HR Attrition Analytics Platform

A comprehensive analytics platform for HR departments to analyze, visualize, and predict employee attrition using interactive dashboards and an AI-powered chatbot.

Table of Contents

• Overview
• Features
• Architecture
• Prerequisites
• Installation
  • Backend Setup
  • Frontend Setup
• Usage Guide
• API Documentation
• Dataset Requirements
• Screenshots
• Tech Stack
• Contributing

Overview

The HR Attrition Analytics Platform helps organizations understand and reduce employee attrition through advanced data visualization and AI-assisted analysis. By leveraging machine learning and interactive dashboards, HR professionals can identify key attrition factors, detect at-risk employees, and develop targeted retention strategies.

Features

Interactive Analytics Dashboard

• Multi-dimensional attrition visualizations by:
  • Age groups
  • Gender
  • Departments
  • Education levels
  • Job satisfaction
  • Salary bands
  • Performance ratings

Advanced Filtering

• Segment employee data by multiple parameters
• Create custom cohorts for analysis
• Save and share filter configurations

AI-Powered Chatbot

• Natural language interface for data queries
• Contextual analysis of attrition factors
• Custom HR insights and recommendations
• Trend identification and prediction

Data Management

• CSV data import functionality
• Data quality checks and validation
• Custom field mapping
• Historical data comparison

Architecture

The platform follows a client-server architecture:

┌───────────────────┐      ┌───────────────────┐
│   Frontend        │      │   Backend         │
│ (React + TypeScript)◄────►(Flask + Python)   │
└───────────────────┘      └─────────┬─────────┘
                                    │
                                    ▼
                           ┌───────────────────┐
                           │  LLM Integration  │
                           │  (Groq API)       │
                           └───────────────────┘

Prerequisites

• Python 3.7+
• Node.js 18+
• npm/yarn
• Groq API key

Installation

Backend Setup

1. Navigate to the backend directory:

   cd attrition-backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   cp .env.example .env
   # Edit .env file to add your GROQ_API_KEY

5. Start the Flask server:

   flask run --port=8000
   # Or with debug mode:
   flask run --debug --port=8000

Frontend Setup

1. Navigate to the frontend directory:

   cd attrition-frontend

2. Install dependencies:

   npm install
   # Or using yarn:
   yarn install

3. Set up environment variables:

   cp .env.example .env
   # Configure API URL if needed:
   # VITE_API_URL=http://localhost:8000/api

4. Start the development server:

   npm run dev
   # Or using yarn:
   yarn dev

5. Open your browser and navigate to http://localhost:5173

Usage Guide

Dashboard Navigation

The platform offers several key views:

1. Overview Dashboard: High-level metrics and KPIs for attrition
2. Detailed Analysis: Deep-dive visualizations for specific metrics
3. Employee Segmentation: Analysis by departments, roles, and demographics
4. Risk Assessment: Identification of employees or groups at risk of attrition
5. AI Assistant: Natural language interface for custom insights

Analyzing Attrition Data

1. Use the filters panel to focus on specific employee segments
2. Compare attrition rates across different dimensions
3. Identify correlating factors with high attrition rates
4. Export visualizations or raw data for reporting

Using the AI Assistant

1. Ask questions in natural language about your HR data
2. Request specific analyses or comparisons
3. Get strategic recommendations for reducing attrition
4. Ask for explanations about attrition factors and trends

Sample questions:

• "What department has the highest attrition rate?"
• "Is there a correlation between overtime and attrition?"
• "What are the main factors contributing to attrition in the Sales department?"
• "Generate a summary of attrition trends by education level"

API Documentation

Authentication

All API endpoints require authentication via API key. Include the key in your request headers:

Authorization: Bearer your-api-key-here

Core Endpoints

Endpoint	Method	Description
/api/attrition-by-age	GET	Attrition data by age groups
/api/attrition-by-gender	GET	Attrition data by gender
/api/attrition-by-department	GET	Attrition data by department
/api/attrition-by-education	GET	Attrition data by education level
/api/attrition-by-job-satisfaction	GET	Attrition data by satisfaction level
/api/attrition-by-salary	GET	Attrition data by salary bands
/api/overall-statistics	GET	Overall attrition statistics
/api/factors-correlation	GET	Correlation between factors and attrition
/api/predictive-factors	GET	Top factors predicting attrition
/api/filtered-data	GET	Filtered attrition data
/api/chat	POST	Chat with the AI assistant
/api/chat/reset	POST	Reset chat conversation
/api/dataset-metadata	GET	Get metadata about the dataset

Refer to the API Documentation for detailed endpoint specifications and response formats.

Dataset Requirements

The platform requires HR data with the following attributes:

Required Fields

• Attrition: Binary indicator (Yes/No)
• Age: Employee age
• Gender: Employee gender
• Department: Department name
• JobSatisfaction: Satisfaction rating (1-4)
• MonthlyIncome: Monthly salary
• YearsAtCompany: Tenure at company

Recommended Fields

• Education: Education level (1-5)
• OverTime: Overtime status (Yes/No)
• JobLevel: Level in organization (1-5)
• WorkLifeBalance: Work-life balance rating (1-4)
• PerformanceRating: Performance rating (1-4)
• DistanceFromHome: Distance from work

The default dataset is located at: /home/Maanu/Documents/RoR Internship/Attrition-Analytics/datasets/HR-Employee-Attrition-All.csv

Screenshots

Landing Page

Dashboard

Chat Assistant

Upload and Data Management

Tech Stack

Backend

• Python: Core language
• Flask: Web framework
• Pandas: Data manipulation and analysis
• NumPy: Numerical computing
• Groq API: LLM integration for chatbot

Frontend

• TypeScript: Type-safe JavaScript
• React: UI library
• Vite: Build tool
• shadcn/ui: Component library
• Tailwind CSS: Utility-first CSS
• Chart.js: Data visualization
• React Query: Data fetching
• Axios: HTTP client

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.