ML Competition
Kaggle Fertilizer Competition
A compact competition project where I practiced building a clean modeling loop: inspect the data, prepare features, compare models, generate predictions, and keep the submission process reproducible.
Period
Jun 2025
Stack
PythonPandasScikit-learnFeature Engineering
Motivation
Kaggle is useful because the feedback is concrete. This project helped me practice making modeling decisions under a metric instead of relying on vague impressions.
Links
Core Features
- Prepared structured competition data for modeling.
- Experimented with features and model choices.
- Organized code for repeatable prediction and submission.
My Contribution
- Handled preprocessing, feature construction, and model experimentation.
- Used the project to strengthen end-to-end competition workflow discipline.
What I Learned
- Improved comfort with evaluation-driven iteration.
- Learned to balance model complexity with a workflow that stays easy to rerun.