Apple stock forecasting.ipynb

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   "source": [
    "# Time-Series Forecasting: Predicting Apple Stock Price Using An LSTM Model\n",
    "Traditionally, most machine learning (ML) models have used some observations (samples / examples) as input features, but the data has no time dimension.\n",
    "\n",
    "Models capable of predicting future values based on previously observed values are known as time-series forecasting models. For non-stationary data, time-series forecasting is widely used. Non-stationary data are those whose statistical features, such as mean and standard deviation, do not remain constant throughout time but instead change.\n",
    "\n",
    "The non-stationary input data used as input to these models is typically called to as time-series. Temperature values over time, stock prices over time, and the price of a property over time are all instances of time-series. As a result, the input is a signal (time-series) characterised by observations recorded in a specific order throughout time."
   ]
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   "cell_type": "markdown",
   "id": "5fe8183b",
   "metadata": {},
   "source": [
    "## The LSTM model\n",
    "- Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning\n",
    "\n",
    "- LSTM Models, in a nutshell, can store data throughout time. When working with Time Series or Sequential Data, this is really handy.\n",
    "\n",
    "- The Human Brain is remarkably similar to LSTM Models. When you hear someone speak to you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4facf177",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader as web # to read data from web\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f24304a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>28.282499</td>\n",
       "      <td>27.552500</td>\n",
       "      <td>28.205000</td>\n",
       "      <td>27.594999</td>\n",
       "      <td>165613600.0</td>\n",
       "      <td>25.020121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>27.860001</td>\n",
       "      <td>26.837500</td>\n",
       "      <td>27.847500</td>\n",
       "      <td>27.332500</td>\n",
       "      <td>212818400.0</td>\n",
       "      <td>24.782112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>27.162500</td>\n",
       "      <td>26.352501</td>\n",
       "      <td>27.072500</td>\n",
       "      <td>26.562500</td>\n",
       "      <td>257142000.0</td>\n",
       "      <td>24.083963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>26.857500</td>\n",
       "      <td>26.157499</td>\n",
       "      <td>26.635000</td>\n",
       "      <td>26.565001</td>\n",
       "      <td>263188400.0</td>\n",
       "      <td>24.086226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>27.049999</td>\n",
       "      <td>26.674999</td>\n",
       "      <td>26.799999</td>\n",
       "      <td>26.937500</td>\n",
       "      <td>160423600.0</td>\n",
       "      <td>24.423967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close       Volume  Adj Close\n",
       "Date                                                                          \n",
       "2014-12-31  28.282499  27.552500  28.205000  27.594999  165613600.0  25.020121\n",
       "2015-01-02  27.860001  26.837500  27.847500  27.332500  212818400.0  24.782112\n",
       "2015-01-05  27.162500  26.352501  27.072500  26.562500  257142000.0  24.083963\n",
       "2015-01-06  26.857500  26.157499  26.635000  26.565001  263188400.0  24.086226\n",
       "2015-01-07  27.049999  26.674999  26.799999  26.937500  160423600.0  24.423967"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the data\n",
    "APPL_data= web.DataReader('AAPL',data_source=\"yahoo\",start='2015-01-01',end='2021-09-30')\n",
    "APPL_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "02262e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1700, 6)"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "APPL_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "978694c2",
   "metadata": {},
   "outputs": [
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       "Text(0.5, 1.0, 'Closing price History')"
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets look at the stock closing price of history\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(16,8))\n",
    "sns.lineplot(x= APPL_data.index,y=APPL_data['Close'])\n",
    "plt.xlabel('Date', fontsize=20)\n",
    "plt.ylabel('closing price USD ($)',fontsize=20)\n",
    "plt.title('Closing price History',fontsize=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d788b8",
   "metadata": {},
   "source": [
    "#### I am using the AAPL dataset, which is already divided into training set and test set, but you can do the divison with a simple command!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dea9b0b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into train and test:\n",
    "data_to_train = APPL_data[:1530]\n",
    "data_to_test = APPL_data[1530:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f1f31f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, we can save the 2 csv files, Train and Test.\n",
    "data_to_train.to_csv('train_data.csv')\n",
    "data_to_test.to_csv('test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf3e3fc0",
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>27.594999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>27.332500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>26.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>26.565001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>26.937500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close\n",
       "Date                 \n",
       "2014-12-31  27.594999\n",
       "2015-01-02  27.332500\n",
       "2015-01-05  26.562500\n",
       "2015-01-06  26.565001\n",
       "2015-01-07  26.937500"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_data= APPL_data.iloc[: , 3:4]\n",
    "aapl_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a586dd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## We want to create a numpy arrary not a vector \n",
    "trainig_set= aapl_data.iloc[:1530,:].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c10d660f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set= aapl_data.iloc[1530:,:].values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e057c645",
   "metadata": {},
   "source": [
    "#### It’s a good idea to normalize the data before model fitting. This will boost the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d720b808",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature scalling, Here we will do normalizatioin \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc= MinMaxScaler(feature_range=(0,1))\n",
    "trainig_set_scaled= sc.fit_transform(trainig_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3c0bd4",
   "metadata": {},
   "source": [
    "#### After scaling the training data, we must format it into a three-dimensional array for use in our LSTM model. We accomplish this by generating 60 timestamps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a43bf07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a data structure with 60 timesteps and 1 output\n",
    "X_train=[] #Independent variables\n",
    "y_train= [] # Dependent variables \n",
    "# I am going to append past 60 days data \n",
    "for i in range(60,1530):\n",
    "    X_train.append(trainig_set_scaled[i-60:i,0]) # Appending prevois 60 days data not including 60\n",
    "    y_train.append(trainig_set_scaled[i,0])\n",
    "    \n",
    "X_train, y_train= np.array(X_train), np.array(y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09a84e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1470, 60), (1470,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lETS CHECK THE SHAPE OF X_train and y_train\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44320ea",
   "metadata": {},
   "source": [
    "#### We have now reshaped the data into the following format (values, time-steps, 1 dimensional output)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a9c0e3e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 60, 1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LSMT Model needs to be 3- dimensional, so need to rehsape the x_train, y_train\n",
    "# Reshaping\n",
    "#numpy.reshape(array, shape, order = 'C')\n",
    "X_train= np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f78a770e",
   "metadata": {},
   "source": [
    "#### Now, it’s time to build the model. We will build the LSTM with 100 neurons and 5 hidden layers. Finally, we will assign 1 neuron in the output layer for predicting the normalized stock price. We will use the MSE loss function and the Adam stochastic gradient descent optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2db39c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Keras libraries and packages\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
    "\n",
    "#  Initialising the RNN\n",
    "model= Sequential()\n",
    "\n",
    "# Adding first LSTM layer and some dropout Dropout regularisation\n",
    "model.add(LSTM(units=100,return_sequences=True, input_shape=(X_train.shape[1],1)))\n",
    "model.add(Dropout(rate=0.2))\n",
    "\n",
    "# Adding second LSTM layer and some dropout Dropout regularisation\n",
    "model.add(LSTM(units=100,return_sequences=True))\n",
    "model.add(Dropout(rate=0.2))\n",
    "\n",
    "# Adding third LSTM layer and some dropout Dropout regularisation\n",
    "model.add(LSTM(units=100,return_sequences=True))\n",
    "model.add(Dropout(rate=0.2))\n",
    "\n",
    "# Adding fourth LSTM layer and some dropout Dropout regularisation\n",
    "model.add(LSTM(units=100,return_sequences=True))\n",
    "model.add(Dropout(rate=0.2))\n",
    "\n",
    "# Adding fifth LSTM layer and some dropout Dropout regularisation\n",
    "model.add(LSTM(units=100))\n",
    "model.add(Dropout(rate=0.2))\n",
    "\n",
    "# Adding the Output Layer\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "# Compiling the Model\n",
    "# Because we're doing regression hence mean_squared_error\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d7dafcf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 60, 100)           40800     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 60, 100)           0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 60, 100)           80400     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 60, 100)           0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 60, 100)           80400     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 60, 100)           0         \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 60, 100)           80400     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 60, 100)           0         \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 100)               80400     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 362,501\n",
      "Trainable params: 362,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c112f265",
   "metadata": {},
   "source": [
    "**We use Dropout layers to avoid Overfitting problems, and besides that, we use the parameter “return_sequences” to determine if the layer will return a sequence compatible with a LSTM. We use “return_sequences=True” when we have a LSTM layer after**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa362c9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "46/46 [==============================] - 38s 427ms/step - loss: 0.0085\n",
      "Epoch 2/100\n",
      "46/46 [==============================] - 19s 406ms/step - loss: 0.0032\n",
      "Epoch 3/100\n",
      "46/46 [==============================] - 19s 405ms/step - loss: 0.0030\n",
      "Epoch 4/100\n",
      "46/46 [==============================] - 18s 385ms/step - loss: 0.0018\n",
      "Epoch 5/100\n",
      "46/46 [==============================] - 17s 378ms/step - loss: 0.0023\n",
      "Epoch 6/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 0.0015\n",
      "Epoch 7/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 0.0016\n",
      "Epoch 8/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 0.0012\n",
      "Epoch 9/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 0.0017\n",
      "Epoch 10/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 0.0017\n",
      "Epoch 11/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 0.0012\n",
      "Epoch 12/100\n",
      "46/46 [==============================] - 17s 373ms/step - loss: 0.0016\n",
      "Epoch 13/100\n",
      "46/46 [==============================] - 17s 373ms/step - loss: 0.0014\n",
      "Epoch 14/100\n",
      "46/46 [==============================] - 17s 373ms/step - loss: 0.0013\n",
      "Epoch 15/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 0.0014\n",
      "Epoch 16/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 0.0016\n",
      "Epoch 17/100\n",
      "46/46 [==============================] - 17s 377ms/step - loss: 0.0012\n",
      "Epoch 18/100\n",
      "46/46 [==============================] - 17s 378ms/step - loss: 0.0011\n",
      "Epoch 19/100\n",
      "46/46 [==============================] - 17s 377ms/step - loss: 0.0011\n",
      "Epoch 20/100\n",
      "46/46 [==============================] - 17s 359ms/step - loss: 0.0014\n",
      "Epoch 21/100\n",
      "46/46 [==============================] - 18s 380ms/step - loss: 0.0011\n",
      "Epoch 22/100\n",
      "46/46 [==============================] - 17s 379ms/step - loss: 9.7642e-04\n",
      "Epoch 23/100\n",
      "46/46 [==============================] - 17s 373ms/step - loss: 9.9201e-04\n",
      "Epoch 24/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 8.0673e-04\n",
      "Epoch 25/100\n",
      "46/46 [==============================] - 17s 373ms/step - loss: 9.9517e-04\n",
      "Epoch 26/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 0.0011\n",
      "Epoch 27/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 9.5443e-04\n",
      "Epoch 28/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 9.5968e-04\n",
      "Epoch 29/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 0.0011\n",
      "Epoch 30/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 0.0010\n",
      "Epoch 31/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 8.7322e-04\n",
      "Epoch 32/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 9.4119e-04\n",
      "Epoch 33/100\n",
      "46/46 [==============================] - 18s 397ms/step - loss: 0.0011\n",
      "Epoch 34/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 9.2280e-04\n",
      "Epoch 35/100\n",
      "46/46 [==============================] - 17s 378ms/step - loss: 8.2354e-04\n",
      "Epoch 36/100\n",
      "46/46 [==============================] - 17s 377ms/step - loss: 9.5226e-04\n",
      "Epoch 37/100\n",
      "46/46 [==============================] - 17s 379ms/step - loss: 8.0796e-04\n",
      "Epoch 38/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 9.5051e-04\n",
      "Epoch 39/100\n",
      "46/46 [==============================] - 17s 378ms/step - loss: 9.6402e-04\n",
      "Epoch 40/100\n",
      "46/46 [==============================] - 17s 377ms/step - loss: 8.6356e-04\n",
      "Epoch 41/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 7.5121e-04\n",
      "Epoch 42/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 7.6412e-04\n",
      "Epoch 43/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 7.1796e-04\n",
      "Epoch 44/100\n",
      "46/46 [==============================] - 17s 377ms/step - loss: 8.3779e-04\n",
      "Epoch 45/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 7.7904e-04\n",
      "Epoch 46/100\n",
      "46/46 [==============================] - 17s 377ms/step - loss: 9.2045e-04\n",
      "Epoch 47/100\n",
      "46/46 [==============================] - 17s 373ms/step - loss: 8.6128e-04\n",
      "Epoch 48/100\n",
      "46/46 [==============================] - 17s 378ms/step - loss: 7.0507e-04\n",
      "Epoch 49/100\n",
      "46/46 [==============================] - 17s 377ms/step - loss: 7.4401e-04\n",
      "Epoch 50/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 8.0927e-04\n",
      "Epoch 51/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 6.8178e-04\n",
      "Epoch 52/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 6.6149e-04\n",
      "Epoch 53/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 7.3909e-04\n",
      "Epoch 54/100\n",
      "46/46 [==============================] - 15s 334ms/step - loss: 6.4877e-04\n",
      "Epoch 55/100\n",
      "46/46 [==============================] - 18s 391ms/step - loss: 7.1886e-04\n",
      "Epoch 56/100\n",
      "46/46 [==============================] - 17s 379ms/step - loss: 6.4773e-04\n",
      "Epoch 57/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 7.6325e-04\n",
      "Epoch 58/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 6.0896e-04\n",
      "Epoch 59/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 6.2899e-04\n",
      "Epoch 60/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 7.4233e-04\n",
      "Epoch 61/100\n",
      "46/46 [==============================] - 17s 379ms/step - loss: 6.0655e-04\n",
      "Epoch 62/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 6.2678e-04\n",
      "Epoch 63/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 6.3083e-04\n",
      "Epoch 64/100\n",
      "46/46 [==============================] - 17s 377ms/step - loss: 7.6947e-04\n",
      "Epoch 65/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 6.9442e-04\n",
      "Epoch 66/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 7.4281e-04\n",
      "Epoch 67/100\n",
      "46/46 [==============================] - 17s 362ms/step - loss: 6.6596e-04\n",
      "Epoch 68/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 6.2649e-04\n",
      "Epoch 69/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 5.9570e-04\n",
      "Epoch 70/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 5.9493e-04\n",
      "Epoch 71/100\n",
      "46/46 [==============================] - 17s 373ms/step - loss: 6.3514e-04\n",
      "Epoch 72/100\n",
      "46/46 [==============================] - 17s 373ms/step - loss: 6.6829e-04\n",
      "Epoch 73/100\n",
      "46/46 [==============================] - 16s 357ms/step - loss: 7.1892e-04\n",
      "Epoch 74/100\n",
      "46/46 [==============================] - 17s 373ms/step - loss: 6.6129e-04\n",
      "Epoch 75/100\n",
      "46/46 [==============================] - 20s 431ms/step - loss: 6.0611e-04\n",
      "Epoch 76/100\n",
      "46/46 [==============================] - 17s 377ms/step - loss: 5.4323e-04\n",
      "Epoch 77/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 6.2197e-04\n",
      "Epoch 78/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 7.7724e-04\n",
      "Epoch 79/100\n",
      "46/46 [==============================] - 17s 372ms/step - loss: 7.3546e-04\n",
      "Epoch 80/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 6.1211e-04\n",
      "Epoch 81/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 5.3323e-04\n",
      "Epoch 82/100\n",
      "46/46 [==============================] - 17s 372ms/step - loss: 5.6726e-04\n",
      "Epoch 83/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 5.9030e-04\n",
      "Epoch 84/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 5.3269e-04\n",
      "Epoch 85/100\n",
      "46/46 [==============================] - 17s 378ms/step - loss: 5.2827e-04\n",
      "Epoch 86/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 5.6455e-04\n",
      "Epoch 87/100\n",
      "46/46 [==============================] - 17s 380ms/step - loss: 5.2100e-04\n",
      "Epoch 88/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 5.3477e-04\n",
      "Epoch 89/100\n",
      "46/46 [==============================] - 17s 372ms/step - loss: 6.3427e-04\n",
      "Epoch 90/100\n",
      "46/46 [==============================] - 16s 339ms/step - loss: 7.2732e-04\n",
      "Epoch 91/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 4.7691e-04\n",
      "Epoch 92/100\n",
      "46/46 [==============================] - 17s 377ms/step - loss: 4.9073e-04\n",
      "Epoch 93/100\n",
      "46/46 [==============================] - 17s 375ms/step - loss: 5.4666e-04\n",
      "Epoch 94/100\n",
      "46/46 [==============================] - 16s 337ms/step - loss: 5.9316e-04\n",
      "Epoch 95/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46/46 [==============================] - 17s 379ms/step - loss: 5.9452e-04\n",
      "Epoch 96/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 5.4686e-04\n",
      "Epoch 97/100\n",
      "46/46 [==============================] - 17s 376ms/step - loss: 6.3875e-04\n",
      "Epoch 98/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 4.8707e-04\n",
      "Epoch 99/100\n",
      "46/46 [==============================] - 17s 378ms/step - loss: 6.2210e-04\n",
      "Epoch 100/100\n",
      "46/46 [==============================] - 17s 374ms/step - loss: 6.6897e-04\n"
     ]
    }
   ],
   "source": [
    "# Fitting the model to the Training set\n",
    "history=model.fit(X_train,y_train,epochs=100,batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2366716c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
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      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
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       "<IPython.core.display.Javascript object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "# Evaluating The Model\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d1fd1b1",
   "metadata": {},
   "source": [
    "## Making Predictions\n",
    "To make predictions, we need to use our Test Dataset and format him like we did in the Train Dataset before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eb7ea4e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#GEtting ready both train and est data set\n",
    "train_data= pd.read_csv('train_data.csv')\n",
    "test_data= pd.read_csv('test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "28e48056",
   "metadata": {},
   "outputs": [],
   "source": [
    "real_stock_price = test_data.iloc[:, 3:4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fa48413d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_stock_price.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f9f9682e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "19dc34f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hence we will concatenate the dataset and then scale them\n",
    "data_total= pd.concat([train_data['Close'], test_data['Close']],  axis=0)\n",
    "inputs= data_total[len(data_total)-len(test_data)-60:].values \n",
    "inputs = inputs.reshape(-1,1) \n",
    "inputs = sc.transform(inputs)\n",
    "\n",
    "X_test = []\n",
    "for i in range(60, 230):\n",
    "    X_test.append(inputs[i-60:i, 0])\n",
    "\n",
    "X_test = np.array(X_test)\n",
    "# 3D format\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d358bbc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(230, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ced5aec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1700,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_total.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cb614e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170, 60, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1ee27e3",
   "metadata": {},
   "source": [
    "#### After format the Test Data, we can make predictions in our X_test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a143e7d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#preict the model\n",
    "predicted_stock_price = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1caabf",
   "metadata": {},
   "source": [
    "#### But before plot our predictions, we need to make a inverse_transform() in the predictions array, because we make predictions using the Scale, so our predictions are between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5df60d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse the scaling\n",
    "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ef62b524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the results\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(real_stock_price, color='Red', label='Real Apple Stock Price')\n",
    "plt.plot(predicted_stock_price, color='Blue', label='Predicted Apple Stock Price')\n",
    "plt.title('Apple Stock Price Prediction',fontsize=20)\n",
    "plt.xlabel('Time', fontsize=15)\n",
    "plt.ylabel('Apple Stock Price',fontsize=15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "90645265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the results\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(test_set, color='Red', label='Real Apple Stock Price')\n",
    "plt.plot(predicted_stock_price, color='Blue', label='Predicted Apple Stock Price')\n",
    "plt.title('Apple Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Apple Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9645d2",
   "metadata": {},
   "source": [
    "### Conclusion:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23790188",
   "metadata": {},
   "source": [
    "Our model performed admirably, as can be seen. It can accurately follow most unusual jumps/drops; however, we can observe that the model expected (predicted) lower values than the actual stock price for the most recent date stamps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9207c1bd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}