CRIME PREDICTION AND ANALYSIS USING MACHINE LEARNING
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Crime prediction has become an important application of artificial intelligence because public safety agencies need faster and more reliable ways to identify crime trends. This project presents CrimeCast, a web-based crime prediction and analysis system developed with Python and Flask, trained on crime data from Tamil Nadu, India. The system uses a Random Forest Classifier to predict the most likely crime type from inputs such as state, city, latitude, longitude, year, and domestic status. The model is designed to classify six major crime categories: Assault, Burglary, Cyber Crime, Domestic Violence, Robbery, and Theft. The application combines machine learning with a secure, responsive web interface built with Bootstrap and an SQLite database for storing user accounts and prediction history. It also includes analytics dashboards that show crime distribution, yearly trends, city-wise frequency, and domestic versus non-domestic comparisons. An interactive heatmap built with Leaflet.js provides a geographic visualization of crime density across Tamil Nadu. With its prediction engine, history tracking, and visual
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D, H. & S, D. (2026). Crime Prediction and Analysis using Machine Learning. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.040
D, Hemanathan, and Dinesh S. "Crime Prediction and Analysis using Machine Learning." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.040.
D, Hemanathan, and Dinesh S. "Crime Prediction and Analysis using Machine Learning." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.040.
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