Active Final Year Projects Showcase

n today's data-driven world, the ability to collect and analyze web data efficiently has become a critical need for businesses, researchers, and analysts. However, traditional web scraping requires significant technical expertise, as developers must manually write custom scripts for each website, define CSS selectors, and handle website structure changes. This makes the process time-consuming, error-prone, and inaccessible to non-technical users who need data for decision-making purposes.
This project proposes the development of an AI-Powered Intelligent Web Data Extraction and Analysis Platform that enables users to extract structured data from any website simply by providing a URL and describing their requirements in natural language. The system eliminates the need for manual coding by using artificial intelligence to automatically identify relevant page elements, understand user intent, and extract the required information accurately.
The technical approach involves integrating a web scraping engine built with Python, Playwright, and BeautifulSoup for browser automation and HTML parsing, combined with a Large Language Model (LLM) API layer that processes natural language queries and intelligently maps them to the scraped content. The backend is developed using FastAPI, while the frontend dashboard is built with React.js, providing a user-friendly interface. Scraped data is stored in PostgreSQL with support for export in CSV, JSON, and Excel formats.
The expected outcome is a fully functional SaaS-style platform that democratizes web data extraction, allowing non-technical users such as business analysts, e-commerce professionals, and researchers to collect and analyze web data efficiently without writing a single line of code. This platform will significantly reduce the time and cost associated with manual data collection while increasing accuracy and scalability.

Business analysts and e-commerce professionals who need product pricing
and competitor data. Academic researchers requiring structured web data
for analysis. Digital marketers monitoring web content and trends.
Non-technical users who need data collection without programming
knowledge. Data scientists requiring automated data pipelines from
web sources.

•User Registration and Authentication: The system shall allow users to register accounts, log in securely using email and password, and manage their profiles. Role-based access control shall differentiate between regular users and administrators.
•Natural Language Query Processing: The system shall allow users to input data extraction requirements in plain English (e.g., 'extract all product names, prices, and ratings from this page'). The AI layer shall interpret the query and map it to the relevant scraped elements automatically.
•Intelligent Web Scraping Engine: The system shall accept a target URL from the user, automatically render and parse the webpage using browser automation, and extract structured data based on the AI-identified elements. The engine shall handle both static and JavaScript-rendered pages.
•Data Preview and Validation: After extraction, the system shall display the scraped data in a structured tabular format for user review before saving. Users shall be able to verify accuracy and re-run extraction with modified queries if needed.
•Data Export and Download Module: The system shall provide options to export extracted data in multiple formats including CSV, JSON, and Excel (.xlsx). Users shall be able to download results directly from the dashboard.
•Scraping History and Project Management: The system shall maintain a history of all scraping jobs performed by the user, including target URL, query, timestamp, and extracted data. Users shall be able to revisit, re-run, or delete previous scraping jobs.
•Admin Dashboard for System Monitoring: The administrator shall have access to a dashboard displaying system usage statistics, registered users, total scraping jobs, and error logs. Admins shall be able to manage user accounts and monitor platform activity.
Notification and Alert System: The system shall notify users via email or in-app notifications upon completion of scraping tasks, especially for scheduled or long-running jobs. Error alerts shall be sent when a scraping job fails due to website restrictions or changes.

Security: JWT-based authentication, encrypted data storage, HTTPS
communication, and role-based access control shall be enforced.
Performance: The system shall process and return scraped data within
10–15 seconds for standard pages. It shall support concurrent scraping
jobs without degradation. Scalability: The architecture shall support
horizontal scaling using containerization (Docker). The database shall
handle large volumes of scraped records efficiently. UI/UX: The
interface shall be responsive, intuitive, and accessible to non-technical
users, with clear error messages and loading indicators.