To develop a Recurrent Neural Network model for stock price prediction.

Include the neural network model diagram.

1. Load the training and test dataset.

2. Prepare the dataset for training.

2. Create and train your model.
3. Predict the output for the test data and compare it with the true stock price. Write your own steps

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

dataset_train = pd.read_csv('trainset.csv')

dataset_train.columns

dataset_train.head()

dataset_train.tail()

dataset_train.iloc[1256:1268]

train_set = dataset_train.iloc[:,1:2].values

type(train_set)

train_set.shape

plt.plot(np.arange(0,1259),train_set)

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

training_set_scaled.shape

X_train_array = [] y_train_array = [] for i in range(60, 1259): X_train_array.append(training_set_scaled[i-60:i,0]) y_train_array.append(training_set_scaled[i,0]) X_train, y_train = np.array(X_train_array), np.array(y_train_array) X_train1 = X_train.reshape((X_train.shape[0], X_train.shape[1],1))

X_train.shape

X_train1.shape

length = 60 n_features = 1

model = Sequential()

model.add(layers.SimpleRNN(50,input_shape=(length,n_features))) model.add(layers.Dense(1)) model.compile(optimizer='adam', loss='mse')

model.summary()

model.fit(X_train1,y_train,epochs=100, batch_size=32)

dataset_test = pd.read_csv('testset.csv')

test_set = dataset_test.iloc[:,1:2].values

test_set.shape

dataset_total = pd.concat((dataset_train['Open'],dataset_test['Open']),axis=0)

inputs = dataset_total.values inputs = inputs.reshape(-1,1) inputs_scaled=sc.transform(inputs) X_test = [] for i in range(60,1384): X_test.append(inputs_scaled[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))

X_test.shape

predicted_stock_price_scaled = model.predict(X_test) predicted_stock_price = sc.inverse_transform(predicted_stock_price_scaled)

plt.plot(np.arange(0,1384),inputs, color='red', label = 'Test(Real) Google stock price') plt.plot(np.arange(60,1384),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()

Recurrent Neural Network model for stock price prediction is developed