Maaz Irfan@Maaz_Ai_enthusiast

Budget Management System is a smart and efficient web application designed to help users manage their personal finances with ease. Whether you're a student, a working professional, or someone trying to build better financial habits, this system offers everything you need in one place. The platform allows users to track their daily expenses, set monthly or weekly budgets, and create personalized savings goals. As you add transactions, the system updates your budget and goal progress in real-time, giving you a clear view of where your money is going. A standout feature of the app is its AI-powered financial suggestions. Based on your spending history and budget patterns, the system gives personalized tips to help you save more, avoid overspending, and plan better. Another useful feature is receipt scanning using OCR (Optical Character Recognition). Users can simply upload a photo of a receipt, and the system automatically reads and adds the transaction details—saving time and reducing manual entry. Built with React.js for the frontend and Tailwind CSS for styling, the app offers a clean, responsive, and user-friendly interface. On the backend, Supabase is used for user authentication and real-time database management, ensuring smooth data handling and secure login/logout flows. All user routes are protected, and the experience is optimized for both desktop and mobile users. Whether you're managing your personal budget or planning for long-term goals, Budget Management System is designed to make your financial life more organized and intelligent. It's an ideal tool for anyone who wants to take control of their finances using smart technology and automation.

An application that leverages neural networks to predict the likelihood of heart disease in users based on their health data. Features User Input: Users can input personal health metrics such as age, gender, blood pressure, cholesterol levels, and other relevant medical history. Neural Network Model: Implement a pre-trained neural network model that processes the input data to predict heart disease risk. The model can be based on existing datasets and algorithms from sources like Kaggle. Real-Time Feedback: Provide users with immediate feedback on their health status, including risk assessment and suggestions for lifestyle changes. Data Visualization: Include graphs and charts to help users understand their health metrics and the prediction results. Educational Resources: Offer information on heart disease, prevention strategies, and when to seek medical advice.

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