Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms

IEEE BASE PAPER ABSTRACT:

Currently, supermarket run-centres, Big Marts keep track of each individual item’s sales data in order to anticipate potential consumer demand and update inventory management. Anomalies and general trends are often discovered by mining the data warehouse’s data store. For retailers like Big Mart, the resulting data can be used to forecast future sales volume using various machine learning techniques like big mart. A predictive model was developed using Xgboost, Linear regression, Polynomial regression, and Ridge regression techniques for forecasting the sales of a business such as Big -Mart, and it was discovered that the model outperforms existing models.

PROJECT OUTPUT VIDEO:

ALGORITHM /MODEL USED:

Decision Tree Regression.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Ranjitha P, Spandana M, “Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms”, IEEE Conference, 2021.

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Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms