Stock Price Prediction.py

# Importing the Libraries
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Importing the Dataset
dataset_train_path = os.getcwd() + "Dataset/Google_Stock_Price_Train.csv"
dataset_train = pd.read_csv(dataset_train_path)
training_set = dataset_train.iloc[:,1:2].values

# Feature Scaling
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler(feature_range = (0,1)) 
scaled_training_set = scaler.fit_transform(training_set)

# Creating new Data Structure
X_train = []
y_train = []

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

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

# Building the Neural Network
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import Dropout

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


# Compiling and Fitting the RNN to the Training set  
regressor.compile(optimizer = 'adam', loss = 'mean_squared_error') 
regressor.fit(X_train, y_train, epochs=100, batch_size=32)

# Making Predictions
# getting the Actual Stock Prices of Jan-2017
dataset_test_path = os.getcwd() + "Dataset/Google_Stock_Price_Test.csv"
dataset_test = pd.read_csv(dataset_test_path)
actual_stock_price = dataset_test.iloc[:,1:2].values

# getting the Predicted Stock Prices of Jan-2017
    # Step1 - preparing the input for the model
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 = scaler.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))

    # Step2 - prediction
predicted_stock_price = regressor.predict(X_test)
predicted_stock_price = scaler.inverse_transform(predicted_stock_price)

# Visualising the Results

plt.plot(actual_stock_price, color = 'red', label = 'Actual 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()