A Streamlit-based web app for 14-day Bitcoin price prediction using LSTM+CNN and XGBoost models. The app visualizes real BTC prices, ML-generated forecasts, and a moving average baseline. Automated daily updates with GitHub Actions.

This study use BTC historical daily price to predict future price direction with various ML models

Predicting Bitcoin price using Multiple Linear Regression based on market indicators. A simple yet effective ML approach to model cryptocurrency trends.

BTC Price Prediction with ML

Experimenting with ML models for btc prices.

BTC Hourly Predictor - ML-based price prediction with Telegram alerts

Using Reddit posts relating to ETH along with BTC price actions to construct ML models to predict ETH prices via PySpark

Forecasting BTC, ETH, and BNB prices using ML and DL models (CatBoost, XGBoost, SVR, LSTM, GRU, RNN, MLP) with 3 data split settings.

End-to-end BTC/USDT trading pipeline using ML price prediction and rule-based execution. Covers data preprocessing, model training, chronological train–test split, and backtesting with equity, drawdown, Sharpe ratio, and benchmark strategies.

A Python ML system for forecasting BTC, ETH, and altcoin prices using real-time data from Binance, CoinGecko, and Fear & Greed Index APIs. Features modular architecture with automated data collection, validation, and SQLAlchemy database storage.