khadar134 / rnn-stock-price-prediction

Stock Price Prediction

AIM

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

Problem Statement and Dataset

We are developing the RNN model to predict the stock prices of Google using the dataset provided. The dataset has many features, but we will be predicting the "Open" feauture alone. We will be using a sequence of 60 readings to predict the 61st reading.we have taken 70 Inputs with 70 Neurons in the RNN Layer (hidden) and one neuron for the Output Layer.These parameters can be changed as per requirements.

DESIGN STEPS

STEP 1:

Import tensorflow library and preprocessing libraries.

STEP 2:

Load the traning dataset and take one column values and scale it using minmaxscaler.

STEP 3:

Split x_train(0-60 values) and y_train(61 st value).

STEP 4:

Create a RNN model with required no of neurons with one output neuron.

STEP 5:

Fit the model and load testing dataset.For x_test,combine the values of both datasets.

STEP 6:

Follow the same splitting.Make the prediction.

STEP 7:

Plot graph and find the mse value.

PROGRAM

# Developed By:khadar bhasha
# Register Number:212220230045


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()
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
model = Sequential()
model.add(layers.SimpleRNN(70,input_shape=(80,1)))
model.add(layers.Dense(1))
model.compile(optimizer='adam', loss='mae')
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 = []
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
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()
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.