Problem: HR managers struggle to optimize workforce planning due to a lack of real-time analytics on employee productivity, turnover, and engagement.
Outcome: Develop an AI-driven workforce analytics system for HR decision-making.
Week 1-2: Data Collection & Cleaning
Gather HR data (attendance, engagement, performance scores).
Preprocess data to handle missing values and standardize formats.
Week 3-4: Exploratory Data Analysis (EDA) & Metric Identification
Identify key HR performance indicators (attrition rate, engagement scores).
Analyze workforce trends over time.
Week 5-6: Predictive HR Analytics Model
Train classification models (Decision Trees, Logistic Regression) for attrition prediction.
Implement clustering algorithms for workforce segmentation.
Week 7-8: Dashboard Development & Visualization
Build HR analytics dashboard using Power BI/Tableau.
Implement filters for department, tenure, and job role analysis.
Week 9-10: AI-Driven Insights & Workforce Optimization
Provide AI-driven recommendations for workforce planning.
Implement predictive alerts for potential turnover risks.
Week 11-12: Final Report & Implementation
Document findings and business impact.
Deploy analytics tool for HR teams.
