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Machine Learning · Major Project
Employee Attrition Prediction Portal
HR portal that compares models and explains attrition risk
2-3 weeks build
Overview
An HR analytics portal built on the IBM HR Analytics Employee Attrition dataset, a public fictional dataset on Kaggle. Five algorithms are trained and compared on an imbalanced target, and SHAP explains the drivers behind each employee's risk score. HR users upload records, view risk by department and read the explanation before acting.
What makes it stand up
- 01Five algorithms compared on precision, recall and ROC-AUC
- 02Class imbalance handled with weighting and threshold tuning
- 03SHAP summary and per-employee explanation plots
- 04Department and role-level risk views for HR
- 05CSV upload with role-based access to the portal
Module breakdown
- 01
EDA
Attrition patterns by role, tenure, overtime and pay.
- 02
Model zoo
Logistic regression, SVM, tree, forest and XGBoost.
- 03
Explainability
Global and local SHAP explanations.
- 04
API
Flask service for scoring and explanation.
- 05
Portal
React dashboard backed by PostgreSQL.
After this, you will be able to
- Explain how SHAP values attribute a prediction to features
- Defend your model choice with the comparison table
- Demo an explained risk score for an uploaded employee
Technology stack
- Python
- scikit-learn
- XGBoost
- Flask
- React
- PostgreSQL
- Level
- Major Project
- Domain
- Machine Learning
- Build time
- 2-3 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support