Please use this identifier to cite or link to this item:
Title: Using AI / machine learning to solve real world problems
Authors: Lok, Ignatius Zhengrong
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Lok, I. Z. (2021). Using AI / machine learning to solve real world problems. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE20-0095
Abstract: The field of Artificial Intelligence and Machine learning has made great advancements over the past few decades and has become more intertwined into the daily lives of people. With the development of technology, it is common to see machine learning methods used and adopted to help solve real-world problems of both individuals and well as large corporations. This project studies how different machine learning algorithms can be used to aid companies make better business decisions and to optimize their investments. In this particular project, we look to help TFI decide on new restaurant investments and potential locations. This is done by predicting the expected annual revenues of Turkish Restaurant based on the data given. The data provided is very imbalanced with a significantly larger test data compared to the training data. Additionally, the data provided contained obfuscated variables which encoded different categorical data types including Demographic, Real Estate and Commercial Data. We look to address the obfuscated data and the small training set during the preprocessing and feature engineering stage. Different supervised machine learning models including Random Forests, Support Vector Machines, XGBoost, LGBM and Ensemble learning methods are then applied to predict the restaurant revenue, allowing a better decision to be made when opening new restaurants and to increase the effectiveness of investments in new restaurant sites.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
SCSE20-0095_Ignatius Lok Zhengrong_U1721578C_FYP_Report.pdf
  Restricted Access
864.25 kBAdobe PDFView/Open

Page view(s)

Updated on May 20, 2022


Updated on May 20, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.