Developed a machine learning system to predict a person’s sleep quality (good or bad) based on daily lifestyle data, including step count, heart rate, caffeine intake, and screen time. Built end-to-end data preprocessing pipelines for feature extraction, encoding, and scaling using pandas and scikit-learn. Processed and prepared the Kaggle Sleep Health dataset into model-ready inputs, generating structured training and testing sets for analysis. Implemented and evaluated predictive models, improving classification accuracy through feature engineering and iterative refinement. Documented project workflows and visualization outputs in Jupyter Notebook, ensuring reproducibility and clarity for future development. #resume
- Back-End Developer