SAFE CITY VISION

👥 Sanjana, Shreya, Khushi, Ms.Aarti Chahal

📙 Abstract : This paper presents Safe City Vision, a machine-learning-based crime detection and analysis project intended to examine historical crime patterns, support prediction, and provide decision-oriented visual analytics. The project uses crime records containing geographic, temporal, and categorical information and applies preprocessing, feature extraction, classification, visualization, and interactive analysis. The source report describes K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, time-series forecasting, and real-time analysis as components or proposed extensions. The documented project evaluation reports an overall accuracy of 82%, while the conclusion reports 85%; these values are retained as reported results rather than reconciled by assumption. The study also considers privacy, secure storage, fairness, and the responsible use of predictive crime analytics.

🔖 Keywords :️ crime detection, crime analysis, machine learning, KNN, SVM, predictive policing, data visualization, safe city.