Anti-Theft-Program-

This is a program that utilizes Kmeans and Decision Tree algorithms in order to detect the occurrences of fraud within the database. The K-means clustering is a method of vector quantization, originally from signal processing. It is popular for cluster analysis in data mining. K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. A decision tree is a flowchart-like tree structure, where each node denotes a test on an attribute value, each branch represents an outcome of the test, and tree leaves represent classes or class distributions. The goal is to figure out how often fraud occurs and what is the estimated amount that people tend to consume. We hope to find fraudulent activity and make it easier to detect. Our results from the dataset we used illustrated that the age range that is most likely to experience fraud is between the ages of twenty-six and thirty two. Written in Python.