Employee Churn Prediction System

Overview

This project aims to predict employee churn using machine learning techniques, specifically deep learning with Keras and TensorFlow. The system is designed with a user-friendly interface using HTML and JavaScript for the frontend, and it employs Spring Boot for the Java backend and Flask for the Python-based machine learning model.

Table of Contents

• Features
• Technologies Used
• Installation
• Running the Application
  • Running the Spring Boot Server
  • Running the Flask Server
• Usage
• Model Training
• Contributing
• License

Features

• Predicts whether an employee will churn based on various input features.
• User-friendly web interface for data input.
• Real-time prediction results displayed on the frontend.
• Integrated API using Flask and Spring Boot for seamless communication.

Technologies Used

• Frontend:

  • HTML
  • JavaScript

• Backend:

  • Spring Boot (Java)
  • Flask (Python)

• Machine Learning:

  • Keras
  • TensorFlow
  • pyhton
  • flask

Installation & Guide

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/employee-churn-prediction.git
   cd employee-churn-prediction

2. Start the Spring Boot Server(after going to specific diretory):

   ./mvnw spring-boot:run

3. Start the Flask Server(after going to specific diretory):

   python app.py

4. Download the LiveServer Extenstion in your IDE then go to diretory Frontend run index.html with live server