Walmart home furnishing data analysis

Walmart is an American company found in Arkansas in the year 1962. Walmart operates approximately 10,500 stores and clubs under 46 banners in 24 countries and eCommerce websites. We employ 2.3 million associates around the world — nearly 1.6 million in the U.S. alone.

Walmart would like to predict sales & demand accurately. The sales data is available for 45 stores and there are certain events and holiday which may impact sales on each day. Business is facing a challenge due to unforeseen demands and runs out of stock some times. Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Dataset Description

This is the historical data that covers sales from 2010–02–05 to 2012–11–01. Within this file you will find the following fields

• Store — the store number
• Date — the week of sales
• Weekly_Sales — sales for the given store
• Holiday_Flag — whether the week is a special holiday week 1 — Holiday week 0 — Non-holiday week
• Temperature — Temperature on the day of sale
• Fuel_Price — Cost of fuel in the region
• CPI — Prevailing consumer price index
• Unemployment — Prevailing unemployment rate

Basic statistic tasks:-

• Which product has the maximum cost after discount
• Which store has the maximum standard deviation and co-efficient of variance.
• Monthly and semester view of sales in units and give insights.
• Which store has a good quarterly growth rate in Q4 2012.
• Holidays which have higher sales than the mean sales in non-holiday season for all stores together

We will now start our analysis

Step 1: Import the data set in MySQL

• Create a new Data base using command: Create database Relevel;
• Now enter 'Use Relevel' command;
• Use table data import wizard to import table, name it as 'sales'.
• SELECT * FROM sales;

Step 2: Understand the dataset. By running the command 'DESCRIBE sales' we get following information.

Now we will analyze the data.

Question 1. Which store has the maximum sales in the dataset?

We will run the following command :

'SELECT Store, sum(Weekly_sales) AS total_sales

FROM sales

GROUP BY 1

ORDER BY 2 DESC

LIMIT 1;

We can clearly see store 1 has the maximum sales.

Question 2. Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of variance (C0V).

The mean of 45 stores is 1528277.86 and Standard Deviation is 153898.17

Question 3. Provide monthly and yearly sales.

Monthly Sales

You can clearly see the monthly sales of all 45 stores.

Yearly sales

we can clearly see has a rise at the end of first quarter, second quarter and fourth quarter.

The yearly sales has declined in 2012 after a rise in 2011.