Machine Learning
Adult Census Preprocessing Pipeline
A supervised learning pipeline project on the Adult Census dataset, focused on feature type separation, imputation, encoding, scaling, leakage prevention, and baseline comparison.
Period
May 2026
Tools / Tech
PythonScikit-learnColumnTransformerPipelineDecision TreeOne-hot Encoding
Why I built it
I used this project to practice the less glamorous but important parts of ML: preprocessing discipline, pipeline structure, and fair evaluation.
Links
What it includes
- Separates numerical and categorical features.
- Uses imputation, one-hot encoding, scaling, and model steps inside a pipeline.
- Compares baselines while reducing leakage risk.
What I worked on
- Designed preprocessing steps with ColumnTransformer and Pipeline.
- Implemented baseline classification with attention to train/test separation.
- Completed the notebook workflow myself and checked how preprocessing choices affect model evaluation.
What I Learned
- Learned why pipelines are safer than ad hoc preprocessing.
- Practiced thinking about leakage before model scores.
- Practiced a more organized ML experimentation workflow.