Tagged scikit-learn

An in depth exploration of stock market data through feature engineering and pattern recognition. This study analyzes historical price changes and volatility to identify trends across diverse companies using custom statistical metrics and datetime features.

This project conducts a detailed evaluation of popular machine learning algorithms and their performance characteristics. It benchmarks Naive Bayes, Random Forest, and k Nearest Neighbors across multiple datasets ranging from simple iris data to complex geospatial and text categories. The analysis explores the relationship between hyperparameter tuning and model efficiency while providing quantitative results on accuracy and execution time.