This application is designed to predict employee attrition using machine learning techniques, providing insights into factors influencing attrition and helps organizations proactively manage employee.
The Employee Attrition Prediction Application is a machine learning-powered web application designed to help organizations predict and analyze employee attrition risks. Built with React and TensorFlow.js, this tool enables HR professionals and managers to proactively identify employees at risk of leaving, understand key factors influencing attrition, and make data-driven retention decisions.
Interactive Dashboard – Visualizes key metrics, feature importance, and model performance.
Attrition Prediction – Predicts the likelihood of an employee leaving based on input features.
Model Performance Tracking – Displays accuracy, loss, confusion matrix, and other evaluation metrics.
Data Exploration – Provides interactive charts to analyze trends and correlations in employee data.
Responsive UI – Built with Tailwind CSS for a clean, modern, and mobile-friendly interface.
Frontend: Developed with React for dynamic user interactions.
State Management: Uses AttritionContext to manage model state, training progress, and predictions.
Machine Learning: Powered by TensorFlow.js, implementing a neural network for binary classification.
Data Processing: Includes normalization, one-hot encoding, and feature importance analysis.
Visualization: Leverages Chart.js for interactive graphs and performance metrics.
Data Loading – Employee data is loaded from a structured dataset.
Preprocessing – Features are normalized and encoded for model training.
Model Training – A neural network is trained to predict attrition risk.
Prediction – Users input employee details to receive real-time attrition predictions.
Analysis – Insights are displayed via dashboards, charts, and performance metrics.
Frontend: React, Tailwind CSS, Vite
Machine Learning: TensorFlow.js
Data Visualization: Chart.js
Icons: Lucide React
This application serves as a valuable tool for HR analytics, helping organizations reduce turnover by identifying at-risk employees and addressing retention challenges effectively.
Model is ready. Working on improving the accuracy of our model.
Not Applicable