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• HR Employee Attrition Analysis
• •Overview
Analyzed 1,470 employees from IBM HR dataset to identify key drivers of employee turnover. Built machine learning models and an interactive dashboard with actionable business recommendations.
•• Key Findings
"Overtime is the strongest risk factor" — employees working overtime are 3x more likely to leave (30.5% vs 10.4%)
"Sales Representatives" have the highest attrition at 39.8%
"Young employees (18-25)" show 34.8% attrition rate
"Income gap" — leavers earn $2,046 less per month on average
"Total annual cost" of attrition estimated at $20.4 million
•• Tools Used
Python (pandas, numpy, matplotlib, seaborn, scikit-learn)
Random Forest Classifier (87.8% accuracy)
Groq API (Llama 3.3 70B) for AI-generated insights
Lovable (interactive dark theme dashboard)
•• What's Included
Interactive dashboard with KPIs, charts, and filters
Feature importance analysis (top predictors: Monthly Income, Overtime, Age)
Financial impact calculation
AI-generated business recommendations
Full Python code in GitHub repository
The network for creativity
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BEFORE: Client had messy Excel inventory with 500 rows, duplicates (A12 / a12), blanks, wrong prices like "$ 2.5" and "N/A", and different warehouse names (KER / ker / Kericho).
AFTER: I did:
Removed 12 duplicate SKUs
Standardized item names to Title Case
Fixed QTY and Prices to 2 decimals
Unified Warehouse to Kericho
Made sheet ready for pivot dashboard
Result: Clean, ready-to-use inventory in 2 days using Excel.
Tools: Excel, Remove Duplicates, Text to Columns, TRIM, PROPER
