In this project, we preprocess a real-world dataset and perform regression and classification tasks using scikit-learn.
The goal is to understand the key steps in a supervised learning pipeline — from data preprocessing to model evaluation.
- Data preprocessing and feature engineering
- Regression and classification models
- Model training, validation, and performance evaluation
- Understanding key supervised learning mechanisms
- Python
- scikit-learn
- Pandas, NumPy
- Matplotlib, Seaborn