Comparative analysis of machine learning techniques in optimal site selection
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
| Autores principales: | , , , , |
|---|---|
| Otros Autores: | |
| Formato: | Tesis |
| Lenguaje: | English |
| Publicado: |
Brac University
2023
|
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/10361/19150 |
| id |
10361-19150 |
|---|---|
| record_format |
dspace |
| spelling |
10361-191502023-07-30T21:02:34Z Comparative analysis of machine learning techniques in optimal site selection Aurnab, Aukik Choudhury, Shaktiman Ruhan, Shoubhick Roy Rifaiya Abrar, Shikh Muhammad Hossain Rabbi, S.M. Riyadh Khan, Rubayat Ahmed Department of Computer Science and Engineering, Brac University Optimal Site Selection (OSS) Comparative analysis Boosting and stacking SVR Random forest XGBoost Ridge regression Lasso regression Elasticnet Convolutional Neural Network (CNN) Ensemble learning VGG16 VGG19 ResNet DenseNet InceptionV3 Root Mean Squared Error (RMSE) Mean Squared Error (MSE) Mean Absolute Error (MAE) Median Absolute Error (MedAE) Max error (ME) Median Absolute Deviation(MAD) Machine learning. Artificial intelligence. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 68-71). Site selection is a crucial aspect of many businesses, as a company’s location can sig nificantly impact its success. In recent years, machine learning techniques have been increasingly used to assist with optimal site selection by providing data-driven pre dictions about the potential success of a given location. Machine learning techniques can be used to assist in the process of selecting the optimal site by analyzing the patterns in data such as demographics, lifestyle services, and geographic features. In this paper, we compare several machine learning techniques for their perfor mance in optimal site selection for features extracted from Open Street Map (OSM) data, WorldPop population data, and Bing satellite imagery. A target dataset cor responding to the features extracted was collected from Yelp data on restaurant check-ins, and this was used as a parameter to determine the human engagement rate of that location with the businesses in that area. Our analysis methods in cluded SVR, Random Forest, XGBoost, Ridge Regression, Lasso Regression, and ElasticNet. The satellite imagery collected from Bing maps were used to train CNN architectures such as; VGG16, VGG19, ResNet, DenseNet, and InceptionV3 and the results were compared. We evaluated the techniques using several metrics, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error(MedAE), Max Error(ME), and Median Abso lute Deviation(MAD). We used algorithm and strategies that performed the best in related works for this research. One meta model was also implemented in this work by an ensemble learning technique known as stacking. The model that performed the best for the data collected was then determined by looking at the error scores of different models. This work provides an insight into the strengths and limitations of each technique and recommendations for practitioners considering the use of ma chine learning in site selection. This study demonstrates the potential of machine learning for improving site selection processes and highlights the importance of con sidering multiple approaches. Aukik Aurnab Shaktiman Choudhury Shoubhick Roy Ruhan Shikh Muhammad Rifaiya Abrar S.M. Riyadh Hossain Rabbi B. Computer Science and Engineering 2023-07-30T07:34:54Z 2023-07-30T07:34:54Z 2023 2023-01 Thesis ID: 19101485 ID: 19101506 ID: 19101124 ID: 19101508 ID: 19101511 http://hdl.handle.net/10361/19150 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. 71 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Optimal Site Selection (OSS) Comparative analysis Boosting and stacking SVR Random forest XGBoost Ridge regression Lasso regression Elasticnet Convolutional Neural Network (CNN) Ensemble learning VGG16 VGG19 ResNet DenseNet InceptionV3 Root Mean Squared Error (RMSE) Mean Squared Error (MSE) Mean Absolute Error (MAE) Median Absolute Error (MedAE) Max error (ME) Median Absolute Deviation(MAD) Machine learning. Artificial intelligence. |
| spellingShingle |
Optimal Site Selection (OSS) Comparative analysis Boosting and stacking SVR Random forest XGBoost Ridge regression Lasso regression Elasticnet Convolutional Neural Network (CNN) Ensemble learning VGG16 VGG19 ResNet DenseNet InceptionV3 Root Mean Squared Error (RMSE) Mean Squared Error (MSE) Mean Absolute Error (MAE) Median Absolute Error (MedAE) Max error (ME) Median Absolute Deviation(MAD) Machine learning. Artificial intelligence. Aurnab, Aukik Choudhury, Shaktiman Ruhan, Shoubhick Roy Rifaiya Abrar, Shikh Muhammad Hossain Rabbi, S.M. Riyadh Comparative analysis of machine learning techniques in optimal site selection |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Khan, Rubayat Ahmed |
| author_facet |
Khan, Rubayat Ahmed Aurnab, Aukik Choudhury, Shaktiman Ruhan, Shoubhick Roy Rifaiya Abrar, Shikh Muhammad Hossain Rabbi, S.M. Riyadh |
| format |
Thesis |
| author |
Aurnab, Aukik Choudhury, Shaktiman Ruhan, Shoubhick Roy Rifaiya Abrar, Shikh Muhammad Hossain Rabbi, S.M. Riyadh |
| author_sort |
Aurnab, Aukik |
| title |
Comparative analysis of machine learning techniques in optimal site selection |
| title_short |
Comparative analysis of machine learning techniques in optimal site selection |
| title_full |
Comparative analysis of machine learning techniques in optimal site selection |
| title_fullStr |
Comparative analysis of machine learning techniques in optimal site selection |
| title_full_unstemmed |
Comparative analysis of machine learning techniques in optimal site selection |
| title_sort |
comparative analysis of machine learning techniques in optimal site selection |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/19150 |
| work_keys_str_mv |
AT aurnabaukik comparativeanalysisofmachinelearningtechniquesinoptimalsiteselection AT choudhuryshaktiman comparativeanalysisofmachinelearningtechniquesinoptimalsiteselection AT ruhanshoubhickroy comparativeanalysisofmachinelearningtechniquesinoptimalsiteselection AT rifaiyaabrarshikhmuhammad comparativeanalysisofmachinelearningtechniquesinoptimalsiteselection AT hossainrabbismriyadh comparativeanalysisofmachinelearningtechniquesinoptimalsiteselection |
| _version_ |
1814306936650203136 |