End-to-end tabular ML pipeline: example dataset + single script to train and evaluate all models, SHAP explainability, and cross-model consistency analysis.

Comprehensive Machine Learning Portfolio: Real-world data science, classification, regression, and business analytics in Python

This comprehensive course covers the fundamental concepts and practical techniques of Scikit-learn, the essential machine learning library in Python. Learn to build, train, and evaluate machine learning models using various algorithms and preprocessing techniques.

Automated classification of 7 different types of dry beans using machine learning techniques. This project leverages computer vision-extracted geometric and shape features (such as Area, Perimeter, and Shape Factors) to accurately identify bean varieties including Barbunya, Bombay, Cali, Dermason, Horoz, Seker, and Sira.

Comprehensive AutoML framework that automates data preprocessing, feature engineering, model selection, hyperparameter tuning, and deployment. Features neural architecture search and automated data cleaning pipelines.

Credit Risk Prediction System is an end-to-end machine learning project that predicts loan default risk using customer financial data. It applies EDA, feature encoding, and advanced models like Random Forest and XGBoost, and is deployed via Streamlit for real-time credit risk assessment.

An end-to-end Machine Learning project predicting laptop prices using hardware specs. Includes advanced data cleaning, Feature Engineering (Regex for Resolution, Touchscreen extraction), and benchmarking between Linear Regression and Random Forest Regressors. Achieved a 14% improvement in MAE via ensemble modeling. Built with Python & Scikit-Learn.

🔍 Streamline tabular binary classification with model interpretability and SHAP consistency analysis for clear insights and robust evaluation.