Product Recommendation Engine

This project showcases a Product Recommendation Engine that uses machine learning algorithms to suggest products based on user behavior, purchase history, and preferences. The engine helps users discover new products tailored to their interests.

Features

• Personalized product recommendations based on browsing and purchase history.
• Collaborative filtering: Analyzes user similarities to recommend products.
• Content-based filtering: Suggests products based on attributes like category, brand, and price range.
• Hybrid recommendation model: Combines collaborative and content-based filtering for accurate results.
• Real-time recommendations that adapt to changing user behavior and preferences.

Installation

To set up the Product Recommendation Engine on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/product-recommender.git
   cd product-recommender

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To use the Product Recommendation Engine, start the application and begin exploring product suggestions tailored to your needs:

python app.py

Access the application through your web browser, and start receiving personalized product recommendations based on your preferences.

Results

Experience personalized product suggestions that match your browsing history, shopping habits, and preferred categories, enhancing your shopping experience with relevant recommendations.