Portfolio Details

Each project in this portfolio reflects my hands-on experience in solving real-world problems using data science, machine learning, and full-stack development. From predictive models to intelligent web applications, these solutions are built with a focus on scalability, functionality, and impact.

Project Information

  • Project Name: Diabetes Risk Predictor
  • Category: Health Monitoring System
  • Model Type: Support Vector Classifier (SVC)
  • Github Repo: Link

Technologies Used

This project uses a Support Vector Machine (SVC) for binary classification, predicting whether a user is at risk of diabetes. The backend is built with Python and Flask, while the frontend is implemented using HTML, CSS, and JavaScript to provide a clean and responsive user experience. The application is designed to offer real-time predictions with a simple interface, ensuring high accessibility and performance.

Details of Project

Diabetes Risk Predictor is a machine learning–powered web application aimed at early detection of diabetes. It allows users to input key health metrics and instantly receive a prediction on their diabetes risk. Built using a Support Vector Classifier (SVC), the model ensures reliable performance across a diverse range of input data.

The tool is designed for accessibility and ease of use, featuring a frontend built with HTML, CSS, and JavaScript, and a Flask-based backend that handles prediction logic. This project bridges the gap between predictive modeling and user-friendly health tech, helping users take proactive steps toward their well-being.

Project Features

  • Accurate diabetes prediction using Support Vector Classifier (SVC).
  • Interactive and responsive frontend interface with clean design.
  • Flask backend for real-time prediction and input processing.
  • Health metric–based risk assessment with instant results.
  • Lightweight and accessible — optimized for desktop and mobile users.