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Multilevel Regression analysis of Big Mart Sales dataset which aimed at forecasting sales, seasonality metrics, and recommending strategies to the business retailers by identifying top price elastic products and best performing outlet types.

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Statistical Analysis of Big Mart Sales

Overview and Problem Statement

This dataset contains sales data on different items at multiple outlets of a major retail chain and the data is multi-level.The client is a business entrepreneur considering franchising one or more stores of this retail chain and is looking for the following answers, with adequate justification:

  1. What type of outlet will return him the best sales: Grocery store or Supermarket Type 1, 2, or 3.
  2. What type of city will return him the best sales: Tier 1, 2 or 3.
  3. What are the top 3 highest performing and lowest performing stores in the sample.

Data Source and Description

Attributes of the dataset:

  • Item_Weight: Weight of the product
  • Item_Fat_Content: Low Fat or Regular
  • Item_Visibility: Percentage of total display area of all products in a store allocated to this product
  • Item_Type: Dairy, Soft Drinks, Meat, Fruits and Vegetables, Household, Baking Goods, Snack Foods, Frozen Foods, Breakfast, Health and Hygiene, Hard Drinks, Canned, Breads, Starchy Foods, Others, Seafood
  • Item_MRP: Maximum Retail Price (list price) of the product
  • Outlet_ID: Unique store ID
  • Outlet_Year: Year in which store was opened
  • Outlet_Size: Store size**: [High, Medium, Small]
  • City_Type: Size of city where store is located [Tier 1, Tier 2, Tier 3]
  • Outlet_Type: Grocery Store, Supermarket Type1, Supermarket Type2, Supermarket Type3
  • Item_Sales: Sales of product in this store

Variable selection for Multi/Mixed Level Analysis

Data Visualizations

Model Buidling and Results

1) What type of outlet will return him the best sales: Grocery store or Supermarket Type 1, 2, or 3.

Interpreatation: After looking at the outputs here and analyzing the results, I would suggest my client to consider franchising with the outlet type of “Suptermarket 3”. Because as I add those random effects values of each type of outlet in average block, I am getting maximum sales at Supermarket 3. It will get the maximum sales based in this analysis.

2) What type of city will return him the best sales: Tier 1, 2 or 3.

Interpreatation: After looking at the outputs here and analysing the results, I would say that city from “Tier 2” could get the maximum sales. As I those random effects values of each type of city in average block, city from “Tier 2” can get the maximum sales to business owner.

3) What are the top 3 highest performing and lowest performing stores in the sample.

Interpreatation: After looking the outputs here and analyzing the results I would say rank outlet as below: TOP 3: 1) OUT027, 2) OUT017, 3) OUT035 BOTTOM 3: 10) OUT019, 9) OUT010, 8) OUT018

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Multilevel Regression analysis of Big Mart Sales dataset which aimed at forecasting sales, seasonality metrics, and recommending strategies to the business retailers by identifying top price elastic products and best performing outlet types.

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