Analysis of Demographic Data to Improve Business Performance Using K-Means Clustering Machine Learning Algorithm

Abstract

Businesses such as gyms, cafes, and department stores often face difficulties in choosing the optimal location for their new outlet. It's crucial to select an area that has a favorable market with a low level of competition. For instance, departmental brands avoid opening their stores in areas that already have too many stores, and similarly, gym brands do not prefer areas with many existing gyms. Many factors need to be considered to maximize profits while selecting a location for a new outlet. Furthermore, students and working professionals coming from outside may also face difficulties in selecting accommodation in an area that caters to their needs, such as a locality with more department stores.

In this paper, our primary goal is to solve these problems by assisting businesses and individuals in making informed decisions about their preferred amenities, budget, and proximity to the location. The           project involves data preparation of real-life datasets, also by visualizing the data, running machine learning algorithms, and presenting the results. K-Means Clustering is used to assist businesses and professionals in making decisions according to their preferences.

By using data analysis and machine learning algorithms, this project aims to provide valuable insights to businesses and individuals and help them make informed decisions about selecting the best location for their new outlet or accommodation. This can lead to better profits for businesses and more convenience for individuals.

In addition, the program is created to be user-friendly and available to a variety of users, regardless of their level of technical proficiency. The goal of the project is to visualize the data simply and understandably so that businesses and people can rapidly decide on their preferred location.