Data Science and Machine Learning Projects

Data Science and Machine Learning Projects#

Welcome to my project portfolio in Jupyter Book format.

This book brings together applied data science, machine learning, statistical analysis, and tutorial notebooks in one clean HTML website. The projects cover classification, regression, clustering, anomaly detection, statistical testing, model evaluation, feature engineering, and practical scikit-learn workflows.

What this portfolio includes#

  • Classification projects using real-world tabular datasets, model comparison, threshold tuning, class imbalance handling, and performance evaluation.

  • Regression projects focused on prediction, validation, error analysis, and practical modeling workflows.

  • Clustering and anomaly detection projects using unsupervised learning, dimensionality reduction, and fraud detection methods.

  • Statistical analysis projects using hypothesis testing, non-parametric tests, assumption checks, and interpretation.

How to read this book#

Use the sidebar to move between project categories. Each notebook is preserved as a portfolio page, with its markdown explanations, code cells, tables, plots, and saved outputs rendered as a static HTML page.

The notebooks are not executed during the website build. This keeps the deployment stable, protects local credentials, and allows the website to display the saved outputs already stored in each notebook.