cool0009 / stock-market-predictions-with-lstm-in-python

This repository contains an implementation of a Stock Market Prediction model using Long Short-Term Memory (LSTM) networks in Python. The goal of this project is to forecast stock prices based on historical data, leveraging the powerful capabilities of LSTM, a type of recurrent neural network (RNN) that is well-suited for sequence prediction tasks

Stock Market Predictions with LSTM in Python

This repository contains an implementation of a Stock Market Prediction model using Long Short-Term Memory (LSTM) networks in Python. The goal of this project is to forecast stock prices based on historical data, leveraging the powerful capabilities of LSTM, a type of recurrent neural network (RNN) that is well-suited for sequence prediction tasks.

Overview

The implemented LSTM model utilizes historical stock market data to make predictions about future price movements. By incorporating sequential information and patterns from past stock prices, the model aims to provide insights that can aid in making informed investment decisions.

Key Features

• LSTM architecture tailored for time-series analysis of stock market data
• Data preprocessing techniques to handle sequential data and ensure model efficiency
• Evaluation metrics for assessing the performance of the LSTM-based stock market prediction model
• Examples demonstrating how to train the model using real-world financial data

Requirements

To run the code in this repository, ensure you have the following:

• Python 3.x
• Pandas
• Numpy
• Matplotlib
• LSTM

Usage

To utilize this repository, clone the project and follow the instructions provided in the documentation. You can train the LSTM model using your own dataset or experiment with the included sample datasets.

Contributions

Contributions to this project are welcome. If you identify issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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