Welcome to my curated portfolio of machine learning projects. This repository contains notebooks, datasets, and documentation from my ML learning journey — covering fundamental concepts, practical implementations, and exploratory data analysis.

ml-portfolio/
├── 01_telcho_terk/                 # Introduction to ML + real-world use cases
├── 02_penguins_classification/     # Supervised classification using the Penguins dataset
├── 03_housing_prices_regression/  # Regression model for predicting housing prices
├── 04_homework4/                   # Applied ML practice notebook
├── 05_homework6/                   # Extended model training task
├── 06_metrics_review/              # Evaluation metrics analysis & summary

• Python 🐍
• Pandas, NumPy
• Scikit-learn
• Google Colab
• Jupyter Notebook

✔ Understand the core principles of machine learning
✔ Gain hands-on experience with real datasets
✔ Apply supervised learning algorithms
✔ Evaluate model performance using standard metrics
✔ Document the learning journey clearly and professionally

A theory-oriented notebook introducing ML fundamentals, differences between AI, ML and Data Science, and real-world sector applications (finance, healthcare, e-commerce, etc.).

Binary classification task using the Penguins dataset with EDA, preprocessing, model training and evaluation.

Regression model predicting house prices based on features. Includes visualization, scaling, model fit and error metrics.

Murat Kömürcü
Electrical & Electronics Engineering Student
LinkedIn: linkedin.com/in/murat-kömürcü
Email: muratkomurrcu@gmail.com

This portfolio is part of my journey through various AI & ML trainings including bootcamps and university projects. All files are structured for clarity and reusability.