Finding suitable locations for live campaigns using different machine learning techniques
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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BRAC University
2018
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| Online dostop: | http://hdl.handle.net/10361/10961 |
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10361-109612022-01-26T10:08:18Z Finding suitable locations for live campaigns using different machine learning techniques Qudar, Mohiuddin Md. Abdul Tazrin, Tahrat Khanam, Kazi Zainab Chakrabarty, Amitabha Department of Computer Science and Engineering, BRAC University LBSN Live campaigns Location, algorithms SVM Random forest Decision tree Machine learning -- Mathematical models. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-45). For business booming, in recent years the idea of find an ideal location for reaching potential customers has been the focus of past research. Traditional approaches have faced many negative responses, now to make business profitable the new marketing strategy is live campaigns. With the growth of location-based social networks collecting data of user mobility and popularity of places has recently become attainable, but not without analyzing to find the optimal location and time for live campaigns with greater accuracy. In this paper, we study the predictive power of various machine learning and mining features on finding suitable location for live campaigns through the use of a dataset collected from Foursquare in New York. We selected 10 candidate areas where the data was preprocessed according to the feature, a score is computed on the candidate areas to do live campaigns based on the features using most suitable algorithm with the accuracy. The results with Random Forest and Decision Tree are shown at the end of the report. Lastly, our proposed model shows how performance varies when using different features and predicting the suitable locations for live campaigns. We achieve 88.25% accuracy in Decision Tree regression model and an accuracy of 88.48% and 70.04% in Support Vector Machine (SVM) and Random Forest respectively. Tahrat Tazrin Mohiuddin Md Abdul Qudar Kazi Zainab Khanam B. Computer Science and Engineering 2018-12-04T08:07:30Z 2018-12-04T08:07:30Z 2018 2018 Thesis ID 15101014 ID 15101131 ID 15101119 http://hdl.handle.net/10361/10961 en BRAC University theses 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. 45 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
LBSN Live campaigns Location, algorithms SVM Random forest Decision tree Machine learning -- Mathematical models. |
| spellingShingle |
LBSN Live campaigns Location, algorithms SVM Random forest Decision tree Machine learning -- Mathematical models. Qudar, Mohiuddin Md. Abdul Tazrin, Tahrat Khanam, Kazi Zainab Finding suitable locations for live campaigns using different machine learning techniques |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Qudar, Mohiuddin Md. Abdul Tazrin, Tahrat Khanam, Kazi Zainab |
| format |
Thesis |
| author |
Qudar, Mohiuddin Md. Abdul Tazrin, Tahrat Khanam, Kazi Zainab |
| author_sort |
Qudar, Mohiuddin Md. Abdul |
| title |
Finding suitable locations for live campaigns using different machine learning techniques |
| title_short |
Finding suitable locations for live campaigns using different machine learning techniques |
| title_full |
Finding suitable locations for live campaigns using different machine learning techniques |
| title_fullStr |
Finding suitable locations for live campaigns using different machine learning techniques |
| title_full_unstemmed |
Finding suitable locations for live campaigns using different machine learning techniques |
| title_sort |
finding suitable locations for live campaigns using different machine learning techniques |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/10961 |
| work_keys_str_mv |
AT qudarmohiuddinmdabdul findingsuitablelocationsforlivecampaignsusingdifferentmachinelearningtechniques AT tazrintahrat findingsuitablelocationsforlivecampaignsusingdifferentmachinelearningtechniques AT khanamkazizainab findingsuitablelocationsforlivecampaignsusingdifferentmachinelearningtechniques |
| _version_ |
1814307254343565312 |