🚗 AURUM — Smart Car Configurator

AURUM is an AI-powered smart car configurator built using Flask, Python, and Machine Learning. The application provides an interactive platform where users can browse available car models, customize vehicles with different engine types, colors, and modifications, estimate vehicle prices, receive intelligent configuration recommendations, and explore buyer segmentation through an intuitive web interface.

The project combines a responsive Flask-based frontend with Machine Learning models for price prediction, recommendation generation, and customer segmentation, demonstrating the practical application of data-driven intelligence in a modern vehicle configuration system.

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✨ Features

• 🚘 Browse car models across multiple categories
• ⚙️ Configure vehicles with different engine types, colors, and modifications
• 💰 Predict estimated vehicle prices using Machine Learning
• 🤖 Receive intelligent modification recommendations based on historical user configurations
• 📊 Explore buyer segmentation using K-Means Clustering
• 🔄 RESTful API endpoints built with Flask enable communication between the frontend and backend
• 📁 Create, manage, switch, save, and delete multiple vehicle configurations within a single session
• 🌐 Interactive and responsive web interface

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🧠 Machine Learning Components

💰 Price Prediction

Predicts the estimated vehicle price using a Linear Regression model based on the selected vehicle configuration.

🤖 Recommendation Engine

Generates intelligent modification recommendations using an Apriori-inspired Association Rule Mining approach trained on historical user configuration data.

📊 User Segmentation

Analyzes customer preferences and groups buyers into meaningful segments using K-Means Clustering.

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🛠️ Technologies Used

Backend

• Python
• Flask
• REST APIs

Machine Learning

• Scikit-learn
• Pandas
• NumPy

Frontend

• HTML5
• CSS3
• JavaScript
• Jinja2 Templates

Data Storage

• CSV Datasets
• Trained Machine Learning Models (.pkl)

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📂 Project Structure

AURUM/
│
├── app.py
├── train_models.py
├── requirements.txt
├── README.md
│
├── data/
│   ├── car_specifications_dataset.csv
│   ├── car_modifications_dataset.csv
│   └── car_user_configurations.csv
│
├── models/
│   ├── price_predictor.py
│   ├── recommendation_engine.py
│   └── user_segmentation.py
│
├── utils/
│   ├── car_configurator.py
│   └── data_loader.py
│
├── templates/
│   ├── base.html
│   ├── index.html
│   ├── browse.html
│   ├── configure.html
│   ├── dashboard.html
│   └── segments.html
│
└── saved_models/
    ├── price_predictor.pkl
    ├── recommendation_engine.pkl
    └── user_segmentation.pkl

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🚀 Getting Started

1️⃣ Clone the repository

git clone https://github.com/taqiyaDevHub/AURUM.git
cd AURUM

2️⃣ Install the required dependencies

pip install -r requirements.txt

3️⃣ Train the Machine Learning models (if the trained model files are unavailable)

python train_models.py

4️⃣ Run the application

python app.py

5️⃣ Open your browser and visit

http://localhost:5000

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🌐 Application Pages

Page	Description
🏠 Home	Landing page introducing the application
🚘 Browse Models	Explore available vehicle models by category
⚙️ Car Configurator	Customize vehicles with engines, colors, and modifications
📋 Dashboard	View the selected configuration, estimated price, and AI-generated recommendations
📊 Buyer Insights	Explore customer segmentation generated using Machine Learning

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📊 Datasets

The project uses three custom-designed CSV datasets created for academic and demonstration purposes.

• 🚗 Car Specifications Dataset — Contains vehicle specifications and technical details.
• 🛠️ Car Modifications Dataset — Contains available modifications, pricing, and compatibility information.
• 👤 User Configurations Dataset — Contains historical user configuration data used to train the Machine Learning models.

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🎯 Project Highlights

• ✅ Flask-based web application
• ✅ Interactive vehicle configuration system
• ✅ Machine Learning-powered price prediction
• ✅ Intelligent recommendation engine
• ✅ Customer segmentation using K-Means Clustering
• ✅ REST API integration between frontend and backend
• ✅ Session-based management of multiple vehicle configurations

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🤝 Contributors

This project was developed collaboratively by the following team members:

• Syeda Taqiya Noman
• Zainab Siddiq
• Bushra Zafar
• Jawerya Shafi

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👩‍💻 Repository Maintainer

Syeda Taqiya Noman

BS Artificial Intelligence
Dawood University of Engineering & Technology

📧 Email: nomantaqiya31@gmail.com

💼 LinkedIn: https://www.linkedin.com/in/syeda-taqiya-noman

🐙 GitHub: https://github.com/taqiyaDevHub

This repository is maintained by Syeda Taqiya Noman and contains the final version of the group project.

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📄 License

This project was developed as part of an academic group project for educational purposes.