Project: No Spam.

The project NoSpam aims to develop a machine learning algorithm that can accurately classify comments on YouTube videos as either spam or legitimate, based on various features such as the text content, the user's history, and the frequency of the comments. By identifying and filtering out spam comments, the project seeks to improve the user experience for viewers and creators on the platform, reduce the amount of unwanted content, and mitigate the negative effects of spam such as phishing attempts, scams, and malware.

Project features:

• Highly predictive datasets crucial for effective and accurate decision-making
• Free for everyone
• Designed as an open-source project
• Completely responsive and accessible from the vast majority of screen devices

Methodology:

The project involves the following key steps:

1. Literature Review: Understanding existing spam detection techniques.
2. Design of User Interface: Creating wireframes and prototypes for the front end using tools like Figma.
3. Implementation: Developing and testing models using frameworks like scikit-learn, numpy, pandas, gunicorn, flask, skllearn, etc.
4. Results & Discussion: Analyzing the effectiveness of the spam detection system.
5. Future Outcome Expectations: Identifying factors for improving spam detection mechanisms.

To know more about the project, check out the project report click here.