shrutikahilale/ecommerce_app

The E-Commerce Application using Flutter & Firebase with Product Recommendations project aims to develop and implement a recommendation system within a Flutter-based e-commerce application that focuses on selling shoes. This document outlines the vision, purpose, and scope of the project, detailing the innovative use of machine learning techniques.

ecommerce_app

The E-Commerce Application using Flutter & Firebase with Product Recommendations project aims to develop and implement a recommendation system within a Flutter-based e-commerce application that focuses on selling shoes. This document outlines the vision, purpose, and scope of the project, detailing the innovative use of machine learning techniques and the seamless integration of frontend and backend components.

The primary purpose of this project is to enhance user engagement and drive conversion rates within an e-commerce application through the implementation of a personalized product recommendation system. By leveraging advanced machine learning techniques and ensuring seamless communication between frontend and backend components, the project aims to:

Evaluate the effectiveness of recommendation algorithms in improving user engagement. Analyze the impact of personalized recommendations on conversion rates. Contribute to the existing body of knowledge on recommendation systems in e-commerce. Provide insights for improving user experience and expanding revenue prospects in the e-commerce sector.

The scope of this project includes the following key components:

1. Frontend Development: Building a user-friendly e-commerce application using Flutter. Designing intuitive UI/UX interfaces for browsing and purchasing shoes.

2. Backend Development: Developing backend services using Python Flask. Implementing Firebase for authentication, database management, and hosting.

3. Recommendation System: Utilizing machine learning techniques such as TF-IDF vectorization and KMeans clustering. Analyzing user search history to provide personalized product recommendations.

4. Integration: Establishing RESTful API endpoints for seamless communication between frontend and backend components. Ensuring real-time data synchronization and efficient API performance.

5. Evaluation and Analysis: Measuring the impact of the recommendation system on user engagement. Analyzing conversion rates and user satisfaction. Collecting and interpreting data to provide insights for future improvements.

6. Documentation and Reporting: Documenting the development process, algorithms used, and integration techniques. Reporting findings and providing recommendations for further research and development.