A machine learning approach to predict crime using time and location data
Cataloged from PDF version of thesis report.
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BRAC University
2017
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| Online Access: | http://hdl.handle.net/10361/8197 |
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10361-81972022-01-26T10:10:34Z A machine learning approach to predict crime using time and location data Shama, Nishat Majumdar, Dr. Mahbub Alam Department of Computer Science and Engineering, BRAC University Machine learning Crime Time and location Cataloged from PDF version of thesis report. Includes bibliographical references (page 51-52). This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017. Recognizing the patterns of criminal activity of a place is paramount in order to prevent it. Law enforcement agencies can work effectively and respond faster if they have better knowledge about crime patterns in different geological points of a city.The aim of this paper is to use machine learning techniques to classify a criminal incident by type,depending on its occurrence at a given time and location.The experimentation is conducted on a data set containing San Francisco’scrimerecordsfrom2003-2015.For this supervised classification problem, Decision Tree, Gaussian Naive Bayes, k-NN, Logistic Regression, Ada boost, Random Forest classification models were used. As crime categories in the data set are imbalanced, oversampling methods, such as SMOTE and under sampling methods such as Edited NN, Neighborhood Cleaning Rule were used. Solving the imbalanced class problem, the machine learning agent was able to categorize crimes with approximately 81% accuracy. Nishat Shama B. Computer Science and Engineering 2017-05-29T05:37:27Z 2017-05-29T05:37:27Z 2017 4/18/2017 Thesis ID 15141009 http://hdl.handle.net/10361/8197 en BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 52 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Machine learning Crime Time and location |
| spellingShingle |
Machine learning Crime Time and location Shama, Nishat A machine learning approach to predict crime using time and location data |
| description |
Cataloged from PDF version of thesis report. |
| author2 |
Majumdar, Dr. Mahbub Alam |
| author_facet |
Majumdar, Dr. Mahbub Alam Shama, Nishat |
| format |
Thesis |
| author |
Shama, Nishat |
| author_sort |
Shama, Nishat |
| title |
A machine learning approach to predict crime using time and location data |
| title_short |
A machine learning approach to predict crime using time and location data |
| title_full |
A machine learning approach to predict crime using time and location data |
| title_fullStr |
A machine learning approach to predict crime using time and location data |
| title_full_unstemmed |
A machine learning approach to predict crime using time and location data |
| title_sort |
machine learning approach to predict crime using time and location data |
| publisher |
BRAC University |
| publishDate |
2017 |
| url |
http://hdl.handle.net/10361/8197 |
| work_keys_str_mv |
AT shamanishat amachinelearningapproachtopredictcrimeusingtimeandlocationdata AT shamanishat machinelearningapproachtopredictcrimeusingtimeandlocationdata |
| _version_ |
1814307909735022592 |