Agent Performance Analysis

A full-stack web application for analyzing and predicting insurance agent performance using machine learning algorithms and interactive data visualizations.

📝 Methodology

1. Data Preprocessing:

   • Selected relevant performance features such as new policy count, net income, conversion rates, and others.
   • Normalized the data to ensure uniformity and scalability, preparing it for machine learning models.

2. K-means Clustering:

   • Applied K-means clustering to categorize agents into performance levels (High, Medium, Low) based on their individual performance metrics.

3. PCA (Principal Component Analysis) Analysis:

   • Performed PCA to reduce the dimensionality of the dataset, improving computational efficiency.
   • Identified key performance indicators (KPIs) that contributed the most to clustering, facilitating better decision-making.

4. Cluster Ranking:

   • Calculated composite scores for each cluster by assigning weighted importance to key performance features.
   • Ranked the agents by their performance level (High, Medium, Low) based on the cluster scores.

🌟 Features

• Real-time Agent Performance Predictions:
  Predict the performance level of agents based on the latest data.

• Interactive Data Visualizations:
  Dynamic and interactive charts displaying agent performance trends, conversion rates, and other key metrics.

• Performance Distribution Analysis:
  Visualize and analyze the distribution of agents across different performance levels (High, Medium, Low).

• Modern, Responsive UI:
  A sleek and user-friendly interface built using Material-UI, designed for both desktop and mobile devices.

• RESTful API Backend:
  A Python-based backend using FastAPI to handle requests and serve predictions.

🎥 Demo

Here’s a quick demo of the Agent Performance Analysis Dashboard:

⚙️ Tech Stack

Frontend

• React 19
• Material-UI (MUI) v7
• Chart.js for data visualization
• React Router for navigation
• Axios for API communication

Backend

• Python
• FastAPI
• Pandas
• Scikit-learn
• NumPy

🛠️ Prerequisites

• Node.js (v14 or later)
• Python 3.7+
• npm or yarn
• pip

📥 Installation

1. Clone the repository:

git clone <repository-url>
cd <repository-directory>

2. Set up the frontend:

cd frontend
npm install

3. Set up the backend:

cd backend
pip install -r requirements.txt

🚀 Running the Application

1. Start the backend server:

cd backend
uvicorn main:app --reload

The backend will be available at http://127.0.0.1:8000

2. Start the frontend development server:

cd frontend
npm start

The frontend will be available at http://localhost:3000

📂 Project Structure

.
├── frontend/               # React frontend application
│   ├── public/            # Static files
│   │   └── theme.js     # Material-UI theme configuration
│   └── package.json     # Frontend dependencies
│
└── backend/              # Python backend application
    ├── main.py          # FastAPI application entry point
    └── train.py         # Machine learning model training

📄 Available Scripts

In the frontend directory, you can run:

• npm start - Runs the app in development mode
• npm test - Launches the test runner
• npm run build - Builds the app for production
• npm run eject - Ejects from Create React App

🙏 Acknowledgments

• Create React App for the frontend boilerplate
• Material-UI for the component library
• FastAPI for the backend framework
• Chart.js for data visualization

👥 Contributors

• Lasitha Amarasinghe
• Induwara Morawakgoda
• Thakshaka Rathnayake