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An Interactive Visualization and Data Analysis of Supermarket Store

Likita H. L.*, Hiresh Yadav**, Anjali Hedaoo***, Swagota Bera****, Siddhartha Choubey*****

*-***** Shri Shankaracharya Engineering College, Bhilai, Chhattisgarh, India.

Periodicity:July - December'2024

Abstract

In the ever-evolving landscape of retail, the utilization of data-driven insights plays a pivotal role in enhancing decision- making processes. This paper presents an advanced approach to the development and implementation of an interactive visualization system tailored specifically for the analysis of data within a supermarket store environment. In order to predict the sales of a business, an intelligent model was built using Linear Regression, LASSO Regression, and XGBoost techniques, which have been shown to be more effective than existing models. The proposed system integrates data pre-processing, feature engineering, and algorithmic enhancements to analyze customer behavior, sales trends, and inventory efficiency. Key performance metrics like RMSE and R-squared validate the efficacy of the models, highlighting the XGBoost algorithm's exceptional performance. This study aims to optimize decision-making in inventory management, marketing strategies, and sales forecasting, ensuring data-driven insights for improved supermarket operations.

Keywords

Interactive Visualization, Data Analysis, XG-Boost Regression, Linear-Regression, LASSO-Regression, Random Forest, Sales, Prediction, Supermarket.

How to Cite this Article?

Likita, H. L., Yadav, H., Hedaoo, A., Bera, S., and Choubey, S. (2024). An Interactive Visualization and Data Analysis of Supermarket Store. i-manager’s Journal on Data Science & Big Data Analytics, 2(2), 28-34.

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