🤖 AI Product Recommendation System

DecodeLabs Artificial Intelligence Internship – Project 3

An AI-powered Product Recommendation System built with Python that recommends products based on user preferences using content-based filtering, weighted similarity scoring, and pattern matching.

This project demonstrates the fundamentals of recommendation systems without requiring machine learning models, making it an excellent introduction to AI recommendation logic.

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📌 Project Overview

The AI Product Recommendation System analyzes user preferences and compares them with historical e-commerce order data to recommend the most relevant products.

The recommendation engine uses a weighted similarity algorithm to rank products based on multiple factors such as:

• Product
• Payment Method
• Coupon Code
• Referral Source
• Order Status
• Purchase Quantity
• Total Price

The system then displays the Top 5 personalized recommendations.

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✨ Features

• Interactive command-line interface
• Product recommendation based on user preferences
• Weighted similarity scoring algorithm
• Intelligent product ranking
• Duplicate recommendation removal
• Dataset validation and cleaning
• Professional modular architecture
• Error handling and input validation
• Clean and readable output
• Easy to extend with machine learning models

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🧠 AI Concepts Used

This project demonstrates several core Artificial Intelligence concepts:

• Recommendation Systems
• Content-Based Filtering
• Similarity Matching
• Pattern Recognition
• Decision Logic
• Weighted Scoring Algorithm
• Ranking and Sorting

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📂 Project Structure

AI-Product-Recommendation-System/
│
├── data/
│   └── ecommerce_dataset.xlsx
│
├── src/
│   ├── __init__.py
│   ├── data_loader.py
│   ├── similarity.py
│   ├── recommender.py
│   └── utils.py
│
├── screenshots/
│
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
└── main.py

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⚙️ Technologies Used

• Python 3
• Pandas
• OpenPyXL
• VS Code
• Git
• GitHub

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📊 Dataset

The project uses an e-commerce dataset containing historical customer orders.

Dataset fields include:

• Order ID
• Date
• Customer ID
• Product
• Quantity
• Unit Price
• Shipping Address
• Payment Method
• Order Status
• Tracking Number
• Items in Cart
• Coupon Code
• Referral Source
• Total Price

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🚀 Installation

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/AI-Product-Recommendation-System.git

2. Navigate to the project

cd AI-Product-Recommendation-System

3. Create a virtual environment

Windows

python -m venv venv

Activate it

venv\Scripts\activate

4. Install dependencies

pip install -r requirements.txt

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📦 Dependencies

Install all required packages using:

pip install -r requirements.txt

▶️ Run the Project

python main.py

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💻 Example Workflow

AI PRODUCT RECOMMENDATION SYSTEM

Select Product

Phone

Select Payment Method

Credit Card

Select Coupon Code

SAVE10

Select Referral Source

Facebook

Generating recommendations...

Top 5 Recommendations

Phone
Similarity Score : 95%

Monitor
Similarity Score : 90%

Tablet
Similarity Score : 86%

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🧮 Recommendation Logic

The recommendation engine assigns weighted scores to different features.

Feature	Weight
Product Match	40
Payment Method	15
Coupon Code	10
Referral Source	10
Successful Order	10
Popular Product	10
Affordable Price	5
Total	100

Products with higher scores are ranked first.

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📷 Screenshots

Home Screen

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Product Selection

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Payment Selection

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AI Recommendations

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Complete Program Execution

🏗️ System Architecture

📈 Future Improvements

Possible future enhancements include:

• Machine Learning Recommendation Models
• Cosine Similarity
• TF-IDF Recommendation
• Collaborative Filtering
• Hybrid Recommendation System
• Streamlit Web Interface
• Flask Web Application
• User Authentication
• Recommendation History
• SQLite Database Integration

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🧪 Testing

The project has been tested for:

• Dataset loading
• Missing file handling
• Invalid user input
• Similarity calculation
• Recommendation ranking
• Duplicate removal
• Empty dataset handling

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📜 License

This project is licensed under the MIT License.

See the LICENSE file for details.

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👨‍💻 Author

Syed Faran Ali

BS Robotics & Artificial Intelligence

The University of Lahore

GitHub: https://github.com/Faran70177784

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🙏 Acknowledgements

• DecodeLabs
• Artificial Intelligence Internship Program
• Python Community
• Pandas Documentation

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⭐ Support

If you found this project useful:

⭐ Star this repository

🍴 Fork the repository

📢 Share it with others

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📬 Contact

For questions or collaboration:

GitHub: https://github.com/Faran70177784

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Built with ❤️ using Python and Artificial Intelligence concepts as part of the DecodeLabs AI Internship Program.