A growing collection of hands-on machine learning projects focused on solving real-world problems using Python and scikit-learn. This repository is part of my self-driven journey through the "Learn by Building" ML series, where I apply key concepts through practical implementation.
- Apply core ML concepts through real-world projects
- Build an end-to-end ML portfolio for interviews
- Strengthen data preprocessing, model selection, and evaluation skills
- Learn by building — not just theory
| Project | Description |
|---|---|
fraud_detection/ |
Detect fraudulent credit card transactions using anomaly detection and supervised models. |
diabetes_prediction/ |
Predict the likelihood of diabetes using logistic regression and decision trees. |
heart_disease/ |
Heart disease classification using feature engineering and classification algorithms. |
- Python
- Scikit-learn
- Pandas & NumPy
- Streamlit (for app interfaces)
- Matplotlib / Seaborn (for visualization)
- Housing price prediction
- Customer churn analysis
- Breast cancer classification
- Projects from LinkedIn’s “Applied ML: Learn by Building” Series
- Many More..
This repo will serve as a live coding companion to my progress through the LinkedIn Learning or self-guided "Learn by Building" series, with every project being practical and production-minded.