Night Guardian: Empowering Women’s Safety with Hand Sign – Based Communication and AI

In an era where technology continually shapes the way we interact with the world, there arises a pressing need for innovative solutions that enhance women's safety. Ensuring women's safety, especially at night, is crucial due to the unique challenges and risks they may face during nighttime hours. Our project aims to address critical issues related to women's safety, such as harassment, assault, and emergencies, particularly in public spaces. We leverage advanced technologies like MediaPipe and machine learning algorithms to create an effective and responsive safety solution. The project employs various machine learning algorithms, including random forest, support vector machine, Hybrid model and k-nearest neighbor, to train the model. Rigorous testing is conducted to evaluate and select the optimal model, utilizing key performance metrics such as F1 score, precision, and recall.