Welcome to my GitHub! I'm a data-driven developer, passionate about building intelligent systems that turn raw data into smart predictions.I love crafting clean pipelines and deploying models that work.

• 📊 Machine Learning Pipelines with scikit-learn & XGBoost
• 🧠 Ensemble Models that outperform baselines
• 📈 Real-world Predictive Systems for health, education, and more
• ⚙️ Scalable solutions ready for deployment via FastAPI

Predicts calories burned during exercise using biometric and activity data.
Tech: OneHotEncoder, StandardScaler, Ridge, Random Forest, XGBoost
Performance: MAE ~1.37 across a calorie range of 1–314

Predicts academic performance based on student demographics and study habits.
Tech: Logistic Regression, Random Forest, Gradient Boosting
Files: student_performance_prediction.ipynb, Student_Performance.csv, performance_scaler.pkl
Goal: Identify key factors influencing student success

• Python • Pandas • NumPy • scikit-learn • XGBoost
• Jupyter • Matplotlib • Seaborn
• FastAPI (coming soon)
• Git • GitHub • VS Code

• Model deployment with FastAPI
• Real-time data integration
• Feature engineering and interpretability techniques

• Email - Peteroluwasegun2002@gmail.com
• X - @Codaksmade, @Oluwasegun_py
• 🌐 Portfolio site in progress...

Thanks for visiting! Feel free to explore my repos, fork what inspires you, and reach out if you want to collaborate or learn together.