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walmart_sales_prediction's Introduction

About Dataset

Walmart Inc. is an American multinational retail corporation that operates a chain of hypermarkets (also called supercenters), discount department stores, and grocery stores in the United States, headquartered in Bentonville, Arkansas. The company was founded by Sam Walton in nearby Rogers, Arkansas in 1962 and incorporated under Delaware General Corporation Law on October 31, 1969. It also owns and operates Sam's Club retail warehouses. In India, Walmart operates under the name of Flipkart Wholesale.

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As of July 31, 2022, Walmart has 10,585 stores and clubs in 24 countries, operating under 46 different names. Out of which we have chosen 45 stores for basic analysis.

Walmart is the world's largest company by revenue, with about US$570 billion in annual revenue, according to the Fortune Global 500 list in May 2022.

How Walmart uses Big Data?

Improving Store Checkout: By using Predictive Analysis, the stores can anticipate demand at a certain week and determine how many Sales Representatives / Employees are needed. Managing the Steps of Supply Chain: The company optimizes the routes to the shipping dock and tracks the number of times the product is accessed before it reaches the Customer's destination. Also, it uses the data to analyze transportation lanes and routes for the company's trucks. These data help Walmart keep transportation costs down and schedule an appropriate time for drivers. Optimizing Product Assortment: By analyzing customer preferences and shopping patterns, Walmart accelerates the decision-making on how to maintain stocks. Big Data provides insights on new items and discontinued products. Personalizing Shopping Experience: With Big Data, Walmart analyzes the shopping preferences of the customers to develop a consistent and delightful shopping experience. and much more…

Features Description:

Store : This column represents the store number or identifier. It is numerical and each number corresponds to a specific store.

Date: Indicates the date of data collection or event, formatted as day-month-year.

Weekly_Sales: A numerical value representing the total sales achieved in a week. The values are in decimal format, allowing for precise measurement of sales.

Holiday_Flag: This is a binary flag indicating whether the given date was a holiday (1) or not (0).

Temperature: Represents the temperature on that specific day, it’s in decimal format which allows for an accurate representation of the temperature.

Fuel_Price: Indicates the price of fuel on that particular day, represented as a decimal number to provide precise values.

CPI: Stands for Consumer Price Index, a measure that examines the average change over time in prices paid by consumers for goods and services. It’s also represented as a decimal number.

Unemployment: This column shows unemployment rates, represented as decimals to provide detailed information.

Observation:

-2011 has recorded highest number of sales then followed by 2010 and 2012.

-More than 50% of sales happen on Thursday.

-Highest sales are recorded in April then followed by May, October, December ,September although their is little variance.

-Most sales comes on non special holiday week which makes sense people may not want to spend their special grocery shopping they would probably shop before to celebrate their special holiday. Like for Christmas week, people go for shopping on weeks before.

-All stores have equal number of sales yet store 20 has recorded highest sales.

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