Tag
#random-forest
15 repositories
Repos
CryptoCurrency prediction using machine learning and deep learning
Detecting Fraudulent Blockchain Accounts on Ethereum with Supervised Machine Learning
Bitcoin price prediction using both traditonal machine learning and deep learning techniques, based on historical price and sentiment extracted from Twitter posts. Fear of missing out analysis after Elon Musk tweeted about Dogecoin.
Rule-based Algorithmic Trading using a Genetic Algorithm and Machine Learning Signals for the Cryptocurrency Market.
Cryptocurrency Trading Bot helps to backtest with Machine Learning Models and use it for trading the crypto
An attempt to determine the direction of crypto asset price movement based on selected market information as well as to identify if there are leading indicators that could point the direction of movement.
♠️ Dive into our ETH Denver 2023 Hackathon project where we built a Dynamic PMF Random Variate Generator using Solidity for blockchain-based games. Discover how this innovation enhances fairness for on-chain games. ♠️
Prediction of avalanches with use of machine learning (Random Forest model, XGBoost) for 22 massifs in French Alps. Bokeh is used for data visualization and Flask for avalanche danger index app.
Rule-based Algorithmic Trading using a Genetic Algorithm and Machine Learning Signals for the Cryptocurrency Market.
Bitcoin price analysis and forecast with deep learning
This project enables rusty-blockparser user to manufacture the csv files into a ML dataset.
TradeForge: A Python-based trading platform with real-time market data visualization, technical indicators, ML-driven predictions, backtesting, and simulated trade execution — all in a Streamlit dashboard.
Embedding transaction graphs to classify fraudulent Ethereum wallet addresses.
Harvest Helpers is a blockchain-based system that streamlines the crop supply chain, automates verification and harvest via smart contracts, enables crop/fertilizer prediction using ML, and empowers farmers with real-time tracking while ensuring transparency and reducing fraud.