Intro

Intro#

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.

Machine Learning Portfolio Cover

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.

  • ML Concepts [New] contains specific methodologies of machine learning.