Time-Series LSTM Model/Time-Series Stock Price Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import yfinance as yf\n",
    "import scipy.stats as stats\n",
    "import datetime\n",
    "import time\n",
    "import requests\n",
    "import io\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Give the Name of Stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Symbols = ['GOOGL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to download Data From Yahoo Finance\n",
    "def download_data(start,end):\n",
    "    stock_final = pd.DataFrame()\n",
    "    # iterate over each symbol\n",
    "    for i in Symbols:  \n",
    "\n",
    "        # print the symbol which is being downloaded\n",
    "        print( str(Symbols.index(i)) + str(' : ') + i, sep=',', end=',', flush=True)  \n",
    "\n",
    "        try:\n",
    "            # download the stock price \n",
    "            stock = []\n",
    "            stock = yf.download(i,start=start, end=end, progress=False)\n",
    "            # append the individual stock prices \n",
    "            if len(stock) == 0:\n",
    "                None\n",
    "            else:\n",
    "                stock['Name']=i\n",
    "                stock_final = stock_final.append(stock,sort=False)\n",
    "        except Exception:\n",
    "            None\n",
    "    return stock_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Give the start and End Time duration of Train and Test Data\n",
    "train_start = datetime.datetime(2007,1,1)\n",
    "train_end = datetime.datetime(2018,1,1)\n",
    "test_start = datetime.datetime(2018,1,1)\n",
    "test_end = datetime.datetime(2020,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : GOOGL,0 : GOOGL,"
     ]
    }
   ],
   "source": [
    "#Call The Functio\n",
    "df_train = download_data(train_start,train_end)\n",
    "df_test = download_data(test_start,test_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-03</th>\n",
       "      <td>233.233231</td>\n",
       "      <td>238.568573</td>\n",
       "      <td>230.785782</td>\n",
       "      <td>234.029022</td>\n",
       "      <td>234.029022</td>\n",
       "      <td>15397587</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-04</th>\n",
       "      <td>234.734741</td>\n",
       "      <td>242.217224</td>\n",
       "      <td>234.409409</td>\n",
       "      <td>241.871872</td>\n",
       "      <td>241.871872</td>\n",
       "      <td>15759425</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-05</th>\n",
       "      <td>241.491486</td>\n",
       "      <td>243.993988</td>\n",
       "      <td>239.294296</td>\n",
       "      <td>243.838837</td>\n",
       "      <td>243.838837</td>\n",
       "      <td>13730456</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-08</th>\n",
       "      <td>244.089096</td>\n",
       "      <td>245.180176</td>\n",
       "      <td>241.341339</td>\n",
       "      <td>242.032028</td>\n",
       "      <td>242.032028</td>\n",
       "      <td>9499291</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-09</th>\n",
       "      <td>242.967972</td>\n",
       "      <td>244.369370</td>\n",
       "      <td>240.840836</td>\n",
       "      <td>242.992996</td>\n",
       "      <td>242.992996</td>\n",
       "      <td>10752037</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2007-01-03  233.233231  238.568573  230.785782  234.029022  234.029022   \n",
       "2007-01-04  234.734741  242.217224  234.409409  241.871872  241.871872   \n",
       "2007-01-05  241.491486  243.993988  239.294296  243.838837  243.838837   \n",
       "2007-01-08  244.089096  245.180176  241.341339  242.032028  242.032028   \n",
       "2007-01-09  242.967972  244.369370  240.840836  242.992996  242.992996   \n",
       "\n",
       "              Volume   Name  \n",
       "Date                         \n",
       "2007-01-03  15397587  GOOGL  \n",
       "2007-01-04  15759425  GOOGL  \n",
       "2007-01-05  13730456  GOOGL  \n",
       "2007-01-08   9499291  GOOGL  \n",
       "2007-01-09  10752037  GOOGL  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2769 entries, 2007-01-03 to 2017-12-29\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       2769 non-null   float64\n",
      " 1   High       2769 non-null   float64\n",
      " 2   Low        2769 non-null   float64\n",
      " 3   Close      2769 non-null   float64\n",
      " 4   Adj Close  2769 non-null   float64\n",
      " 5   Volume     2769 non-null   int64  \n",
      " 6   Name       2769 non-null   object \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 173.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training Data is from 2007 till 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Time Series Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.plot(df_train.Close)\n",
    "plt.title('Google Time-series plot')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume', 'Name'], dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We will use close price as a Target and drop rest of the columns\n",
    "df_train = df_train.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume', 'Name'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "df_train_scaled = scaler.fit_transform(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Time-Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating Data Stricture with 60 time-steps \n",
    "#60 time-step means we will try to predict on the basis of past 60 days of historical data\n",
    "\n",
    "time_step = 60\n",
    "\n",
    "X_train = []\n",
    "\n",
    "y_train = []\n",
    "\n",
    "for i in range(time_step,len(df_train_scaled)):\n",
    "    \n",
    "    X_train.append(df_train_scaled[i-time_step:i,0])\n",
    "    \n",
    "    y_train.append(df_train_scaled[i,0])\n",
    "\n",
    "X_train,y_train = np.array(X_train),np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2709, 60, 1), (2709,))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train.reshape((X_train.shape[0],X_train.shape[1],1))\n",
    "X_train.shape,y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.layers import Dense,LSTM,Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LSTM_model():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    #First LSTM layer\n",
    "    model.add(LSTM(units = 64, return_sequences = True, input_shape = (X_train.shape[1],1)))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    #Second LSTM Layer\n",
    "    model.add(LSTM(units = 64, return_sequences = True))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    #Third LSTM Layer\n",
    "    model.add(LSTM(units = 64, return_sequences = True))\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    #Forth LSTM Layer\n",
    "    model.add(LSTM(units = 64))\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "\n",
    "    #Output Layer(Dense)\n",
    "    model.add(Dense(units=1))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "153/153 [==============================] - 19s 94ms/step - loss: 0.0052 - mean_squared_error: 0.0052 - cosine_proximity: 0.9979 - val_loss: 0.0044 - val_mean_squared_error: 0.0044 - val_cosine_proximity: 1.0000\n",
      "Epoch 2/15\n",
      "153/153 [==============================] - 13s 88ms/step - loss: 0.0017 - mean_squared_error: 0.0017 - cosine_proximity: 0.9996 - val_loss: 0.0048 - val_mean_squared_error: 0.0048 - val_cosine_proximity: 1.0000\n",
      "Epoch 3/15\n",
      "153/153 [==============================] - 13s 86ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - cosine_proximity: 0.9996 - val_loss: 8.9332e-04 - val_mean_squared_error: 8.9332e-04 - val_cosine_proximity: 1.0000\n",
      "Epoch 4/15\n",
      "153/153 [==============================] - 11s 69ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - cosine_proximity: 0.9996 - val_loss: 0.0136 - val_mean_squared_error: 0.0136 - val_cosine_proximity: 1.0000\n",
      "Epoch 5/15\n",
      "153/153 [==============================] - 11s 69ms/step - loss: 0.0013 - mean_squared_error: 0.0013 - cosine_proximity: 0.9971 - val_loss: 0.0036 - val_mean_squared_error: 0.0036 - val_cosine_proximity: 1.0000\n",
      "Epoch 6/15\n",
      "153/153 [==============================] - 12s 77ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - cosine_proximity: 0.9971 - val_loss: 0.0111 - val_mean_squared_error: 0.0111 - val_cosine_proximity: 1.0000\n",
      "Epoch 7/15\n",
      "153/153 [==============================] - 13s 87ms/step - loss: 0.0012 - mean_squared_error: 0.0012 - cosine_proximity: 0.9971 - val_loss: 0.0031 - val_mean_squared_error: 0.0031 - val_cosine_proximity: 1.0000\n",
      "Epoch 8/15\n",
      "153/153 [==============================] - 13s 82ms/step - loss: 0.0010 - mean_squared_error: 0.0010 - cosine_proximity: 0.9971 - val_loss: 6.6575e-04 - val_mean_squared_error: 6.6575e-04 - val_cosine_proximity: 1.0000\n",
      "Epoch 9/15\n",
      "153/153 [==============================] - 11s 74ms/step - loss: 9.2465e-04 - mean_squared_error: 9.2465e-04 - cosine_proximity: 0.9947 - val_loss: 0.0019 - val_mean_squared_error: 0.0019 - val_cosine_proximity: 1.0000\n",
      "Epoch 10/15\n",
      "153/153 [==============================] - 11s 73ms/step - loss: 7.9177e-04 - mean_squared_error: 7.9177e-04 - cosine_proximity: 0.9971 - val_loss: 0.0022 - val_mean_squared_error: 0.0022 - val_cosine_proximity: 1.0000\n",
      "Epoch 11/15\n",
      "153/153 [==============================] - 11s 74ms/step - loss: 8.7739e-04 - mean_squared_error: 8.7739e-04 - cosine_proximity: 0.9922 - val_loss: 7.0307e-04 - val_mean_squared_error: 7.0307e-04 - val_cosine_proximity: 1.0000\n",
      "Epoch 12/15\n",
      "153/153 [==============================] - 11s 73ms/step - loss: 6.9985e-04 - mean_squared_error: 6.9985e-04 - cosine_proximity: 0.9955 - val_loss: 0.0013 - val_mean_squared_error: 0.0013 - val_cosine_proximity: 1.0000\n",
      "Epoch 13/15\n",
      "153/153 [==============================] - 11s 72ms/step - loss: 7.6216e-04 - mean_squared_error: 7.6216e-04 - cosine_proximity: 0.9971 - val_loss: 0.0017 - val_mean_squared_error: 0.0017 - val_cosine_proximity: 1.0000\n",
      "Epoch 14/15\n",
      "153/153 [==============================] - 12s 76ms/step - loss: 7.7644e-04 - mean_squared_error: 7.7644e-04 - cosine_proximity: 0.9930 - val_loss: 0.0047 - val_mean_squared_error: 0.0047 - val_cosine_proximity: 1.0000\n",
      "Epoch 15/15\n",
      "153/153 [==============================] - 13s 84ms/step - loss: 7.0080e-04 - mean_squared_error: 7.0080e-04 - cosine_proximity: 0.9922 - val_loss: 0.0022 - val_mean_squared_error: 0.0022 - val_cosine_proximity: 1.0000\n"
     ]
    }
   ],
   "source": [
    "model = LSTM_model()\n",
    "model.compile(optimizer='adam',loss='mean_squared_error',metrics=['mean_squared_error',  'cosine_proximity'])\n",
    "\n",
    "earlystopping=EarlyStopping(monitor=\"mean_squared_error\", patience=5, verbose=1, mode='auto')\n",
    "\n",
    "history = model.fit(X_train,y_train,epochs = 15,validation_split = 0.1, batch_size = 16,callbacks=[earlystopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.plot(history.history['mean_squared_error'], label='MSE (training data)')\n",
    "plt.plot(history.history['val_mean_squared_error'], label='MSE (validation data)')\n",
    "plt.title('MSE for Google Stock')\n",
    "plt.ylabel('MSE value')\n",
    "plt.xlabel('No. epoch')\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving The Model\n",
    "model.save('time_series_lstm.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test our Model on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 503 entries, 2018-01-02 to 2019-12-31\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       503 non-null    float64\n",
      " 1   High       503 non-null    float64\n",
      " 2   Low        503 non-null    float64\n",
      " 3   Close      503 non-null    float64\n",
      " 4   Adj Close  503 non-null    float64\n",
      " 5   Volume     503 non-null    int64  \n",
      " 6   Name       503 non-null    object \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 31.4+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>1053.020020</td>\n",
       "      <td>1075.979980</td>\n",
       "      <td>1053.020020</td>\n",
       "      <td>1073.209961</td>\n",
       "      <td>1073.209961</td>\n",
       "      <td>1588300</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>1073.930054</td>\n",
       "      <td>1096.099976</td>\n",
       "      <td>1073.430054</td>\n",
       "      <td>1091.520020</td>\n",
       "      <td>1091.520020</td>\n",
       "      <td>1565900</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>1097.089966</td>\n",
       "      <td>1104.079956</td>\n",
       "      <td>1094.260010</td>\n",
       "      <td>1095.760010</td>\n",
       "      <td>1095.760010</td>\n",
       "      <td>1302600</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>1103.449951</td>\n",
       "      <td>1113.579956</td>\n",
       "      <td>1101.800049</td>\n",
       "      <td>1110.290039</td>\n",
       "      <td>1110.290039</td>\n",
       "      <td>1512500</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>1111.000000</td>\n",
       "      <td>1119.160034</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>1114.209961</td>\n",
       "      <td>1114.209961</td>\n",
       "      <td>1232200</td>\n",
       "      <td>GOOGL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2018-01-02  1053.020020  1075.979980  1053.020020  1073.209961  1073.209961   \n",
       "2018-01-03  1073.930054  1096.099976  1073.430054  1091.520020  1091.520020   \n",
       "2018-01-04  1097.089966  1104.079956  1094.260010  1095.760010  1095.760010   \n",
       "2018-01-05  1103.449951  1113.579956  1101.800049  1110.290039  1110.290039   \n",
       "2018-01-08  1111.000000  1119.160034  1110.000000  1114.209961  1114.209961   \n",
       "\n",
       "             Volume   Name  \n",
       "Date                        \n",
       "2018-01-02  1588300  GOOGL  \n",
       "2018-01-03  1565900  GOOGL  \n",
       "2018-01-04  1302600  GOOGL  \n",
       "2018-01-05  1512500  GOOGL  \n",
       "2018-01-08  1232200  GOOGL  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_test.info())\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing of Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((443, 60, 1), (443,))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_test = df_test.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume', 'Name'],axis=1)\n",
    "df_test_scaled = scaler.transform(df_test)\n",
    "\n",
    "\n",
    "X_test = []\n",
    "\n",
    "y_test= []\n",
    "\n",
    "for i in range(time_step,len(df_test_scaled)):\n",
    "    \n",
    "    X_test.append(df_test_scaled[i-time_step:i,0])\n",
    "    \n",
    "    y_test.append(df_test_scaled[i,0])\n",
    "\n",
    "X_test,y_test = np.array(X_test),np.array(y_test)\n",
    "\n",
    "X_test = X_test.reshape((X_test.shape[0],X_test.shape[1],1))\n",
    "X_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict on Test Dataset\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(443, 1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test=y_test.reshape((-1,1))\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating test result Dataset\n",
    "df = pd.DataFrame()\n",
    "y_pred_actual= scaler.inverse_transform(y_pred)\n",
    "\n",
    "y_test_actual = df_test[time_step:]['Close']\n",
    "\n",
    "df['y_test'] = y_test_actual\n",
    "\n",
    "df['y_pred'] = y_pred_actual\n",
    "\n",
    "df.index = df_test.index[time_step:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
       "      <td>1037.140015</td>\n",
       "      <td>1016.166809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02</th>\n",
       "      <td>1012.630005</td>\n",
       "      <td>1004.897583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-03</th>\n",
       "      <td>1018.679993</td>\n",
       "      <td>995.485107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04</th>\n",
       "      <td>1029.709961</td>\n",
       "      <td>988.292603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-05</th>\n",
       "      <td>1032.640015</td>\n",
       "      <td>983.642395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 y_test       y_pred\n",
       "Date                                \n",
       "2018-03-29  1037.140015  1016.166809\n",
       "2018-04-02  1012.630005  1004.897583\n",
       "2018-04-03  1018.679993   995.485107\n",
       "2018-04-04  1029.709961   988.292603\n",
       "2018-04-05  1032.640015   983.642395"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(df['y_test'],color='b')\n",
    "plt.plot(df['y_pred'],color='y')\n",
    "plt.title(\"Actual vs Predicted\")\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}