stock.txt

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense

# Load the stock price data
data = pd.read_csv('stock_prices.csv')  # Replace 'stock_prices.csv' with your dataset file

# Preprocess the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))

# Split the data into training and test sets
train_size = int(len(scaled_data) * 0.8)
train_data = scaled_data[:train_size]
test_data = scaled_data[train_size:]

# Prepare the training data
window_size = 30  # Number of previous days' prices to consider
X_train, y_train = [], []

for i in range(window_size, len(train_data)):
    X_train.append(train_data[i - window_size:i, 0])
    y_train.append(train_data[i, 0])

X_train, y_train = np.array(X_train), np.array(y_train)

# Reshape the data for LSTM
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))

# Build the LSTM model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(units=50))
model.add(Dense(units=1))

# Compile and train the model
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(X_train, y_train, epochs=50, batch_size=32)

# Prepare the test data
inputs = data['Close'][len(data) - len(test_data) - window_size:].values
inputs = inputs.reshape(-1, 1)
inputs = scaler.transform(inputs)

X_test = []
for i in range(window_size, len(inputs)):
    X_test.append(inputs[i - window_size:i, 0])

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

# Predict the stock prices
predicted_prices = model.predict(X_test)
predicted_prices = scaler.inverse_transform(predicted_prices)

# Plot the predicted prices
plt.plot(data['Close'][train_size + window_size:].values, color='blue', label='Actual Price')
plt.plot(predicted_prices, color='red', label='Predicted Price')
plt.title('Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
plt.show()