\n",
      "Epoch 62/100\n",
      "38/38 [==============================] - 10s 260ms/step - loss: 0.0022\n",
      "Epoch 63/100\n",
      "38/38 [==============================] - 9s 232ms/step - loss: 0.0021\n",
      "Epoch 64/100\n",
      "38/38 [==============================] - 9s 237ms/step - loss: 0.0017\n",
      "Epoch 65/100\n",
      "38/38 [==============================] - 8s 219ms/step - loss: 0.0019\n",
      "Epoch 66/100\n",
      "38/38 [==============================] - 6s 156ms/step - loss: 0.0018\n",
      "Epoch 67/100\n",
      "38/38 [==============================] - 8s 218ms/step - loss: 0.0017\n",
      "Epoch 68/100\n",
      "38/38 [==============================] - 10s 260ms/step - loss: 0.0020\n",
      "Epoch 69/100\n",
      "38/38 [==============================] - 9s 247ms/step - loss: 0.0019\n",
      "Epoch 70/100\n",
      "38/38 [==============================] - 10s 262ms/step - loss: 0.0017\n",
      "Epoch 71/100\n",
      "38/38 [==============================] - 10s 250ms/step - loss: 0.0019\n",
      "Epoch 72/100\n",
      "38/38 [==============================] - 10s 258ms/step - loss: 0.0018\n",
      "Epoch 73/100\n",
      "38/38 [==============================] - 10s 255ms/step - loss: 0.0022\n",
      "Epoch 74/100\n",
      "38/38 [==============================] - 9s 245ms/step - loss: 0.0018\n",
      "Epoch 75/100\n",
      "38/38 [==============================] - 10s 257ms/step - loss: 0.0017\n",
      "Epoch 76/100\n",
      "38/38 [==============================] - 9s 245ms/step - loss: 0.0018\n",
      "Epoch 77/100\n",
      "38/38 [==============================] - 11s 298ms/step - loss: 0.0017\n",
      "Epoch 78/100\n",
      "38/38 [==============================] - 10s 268ms/step - loss: 0.0017\n",
      "Epoch 79/100\n",
      "38/38 [==============================] - 8s 223ms/step - loss: 0.0017\n",
      "Epoch 80/100\n",
      "38/38 [==============================] - 8s 204ms/step - loss: 0.0015\n",
      "Epoch 81/100\n",
      "38/38 [==============================] - 8s 217ms/step - loss: 0.0015\n",
      "Epoch 82/100\n",
      "38/38 [==============================] - 7s 192ms/step - loss: 0.0017 4s  - ETA: 2s - \n",
      "Epoch 83/100\n",
      "38/38 [==============================] - 10s 265ms/step - loss: 0.0015\n",
      "Epoch 84/100\n",
      "38/38 [==============================] - 9s 239ms/step - loss: 0.0016\n",
      "Epoch 85/100\n",
      "38/38 [==============================] - 12s 306ms/step - loss: 0.0015\n",
      "Epoch 86/100\n",
      "38/38 [==============================] - 9s 246ms/step - loss: 0.0014\n",
      "Epoch 87/100\n",
      "38/38 [==============================] - 8s 201ms/step - loss: 0.0013\n",
      "Epoch 88/100\n",
      "38/38 [==============================] - 10s 254ms/step - loss: 0.0015\n",
      "Epoch 89/100\n",
      "38/38 [==============================] - 10s 265ms/step - loss: 0.0015\n",
      "Epoch 90/100\n",
      "38/38 [==============================] - 10s 264ms/step - loss: 0.0014\n",
      "Epoch 91/100\n",
      "38/38 [==============================] - 11s 291ms/step - loss: 0.0014\n",
      "Epoch 92/100\n",
      "38/38 [==============================] - 8s 210ms/step - loss: 0.0016\n",
      "Epoch 93/100\n",
      "38/38 [==============================] - 10s 268ms/step - loss: 0.0017\n",
      "Epoch 94/100\n",
      "38/38 [==============================] - 10s 248ms/step - loss: 0.0014\n",
      "Epoch 95/100\n",
      "38/38 [==============================] - 10s 263ms/step - loss: 0.0021\n",
      "Epoch 96/100\n",
      "38/38 [==============================] - 10s 257ms/step - loss: 0.0015\n",
      "Epoch 97/100\n",
      "38/38 [==============================] - 9s 248ms/step - loss: 0.0012\n",
      "Epoch 98/100\n",
      "38/38 [==============================] - 7s 193ms/step - loss: 0.0015\n",
      "Epoch 99/100\n",
      "38/38 [==============================] - 7s 193ms/step - loss: 0.0015T - ETA: 2s -\n",
      "Epoch 100/100\n",
      "38/38 [==============================] - 7s 188ms/step - loss: 0.0014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1640a202040>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the data\n",
    "regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FmBT2zqukxTz"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>778.81</td>\n",
       "      <td>789.63</td>\n",
       "      <td>775.80</td>\n",
       "      <td>786.14</td>\n",
       "      <td>1,657,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>788.36</td>\n",
       "      <td>791.34</td>\n",
       "      <td>783.16</td>\n",
       "      <td>786.90</td>\n",
       "      <td>1,073,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>786.08</td>\n",
       "      <td>794.48</td>\n",
       "      <td>785.02</td>\n",
       "      <td>794.02</td>\n",
       "      <td>1,335,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>795.26</td>\n",
       "      <td>807.90</td>\n",
       "      <td>792.20</td>\n",
       "      <td>806.15</td>\n",
       "      <td>1,640,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/9/2017</td>\n",
       "      <td>806.40</td>\n",
       "      <td>809.97</td>\n",
       "      <td>802.83</td>\n",
       "      <td>806.65</td>\n",
       "      <td>1,272,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/10/2017</td>\n",
       "      <td>807.86</td>\n",
       "      <td>809.13</td>\n",
       "      <td>803.51</td>\n",
       "      <td>804.79</td>\n",
       "      <td>1,176,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/11/2017</td>\n",
       "      <td>805.00</td>\n",
       "      <td>808.15</td>\n",
       "      <td>801.37</td>\n",
       "      <td>807.91</td>\n",
       "      <td>1,065,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/12/2017</td>\n",
       "      <td>807.14</td>\n",
       "      <td>807.39</td>\n",
       "      <td>799.17</td>\n",
       "      <td>806.36</td>\n",
       "      <td>1,353,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/13/2017</td>\n",
       "      <td>807.48</td>\n",
       "      <td>811.22</td>\n",
       "      <td>806.69</td>\n",
       "      <td>807.88</td>\n",
       "      <td>1,099,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/17/2017</td>\n",
       "      <td>807.08</td>\n",
       "      <td>807.14</td>\n",
       "      <td>800.37</td>\n",
       "      <td>804.61</td>\n",
       "      <td>1,362,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/18/2017</td>\n",
       "      <td>805.81</td>\n",
       "      <td>806.21</td>\n",
       "      <td>800.99</td>\n",
       "      <td>806.07</td>\n",
       "      <td>1,294,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/19/2017</td>\n",
       "      <td>805.12</td>\n",
       "      <td>809.48</td>\n",
       "      <td>801.80</td>\n",
       "      <td>802.17</td>\n",
       "      <td>919,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1/20/2017</td>\n",
       "      <td>806.91</td>\n",
       "      <td>806.91</td>\n",
       "      <td>801.69</td>\n",
       "      <td>805.02</td>\n",
       "      <td>1,670,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1/23/2017</td>\n",
       "      <td>807.25</td>\n",
       "      <td>820.87</td>\n",
       "      <td>803.74</td>\n",
       "      <td>819.31</td>\n",
       "      <td>1,963,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1/24/2017</td>\n",
       "      <td>822.30</td>\n",
       "      <td>825.90</td>\n",
       "      <td>817.82</td>\n",
       "      <td>823.87</td>\n",
       "      <td>1,474,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1/25/2017</td>\n",
       "      <td>829.62</td>\n",
       "      <td>835.77</td>\n",
       "      <td>825.06</td>\n",
       "      <td>835.67</td>\n",
       "      <td>1,494,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1/26/2017</td>\n",
       "      <td>837.81</td>\n",
       "      <td>838.00</td>\n",
       "      <td>827.01</td>\n",
       "      <td>832.15</td>\n",
       "      <td>2,973,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1/27/2017</td>\n",
       "      <td>834.71</td>\n",
       "      <td>841.95</td>\n",
       "      <td>820.44</td>\n",
       "      <td>823.31</td>\n",
       "      <td>2,965,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1/30/2017</td>\n",
       "      <td>814.66</td>\n",
       "      <td>815.84</td>\n",
       "      <td>799.80</td>\n",
       "      <td>802.32</td>\n",
       "      <td>3,246,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1/31/2017</td>\n",
       "      <td>796.86</td>\n",
       "      <td>801.25</td>\n",
       "      <td>790.52</td>\n",
       "      <td>796.79</td>\n",
       "      <td>2,160,600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Open    High     Low   Close     Volume\n",
       "0    1/3/2017  778.81  789.63  775.80  786.14  1,657,300\n",
       "1    1/4/2017  788.36  791.34  783.16  786.90  1,073,000\n",
       "2    1/5/2017  786.08  794.48  785.02  794.02  1,335,200\n",
       "3    1/6/2017  795.26  807.90  792.20  806.15  1,640,200\n",
       "4    1/9/2017  806.40  809.97  802.83  806.65  1,272,400\n",
       "5   1/10/2017  807.86  809.13  803.51  804.79  1,176,800\n",
       "6   1/11/2017  805.00  808.15  801.37  807.91  1,065,900\n",
       "7   1/12/2017  807.14  807.39  799.17  806.36  1,353,100\n",
       "8   1/13/2017  807.48  811.22  806.69  807.88  1,099,200\n",
       "9   1/17/2017  807.08  807.14  800.37  804.61  1,362,100\n",
       "10  1/18/2017  805.81  806.21  800.99  806.07  1,294,400\n",
       "11  1/19/2017  805.12  809.48  801.80  802.17    919,300\n",
       "12  1/20/2017  806.91  806.91  801.69  805.02  1,670,000\n",
       "13  1/23/2017  807.25  820.87  803.74  819.31  1,963,600\n",
       "14  1/24/2017  822.30  825.90  817.82  823.87  1,474,000\n",
       "15  1/25/2017  829.62  835.77  825.06  835.67  1,494,500\n",
       "16  1/26/2017  837.81  838.00  827.01  832.15  2,973,900\n",
       "17  1/27/2017  834.71  841.95  820.44  823.31  2,965,800\n",
       "18  1/30/2017  814.66  815.84  799.80  802.32  3,246,600\n",
       "19  1/31/2017  796.86  801.25  790.52  796.79  2,160,600"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get actual stocks for Jan.2017 contains 20 days\n",
    "test = pd.read_csv('Google_Stock_Price_Test.csv')\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([736334., 736338., 736342., 736346., 736350., 736354., 736358.,\n",
       "        736361.]),\n",
       " <a list of 8 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ta/SUV7KqR4u8bq+P3ZUOskb3Pqtr6/G5/c2EFJ5at4eDTjfDNGiddPVzpkoxWbp0KRMmTGDevHmUl5dzww038M477/Dxxx/z3HPPsWTJEqKiorDZbDidzpbHOZ3OU4rL6Xi9CrW1DWrEbreIiBC/y9QaPeWVrOrRIu+uQ/W4PD5So87u2np8bi8dGMMLG/bx14/28KcrhmiS4eTnzG5Xd4xKlXZmWFhYS1EIDw/H4/Hw3nvvsXz5cpYtW0bv3r0ByMjIICcnB5fLRX19PXv37iUtLU2NSEIIP9DdB99PZgs088sRCXxWWE1hlfPHH6BzqrRMZs+ezYIFC8jKysLtdnPHHXfw6KOPEh8fz2233QbAqFGjuP3225k1axZZWVkoisLcuXMJDAxUI5IQwg/sLK8jKiSAuNCe8Tq/JjOR174u5eXNxSy6LF3rOKpSpZhYrVaeeeaZUz53+eWXn/bYGTNmMGPGDDViCCH8TG55PUPjw3S1PW9HhAcH8IvMBF7dcpCbxvWhb5R+xn3OVve9nUII4VfqmtwU1zT2iC6uk2Wdm4jFbGTploNaR1GVFBMhRJfYWXFsvGRIXM8qJlEhFqZnxLP220OUHm3UOo5qpJgIIbpEXnk9BmBwDysmANeNTMJoNPDqlhKto6hGiokQokvsLK8nJToEW6AqQ7V+LTY0kMuHxvHOzgoO1bu0jqMKKSZCCNV155WC2+r6Ub3xKbDsq+45diLFRAihupLaJo42eRjSDVcKbquE8CAuSY9ldW4F1c5mreN0OikmQgjV5VXUATC0B46XnGz2mGTcXh+vfd39xk6kmAghVLezvJ4gs5F+3XSl4LZKjgzm4oF2Vm4vo7bRrXWcTiXFRAihurzyegbHhWI29ozJiq25cUwyjW4f2VtLtY7SqaSYCCFU1ezxsafK0aMH30/WP8bKpNQY/v1NKQ6XR+s4nUaKiRBCVbsrHbi9So8efP++X49JxuHysuKbMq2jdBopJkIIVeUdn/ne0wffTzawl40J/aL4V04JDc1ereN0CikmQghV7SyvI9ZmIbaHrBTcVjeOSeZok4f/5FVoHaVTSDERQqgq7/hKweJUGQlhJIYHsb30qNZROoUUEyGEamoamik92iSD72eQareyp5tsnCXFRAihmpaVgqWYnFaa3cbBmkYa3fofN5FiIoRQTW55PSYDpPeSYnI6qXYrCrDvsP5bJ1JMhBCq2VleR78YK8EBJq2j+KUB9mMrAnSHri4pJkIIVfgUhZ0V9TJe0oqE8CCsFhMFUkyEEOL0io804nB55U6uVhgNBgbEWCmscmgdpcOkmAghVNGyUrC0TFo1wG6l4LATRVG0jtIhUkyEEKrIK6/HajHRNypE6yh+Lc1uxeHyUl6n7x0YpZgIIVRxYqVgo0FWCm5Nqt0GoPtxEykmQohO1+T2UigrBbdJ/xgrBqBA5+MmUkyEEJ0u/5ADrwJD4mTw/ceEWEwkRQTpvmVibstBu3bt4t///jcu13d9eo8//rhqoYQQ+tayUrC0TNok1W6jUOcTF9tUTObPn891111HXFxcm07qdruZP38+paWlGI1GFi5cSP/+/QF47LHHSElJYebMmQCsWLGC7OxszGYzc+bMYdKkSe38VoQQ/mJneR0JYYFEWy1aR9GFAXYrnxYcptHt1e0EzzYVk5iYGH7xi1+0+aTr16/H4/GQnZ3Nxo0bWbx4MQ8//DB/+MMf2L9/P7/+9a8BqKqqYtmyZaxatQqXy0VWVhbjx4/HYpFfQCH0LLe8nowE6eJqq7Tjy6oUVjkZptPnrU3FJDExkSVLlpCeno7h+J0ZEyZMOOPxKSkpeL1efD4fDocDs9mM0+nktttuY8OGDS3H7dixg8zMTCwWCxaLheTkZPLz88nIyDjjuU0mAxER/nWroclk9LtMrdFTXsmqHrXyVtY3cajexah+0Z12/u7+3J7bPwaAUqeb83X6nLWpmLjdboqKiigqKmr5XGvFJCQkhNLSUqZNm0ZNTQ3PP/88vXv3pnfv3qcUE4fDQWjod32qVqsVh6P1Oxq8XoXa2oa2xO4yEREhfpepNXrKK1nVo1beLwsOA9AvPLDTzt/dn1srClaLie0HjvDT1GhVMtjt6o5ftamYPP744xQVFVFcXMzAgQOJjY1t9filS5cyYcIE5s2bR3l5OTfccAPvvPMOgYGn7rRms9lwOr8bdHI6nacUFyGE/uRV1GM2GhgYa9M6im4YDAZS7VZd39HVpmKyfPly1q1bx9GjR7nqqqs4cOAADzzwwBmPDwsLIyAgAIDw8HA8Hg9e7w/X68/IyGDx4sW4XC6am5vZu3cvaWlp7fxWhBD+YGd5Hal2K0E6HUjWSqrdxnvfHkJRlJbhBD1p0zyTNWvWsHTpUkJDQ7nhhhvYvn17q8fPnj2bnTt3kpWVxQ033MDcuXMJCflh353dbmfWrFmnHPf91osQQj+8PoVvKxyyuGM7DLBbcTZ7Katr0jpKu7SpZXJiAbIT1fLH7rayWq0888wzp/3abbfddsrHM2bMYMaMGW2JIYTwc0XVDTS4vTK/pB3Sju9tUlDpJDE8WOM0Z69NLZPLLruMa6+9luLiYm666SYuuugitXMJjSmKgrPZo3UMoTN55cdWCh4SJ8XkbLUsq6LTyYttaplcd911nHfeeRQUFJCSksLAgQPVziU04vH6+HjPYZZ/XUJBlYP5F6VyZUa81rGETuRV1BMWZCY5Un9/WWstOMBE78hg3Q7Ct6mYFBUV8dRTT1FUVERaWhr33HMPiYmJamcTXcjh8rA6t4LsraUcqnfRJzKYjIQwFq0r4FC9i5vH9en0QUFFUahr8lDlaKbK6aKqvplKh4sqx7H/H3Y041HAbACL2YjFbCTQdOz/FpMBy/F/B5qN3/37xNeP/zvAZDj29ePHnHLsiX+fdE49Dnz6A5+i0OzxkVdex5C4UHke2ynVbmV3pT4XfGxTMbnnnnu49dZbGTFiBDk5OcyfP59ly5apnU10gYq6JrK3lrE6txxns5cRSeHcM2UA4/tF4fMpPP5RAS9tKqai3sW9F6diNrV/bVCvT2HFtjI+LThM1fGi4fL4fnBcRHAAdpsFu81CaLAFZ5ObZo+PZq8Pp8tDs9d3/GOl5fMujw+Pr+ObC1lMhtMWnh8UK5ORQPOpx8aEBzMmKYx+0dYO5+gIRVFwexVcHh8uj5cmz7Hnp9nrw+U+9u8mjw9z4FGOHG08fpzvB8ef+K+5la+d+Hyz97vnfkqqXcPvXt8GxFj5eM9hGpq9hFj0dTdcm4pJcHAwF1xwAQAXXnghL7/8sqqhhPp2Harnta9L+Gh3FQAXDbSTdW4Sg0/q6zaaDNz3kzTiQoNY8t8DVDlcPPGzwdgC2/Rrc4qi6gYWfrCb3PJ60nvZGNwrFHv/QGJDLcRYLcTaArGHWrBbA7GYvytYZzP5y+tTcHu/e+M8VnSOFRzX8QJ04v/NJx3jOuljl0c5qVj5TilWzV4fzmYPRxpOPPbUYnaiMGYkhHHFsDguHmjvsnWW9lQ6ePbzIr4pOYrL46O9ZdUABB5vtQWajQQFmE75ODIkoKV4BplP/Vqg2UiIxcTUQa3PQxNndmJvk8LDTt0tR9Omd4X4+Hj+/ve/M3bsWHbu3InFYuGLL74AWp8JL/yLoih8WVTD69vy2Fx0BKvFxDUjEpk5IpG4sKDTPsZgMHDTuD70Cg3ksXV7+J9/b2fx9KHYbW27hdvjU1j+1UFe/O8BggNMLLxkEFMH2VXpBjEZDZiMJs3mN3jNJl7fdIC3c8tZ+MEenv50Lz8ZZOeKYfEM7mVT5Xs+VO/i+Y37WbPzEKFBZi4fGoct0ETgad7oj33O0PI1e2QIrsZmglq+dqxbULqotJMWe/yOripH9ywmiqKwatUqiouLMRgMxMTEsGbNGkCKiZ68+lUJz35eRFxYELdPTOGqjPg2tzIuHxZHjM3C/76zi1/9axuLpw+lf0zr3TmFVU4e+WA3uw45mJwawx+mDOjWq8hG2wK5bmQS156byI6yOt7KreC9byt5a0cFqXYrVw6L46fpsYQFBXT4Wg6Xh1e/Osi/ckrxKQrXjkzixjG9z+rceluipCeICw3EFmjS5SC8QWllF3un08m8efOoqakhKSmJ/fv3Ex0dzdNPP43Nps1SCW631+9eAHp4UX6wq5L73svnJwPtLJ6ZibO+fROj8g/Vc8dbO2n2+PjTFYM5t3fED47xeH28vOUg/9xUTGigmXsuGsCUtPb1o+vhuT3hdFkdLg8f5FeyekcF+ZUOLCYDk9PsXDakFxkJYWfdDebx+ngrt4IXvzxATaObqYPszJnQt13zEvT+3PqzjuS9OXsbXgX+MXN4p2ZQe22uVovJI488QkZGBldeeWXL59544w1yc3N55JFHVA12JlJMzt7Wklp+tzKXoXGhPHt1BrExtg7lLTvaxO/fzKX0aBMP/XQgPzmpj3z3IQcPf7CbgionUwfZuWvSACJC2v+XuL8/tyf7saz5h+p5O7eC93dV4mz2YjJAvxgrQ+JCGRofypC4MFKiQzAZf9jNpCgK6wur+dvnRRTXNDIiKZzbL+jXofkc3em59Tcdyfunjwt5d+chPr1tHMYOdDn61UKP+fn5P1iD6xe/+AUrV65UNZToPEXVDdy1+lsSwoL40xVDThncbq+E8CBe+uVw7n57J/euyedQvYtrMhP5x6YDvLLlIBEhFp66YjAXDIjphO+g+xjUK5RBvUL5/QX92FJcy86KenaW1/HRnipW51YAEBxgJL3XieISypD4MA47XDyzfh/flNbRNyqYp64YwsT+UTK20U0NsFtpcHspO9pEUoR+5uu0WkzM5tN/2WTS1y1rPdVhZzN3vJlLgMnAMz8fSnhwx/vqTwgPDuBvV2fw0Pv5/HVDEcu/LuFIg5tLB8cy98L+nXqt7iYowMTE/tFM7H9sqXGfonCwpvF4calnZ0U9r28txX3S7bZRIQHMv2gAVwyLx3yalovoPlqWValydp9iEhERQW5uLsOGDWv5XG5uLuHh4aoHEx3T6PZy51t5HGlw88I156iy1k+g2ciiy9KJ31DE+r3V3D81jQn9Omcvhp7EaDDQJyqEPlEhXDK4FwDNHh8FVQ52VtTj9ipcmRGH1XL2t2QL/WlZVqXKwaRU/bTuW/3t/MMf/sCcOXMYM2YMvXv3pqSkhP/+978899xzXZVPtIPHp7Dg3V3srnTwpyuGnDJ3pLMZDQZuv6Aft1/QT7Vr9EQWs5Eh8WEMkdV3e5wgnS6r0moHelJSEitXrmTUqFG43W4yMjJYsWIFvXv37qp84iwpisJTnxTyxb4jzJs0oKUrRQihH2k63CjrR9vNgYGBTJ06tSuyiE6w/OsSVm0vZ9bIJGZkJmgdRwjRDql2Gx/tOYzD5WnXihNa6PitPcJvrNtdxV83FHFRmp3fTUzROo4Qop0GHB+E36uj5eilmHQT35Qc5cH38zknIYyHpg3s0P3pQghtnXxHl15IMekG9h9p4K63dxIfFsRTVw4hsBPmkgghtNMrNJDQQLMUE9F1qp3N/P7NPEwGA89MH0qEzO8QQvcMBgMDdDYIL8VExxrdXu5cvZNqZzNPXzVEVxOchBCtS7NbKTzswHfmFa/8ihQTnfL6FO5bk8+uinoWXTqIoTIfQYhuJdVupdHto7S2fYuydjUpJjqkKApPf7qXDXuruWtyf1kDS4huaMDxjbIKdHJHlxQTHfpXTikrtpWRdW4iMzITtY4jhFBB/+gQjAYo0Mme8FJMdObjPVUsXr+Pyakx/F6WMBGi2woKMJGso2VVpJjoyPbSozzwXj4ZCWE8LHNJhOj2BsTYdNPNpco8fbfbzfz58yktLcVoNLJw4ULMZjPz58/HYDCQmprKgw8+iNFoZMWKFWRnZ2M2m5kzZw6TJk1SI5LuHTjSwLzVO4kLC+LPVwzRbJ9zIUTXSYu18tGeKl0sq6JKuvXr1+PxeMjOzmbjxo0sXrwYt9vNHXfcwZgxY3jggQf4+OOPGT58OMuWLWPVqlW4XC6ysrIYP348Fkv33Se8PWoajs0lMRgMLL5qaId2LhRC6Efq8ZnwhVVOhif599YfqnRzpaSk4PV68Tkm3pMAAB5uSURBVPl8OBwOzGYzO3fuZPTo0QBMnDiRL7/8kh07dpCZmYnFYiE0NJTk5GTy8/PViKRbTcfnkhx2NvP0lUPoHSlzSYToKQbEHF9WRQddXaq0TEJCQigtLWXatGnU1NTw/PPP89VXX7VsM2q1Wqmvr8fhcBAa+t1eG1arFYej9TsXTCYDEREhasRuN5PJqEomr09hQfY2dlbU8+wvMzn/+MZJHaVWXjVIVvXoKa+eskLn5Q0PDyY8OIADR5vO+nxd/ZypUkyWLl3KhAkTmDdvHuXl5dxwww243e6WrzudTsLCwrDZbDidzlM+f3JxOR2vV6G2tkGN2O0WERGiSqY/f7qXdbsOceek/oxOCO20a6iVVw2SVT16yqunrNC5eQfEhLCz9OhZn+/7Gex29TbJA5W6ucLCwlqKQnh4OB6Ph8GDB7N582YANmzYwMiRI8nIyCAnJweXy0V9fT179+4lLS1NjUi686+cErK3ljJzRCIzR8hcEiF6qgExVgqrnH6/rIoqLZPZs2ezYMECsrKycLvdzJ07l6FDh3L//ffz9NNP069fP6ZOnYrJZGLWrFlkZWWhKApz584lMDBQjUi68knBYRZ/to9JMpdEiB4vzW6jyeOjpLaJZD8eM1WlmFitVp555pkffH758uU/+NyMGTOYMWOGGjF0aUdZHQ+8l8/Q+FAemTYQk1HmkgjRk6XGntjbxOHXxUQmLfqRgzWNzFu9E7vNwp+vlLkkQghIiTq+rIqfz4SXYuInahvc/P7NXBRF4Znpw4gMkbk2Qohjy6r0iQyRYiJ+3Im5JIfqXfz5yiF+3ZQVQnS9VLuVgir/XvBRionGfIrCg+/vJq+8joWXDOKcRP+e5SqE6HoD7FbK61w4XB6to5yRFBONPbN+H58UHOaOC/sxOc2udRwhhB9KO7G3iR93dUkx0dC/t5byr5xSrslMkLkkQogzGtjLhsVkoK7J/eMHa8S/l6HsxtYXHubPn+7lgv7RzL2wf8tSM0II8X0xVgtv/2a0X9+YI8VEA3nlddy7Jp/BcaE8eukgmUsihPhRMTb/ntAt3VxdrKS2kTvf2kmM1cLTV8lcEiFE9yDFpAvVNrr5/Zt5+BSFZ6YPJcqPm6xCCHE2pJurCz3wXj4VdU3839UZ9InSz3LaQgjxY6Rl0kVKahv57/4afj22j9/vmCaEEGdLikkX+TC/CoBpg2M1TiKEEJ1PikkXUBSFtbsqyUwMIz4sSOs4QgjR6aSYdIE9VU6KjjQwNV1aJUKI7kmKSRf4YFclJqOBKamyXIoQonuSYqIyn6LwQX4l5/WNJCIkQOs4QgihCikmKvum5CiVjmZ+Oki6uIQQ3ZcUE5Wt3VVJcICRiQOitY4ihBCqkWKiomaPj08KDnPBgBiCZdkUIUQ3JsVERf/df4S6Jg8/lbu4hBDdnBQTFa3dVUVEcABjkiO0jiKEEKqSYqISZ7OHz/dVc/FAO2aTPM1CiO5N3uVU8llBNS6Pj6mDZG6JEKL7k2KikrX5lSSEBZKREKZ1FCGEUJ0UExVUO5vZcqCGqemxsh2vEKJHUGU/kzfffJO33noLAJfLxa5du8jOzubBBx/EYrGQnp7Ovffei9FoZMWKFWRnZ2M2m5kzZw6TJk1SI1KX+mh3FT4FuYtLCNFjqFJMpk+fzvTp0wF4+OGH+fnPf87999/Pfffdx4gRI/jLX/7CO++8w7hx41i2bBmrVq3C5XKRlZXF+PHjsVj0vQPh2vxKUu1W+kVbtY4ihBBdQtWdFnNzcyksLOTBBx/kr3/9KyNGjABgxIgRfPzxx9hsNjIzM7FYLFgsFpKTk8nPzycjI+OM5zSZDERE+NcuhSaTsSXTgSMN5JXXc/dP0vwu5wkn5/V3klU9esqrp6zgH3m7OoOqxeSFF17g1ltvBaB3795s2bKF0aNH8+mnn9LY2IjD4SA0NLTleKvVisPhaPWcXq9CbW2DmrHPWkRESEumNzYfAGBinwi/y3nCyXn9nWRVj57y6ikr+Efe72ew20NbObrjVBuAr6urY9++fYwdOxaAxx57jBdeeIGbb76Z6OhoIiMjsdlsOJ3Olsc4nc5TiovetGyClRROnGyCJYToQVQrJl999RXjxo1r+Xj9+vU89thjLFmyhNraWsaPH09GRgY5OTm4XC7q6+vZu3cvaWlpakVS3e5KBwdqGmXgXQjR46jWzVVUVERSUlLLx3369OHmm28mODiYMWPGcMEFFwAwa9YssrKyUBSFuXPnEhgYqFYk1a3dVYXZaGBKaozWUYQQoksZFEVRtA5xNtxur+p9kZ8VHGb93mpuPT+FGOuP31kWERFC9REnP3txM+m9QvnzlUNUzddR/tCf21aSVT16yqunrOAfebvNmImeLfnvAd7deYiZr+TwWcHhNj3mm5KjVDmaZfkUIUSPJMXke/ZXN1BQ5eSazATiQgO5+z/f8uiHe2ho9rb6uLX5lYQEmJjYXzbBEkL0PKreGqxH63ZXYQBuGN2biOAAlnx5gFe2HCTnYC2PTBvEsNOsteXy+Phkz2EuTI0mSDbBEkL0QNIyOYmiKKzbXcXwpHDstkACTEZuPT+FF645B69P4absbSz5cj8e36nDTBv2VFHvkk2whBA9lxSTk+w93EDRkQYuHnjquEdmUjj/uv5cfpoey4v/Leam7G0crGls+fp/dpQRFRLAqOTIro4shBB+QYrJSdbtqcJogMmnubXXFmjmoWmDeOyydIprGrl2WQ6rd5TjcHn4ZHfVsU2wjLJCsBCiZ5Ixk+MUReGj3VWc2zuC6FZuB754oJ2MhDAeXrubResKWP51Cc0eH1MHSReXEKLnkpbJcXsqnRTXNP6gi+t0eoUG8uzVw5h7YT/K6ppIjgphaLx+l4ERQoiOkpbJcR/ursJkNDCpjbPXjQYDWecmMbF/NOHhwcgeWEKInkxaJpzo4qpkdHIEEcEBZ/XYpIhgekfqZ2lsIYRQgxQT4NuKesrqXG3q4hJCCPFDUkw41sUVYDJw4QBZoFEIIdqjxxcT3/G7uMb2iSQ0SIaQhBCiPXp8Mcktq6PS0czFskCjEEK0W48vJut2VxFoNsoCjUII0QE9uph4fQof7TnMuJQorBbp4hJCiPbq0cVkW+lRqp3NcheXEEJ0UI8uJut2VxFkNjKhX5TWUYQQQtd6bDHx+BQ+2XOY8/tHEyx7kAghRIf02GKSU1xLTaNburiEEKIT9Nhism53FVaLiXEp0sUlhBAd1SOLidvr49PCw0zsH02guUc+BUII0al65DvplgO11DV5pItLCCE6SY8sJut2V2ILNDGmj2yzK4QQnaHHFROXx8dnhdVcOCAGi3RxCSFEp1Bl2vebb77JW2+9BYDL5WLXrl2sWLGCBx98EJPJRN++fVm0aBFGo5EVK1aQnZ2N2Wxmzpw5TJo0SY1ILTbtP4Kz2StdXEII0YlUKSbTp09n+vTpADz88MP8/Oc/59lnn+XWW2/lggsuYN68eXz22WcMGzaMZcuWsWrVKlwuF1lZWYwfPx6L5cx7sHfUut1VhAeZGZ0codo1hBCip1G1nyc3N5fCwkKuueYa0tPTqa2tRVEUnE4nZrOZHTt2kJmZicViITQ0lOTkZPLz81XL0+T2smFvNZNSYzCbpItLCCE6i6qrG77wwgvceuutAPTt25dHHnmE5557jtDQUMaMGcPatWsJDQ1tOd5qteJwOFo9p8lkICKifdvkvp9XQaPbx1Xn9m73OU6fydip51ObnvJKVvXoKa+esoJ/5O3qDKoVk7q6Ovbt28fYsWMBWLRoEa+99hqpqam89tprPPHEE0yYMAGn09nyGKfTeUpxOR2vV6G2tqFdmd7+poSokADSIoPafY7TiYgI6dTzqU1PeSWrevSUV09ZwT/yfj+D3d76e2tHqdbX89VXXzFu3LiWj8PDw7HZbADExsZSV1dHRkYGOTk5uFwu6uvr2bt3L2lpaarkaWj28sW+I0xOjcFsNKhyDSGE6KlUa5kUFRWRlJTU8vGjjz7K3LlzMZvNBAQEsHDhQux2O7NmzSIrKwtFUZg7dy6BgYGq5Nm0/wguj092VBRCCBUYFEVRtA5xNtxub7uaj/urG1jz7SHmTOiL0dC5LRN/aNKeDT3llazq0VNePWUF/8jb1d1cPWZ7wb7RIdx6forWMYQQoluS+2OFEEJ0mBQTIYQQHSbFRAghRIdJMRFCCNFhUkyEEEJ0mBQTIYQQHSbFRAghRIdJMRFCCNFhupsBL4QQwv9Iy0QIIUSHSTERQgjRYVJMhBBCdJgUEyGEEB0mxUQIIUSHSTERQgjRYVJMhBBCdJgUEyE0prepXnrLK9quIz9bKSYa8Pl8LFiwgNLSUq2j/ChFUaisrKSiokLrKG2iKApVVVWUlZW1fOyvfD4fM2bMYM2aNVpHaZMTz6XBYMDn82mcpnV6eo2B9q+zzvjZSjHpYj6fj3nz5tG3b18SExP9+kXp8/n4zW9+w5IlS7jppptYt26d1pFa5fP5mD17Nn/729+48cYb+frrrzEYDFrHOi2fz8ejjz6Kx+Ph6NGjAHi9Xo1Tte7ll1/mxhtvBMBoNPrt766eXmPgH6+zzvjZmh566KGHOjmXaMWiRYuIj4/nt7/9LY888ghvvvkmTqeTuLg4goODtY53iiVLlmCxWLjvvvtIS0vj/vvvJzQ0lMGDB6Moit+9Ub/22muYTCYefPBBQkJC2L9/PwkJCdhsNq2jnUJRFO644w4SEhKYMWMG77//PpdccglGo3//bZeXl8ebb77Jl19+yVVXXdXyV6y//R7o6TUG/vE664yfrX//9nZDAwYM4JNPPuH2228nIyODmTNn8sUXX/Dpp59qHe0HbDYbLpeL5uZmRo4cyfTp01m9ejVbtmzxuzcQONZEb25uBmDjxo189NFH/M///A/Lly/XONmpiouLmTx5Mrfffjtjx47FZrNRUlIC+G+3nMvloqmpiTVr1pCSksJ1110H+GcLZeDAgbp5jQFERETQ1NSk2evM6XTicrk6/LOVlkkXUBSFvLw8IiMjOeecc2hoaODAgQPcfffd9O7dm169erFy5UouuugizGazX2SNiIjA6/VSU1PD5s2baWpq4ptvvmHixIls27aNCRMmaJrzZIcOHcJms5GcnMyoUaMICQmhvr6eBx54gMzMTN566y0mTpyIxWLROirV1dXEx8czaNAgABoaGli/fj319fUMHz7cr4q0oii8//77+Hw+rFYriYmJREREMHXqVDZt2sTy5ctb/orVmqIovPfeexgMBs4//3waGxspLi7mrrvu8rvX2Ml5FUXB4/HQ2NjIli1baGxs7JLXmaIofPvtt4SHhxMcHExMTAzh4eH89Kc/bffPVvtntZvz+XzccccdGAwGzGYzY8aMYfLkyUyePJnGxkaCg4NpbGzEZrNp/qI8kRXAarUydOhQ4uLiaGpq4osvvuC6667D5XJRUFCgac6TvfTSS1RVVTF79mzi4+NbPn/11VcDUFVVhcFg8IsupJdeeonKykpuuOEGEhMTAQgJCeGWW27hgQceoE+fPlx44YXahjzO5/Nx11134fF4iI6OJjU1laysrJa/VP/4xz/y29/+lptvvpklS5b4RVav18uWLVsYNGgQM2fOZMSIEbhcLgIDA/3mNXZyXo/Hw/bt20lKSmL48OFs376djRs3MmvWLJqamlR7nX3/PWn06NFMmTKFsLAwoP0/W2mZqGzlypU0Njby+OOPYzKZKC4uZs+ePQwfPpwPPviAxx9/nJycHO644w569erlF1mfeOKJlruijh49SlZWFna7nQ8//JBVq1Zx6623EhMTo2nWE9avX8/7779PYGAgMTExREREALB69WqeffZZNm3axF133UVcXJzGSY9lXbt2LUFBQadkPfH/vLw8Ro4c6Rd/OWdnZ6MoSstNAuvXr2fq1KkYDAY8Hg9Go5FLL72U0aNHaz4mdSLrwoUL8Xq9bNiwgUsvvZRevXrx+uuv88c//tFvXmMn53300UdbWiS//vWvGT16NMHBwXz88cesXLmS3/3ud6q8zk5+TzKbzRw4cIC8vDz69+9PSEgIQLt+ttr/1nZziqIQEBAAwEUXXURERAQbN25k9+7dTJkyhfT0dOLj47Hb7RonPTXrxRdfzNdff83nn3/ON998w4gRI6iurmbGjBkkJydrnPQYj8dDWFgYc+fOZfv27axdu5Zp06aRnJzM+eefT3JyMomJiX7xBtJaVqClxRoYGKhx0mOqqqpaBqszMzNZsWJFy9dO7kf3h+f2TFkNBgMXXngh48aNIzQ0lNjYWC1jtjg577nnnssbb7zR8rX+/fvj8XhUfZ2d/DqfMmUK4eHhbNy4kV27djFhwgTcbjcBAQFn/bOVlonK4uPjee6553A6nWRmZpKQkEBBQQF5eXlMnTqVXr16YbVatY4JnD7r3r172bFjB5MnTyYlJYXw8HCtY7YwGo2Eh4czduxY+vXrx2effUZlZSUxMTHEx8cTHx+v+V/NJ5wua1VVFZGRkURGRhIaGkpQUJDWMVsMHDgQu92O3W6nvLycd999lxkzZrBu3Try8vIYOHCgX3QZwZmzfvTRRxw4cIBx48b5zWsMWs9bUFDAxRdfrOrr7Eyv8+3btzNx4kRMJlO7zivFREUejwer1crIkSN59dVXKS8vZ9SoUZSVlVFQUMD48eP9oksDzpy1tLSUgoICJkyY4DdZgZZbJqOiogAICwsjNTWVd999l4aGBoYMGdLuF0Vnay1rU1OTX2WFY78LISEhLa3lI0eOcPjwYeDYuM/111/f0jWntdayvvjii8yaNcuv/gBqLe+SJUu4/vrrVcl74newubkZm8122td5YWFhh96TZNveTubz+TAajafMKD106BBOp5N58+YxbNgwtm3bxtNPP82AAQMk61k4Xd4DBw4QFRXVMrhaVlaG2WzWvEtDT1nhzHmjo6MpKSnhxhtvZPjw4dx9993069dPsp4FrfM2NjZiMBgICgpqKSqqvM4V0WlefvllZevWrYrX62353Pbt25Vp06YpVVVVSmNjo1JZWalUV1drmPIYPWVVlDPnveyyy5Rdu3YpiqIoPp9Pq3in0FNWRWk9b2FhoVJdXa1cfvnlSkFBgYYpj9FTVkXRNq/X61X+8Ic/KPPmzVMWL16s7Nmzp+X6arzO/affoht4++232b17NyaTicGDB+PxePjggw+47777Wu7K8Jd+cT1lhTPn/d///d+WORv+0oevp6zQet7+/fsD8MYbb/jFPB09ZQVt8955550MGDCAiy++mNWrV3Po0CFSU1P58MMPVXmdSzHpBF6vl8rKSoKCgggICOD999/HYDAwbNgw5syZ4zeDwKCvrKCvvHrKCm3PqyiK5m/OesoK2udtbm5GURR+97vfARAZGclHH33EhAkTuPnmm1vmlCiduFyLDMB3AoPBQGhoKKmpqVxzzTVs3bqVHTt2EBUVRVxcnF9MmDtBT1lBX3n1lBXantcfWlF6ygra5zWZTOzcuZPIyEjsdjsHDhygqamJ8847j/z8fJqbmwkPD+/U60sxaSefz8f8+fPZtWsXn3/+Oeeccw7JyckYDAZGjx5Nbm4umzZtIj4+XvMBVj1l1VtePWXVW149ZfWHvCeun5eXR05ODjfddBMJCQkAbN68mZSUFKqrq1myZAlTp07t9Nul/etPJR259957iYuL47LLLgPg+uuvb1lK3Gg0MmfOnJY1gbSmp6ygr7x6ygr6yqunrKB93hPXv+KKK2hsbOS6666jtrYWONYCWrp0Kf/4xz+4++671Zkk3eEh/B5q4cKFSk5OTsvHTz31lHLttdcqDodDw1Snp6esiqKvvHrKqij6yqunrIqifd7TXT8rK0txuVzKc889pwwfPlw5ePCgateXbq6z5PP5UBSFXbt2cfjwYQYOHEhAQADjxo1j9+7dAKSkpGic8hg9ZQV95dVTVtBXXj1lBe3ztnb9/Px8zGYzP/vZz7jiiitISkpSLYd0c50FRVEwGo0YjUYuvvhivvzyS9asWcORI0cACAwMpLq6WuOUx+gpK+grr56ygr7y6ikraJ/3x64fHBxMeXk5FotF1UICMgDfJj6fj/z8fOx2O4qi4PV6iYmJwW638+6771JcXMznn3/Otm3buPbaa4mMjJSs3TCvnrLqLa+esvpDXq2vfzqynMqPUBSFOXPm4HQ6efXVV/H5fJhMJnJycti0aRPjx4+nsbGRwsJCJk6cSJ8+fSRrN8yrp6x6y6unrP6QV+vrn4lMWmyFz+fj7rvvxmAwEBcXh8FgwGQyUVFRweOPP85tt93G8OHDATjvvPMk61nQU149ZQV95dVTVtA+r9bXb410c7XiySefJDY2lkceeYRNmzYRHh7e8gM8//zzOeecc05ZvE2ytp2e8uopK+grr56ygvZ5tb5+a6RlcgZer5eLLrqIkSNHAsealjt27GD48OFYrdaWCT/+8Auup6ygr7x6ygr6yqunrKB9Xq2v/6M6+15jvfP5fMr69et/8PmCggJl9uzZyvbt2zVIdXp6yqoo+sqrp6yKoq+8esqqKNrn1fr6bSXdXN9TUlLCjBkz6N+/P6mpqQAtd0o0NTVx8OBB+vTp07Ltppb0lBX0lVdPWUFfefWUFbTPq/X120rmmXxPZWUlcXFx/OlPf2LVqlUALbvg9enTh7Kyspb9k7Wmp6ygr7x6ygr6yqunrKB9Xq2v32ZaN438zY4dO5Rvv/1W2bdvnzJlyhRl1apVp3zdn5Zy0FNWRdFXXj1lVRR95dVTVkXRPq/W12+rHt/NpSgKr7zyCkePHsXtdpOeno7BYCAxMZHhw4fzxBNPYDKZGDZsGICmeyXoKave8uopq97y6imrP+TV+vrt1aOLiaIo3HLLLRgMBsrLy9m2bRtlZWWMGTMGgF69ejFkyBD+/ve/c+mllxIYGChZu2FePWXVW149ZfWHvFpfv0O6vjHkP0pLS5X58+criqIo9fX1ypYtW5R77723pRl5Yp/upqYmzTKeoKesiqKvvHrKqij6yqunrIqifV6tr98RPXKeic/nY/369ezfv5/GxkYqKyuJjY0lPT0dp9PJV199RX19fcvWmlo2I/WUVW959ZRVb3n1lNUf8mp9/c7Q4+7mUhSF3/72t3z++eds2LCBtWvXcsstt3Do0CFsNhtjx46luLiYmpqalsk/Wk0C0lNWveXVU1a95dVTVn/Iq/X1O0uPa5m88sorREVF8cADD+D1evnjH/+IyWTi+uuv58knn2T//v3U1dURFBSkdVRdZQV95dVTVtBXXj1lBe3zan39ztLjiklSUhK1tbU0NTVRW1vLrl27ePXVV0lPT+eTTz6hrKyM++67zy/2lNZTVtBXXj1lBX3l1VNW0D6v1tfvLD2umIwYMYIhQ4YQFBSEyWSiqakJOLaJTK9evfj973/fMiFIa3rKCvrKq6esoK+8esoK2ufV+vqdpcfdGhwcHExoaChwrK9y//79uN1uli1bxsyZM4mJidE44Xf0lBX0lVdPWUFfefWUFbTPq/X1O03X30DmP8rLy5WBAwcqM2bMUIqKirSO0yo9ZVUUfeXVU1ZF0VdePWVVFO3zan39juhxLZOTmUwmSkpKuPfee+nbt6/WcVqlp6ygr7x6ygr6yqunrKB9Xq2v3xE9ftve5uZmv7xn+3T0lBX0lVdPWUFfefWUFbTPq/X126vHFxMhhBAd1+MmLQohhOh8UkyEEEJ0mBQTIYQQHSbFRAghRIf1uBnwQrTH5s2bueOOOxgwYACKouDxeLj++uu55JJLTnt8WVkZ+fn5TJ48uYuTCqENKSZCtNHYsWP5y1/+AoDT6WTWrFmkpKSQnp7+g2M3bdrEvn37pJiIHkOKiRDtYLVaueaaa3jvvfdYvnw5FRUV1NTUMHHiRG677TaWLFlCU1MTmZmZJCUl8eijjwIQERHBY4891rJ8hhDdhYyZCNFO0dHRfPvttwwfPpx//OMfvP7667z++uuYTCZuvvlmLrvsMqZMmcL999/Pgw8+yLJly5g4cSIvvfSS1tGF6HTSMhGincrKysjMzCQ3N5dNmzZhs9lobm7+wXF79+7l4YcfBsDtdpOSktLVUYVQnRQTIdrB4XDwxhtvcPXVV9PY2MgjjzzCgQMHWLFiBYqiYDQa8fl8AKSkpPDkk0+SkJBATk4OVVVVGqcXovNJMRGijTZt2sSsWbMwGo14vV5uu+02UlJSuPPOO8nJySE4OJg+ffpQWVlJWloazz33HEOGDOGhhx7innvuwev1ArBo0SKNvxMhOp+szSWEEKLDZABeCCFEh0kxEUII0WFSTIQQQnSYFBMhhBAdJsVECCFEh0kxEUII0WFSTIQQQnTY/wNuEC9Vf4vjCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the trend of actual stocks\n",
    "\n",
    "sns.lineplot(pd.to_datetime(test['Date']),test['Open'])\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is what we are trying to predict. We might not get the exact values but can definetly get the trend. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[778.81],\n",
       "       [788.36],\n",
       "       [786.08],\n",
       "       [795.26],\n",
       "       [806.4 ],\n",
       "       [807.86],\n",
       "       [805.  ],\n",
       "       [807.14],\n",
       "       [807.48],\n",
       "       [807.08],\n",
       "       [805.81],\n",
       "       [805.12],\n",
       "       [806.91],\n",
       "       [807.25],\n",
       "       [822.3 ],\n",
       "       [829.62],\n",
       "       [837.81],\n",
       "       [834.71],\n",
       "       [814.66],\n",
       "       [796.86]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_stock_price = test.iloc[:, 1:2].values\n",
    "real_stock_price  ## actual stock price for 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GrvrLblxkz42"
   },
   "source": [
    "### Getting the predicted stock price of 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "emikTvUpk3Ck"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    325.25\n",
       "1    331.27\n",
       "2    329.83\n",
       "3    328.34\n",
       "4    322.04\n",
       "Name: Open, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To predict each day stcok of 2017, we need 60 previous stock prices. For the first finanacial day of Jan 2017 i.e 3rd Jan, we need data of Nov 2016 and Dec 2016.\n",
    "# To do this we will combine the train and test data here.  \n",
    "total = pd.concat((train['Open'], test['Open']), axis = 0)\n",
    "\n",
    "total.head()  # contains data from 2012 to 2017. 2012-2016 : we have , 2017: we wnt to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[779.   779.66 777.71 786.66 783.76 781.22 781.65 779.8  787.85 798.24\n",
      " 803.3  795.   804.9  816.68 806.34 801.   808.35 795.47 782.89 778.2\n",
      " 767.25 750.66 774.5  783.4  779.94 791.17 756.54 755.6  746.97 755.2\n",
      " 766.92 771.37 762.61 772.63 767.73 764.26 760.   771.53 770.07 757.44\n",
      " 744.59 757.71 764.73 761.   772.48 780.   785.04 793.9  797.4  797.34\n",
      " 800.4  790.22 796.76 795.84 792.36 790.9  790.68 793.7  783.33 782.75\n",
      " 778.81 788.36 786.08 795.26 806.4  807.86 805.   807.14 807.48 807.08\n",
      " 805.81 805.12 806.91 807.25 822.3  829.62 837.81 834.71 814.66 796.86]\n"
     ]
    }
   ],
   "source": [
    "# Next from total, we will take data from Jan3 - 60 days. Coz we have to start predicting from Jan 3. To predict Jan 3, we need only 60 day previous data not the whole total dataset. \n",
    "# To get the index of Jan3 we do, len(total) - len(test). From this we will subtract 60 days. This will give us stocks from Jan3 - 60 th day. \n",
    "inputs = total[len(total) - len(test) - 60:].values\n",
    "print(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[779.  ],\n",
       "       [779.66],\n",
       "       [777.71],\n",
       "       [786.66],\n",
       "       [783.76],\n",
       "       [781.22],\n",
       "       [781.65],\n",
       "       [779.8 ],\n",
       "       [787.85],\n",
       "       [798.24],\n",
       "       [803.3 ],\n",
       "       [795.  ],\n",
       "       [804.9 ],\n",
       "       [816.68],\n",
       "       [806.34],\n",
       "       [801.  ],\n",
       "       [808.35],\n",
       "       [795.47],\n",
       "       [782.89],\n",
       "       [778.2 ],\n",
       "       [767.25],\n",
       "       [750.66],\n",
       "       [774.5 ],\n",
       "       [783.4 ],\n",
       "       [779.94],\n",
       "       [791.17],\n",
       "       [756.54],\n",
       "       [755.6 ],\n",
       "       [746.97],\n",
       "       [755.2 ],\n",
       "       [766.92],\n",
       "       [771.37],\n",
       "       [762.61],\n",
       "       [772.63],\n",
       "       [767.73],\n",
       "       [764.26],\n",
       "       [760.  ],\n",
       "       [771.53],\n",
       "       [770.07],\n",
       "       [757.44],\n",
       "       [744.59],\n",
       "       [757.71],\n",
       "       [764.73],\n",
       "       [761.  ],\n",
       "       [772.48],\n",
       "       [780.  ],\n",
       "       [785.04],\n",
       "       [793.9 ],\n",
       "       [797.4 ],\n",
       "       [797.34],\n",
       "       [800.4 ],\n",
       "       [790.22],\n",
       "       [796.76],\n",
       "       [795.84],\n",
       "       [792.36],\n",
       "       [790.9 ],\n",
       "       [790.68],\n",
       "       [793.7 ],\n",
       "       [783.33],\n",
       "       [782.75],\n",
       "       [778.81],\n",
       "       [788.36],\n",
       "       [786.08],\n",
       "       [795.26],\n",
       "       [806.4 ],\n",
       "       [807.86],\n",
       "       [805.  ],\n",
       "       [807.14],\n",
       "       [807.48],\n",
       "       [807.08],\n",
       "       [805.81],\n",
       "       [805.12],\n",
       "       [806.91],\n",
       "       [807.25],\n",
       "       [822.3 ],\n",
       "       [829.62],\n",
       "       [837.81],\n",
       "       [834.71],\n",
       "       [814.66],\n",
       "       [796.86]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = inputs.reshape(-1,1)\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([779.  , 779.66, 777.71, 786.66, 783.76, 781.22, 781.65, 779.8 ,\n",
       "       787.85, 798.24, 803.3 , 795.  , 804.9 , 816.68, 806.34, 801.  ,\n",
       "       808.35, 795.47, 782.89, 778.2 , 767.25, 750.66, 774.5 , 783.4 ,\n",
       "       779.94, 791.17, 756.54, 755.6 , 746.97, 755.2 , 766.92, 771.37,\n",
       "       762.61, 772.63, 767.73, 764.26, 760.  , 771.53, 770.07, 757.44,\n",
       "       744.59, 757.71, 764.73, 761.  , 772.48, 780.  , 785.04, 793.9 ,\n",
       "       797.4 , 797.34, 800.4 , 790.22, 796.76, 795.84, 792.36, 790.9 ,\n",
       "       790.68, 793.7 , 783.33, 782.75])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs[60-60:60,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the inputs. \n",
    "inputs = sc.transform(inputs)\n",
    "# For each day prediction X_test will have 60 previous data. Hence the below for loop to get the same. \n",
    "# Note: To predict each day stock in Jan, we will use previous day's actual stocks instead of the predicted stocks. \n",
    "X_test = []\n",
    "for i in range(60, 80): #index 60 coz this index is Jan3rd. \n",
    "    X_test.append(inputs[i-60:i, 0])\n",
    "X_test = np.array(X_test)\n",
    "# reshaping it to 3D\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "pred_stock_price = regressor.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.959315  ],\n",
       "       [0.95457375],\n",
       "       [0.95694804],\n",
       "       [0.95979464],\n",
       "       [0.9657303 ],\n",
       "       [0.976237  ],\n",
       "       [0.9840991 ],\n",
       "       [0.98525786],\n",
       "       [0.98498654],\n",
       "       [0.985363  ],\n",
       "       [0.9861822 ],\n",
       "       [0.9866327 ],\n",
       "       [0.9869482 ],\n",
       "       [0.98865956],\n",
       "       [0.9907611 ],\n",
       "       [1.0007708 ],\n",
       "       [1.0134413 ],\n",
       "       [1.0252639 ],\n",
       "       [1.0292534 ],\n",
       "       [1.017756  ]], dtype=float32)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_stock_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[794.8094 ],\n",
       "       [792.2606 ],\n",
       "       [793.537  ],\n",
       "       [795.0672 ],\n",
       "       [798.25793],\n",
       "       [803.90594],\n",
       "       [808.1323 ],\n",
       "       [808.7552 ],\n",
       "       [808.6093 ],\n",
       "       [808.8117 ],\n",
       "       [809.2521 ],\n",
       "       [809.49426],\n",
       "       [809.6639 ],\n",
       "       [810.58386],\n",
       "       [811.71356],\n",
       "       [817.0943 ],\n",
       "       [823.9055 ],\n",
       "       [830.26086],\n",
       "       [832.4054 ],\n",
       "       [826.22485]], dtype=float32)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do inverse tranform for normal values\n",
    "pred_stock_price = sc.inverse_transform(pred_stock_price)\n",
    "pred_stock_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iFTNs3YHk6FQ"
   },
   "source": [
    "### Visualising the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "8OUI8U49k9tH",
    "outputId": "db0ed15e-071b-4bae-955e-0eda3df95238"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(real_stock_price, color = 'red', label = 'Real Stock Price')\n",
    "plt.plot(pred_stock_price, color = 'blue', label = 'Predicted Stock Price')\n",
    "plt.title('Google Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Google Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see that the trend is almost similar to that of actual stocks of 2017. \n",
    "# We can also see in some places the actual values do not match the predicted values, \n",
    "# this is because the model cannot predict the sudden behaviour change in trend of the stocks such as sudden upward raise or downward fall."
   ]
  },
  {
   "cell_type": "code",
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