Stock Price Prediction using LSTM This repository contains code for predicting stock prices using Long Short-Term Memory (LSTM) networks. Specifically, it focuses on predicting the stock price of Tesla, Inc. (TSLA), but it can be easily adapted for other stocks.

Overview The project uses a deep learning model with multiple LSTM layers to predict the future stock prices of a given company. The code leverages historical stock data, trains the model on it, and then predicts future prices. It also includes a visualization of the actual vs. predicted prices.

Requirements Python 3.7+ TensorFlow 2.x yfinance scikit-learn matplotlib pandas Installation Clone the repository and navigate to the directory:

bash Copy code git clone https://github.com/yourusername/stock-price-prediction.git cd stock-price-prediction Install the necessary packages using pip:

bash Copy code pip install -r requirements.txt Note: It is recommended to create a virtual environment before installing the packages to avoid conflicts.

Usage Run the main script:

bash Copy code python main.py The script will download the required data, train the model, and display the prediction plot.

Customization You can change the company variable in the code to predict the stock price for a different company. Make sure the company symbol is valid and available on Yahoo Finance.

Acknowledgments This project is inspired by various research in the field of financial forecasting and leverages open-source tools such as TensorFlow, yfinance, and scikit-learn.

Contact Feel free to reach out if you have any questions or if you want to contribute to the project.

Email: azaboni@sfsu.edu
GitHub: @azaboni510