Cyberbullying Detection Using Machine Learning | Python Final Year IEEE Project

Summary

This video introduces a project that detects cyberbullying using machine learning. The project involves building a full-stack web application with front-end technologies like HTML and CSS, back-end Python code, and machine learning algorithms. The system analyzes user input to determine whether it’s a case of cyberbullying, with an accuracy rate of 80-90%. Viewers are encouraged to use the application to detect bullying on social media and take appropriate actions, such as blocking or reporting offenders. The project is also supported by research papers, with further assistance available through the **Final Pro** platform for students.

Takeaways

• 😀 The project is a **Cyber Bullying Detection System** using machine learning technology.
• 😀 The system uses a **full-stack approach** with **HTML, CSS** for the front end, and **Python** with the **Flask framework** for the back end.
• 😀 The goal of the project is to detect and prevent **cyber bullying**, which is becoming an increasing issue due to the rise of the internet.
• 😀 The **machine learning model** achieves an accuracy rate of **88-90%** in detecting cyber bullying content.
• 😀 The project allows users to input text (e.g., comments from social media) to determine if it is cyber bullying or not.
• 😀 The system provides users with actionable insights, such as blocking or reporting the individual who made the hurtful comment.
• 😀 The project is based on research papers, and the development was informed by academic sources on cyber bullying detection.
• 😀 The website **FinalPro.in** offers a range of resources, including project help, research papers, and coding explanations across various domains.
• 😀 FinalPro.in helps students by providing affordable project development services for various degree levels (BTech, MTech, PhD).
• 😀 Over **10,000 students** worldwide have benefited from the services offered by FinalPro.in, which has received a **4.5/5 rating** from users.
• 😀 Viewers are encouraged to visit the social media channels (Instagram, Facebook, YouTube) for more knowledge, tutorials, and ideas to help with their own projects.

Q & A

What is the main focus of the project presented in the video?

The project focuses on building a cyber-bullying detection system using machine learning. It aims to identify and prevent cyberbullying by detecting harmful messages on social media platforms.

What technologies were used in this cyber-bullying detection project?

The project uses a full-stack approach, utilizing HTML and CSS for the frontend, Python for the backend, and machine learning algorithms for predicting and detecting cyberbullying messages.

How does the project work to detect cyberbullying?

The project trains a machine learning model using a dataset. It then tests and applies this model to detect cyberbullying messages, which are inputted through a web interface. The system provides feedback on whether the message is considered bullying or not.

What is the purpose of the website 'FinalPro.in' mentioned in the video?

The website 'FinalPro.in' offers a variety of services for students, including project guidance, research paper writing, programming help, and project development in various domains such as machine learning, cyber security, and blockchain.

How accurate is the machine learning model used in this project?

The machine learning model used in the project has an accuracy rate of approximately 88% to 90% in detecting cyberbullying messages.

What action can a user take after receiving a bullying message detected by the system?

After the system detects a bullying message, users can take actions such as blocking the person, reporting the message, or seeking legal help to address the issue.

What is the significance of the research papers mentioned in the video?

The research papers mentioned in the video were studied to understand existing approaches and improve the development of the cyberbullying detection system. They provide foundational knowledge and insights into machine learning techniques used in the project.

What kind of support can users expect from the company behind this project?

The company offers support for students by providing the project code, documentation, and live explanations. They also assist with project customization for different academic levels (BTech, MTech, PhD, etc.) for a minimal charge.

What is the rating of the company offering these services?

The company has a rating of 4.5 stars out of 5 and has assisted over 5,000 students globally with their projects across various domains.

How can users access and run the project presented in the video?

Users can download and install Visual Studio Code, open the 'app.py' file, and run the project by clicking the 'run' button. Once running, they can access the cyberbullying detection webpage through a provided link and input text for detection.