Architecture & Scope
Supervised Machine Learning Pipeline
Benchmarked predictive ML pipeline featuring automated feature engineering, cross-validation, and multi-model performance evaluation.
Designed and trained multiple classification models including Random Forests, Logistic Regression, and Support Vector Machines. Tuned hyperparameters using GridSearchCV and constructed ROC curves and confusion matrices for validation.
Technical Execution & Highlights
End-to-end data preparation, categorical encoding, and feature scaling
Grid search hyperparameter optimization across four algorithms
API-ready inference pipeline for real-time prediction integration
Technologies & Libraries
Python
Scikit-Learn
Pandas
Jupyter
Algorithms