Design and Development of Machine Learning Algorithm for Forecasting Crime Rate

Controlling crime is one of the necessary things for a peaceful life. Forecasting the crime helps in planning the strategies in this task. Modern data analysis techniques like classification and prediction may be utilized for this purpose. Classification is a data mining approach that allocates items in a group to target categories or classes. It also may be used to label a target item into any one of the classes identified.Among many available classification techniques, clustering is one of the unsupervised machine learning approaches that could be used for creating clusters as features to enhance classification models. There are various clustering algorithm available like K-mean clustering, Kernel K-mean algorithm etc.PCA algorithm is used to reduce the dimension of the huge amount of data used so that the data can be represented in smaller database with reduced noise in the dataset. In general, mode is a set of values which occurs frequently. Hence, instead of k-mean which is an average value, frequent values may produce better result.K-Mean algorithm creates clusters and groups data properly. But randomly assuming centroid for clusters in the initial stage leads to too much of computational cost. So, in this work, K mode Clustering algorithm was used for clustering asit replaces the Euclidean distance function with the simple matching dissimilarity measure. Once the clusters were formed, a new algorithm was used to forecast the crime rate or future values of the data in the cluster.The proposed approach was tested on crime dataset and found efficient in this domain while comparing with some existing approaches