Stock Market Prediction and Forecasting using Stacked LSTM

This project aims to predict and forecast stock market prices using a stacked Long Short-Term Memory (LSTM) neural network. The stacked LSTM architecture enables the model to capture complex patterns and dependencies in historical stock market data, thereby enhancing its predictive capabilities.

Project Structure

The project structure is organized as follows:

1. nse_tataglobal.csv file: This file contains the historical stock market data of tata required for training and testing the model. Ensure that the data is in a suitable format, such as CSV or Excel, and stored in this directory.

2 stock_market.ipynb file: This script is responsible for training the stacked LSTM model using the historical stock market data and allows users to make predictions and forecasts using the trained model. Users can input a specific date or time range to generate predictions.

Getting Started

To get started with the project, follow these steps:

1. Clone the project repository to your local machine.

2. Ensure that you have the required dependencies(modules) installed as per your requirement.

3. Place the historical stock market data in the nse_tataglobal.csv file. Ensure that the data is in a suitable format and contains relevant features such as date, opening price, closing price, volume, etc.

4. Open the stock_market.ipynb script and modify the configuration parameters according to your requirements. These parameters include the data file path, model hyperparameters, training duration, etc.

5. Run the stock_market.ipynb script to train the stacked LSTM model. The script will preprocess the data, split it into training and testing sets, train the model, and save it in the models directory.

6. Once the model is trained, you can use the code script to make predictions and forecasts. Specify the input date or time range to generate the desired predictions.

Model Evaluation

To evaluate the performance of the trained model, you can use various evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc. Additionally, you can visualize the predicted and actual stock market prices using suitable plotting libraries like Matplotlib or Plotly.

Conclusion

This project provides a foundation for predicting and forecasting stock market prices using stacked LSTM neural networks. However, keep in mind that stock market prediction is a complex and challenging task, and the accuracy of the predictions may vary depending on various factors. It is always recommended to conduct thorough analysis and consider other fundamental and technical factors when making investment decisions.

Feel free to modify and enhance this project according to your specific requirements and experiment with different model architectures and techniques to further improve the accuracy of stock market predictions.