GitHub

Project Title: Feature Engineering and Imbalanced Data in Money Laundering Detection: A Comparative Study of ML Algorithms

This project primarily used the small dataset with a high illicit ratio, which contains 5,078,345 financial transactions spanning 10 days.It effectively addresses challenges such as overlap and ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...
IEEE

Similarity and Geographically Weighted Random Forest Regression Model

Abstract: With the improvement of computer computing power, machine learning such as random forests, extreme gradient boosting, and support vector machines have ushered in many optimizations and ...
IEEE

Typhoon Classification and Path Prediction Based on XGBoost and Random Forest Modeling

Abstract: The aim of this paper is to construct a typhoon classification and path prediction system based on XGBoost and random forest model to improve the accuracy of typhoon prediction and disaster ...