Project Description:

The AI-Based Expense Categorizer is a smart web application that helps users automatically organize their expenses by analyzing the text or receipt data using machine learning and natural language processing (NLP). Users can upload their expense entries manually or via receipt images, and the system intelligently classifies them into predefined categories like Food, Travel, Bills, Entertainment, etc.

This project enhances personal finance management and reduces the manual effort of sorting expenses.

Core Objective:

To develop a system that automatically identifies the category of each expense using AI/ML and provides insightful spending analytics via an interactive dashboard.

Key Features:

1. Expense Entry Methods:

   • Manual entry: User enters expense name, amount, date.

   • Receipt upload: Uses OCR to extract text from receipts.

2. AI-Based Categorization:

   • Uses NLP to analyze text like “Uber Ride”, “Domino’s Pizza” and auto-categorizes it.

   • Categories may include: Food, Travel, Utilities, Shopping, Medical, Education, etc.

3. Spending Dashboard:

   • Graphs for monthly category-wise expenses (pie/bar chart).

   • Daily, weekly, monthly trend analysis.

4. Calendar View:

   • Displays expenses by date to track spending habits.

5. Search & Filter:

   • Users can search by name, category, or amount.

   • Filter by date range or category.

6. User Authentication:

   • Login/signup to access private financial data.

   • Secure data handling with session management.

Tech Stack:

 Backend (Choose One):

• PHP with Laravel / Core PHP

• Node.js with Express

• Java with Spring Boot

 Frontend:

• HTML, CSS, Bootstrap

• JavaScript (with Chart.js or D3.js for graphs)

 AI/ML Layer:

• Python (Flask or FastAPI microservice) using:

  • NLP with Scikit-learn or spaCy

  • Pre-trained classification models

  • Optional: TensorFlow or PyTorch for advanced classification

 Database:

• MySQL / MongoDB (to store user, expenses, and category data)

 Optional Add-ons:

• OCR using Tesseract (for receipt scanning)

• REST API or internal API for frontend-backend communication