Template-Type: ReDIF-Article 1.0
Author-Name: Danish Anwar
Author-Email: 
Author-Name: Altaf mallik
Author-Email: 
Author-Name:  Md. Faizanuddin
Author-Email: 
Author-Name: Amitabh Chandan
Author-Email: 
Title: Artificial Intelligence and ICT in Enhancing Economic and Productivity Indicators for Smart Cities
Abstract: Introduction: The demand for housing in major cities is exceptionally high due to the concentration of offices and economic hubs in these areas. The combination of limited available land and increased demand drives house prices upward.Objective: These developers compete by offering competitive pricing, diverse housing options, simplified mortgage processes, and attractive promotions like zero down payments. Buying a house is a significant long-term investment, as property values typically appreciate over time. Method: This study adopts a quantitative approach, which involves systematically investigating phenomena by collecting measurable data and analyzing it through statistical, mathematical, or computational methods. Result: This paper discusses the most effective techniques for data collection, pre-processing, feature extraction, model training, and evaluation. The purpose of this research method is to develop theoretical frameworks related to real-world phenomena. Conclusions: Measurement plays a pivotal role in this quantitative study, as it is central to understanding the data and drawing meaningful conclusions. Finally, we evaluate the current state of research, identifying trends and gaps in the field.
Keywords: Data, Computational methods, House Prediction
Journal: Edu - Tech Enterprise
Pages: 36
Volume: 3
Issue: 
Year: 2025
Subtitle : 
File-URL: https://ete.sciten.org/index.php/ete/article/view/36/41
File-Format: application/pdf
Handle: RePEc:cua:edutec:v:3:y:2025:i::p:36:id:36