PATTERN DETECTION ANALYSIS & FORECASTING OF CRIME ACTIVITIES USING MACHINE LEARNING

Abstract

Pattern Detection Analysis and Forecasting of
Crime Activities using Machine Learning is an
intelligent predictive system developed to analyze
historical crime data and forecast future crime
trends using advanced machine learning and deep
learning techniques. The increasing rate of criminal
activities in urban and rural regions has created the
need for proactive crime prevention mechanisms
that support law enforcement agencies in decisionmaking
and resource allocation. Traditional crime
analysis systems mainly focus on historical
reporting and manual interpretation, which are
often inefficient for handling large-scale datasets
and identifying hidden crime patterns. To overcome
these limitations, the proposed system applies
ARIMA, SARIMA, and LSTM models for accurate
time series forecasting of crime activities. ARIMA
is effective for identifying linear trends and
temporal dependencies in sequential crime records,
while SARIMA enhances forecasting by capturing
seasonal variations and repetitive crime behaviors.
LSTM, a recurrent neural network architecture, is
capable of learning long-term and non-linear
relationships in crime data, thereby improving
prediction accuracy. The system performs data
collection, preprocessing, normalization,
visualization, model training, forecasting, and
evaluation in an automated manner. Performance
metrics such as Mean Absolute Error (MAE), Root
Mean Square Error (RMSE), Mean Absolute
Percentage Error (MAPE), and R2 Score are used
to compare model efficiency. The proposed
framework also includes graphical visualization of
crime trends and hotspot analysis for better
interpretation of predictions. The comparative
analysis demonstrates that machine learning-based
forecasting models can significantly improve
predictive policing and crime prevention strategies.
The system provides a scalable, reliable, and
intelligent solution that supports data-driven public
safety management and proactive law enforcement
operations.