This project demonstrates a practical machine learning regression workflow using TensorFlow, focusing on comparing a simple baseline model against a feature-engineered model.
The goal is to measure how feature engineering impacts model performance using Mean Absolute Error (MAE) as the evaluation metric.
- Python
- Pandas, NumPy
- TensorFlow / Keras
- Scikit-learn
- Jupyter Notebook
- Supervised Machine Learning
- Regression
- Target variable:
total_fare - Evaluation Metric: Mean Absolute Error (MAE)