Crime Prediction and Analysis Using Machine Learning

Crime prediction and analysis have been the backbone within which public safety is expected and effective policies can be made. Using time series forecasting techniques such as SARIMA and Prophet, this project describes crime forecasting in Andhra Pradesh, Telangana, and Maharashtra. The overall analysis will be directed towards producing actionable insights for police and policymakers by analysing historical crime data. After thorough data preparation involving cleaning and feature enrichment, Exploratory Data Analysis (EDA) will be conducted to identify important crime patterns and seasonal behaviours. The predictive model of SARIMA captures seasonal variations, while Prophet takes care of the irregularities and missing data with flexibility and accuracy. A comparative assessment of these two models would help to highlight their strength in forecasting crime trends. Crime patterns significantly differ with each region with specific traits attributed to socio-economic and geographical aspects. Some problems include data imbalance, scant historical data, and inconsistencies in records. But the project highlights the application of machine learning to handle real-life problems in crime prevention. It opens up a future direction integrating new, real-time data, socio-economic variables, and geospatial analyses to make predictions more precise and provide actionable insights dynamically. Overall, the project shows the promise of data-driven approaches in improving public safety and reducing crime rates