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# Machine Learning Portfolio
A collection of end-to-end machine learning projects covering regression, classification, and linear models built from first principles.
![Python](https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white)
![scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?style=flat&logo=scikit-learn&logoColor=white)
![pandas](https://img.shields.io/badge/pandas-150458?style=flat&logo=pandas&logoColor=white)
![NumPy](https://img.shields.io/badge/NumPy-013243?style=flat&logo=numpy&logoColor=white)
## Projects
| Project | Task | Highlight |
|---|---|---|
| [housing-price-regression](housing-price-regression/) | Regression | Full pipeline (EDA → feature engineering → tuning → evaluation), test R² 0.72 |
| [classification-project](classification-project/) | Classification | Binary/multiclass/multilabel/multioutput on MNIST, Random Forest ROC AUC 0.997 |
| [linear-models](linear-models/) | Linear models | Normal equation, gradient descent, and regularization implemented from scratch |
Each project folder has its own README with the specific approach, results, and notebook.
## Tech stack
Python, pandas, NumPy, scikit-learn, matplotlib, seaborn
## Author
**Reza** — Computer Networks student, LPIC-1
[GitHub](https://github.com/rezayaghobi-dev)