Developed and deployed end-to-end machine learning pipelines in Python, covering data ingestion, preprocessing, model training, and evaluation for real-world classification and prediction tasks. Performed exploratory data analysis (EDA) and data cleaning using Pandas and NumPy, improving dataset quality and model readiness across multiple project deliverables. Built and evaluated supervised ML models including Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN), applying feature engineering and hyperparameter tuning to boost model accuracy. Wrote modular, reusable Python scripts for data processing and model evaluation, following OOP principles and maintaining clean, documented code. Debugged model performance issues and resolved data inconsistencies through systematic testing and root-cause analysis, strengthening problem-solving and debugging skills. Collaborated with mentors to manage project milestones, delivered all assignments on schedule, and maintained code repositories using Git and GitHub. Visualized model performance metrics and data distributions using Matplotlib and Seaborn, communicating findings clearly through charts and summary reports.