: Crimes damage any society each socially and economically. enforcement bodiesface varied challenges whereas making an attempt to
stop crimes. we have a tendencyto propose acriminaloffense information Analytics Platform (CDAP) to help enforcement bodies to perform
descriptive, predictive, and prescriptive analysis on crime information. CDAP contains a standard design wherever every part is constructed one
by one from the others. CDAP conjointly supports plugins sanctionative future feature expansions. The platform will ingest any crime dataset
that has needed the specified the desired} attributes to map the dataset to attributes required by the platform. It will then analyze them, train
models, so visualize information. CDAP conjointly combines census information with crime information to realize a additional comprehensive
crime analysis and its impact on society. Moreover, with the mix of census information and crime information, CDAP provides method
reengineering steps to optimize resource allocations of police forces. we have a tendency to demonstrate the utility of the platform by visualizing
spacial and temporal relationships in a very set of real-world crime datasets. prognosticative capabilities of the platform ar incontestable by
predicting crime classes, that a machine learning approach is employed. To construct a model area theorem, Random Forest Classifier, and
Multi-layer Perceptron Network classification algorithms ar provided. Identification of optimized police district boundaries and allocating patrol
beats ar accustomed demonstrate the prescriptive analytics capabilities of the tool. Heuristic-based clump approach was taken to outline police
district boundaries in a very manner that the known districts have equitable population distribution with compact shapes. The ensuing districts
ar then evaluated on the difference of population and also the compactness exploitation the Gini constant and Isoperimetric Quotient. Another
heuristic-based approach was taken to outline new police patrol beats to be optimized on equitable work distribution, compactness, and
minimizing reaction time for brand spanking new police patrol beats.
Keywords: CDAP, Nave Bayesian, Random Forest, Multi-layer perceptron.