AI-Powered Data Plan Recommender

Machine Learning, Data Analysis

Project Overview

The AI-Powered Data Plan Recommender is an intelligent system that analyzes user behavior patterns and suggests the most suitable mobile data plans based on individual usage. The system leverages advanced machine learning algorithms to provide personalized recommendations that optimize cost and coverage for users.

This project addresses the common challenge of selecting appropriate data plans by offering data-driven insights that enhance decision-making. By analyzing usage patterns, the recommender system helps users avoid overpaying for unused data or experiencing coverage limitations.

React Flask Python MongoDB CatBoost Machine Learning

Client

HCLTech(Internship)

Timeline

December 2024 - Februrary 2025

Role

ML Engineer, Full-Stack Developer

Project Link

GitHub Repository

Project Details

The AI-Powered Data Plan Recommender delivers several key features and benefits:

Personalized Recommendations: Built a recommender system that suggests the most suitable data plans based on individual user behavior patterns.
Interactive User Interface: Designed an engaging front-end interface using React for intuitive user interaction and visualization of recommendations.
Enhanced Decision-Making: Improved recommendation accuracy to help users make better-informed choices about their data plans.
Data-Driven Insights: Provided valuable analytics on customer behavior that telecom service providers can leverage for business decisions.
Scalable Architecture: Implemented a robust system capable of handling large datasets and multiple users efficiently.

The system leverages CatBoost machine learning algorithms to analyze usage patterns and predict optimal plans, achieving high accuracy in its recommendations.

Technical Implementation

The system was built using a modern tech stack consisting of React for the front-end, Flask (Python) for the backend API, MongoDB for data storage, and CatBoost for the machine learning model.

The architecture was designed to be scalable, allowing for efficient processing of large datasets while maintaining responsive user interactions. The recommendation engine processes user behavior data to generate insights that drive the suggestion algorithm.

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