BREAST-CANCER-AND-HOUSE-VOTES.MD
CLASSICAL ML + COMPARATIVE ANALYSIS
Breast Cancer and House Votes
A structured machine learning project that compares model behavior across two benchmark tabular datasets with different feature characteristics.
Overview
This project works across two classic benchmark datasets to study classification performance, preprocessing decisions, and model behavior in tabular machine learning settings.
Problem
Structured datasets can behave very differently depending on feature quality, missing values, and class structure. Comparative analysis helps build stronger intuition than training on just one dataset in isolation.
Role
I focused on dataset preparation, model experimentation, and comparative reasoning so the project could be useful as both a learning exercise and a disciplined ML workflow.
Implementation
- Worked across two benchmark datasets to compare data behavior and classification outcomes.
- Applied preprocessing and model-selection logic suitable for structured tabular problems.
- Used the project to study how dataset-specific characteristics influence model performance.
Outcomes
- Strengthened comparative reasoning across two different tabular ML problems.
- Improved understanding of preprocessing, feature handling, and classification workflow design.
- Created a solid reference project for classical machine learning foundations.