rnn.py

#Importing libraries

from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Importing the training set
dataset_train = pd.read_csv("Google_Stock_Price_Train.csv")
training_set = dataset_train.iloc[:, 1:2].values

#Feature Scaling
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)

#Reshaping
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))

#Building the RNN

#Importing the Keras libraries and packages

#Initialising the RNN
regressor = Sequential()

#Adding the first LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
regressor.add(Dropout(0.2))

#Adding the second LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))

#Adding the third LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50, return_sequences=True))
regressor.add(Dropout(0.2))

#Adding the fourth LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50))
regressor.add(Dropout(0.2))

#Adding the output layer
regressor.add(Dense(units=1))

#Compiling the RNN
regressor.compile(optimizer="adam", loss='mean_squared_error')

#Fitting the RNN to the Trainning set
regressor.fit(x_train, y_train, epochs=100, batch_size=32, steps_per_epoch=1198)

#Making the predictions and vanishing the results

#Getting the real stock price of 2017
dataset_test = pd.read_csv("Google_Stock_Price_Test.csv")
real_stock_price = dataset_train.iloc[:, 1:2].values

#Getting the Predictied stock price of 2017
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)

#visualising the results
plt.plot(real_stock_price, color="red", label="Real Google Stock Price")
plt.plot(predicted_stock_price, color="blue", label="Predicted Google Stock Price")
plt.title("Google Stock Price Prediction")
plt.xlabel("Time")
plt.ylabel("Google Stock Price")
plt.legend()
plt.show()