All Work
Education · Prediction
Dropout Prevention System
Predictive system for online learning completion risk using Python and classical ML tooling.
PythonScikit-learnXGBoostPandas
- DATA↓
- CLEAN↓
- ANALYZE↓
- VISUALIZE↓
- INSIGHT↓
- ACTION
Business Problem
Learners were leaving courses before teams had a clear early signal to intervene.
Objective
Build a machine-learning workflow that flags at-risk learners early enough for outreach.
Dataset
Online learning engagement and completion signals used for prediction modeling.
Tech Stack
Python, Scikit-learn, XGBoost, Pandas
Data Preparation
- Prepared engagement features for modeling
- Handled missing and noisy learner signals
- Structured train/evaluation datasets
Analysis Process
- Feature engineering for fatigue and engagement risk
- Classification modeling with Scikit-learn / XGBoost
- Model evaluation and actionable scoring lists
Key Insights
- Early risk signals can surface before completion drops become obvious
- Actionable score lists help teams prioritize outreach
Business Recommendations
- Review high-risk learners on a fixed cadence
- Pair model scores with human outreach workflows
- Monitor precision and recall as data drifts
Outcome
See full case study for process detail and deliverables.
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