HR Attrition Prediction System is a machine learning-based analytics project designed to predict whether an employee is likely to leave an organization. The system analyzes key employee attributes and applies a trained classification model to generate real-time attrition risk predictions.
The project is built using traditional machine learning techniques and deployed as an interactive web application using Streamlit. It demonstrates an end-to-end data science workflow including data preprocessing, feature engineering, model training, evaluation, and deployment.
This project was developed as a portfolio-level data science application focused on solving real-world HR analytics problems using predictive modeling.
The primary objectives of this project are:
- Predict employee attrition using machine learning techniques
- Identify key behavioral and organizational factors influencing employee turnover
- Build a real-time prediction system for HR decision support
- Deploy a user-friendly web application for live inference
- Demonstrate an end-to-end data science pipeline
- Improve understanding of workforce analytics and retention patterns
- Programming Language
- Python
- Machine Learning & Data Processing
- Pandas
- NumPy
- Scikit-learn
- Random Forest Classifier
- Visualization
- Matplotlib
- Seaborn
- Deployment
- Streamlit
- Joblib
- Development Environment
- Google Colab
- Jupyter Notebook
- VS Code
The system follows a structured machine learning pipeline:
Data Collection → Data Cleaning → Feature Selection → Model Training → Model Evaluation → Model Deployment
- Data Preprocessing
- Handling missing values
- Removing irrelevant columns
- Encoding categorical variables
- Mapping target variable (Attrition: Yes/No → 1/0)
- Feature Selection
The final model uses the following key features:
- Age
- Monthly Income
- Years at Company
- OverTime
- Job Satisfaction
- Model Training
- Algorithm: Random Forest Classifier
- Problem Type: Binary Classification
- Target Variable: Employee Attrition
- Output: Probability of employee leaving the organization
- Evaluation Metrics
- Accuracy Score
- Precision
- Recall
- F1 Score
- Confusion Matrix
The trained model achieved:
Accuracy: ~83% Stable generalization on unseen data Balanced prediction between attrition classes
- User inputs employee details
- Data is converted into feature vector
- Trained model processes input
- Attrition probability is generated
- Risk level is classified (Low / Medium / High)
- Result is displayed in real time via Streamlit interface
- Employee Attrition Prediction
- Real-time prediction system
- Binary classification (Stay / Leave)
- Risk Analysis System
- Low / Medium / High risk classification
- Probability-based scoring
- Interactive Web Interface
- Streamlit-based UI
- Clean and minimal design
- Instant prediction output
- Data-Driven Insights
- Identifies high-risk employee profiles
- Supports HR decision-making process
Employees with higher overtime show increased attrition probability Low job satisfaction strongly correlates with employee turnover Salary stagnation increases likelihood of leaving Early-career employees exhibit higher mobility rates
Add SHAP-based model explainability Integrate HR dashboard analytics (KPIs & trends) Deploy on cloud platforms (AWS / Streamlit Cloud) Connect real-time employee database Enhance UI with advanced analytics visualization Add AI-based recommendation system for retention strategies
This project contributes to the field of Human Resource Analytics and Predictive Modeling through:
- End-to-end machine learning pipeline implementation
- Real-time attrition prediction system
- Practical application of classification algorithms in HR domain
- Deployment of ML model using Streamlit
- Business-oriented data science solution for workforce management
Divya R. Pichika