A machine learning study utilizing XGBoost and technical indicators to forecast daily Bitcoin price movements.
A mathematical and programmatic exploration of K-Means clustering, Hierarchical clustering, and K-Nearest Neighbors (KNN) classification.
A comprehensive Python analysis implementing Random Forest and class-balancing techniques to secure financial transactions.
A detailed predictive modeling project using Scikit-Learn to estimate housing prices through simple and multiple linear regression techniques.
A Python-based financial application using Tkinter and MarketStack API to track portfolios, analyze historical data, and manage investments.
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