Performance ML
End-to-end ML pipeline identifying at-risk students with 92% accuracy, comparing 5+ models before selecting the final approach. Processes 1,000+ records weekly.
- Year
- 2023
- Type
- Machine learning · data science
- Stack
- Python · Scikit-learn · Pandas
- Outcome
- 92% accuracy · 5+ models compared · 1,000+ records a week
Read the Case Study →
Problem
Instructors and academic advisors often identify struggling students only after performance has already dropped — by then, the window to intervene effectively has narrowed. The goal was to flag at-risk students earlier, from data that's already available.
Approach
Built a supervised learning pipeline in Python: Pandas for preprocessing and feature preparation, Scikit-learn for model training and evaluation. Compared 5+ model families rather than committing to one architecture upfront.
Result
The selected model reached 92% accuracy on the evaluation set.
Architecture
Data → Preprocessing (Pandas) → Model training & comparison (Scikit-learn) → Evaluation → Selected model. Processes 1,000+ records weekly.
