This project focuses on predicting stock prices using Long Short-Term Memory (LSTM) neural networks. By leveraging historical stock data, including features such as open, high, low, and volume, the model aims to forecast future stock prices. The project involves data preprocessing, model training, and evaluation, providing insights into the model's performance and highlighting potential areas for improvement. Visualizations of actual versus predicted prices are included to assess the accuracy of the predictions.

Programming Language: Python

Libraries: TensorFlow, Keras, scikit-learn, pandas, numpy, matplotlib

Environment: Jupyter Notebook

1. Data Collection: Gather historical stock price data.

2. Data Preprocessing: -Load the data.

-Handle missing values.

-Scale the data using MinMaxScaler.

3. Create Training and Testing Sets:

-Define the time step (window size).

-Split the data into training and testing sets.

4. Build LSTM Model:

-Define the model architecture.

-Compile the model with appropriate loss function and optimizer.

5. Train the Model:

Fit the model on training data.

Use validation split to monitor performance.

6. Evaluate the Model:

Predict on test data.

Inverse transform the predictions and actual values.

Calculate evaluation metrics (MAE, MSE, RMSE, R-squared).

7. Visualize Results:

Plot actual vs. predicted stock prices.

Plot future stock price predictions.

8. Future Predictions:

Generate future dates.

Predict future stock prices.

Plot the predicted future prices