Authors:
K. T. Maheswari, R. Bharanikumar, M. S. Ajay Vishnu, S. Manivannan
Addresses:
Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu, India. Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.
Abstract:
Today's populations are increasing, which is unpredictable for a common individual. Also, it becomes harder for him to construct the correct volume needed for the upcoming year. This can be solved by providing a future population-finding model. Neighbourhood and state legislatures rely on limited regional population figures to make significant decisions about the development of local infrastructure and services, including education, transportation, healthcare, energy, telecommunications, and water supply. Despite their significance, current techniques often yield highly inaccurate results, particularly at the small scale. Over the years, there have been promising improvements in time-series forecasting using Artificial Intelligence across many social and economic factors. In the proposed work, Machine Learning helps us solve many problems, including predicting future population growth. The objective of this paper is to analyse the specific prediction area and determine the need for prediction. Then, gather the best dataset for that specific area of a specific need to be predicted. With the help of a Machine Learning algorithm, the issue can be solved more precisely. Then, select a suitable Machine Learning model for our dataset. Power BI is used to create an interactive dashboard for users that provides a better understanding of the prediction output data. By properly implementing the methodology, the user will be able to determine the construction or business volume to enter.
Keywords: Machine Learning; Dynamic Dashboard; Artificial Intelligence; Linear Regression; Random Forest; Support Vector Machines; Decision Trees; Artificial Neural Networks.
Received: 18/10/2024, Revised: 15/01/2025, Accepted: 06/03/2025, Published: 09/09/2025
DOI: 10.64091/ATICS.2025.000196
AVE Trends in Intelligent Computing Systems, 2025 Vol. 2 No. 3 , Pages: 122-132