shamrathan / rnn-stock-price-prediction

Stock Price Prediction

AIM

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

Problem Statement and Dataset

The given problem is to predict the google stock price based on time. For this we are provided with a dataset which contains features like Date, Opening Price, Highest Price, Lowest Price, Closing Price, Adjusted Closing Price, Volume Based on the given features, develop a RNN model to predict, the price of stocks in future

Neural Network Model

DESIGN STEPS

STEP 1:

Import the necessary tensorflow modules

STEP 2:

Load the stock dataset

STEP 3:

Fit the model and then predict

PROGRAM

Developed By: Sham Rathan S
Register No.: 212221230093

Libraries:

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

Read Train Data & Normalize:

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

dataset_train.columns

dataset_train.head()

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

type(train_set)

train_set.shape

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

length = 60
n_features = 1

Build & Train Model:

model = Sequential()
## Write your code here
model = Sequential([layers.SimpleRNN(50,input_shape=(60,1)),
                    layers.Dense(1)])

model.compile(optimizer='adam',loss='mse')
model.summary()

model.fit(X_train1,y_train,epochs=20, batch_size=32)

Read Test Data & Normalize:

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 = []
y_test = []
for i in range(60,1384):
  X_test.append(inputs_scaled[i-60:i,0])
  y_test.append(inputs_scaled[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

Predict Values & Plot:

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()

Check MSE Score:

from sklearn.metrics import mean_squared_error as mse
mse(y_test,predicted_stock_price)

OUTPUT

True Stock Price, Predicted Stock Price vs time

Mean Square Error

RESULT:

Thus, a Recurrent Neural Network model for stock price prediction is developed.