Welcome to the Stock Market Prediction repository. This project uses machine learning, specifically Long Short-Term Memory (LSTM) models, to predict stock market prices.
The goal of this project is to predict future stock prices based on historical stock price data. We use an LSTM model, a type of recurrent neural network that is well-suited to time series data like stock prices.
The project includes a Jupyter notebook (Main.ipynb) that contains the main code for the project. This includes data loading, data preprocessing, model creation, model training, and prediction steps.
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Main.ipynb: This Jupyter notebook contains the main code for the project. It includes the steps for loading and preprocessing the data, creating and training the LSTM model, and making predictions.
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TATASTEEL.NS .csv: This CSV file contains historical stock price data for TATASTEEL.NS, which is used as input data for the LSTM model.
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STOCK_PREDICTION.log: This log file contains logs generated during the execution of the code, such as information about the training process or any errors or warnings that occurred.
To get started with this project, clone the repository and install the required Python packages. You can then run the Main.ipynb notebook to train the model and make predictions.
Please note that this project is a work in progress, and the model's predictions should not be used for actual trading decisions without further testing and validation.