Employee Attrition Prediction System ML Project Free

Employee Attrition Prediction System

Overview

This project is a professional web application that helps companies predict which employees might leave the organization. It uses machine learning and data analysis to give HR teams helpful insights for improving employee retention.The system includes easy-to-understand data visuals, prediction tools, and a clean web interface. It’s perfect for HR professionals, students, or data analysts working on real-world problems.

Project Summary

Project Name	Employee Attrition Prediction System
Language/s Used	Python
Type	Web Application

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Available Features

Predictive modeling for employee attrition
Interactive web application built with Streamlit
Data analysis and visualizations using Jupyter Notebook
Insightful dashboard view for management reporting
Key employee-related factors such as age, salary, job level, and training
Pre- and post-prediction interface screenshots
Machine learning model packaged for reuse
Power BI integration for visual storytelling

Dataset Description

The dataset used in this project includes the following fields:

Age – Employee’s age category
Work-Life Balance – A score between 1 and 4 representing work-life satisfaction
Monthly Salary – Monthly earnings
Gender – Male or Female
Training Sessions – Number of training programs attended in the previous year
Job Level – Employee level: Entry, Mid, or Senior
Attrition – Indicates whether the employee has left the organization

The data is stored in CSV format and does not rely on any external database system.

Key Insights

Age and Attrition

Employees aged between 26 to 35 years are more likely to leave the organization compared to other age groups.

Work-Life Balance

65% of employees enjoy a healthy work-life balance, while 35% report challenges that may contribute to attrition.

Salary Differences

Employees who left the company earned an average of $4,787, whereas those who stayed earned an average of $6,833.

Training Gaps

Entry-level employees who left the organization had limited access to training, with 97% having attended fewer than 5 sessions in the prior year.

Recommendations

Based on the analysis, here are data-driven recommendations:

Retention Programs for Younger Employees
Develop mentorship, growth paths, and targeted engagement strategies for the 26–35 age group.
Improve Work-Life Balance
Introduce policies such as flexible schedules and mental health programs to address the 35% experiencing imbalance.
Salary Adjustments
Benchmark compensation against industry standards and ensure competitive packages to retain top talent.
Training and Development
Increase the number and quality of training sessions, particularly for entry-level staff, to improve skills and job satisfaction.

Files Included

All essential components required to understand, run, and customize the project are provided:

data/attrition_data.csv – Raw dataset
data/train_data.csv – Cleaned and processed training data
notebooks/Employee_Attrition.ipynb – Jupyter notebook with data analysis and model building
app/app.py – Streamlit app script
model_and_key_components.pkl – Pre-trained model and pipeline
requirements.txt – Python dependencies
screenshots/ – Pre- and post-prediction visuals

Technical Stack

Programming Language: Python
Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn
Visualization: Power BI
Web Framework: Streamlit
Data Storage: CSV files
Environment: Jupyter Notebook for exploration, Streamlit for deployment

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Employee Attrition Prediction System Using Machine Learning