Bitcoin Price Prediction using Machine Learning

## Project Overview

This project aims to predict Bitcoin prices using machine learning techniques. The model is trained on historical Bitcoin price data, leveraging features such as past prices, trading volume, and technical indicators to make future price predictions. The project is implemented using Python and popular data science libraries.

## Features

- **Data Collection:** Uses historical Bitcoin price data from a reliable source.

- **Data Preprocessing:** Cleans and transforms the data for analysis.

- **Feature Engineering:** Extracts relevant features such as moving averages, RSI, and volatility.

- **Model Selection:** Implements regression models such as Linear Regression, Random Forest, and LSTM.

- **Evaluation:** Assesses model performance using RMSE and R-squared metrics.

- **Prediction Visualization:** Plots predicted vs actual prices for better insight.

## Technologies Used

- Python

- Pandas & NumPy

- Scikit-learn

- TensorFlow/Keras (for deep learning models)

- Matplotlib & Seaborn (for visualization)

## Results

- The model successfully captures Bitcoin price trends with reasonable accuracy.

- Further improvements can be made using additional data and hyperparameter tuning.

## Future Enhancements

- Implement real-time Bitcoin price prediction.

- Explore advanced deep learning architectures.

- Integrate API for live predictions.

## Author

Mohamed Ashraf

For any questions or contributions, feel free to reach out!