Problem: High employee turnover leads to productivity loss, but HR lacks predictive insights.
Outcome: Develop a machine learning model that predicts employee attrition risk and suggests retention measures.
Week 1-2: Data Collection & Cleaning
Gather HR data (tenure, salary, performance reviews).
Handle missing values & categorical variables.
Week 3-4: Exploratory Data Analysis (EDA) & Feature Engineering
Identify correlations between features and attrition.
Create new features based on HR insights.
Train classification models (Decision Tree, SVM, Neural Networks).
Test different ML algorithms for accuracy.
Week 7-8: Model Optimization & Interpretation
Fine-tune models for better predictions.
Use SHAP values for explainability.
Week 9-10: Dashboard & Visual Representation
Develop an HR analytics dashboard using Power BI/Tableau.
Show key metrics on attrition risk.
Week 11-12: Final Report & HR Recommendations
Document model findings.
Provide actionable insights for HR teams.
