Date of Award
8-2024
Document Type
Project
Degree Name
Master of Science in Computer Science
Department
School of Computer Science and Engineering
First Reader/Committee Chair
Dr.Yan Zhang
Abstract
This project employs machine learning methods like K Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Decision Tree algorithms to monitor crime data based on location and pinpoint areas with risks. The project implements and tunes the four models to improve the precision of predicting crime levels. These models collaborate to offer a trustworthy evaluation of crime patterns. K Nearest Neighbors (KNN) categorizes locations by examining the proximity of data points considering coordinates and other factors to identify trends linked to increased crime data. Logistic Regression gauges the likelihood of crime incidents by studying the connection, between factors (like location and time ) and the crime activity, assisting in forecasting crimes in various regions. Decision Tree Classifier uses a tree structure to make decisions based on feature values dividing the data into branches representing decision paths. This approach is particularly useful for identifying high-risk areas using crime data. Random Forest Classifier constructs decision trees and combines their results for classification purposes, resulting in enhanced prediction accuracy and robustness by merging outcomes from multiple trees, thus reducing the risks of overfitting and improving generalization to unseen data.
The system’s efficiency is assessed using a crime dataset that includes information, about crime occurrences, geographical locations, and time-related data. Metrics, like accuracy, precision, and recall are employed to assess the model’s ability to anticipate crimes and identify hotspots accurately.
Recommended Citation
Yarlagadda, Sai Bharath, "CRIME DATA PREDICTION BASED ON GEOGRAPHICAL LOCATION USING MACHINE LEARNING" (2024). Electronic Theses, Projects, and Dissertations. 2016.
https://scholarworks.lib.csusb.edu/etd/2016
Included in
Computer and Systems Architecture Commons, Data Storage Systems Commons, Other Computer Engineering Commons