🛠️ Tools & Technologies Used:

• Programming Language: Python

• IDE: Visual Studio Code (VS Code)

• Libraries Used:

  • Tkinter – for GUI (Graphical User Interface)

  • requests – for sending HTTP requests to websites

  • BeautifulSoup – for scraping basic data from web pages (optional)

  • re – for URL pattern checking using regular expressions

  • validators – for URL validation

  • scikit-learn (optional) – for future implementation using Machine Learning

  • joblib – to load/save models (if ML is used)

📌 Project Description:

This project is a Scam Website Detector or Scanner, built using Python. It takes a website URL as input from the user and checks whether the website is suspicious or legitimate based on several features like:

• URL length

• Presence of symbols (like @, //, or -)

• Use of HTTPS protocol

• Domain age (optional – using WHOIS APIs)

• Use of URL shortening services (like bit.ly)

It also allows future integration of machine learning models trained on phishing/scam data to make predictions even more accurate.

The application has a simple GUI built using Tkinter, which makes it user-friendly. The user enters a URL, and the program displays whether it is safe or potentially a scam.

🖼️ Features of the Project:

• Clean and interactive Tkinter GUI for URL input

• Checks for common red flags in scam URLs

• Gives instant feedback: “Safe” or “Scam”

• Future-ready: Can be extended to include a dataset and ML model for higher accuracy

• Lightweight and fast — good for student use and demonstration

📚 What I Learned:

• How to create desktop apps using Tkinter

• Handling HTTP requests and data parsing in Python

• Writing clean and modular Python code in VS Code

• Working with URL validation and using regex patterns

• Basic introduction to cybersecurity and how phishing websites work

• Potential of machine learning in improving scam detection

🔗 Real-World Applications:

• Can be used as a base for browser extensions

• Helpful for educational purposes on internet safety

• Can be improved and used by IT teams in schools or colleges to teach about scam prevention

🔧 Future Improvements:

• Add a machine learning model trained on phishing datasets

• Use WHOIS API to check domain age and registration

• Show more detailed reports about why a site is flagged

• Add a dark mode and improve UI using ttk styling or customtkinter

• Deploy it as a web application using Flask or Streamlit