• Built end-to-end machine learning pipelines including data preprocessing, feature engineering, model training,
and evaluation.
• Implemented supervised and unsupervised models such as Logistic Regression, Random Forests, and clustering
algorithms.
• Performed hyperparameter tuning and cross-validation to improve model generalization.
• Evaluated model performance using F1-score, precision-recall curves, and regression error metrics.
• Communicated insights through visualizations using Matplotlib and Seaborn.
