Recurrent neural network to predict stock exchange data

google_stock_data_prediction.py

# recurrent neural network to predict stock exchange data

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset_train = pd.read_csv('Download/Google_Stock_Price_Train.csv')
dataset_test = pd.read_csv('Download/Google_Stock_Price_Test.csv')
training_set = dataset_train.iloc[:,1:2].values

from sklearn.preprocessing import MinMaxScaler

sc = MinMaxScaler(feature_range=(0,1))
training_set_scaled = sc.fit_transform(training_set)

X_train = []
y_train = []

for i in range(60, 1258):
    X_train.append(training_set_scaled[i-60:i,0])
    y_train.append(training_set_scaled[i,0])
    
    
X_train, y_train = np.array(X_train), np.array(y_train)

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1],1))

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout

regressor = Sequential()

#1th Layer
regressor.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1],1)))
regressor.add(Dropout(0.2))

#2th Layer
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))

#3th Layer
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))

#4th Layer
regressor.add(LSTM(units=50))
regressor.add(Dropout(0.2))

#Exit Layer
regressor.add(Dense(units=1))

regressor.compile(optimizer = 'adam', loss='mean_squared_error')
regressor.fit(X_train, y_train, epochs=100, batch_size=32)

real_stock_price = dataset_test.iloc[:,1:2].values
dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)

inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values

inputs = inputs.reshape(-1, 1)

inputs = sc.transform(inputs)

X_test = []

for i in range(60,80):
    X_test.append(inputs[i-60:i,0])
X_test = np.array(X_test)

X_test = np.reshape(X_test (X_test.shape[0], X_test.shape[1],1))

predicted_stock_price = regressor.predict(X_test)

predicted_stock_price = sc.inverse_transform(predicted_stock_price)


plt.plot(real_stock_price, color = 'red', label = 'Real Google stock exchange data')
plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Exchange Data')
plt.title('Google Stock Exchange Data Prediction')
plt.xlabel('Time')
plt.ylabel('Google stock price')
plt.legend()
plt.show()