Stock Trend Prediction using LSTM

Stock Trend Prediction

Overview

This is my first machine learning project on stock trend prediction using Long Short-Term Memory (LSTM) deep learning model. The project aims to predict the future trend of a stock based on historical data, utilizing the Time Series analysis approach. The dataset used for training and testing the model consists of stock opening, closing, high, and low data values, which were scraped from Yahoo Finance and cover the period from the year 2010 to 2022.

Dependencies

Make sure you have the following libraries installed:

numpy
sklearn
pandas

You can install the dependencies using the following command:

pip install -r requirements.txt

Getting Started

Clone this repository to your local machine.
Open the Jupyter notebook (or any Python IDE of your choice) and load the stock_trend_prediction.ipynb file. Project Structure

The project is structured as follows:

stock_trend_prediction.ipynb
app.py which consists of the Streamlit application
requirements.txt
README.md

Usage

Run the Jupyter notebook cells sequentially in stock_trend_prediction.ipynb to train the LSTM model on the provided dataset and make predictions. The notebook contains detailed explanations of each step, making it easy to follow along.

Results

The trained LSTM model will make predictions on the test data and evaluate its performance.

Contributing

Any contributions to the project are welcome! If you find any issues or improvements, 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 details.

Feel free to add more details and customize the README according to your project's specific requirements. Happy coding! 🚀📈

Prayag2003/stock-trend-prediction-lstm-model