BSc IT Project Guide: Crypto Price Prediction
1. Project Title
Crypto Price Prediction: Forecasting Cryptocurrency Price Movements using Machine Learning
2. Introduction
Cryptocurrencies have gained significant attention in recent years. Their high volatility makes predicting price movements both a challenge and an opportunity. This project aims to build a machine learning model to predict the future prices of cryptocurrencies such as Bitcoin, Ethereum, etc.
3. Objectives
- To collect historical cryptocurrency price data.
- To perform exploratory data analysis (EDA).
- To train and evaluate ML models such as LSTM, ARIMA, or XGBoost.
- To build a system for visualizing price trends and predictions.
4. Tools and Technologies
- Programming Language: Python
- Libraries: NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow/Keras
- Database: SQLite or Firebase (optional)
- Platform: Jupyter Notebook / Streamlit (for visualization)
5. System Requirements
- Python 3.x installed
- Internet access for retrieving live/archived data
- Jupyter Notebook or any IDE for Python
6. Methodology
1. Data Collection: Using APIs like CoinGecko or CryptoCompare to fetch historical prices.
2. Data Preprocessing: Cleaning, normalization, and feature engineering.
3. Model Selection: Train models like LSTM (deep learning), ARIMA (time-series), or ensemble models.
4. Evaluation: Use RMSE, MAE, and accuracy metrics.
5. Visualization: Plot predicted vs actual prices using Matplotlib or Streamlit.
7. Expected Outcome
A functional system that can predict near-future cryptocurrency prices and visualize the forecast. Helps users understand market trends and make better investment decisions.
8. Challenges
- High volatility and non-stationary nature of crypto data
- Need for large datasets for deep learning models
- Model overfitting due to market noise
9. Future Scope
- Integrate with live trading platforms for automated trading suggestions
- Use reinforcement learning for real-time decision making
- Support for multiple cryptocurrencies and fiat conversions
10. References
- https://www.coingecko.com/
- https://www.cryptocompare.com/
- Research papers and tutorials on time series forecasting