Salifort Motors HR Analytics

Advanced Employee Turnover Prediction using Machine Learning & Data Science

Python Scikit-Learn Machine Learning HR Analytics
96.7%
Recall Rate
0.851
ROC-AUC Score
$15.5M
Annual Savings Potential
14,999
Employees Analyzed

Project Overview

A comprehensive machine learning solution to predict and prevent employee turnover

Business Challenge

Salifort Motors faced significant costs due to employee turnover. With an average cost of $50,000 per departure, the company needed a data-driven approach to identify at-risk employees and implement proactive retention strategies.

Solution Approach

  • Analyzed 14,999 employee records with 10 key features including satisfaction levels, performance metrics, and work patterns
  • Developed and compared multiple machine learning models: Logistic Regression, Random Forest, and Gradient Boosting
  • Implemented advanced feature engineering to capture non-linear relationships and interaction effects
  • Optimized models using GridSearchCV with 5-fold cross-validation for maximum predictive power
  • Generated actionable business recommendations backed by statistical evidence

Key Insights & Findings

Data-driven discoveries that transform HR strategy

Critical Retention Lever

Promotions reduce turnover risk by 73% - the strongest predictor of employee retention

4-Year Milestone

Employees at 4-5 years tenure show 143% increased departure risk - a critical intervention window

Satisfaction Impact

Employee satisfaction shows bimodal distribution: both very satisfied and very dissatisfied employees may leave

Workload Balance

Employees with 2 projects (54% turnover) or 7+ projects (100% turnover) need immediate attention

Technical Implementation

Technologies & Tools

Python 3.11 Scikit-learn Pandas NumPy Matplotlib Seaborn Jupyter Notebook GridSearchCV

Methodology Highlights

  • Data Preprocessing: Cleaned 3,008 duplicate records, standardized features, and engineered new variables
  • Feature Engineering: Created satisfaction_squared, hours_per_project, and workload category flags
  • Model Training: Hyperparameter optimization with cross-validation for three distinct algorithms
  • Evaluation Metrics: ROC-AUC, Precision, Recall, F1-Score with emphasis on minimizing false negatives
  • Business Translation: Converted model predictions into actionable retention strategies

Business Impact & Recommendations

Immediate Actions (0-3 months)

  • Deploy monthly risk scoring system using model predictions
  • Implement mandatory career milestone reviews at 4-year tenure mark
  • Audit and accelerate promotion timelines - 73% risk reduction opportunity
  • Investigate post-accident support programs for replication

Strategic Initiatives (3-12 months)

  • Develop differentiated retention strategies for low vs. high satisfaction employees
  • Optimize project allocation to eliminate extreme workloads (2 or 7+ projects)
  • Integrate model outputs into performance management and succession planning
  • Train managers on interpreting and acting on employee risk scores

Expected ROI

Model prevents 311 additional annual departures, representing $15.5 million in cost avoidance through reduced recruitment, training, and knowledge transfer expenses.

🚀 Try the Analysis Yourself

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Interactive Notebook Access

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Explore the Full Analysis

Dive deep into the technical implementation, visualizations, and detailed findings

📊 View Model Results 💬 Q&A Analysis 📖 Documentation 🏠 Portfolio