RNN1.py

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

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

#Preprocessing

#Feature Scaling
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)

#Creating a data structure with 60 timesteps and 1 output
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))

#Building the RNN
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
cols = X_train.shape[1]

regressor = Sequential()
#Adding LSTM layer
regressor.add(LSTM(50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
regressor.add(Dropout(0.2))

regressor.add(LSTM(50, return_sequences = True))
regressor.add(Dropout(0.2))

regressor.add(LSTM(50, return_sequences = True))
regressor.add(Dropout(0.2))

regressor.add(LSTM(50, return_sequences = False))
regressor.add(Dropout(0.2))

regressor.add(Dense(units = 1))

regressor.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])

regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)

dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')
real_stock_price = dataset_test.iloc[:, 1:2].values

#Predictions
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)
#Creating a data structure to store inputs for the test set. No ground truths are required
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)

#Visualising the result
plt.plot(real_stock_price, label = 'Real Stock Price')
plt.plot(predicted_stock_price, label = 'Predicted Stock Price')
plt.legend(loc = 'best')
plt.title("Google Stock Price Prediction")
plt.xlabel("Time")
plt.ylabel("Stock Price")
plt.show()