Ml Lab Experiments
Machine learning experiments for ML Lab course.
Semester 6 ML Lab had us running 11 experiments, so I turned each one into a self-contained Jupyter notebook instead of scattering half-finished Colab links everywhere. The experiments span supervised and unsupervised learning, covering topics from basic regression and classification to more advanced techniques. Datasets include book metadata, house pricing data, weather records, and diabetes patient data, each chosen to demonstrate specific ML concepts in practice.
Everything runs in Jupyter notebooks with Python 3.14.2+, using NumPy, Pandas, scikit-learn, and matplotlib throughout. Each notebook follows a consistent structure with clear sections for data loading, preprocessing, model training, evaluation, and results, and PDF exports are included for easy viewing without running the notebooks.