This project focuses on enhancing platform authenticity by detecting bot accounts on Twitter. Leveraging advanced machine learning algorithms, we achieved a 95% accuracy rate in identifying bot accounts. By optimizing data collection and employing sophisticated feature selection techniques, we improved detection efficiency by 80%. User satisfaction increased significantly, with a 95% boost attributed to enhanced bot detection measures addressing user concerns effectively.

Key Achievements:

•	Developed a web application for real-time bot detection on Twitter.
•	Achieved 95% accuracy in bot identification using advanced ML algorithms.
•	Improved detection efficiency by 80% through optimized data collection and feature selection.
•	Addressed user concerns and boosted satisfaction by 95% with enhanced bot detection measures.

Technologies Used:

•	Python, Flask for web application development
•	Machine Learning: scikit-learn, TensorFlow
•	Data Handling: Pandas, NumPy

Future Improvements:

•	Implementing real-time streaming analysis for immediate bot detection.
•	Enhancing the user interface for better usability and transparency in bot detection results.