Amazon Product Recommendation System

Overview

This project is an Amazon product recommendation system built using Python and Flask. It utilizes a dataset of Amazon products to provide users with personalized recommendations, similar products, and complementary products based on their selected item.

Business Problem Statement

Online shoppers often face difficulties in finding relevant and high-quality product recommendations. This project aims to address this issue by offering a reliable and user-friendly Amazon product recommendation system.

Solution

The Amazon Product Recommendation System is designed to provide users with a seamless and personalized shopping experience. The key features of the system include:

• Product Details: Users can input the name of a product, and the system retrieves and displays detailed information about the product, including its description, reviews, pricing, and discounts.

• Similar Products: The system analyzes the selected product and suggests similar products based on category and customer ratings. This helps users discover alternative options that match their preferences.

• Complementary Products: In addition to similar products, the system also suggests complementary products that go well with the selected item. This encourages cross-selling and helps users find related products that enhance their overall shopping experience.

Installation

1. Clone the repository: git clone https://github./mohammedkayser/amazon-product-recommendation.git

2. Navigate to the project directory: cd amazon-product-recommendation

3. Install the required dependencies: pip install -r requirements.txt

4. Run the application: python app.py

5. Open your web browser and visit http://localhost:5000 to access the Amazon Product Recommendation System.

Usage

1. Enter the name of a product in the search box on the homepage.

2. Click the "Search" button to retrieve the product details.

3. Explore the product details, including description, reviews, pricing, and discounts.

4. Below the product details, you will find two buttons: "Similar Products" and "Complementary Products."

5. Click on either button to view the suggested products in the respective category.

6. Browse through the suggested products and select any item of interest.

7. Repeat the process for different products to discover new recommendations.

Technologies Used

• Python

• Flask

• Pandas

• Html , csss , Js

Contributing

Contributions are welcome! If you'd like to contribute to the project, please follow these steps:

1. Fork the repository.

2. Create a new branch for your feature or bug fix: git checkout -b my-new-feature

3. Make your modifications and commit your changes: git commit -am 'Add new feature'

4. Push your changes to your fork: git push origin my-new-feature

5. Submit a pull request describing your changes.