SALES COMPONENT ANALYSIS & PREDICTION USING LINEAR REGRESSION
Author(s):
Shubham Tiwari, Anubhav Yadav, Abhay Mishra, Mohd Hamd
Keywords:
Abstract
Industrial engineering is about enhancing a process and improving the return of investment both to make more profit. Such is the background behind the Sales Mart Data analysis, the purpose being the determination of the properties of products and stores that help increase the sales. The data analysis was part of a competition launched by the American stop-shop Big Mart with the aim of building a predictive model that could predict the sales of the following year for each of the 1559 products in the 10 different stores of Big Mart. The aim is to build a predictive model and find out the sales of each product at a particular store. Create a model by which Big Mart can analyse and predict the outlet production sales. Motivation came into the mind with the idea of developing excellent Business Strategies. To predict the future of a particular product whether it is in demand or not. The main objective is to understand whether specific properties of products and/or stores play a significant role in terms of increasing or decreasing sales volume. To achieve this goal, we will build a predictive model and find out the sales of each product at a particular store. We will use Data science and ML on Python to create the predictive models that allowed us to have a better understanding of the client’s behaviour and an people estimation of the store’s future sales. It turned out that customers tend to prefer a product with a high MRP because the negotiation margin is bigger and because a high price tends to be associated with a better quality. With this information the corporation hopes we can identify the products and stores which play a key role in their sales and use that information to take the correct measures to ensure success of their business.
Article Details
Unique Paper ID: 152034

Publication Volume & Issue: Volume 8, Issue 2

Page(s): 273 - 276
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