Information Flow in Crypto Order-Books | This repository uses ML models to predict the future price of ETH based on order books of ETH and BTC.

my IIT kharagpur machine learning project

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

Predicting the fluctuation of the Bitcoin prices using ML from the OHLC data of the BTC-USD data and finding the optimum investment strategy.

End-to-end ML pipeline that predicts BTC/USDT price direction (4h horizon) using XGBoost + Optuna + SHAP. 9-phase architecture, Walk-Forward Validation across 15 folds, 37 technical indicators, 98 automated tests. ROC-AUC: 0.5431.

Developed a Bitcoin price prediction model using ML, leveraging time series analysis and deep learning to analyze trends. It incorporates key indicators like trading volume and market sentiment for accurate forecasts. Built with Python, TensorFlow, and scikit-learn, showcasing AI’s potential in cryptocurrency analysis.

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Predict the btc Price using ML .NET

Predicting Closing Price of BTC-USD pair using various statistical and ML methods

ML pipeline to predict BTC price direction (UP/DOWN) 15 min ahead using real-time Binance data, multi-model comparison & backtesting.

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

This is a simple showcase LR ml model for predicting BTC prices using the Quandl API, based on thecodex.me project of stock price prediction

End-to-end ML project: predicting short-term liquidity fragility in high-frequency crypto markets using 3.2M real Binance BTC/USDT trades. Features leakage-safe target engineering, embargo-based chronological split, 6 classification models, walk-forward validation, SHAP interpretability, and a 10-slide presentation deck.

This web application uses Machine Learning (ML) to predict if the price of Bitcoin (BTC) and Ethereum (ETH) will increase or decrease over the next 7 days. It leverages historical price data and sentiment analysis (Fear & Greed Index, Twitter, Reddit) to make predictions.

A Python ML project that pulls real crypto price data via yfinance and predicts the next day's closing price using Ridge Regression and Gradient Boosting. Engineers 30+ features including RSI, MACD, Bollinger Bands, and lagged prices. Outputs an 8-panel chart and a bullish/bearish signal. Supports BTC, ETH, SOL and more.