📌 HR Attrition Prediction

📖 Overview

This project predicts whether an employee is likely to leave the company (attrition) using machine learning techniques. Employee attrition is a major challenge for HR teams, and this project aims to provide insights that can help organizations improve retention strategies.

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🎯 Objectives

• Analyze HR data to identify key factors influencing employee attrition.
• Build and compare multiple machine learning models.
• Evaluate performance using accuracy, precision, recall, F1-score, and ROC-AUC.
• Provide actionable insights for HR decision-making.

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🗂 Dataset

• Source: IBM HR Analytics Employee Attrition Dataset (publicly available).
• Features: Age, JobRole, MaritalStatus, MonthlyIncome, WorkLifeBalance, YearsAtCompany, etc.
• Target: Attrition (Yes/No).

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⚙️ Technologies Used

• Programming Language: Python
• Libraries:
  • Pandas, NumPy (data handling)
  • Matplotlib, Seaborn (visualization)
  • Scikit-learn (ML models & evaluation)
  • Jupyter Notebook (development)

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🧠 Models Tried

• Logistic Regression
• Random Forest Classifier
• XGBoost Classifier

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📊 Model Evaluation

Models were compared based on Accuracy, Precision, Recall, F1-Score, and ROC-AUC.

Model	Accuracy	Precision	Recall	F1-Score	ROC-AUC
Logistic Regression	0.82	0.75	0.68	0.71	0.80
Random Forest	0.87	0.81	0.77	0.79	0.88
XGBoost	0.89	0.83	0.80	0.81	0.90

✅ Best Model: XGBoost with ROC-AUC of 0.90

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🔍 Key Insights

• Monthly Income, Job Role, Years at Company, and Work-Life Balance significantly influence attrition.
• Employees with low income and poor work-life balance are more likely to leave.
• Random Forest and XGBoost provided the most balanced performance.

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📂 Project Structure

HR-Attrition-Prediction/ │

├── data/ # Data files (raw & modified)

│ ├── employee_attrition.csv

│ ├── cleaned_data.csv

│ ├── scripts/ # Scripts for project workflow

│ ├── datacollection.py

│ ├── datacleaning.py

│ ├── eda.py

│ ├── model.py │ ├── relation/ # Feature relationship analysis

│ ├── relation_with_attrition.py

│

├── README.md # Project documentation

└── requirements.txt # Python dependencies

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🚀 How to Run the Project

1. Clone the repository:
   git clone https://github.com/your-username/hr-attrition-prediction.git cd hr-attrition-prediction

2. Install dependencies: pip install -r requirements.txt

3.Run Jupyter Notebook or Python scripts: jupyter notebook

📌 Future Work Deploy the model as a web app using Flask/Streamlit.

Perform hyperparameter tuning for better accuracy.

Try deep learning models for more complex patterns.

🏆 Results Best Model: XGBoost

ROC-AUC: 0.90

Business Use Case: Helps HR teams identify employees at risk of leaving and take preventive measures.

✍️ Author

Your Name : S.RajiReddy

GitHub: https://github.com/RajiReddy15

LinkedIn: https://www.linkedin.com/in/raji-reddy-bb54682aa/