A Predictive Analytics Framework for Crime Type Forecasting
DOI:
https://doi.org/10.15662/IJEETR.2026.0802225Keywords:
Crime Prediction, Machine Learning, Crime Analysis, Random Forest, SVM, Streamlit, Data Visualization, PythonAbstract
Understanding crime patterns is essential for improving public safety and making informed decisions based on data. This project presents a web-based application that studies past crime records and predicts possible crime types using machine learning techniques. The system is developed using Python and Streamlit, which makes it interactive and easy for users to explore. A real-world crime dataset from Kaggle is used to train and test the model. Algorithms such as Support Vector Machine and Random Forest are applied to recognize patterns in the dataset and generate predictions. Apart from prediction, the system also provides charts and visual summaries that allow users to observe trends, frequency, and distribution of crimes more clearly. The purpose of this project is not only to build a prediction model but also to demonstrate how machine learning can be practically used to analyze real-world data and derive useful insights from it
References
1. S. N and A. M. Anusha Bamini, “Criminal activity forecasting using machine learning,” in Proc. 10th Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), Coimbatore, India, 2024, pp. 2151–2156, doi: 10.1109/ICACCS60874.2024.10717111.
2. Yu et al., “Crime prediction using spatial-temporal synchronous graph convolutional networks,” in Proc. 11th Int. Conf. Soft Comput. Mach. Intell. (ISCMI), Melbourne, Australia, 2024, pp. 129–133, doi: 10.1109/ISCMI63661.2024.10851671.
3. P. Phalaagae, A. M. Zungeru, A. Yahya, B. Sigweni, and S. Rajalakshmi, “A hybrid CNN-LSTM model with attention mechanism for improved intrusion detection in wireless IoT sensor networks,” IEEE Access, vol. 13, pp. 57322–57341, 2025, doi: 10.1109/ACCESS.2025.3555861.
4. A. Kumar, A. Maurya, P. Kumar, A. Gupta, and J. Saini, “Optimizing safe routes with machine learning: A crime prediction system for urban safety,” in Proc. Int. Conf. Emerging Technol. Innov. Sustainability (EmergIN), Greater Noida, India, 2024, pp. 368–372, doi: 10.1109/EmergIN63207.2024.10961735.
5. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
6. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
7. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
8. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
9. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
10. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
11. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
12. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
13. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
14. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
15. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
16. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
17. S. Arul, P. Kavitha, and S. Kamalakkannan, “Deep learning crime monitoring and alerting system using hybrid feature engineering technique with IoT technology,” in Proc. 2nd Int. Conf. Res. Methodol. Knowl. Manage., Artif. Intell. Telecommun. Eng. (RMKMATE), Chennai, India, 2025, pp. 1–7, doi: 10.1109/RMKMATE64874.2025.11042792.
18. F. Ersöz, T. Ersöz, F. Marcelloni, and F. Ruffini, “Artificial intelligence in crime prediction: A survey with a focus on explainability,” IEEE Access, vol. 13, pp. 59646–59674, 2025, doi: 10.1109/ACCESS.2025.3553934.
19. B. Huang, “Predicting future incidences of crime based on the CNN-transformer model,” in Proc. 5th Int. Conf. Big Data Artif. Intell. Softw. Eng. (ICBASE), Wenzhou, China, 2024, pp. 766–769, doi: 10.1109/ICBASE63199.2024.10762507.
20. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.
21. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
22. Rajasekar, M. (2024). Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10713-10718.
23. Mathew, A. (2021). Edge Computing and its convergence with blockchain in 6G: Security challenges. International Journal of Computer Science and Mobile Computing, 10(8), 8-14.
24. Prasad, D. R., & Vimal, V. R. (2025, April). Early Detection of Brain Tumor from The MRI scan Using Deep Learning. In 2025 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) (pp. 1-5). IEEE
