Abstract :

This Project Presents A Women Safety Analytics – Protecting Women From Safety Threats System That Uses Machine Learning Techniques To Predict Safety Risk Levels Based On Environmental And Location-related Factors. The System Enhances Womens Safety By Analyzing Parameters Such As City, Area, Time Of Day, Lighting Score, Police Station Distance, Crowd Density, And Weather Conditions. It Ensures Timely Risk Prediction And Provides Appropriate Safety Recommendations To Help Users Make Informed Decisions. The System Uses Decision Tree, Random Forest, And KNearest Neighbors (KNN) Algorithms To Classify The Predicted Safety Risk Into Low, Medium, Or High Categories. Based On The Prediction Result, The Application Displays Personalized Safety Recommendations To Improve Awareness And Reduce Potential Safety Threats. Traditional Women Safety Methods Mainly Depend On Emergency Helplines, Manual Reporting, And Reactive Measures, Which Cannot Predict Unsafe Situations In Advance. These Approaches Often Lack Intelligent Risk Analysis And Preventive Decision-making Capabilities. Therefore, There Is A Need For An Intelligent, Data-driven, And Userfriendly System That Can Analyze Safetyrelated Factors, Predict Risk Levels Accurately, And Provide Preventive Safety Guidance.

Published:

29-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1377-1383

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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