walmart_case_study

Walmart is the largest retailer in the world, but it is facing some challenges. These challenges include: Competition from online retailers

• Competition from online retailers: Amazon is the largest online retailer in the world, and it is taking market share from Walmart.
• Changing customer expectations: Customers are increasingly demanding convenience and personalization, and Walmart is struggling to keep up.
• Labor costs: Walmart's labor costs are rising, and this is putting pressure on the company's margins.
• Regulations: Walmart is facing increasing regulations, both in the United States and abroad.

Analysis

• Non graphical analysis
• Univariate and Bivariate analysis on each feature
• Outliers detection
• Distribution of variables and relationship between them
• Confidence intervals & CLT
• Missing values and Outliers treatment
• Insights and Recommendations

Insights: The insights that I derived from my analysis of the Walmart business problem are as follows:

Comments on the distribution of the variables and relationship between

them

• Product_Category - 1, 5, 8, & 11 have highest purchasing frequency.
• There are 20 differnent types of occupations in the city
• 35% Staying in the city from 1 year, 18% from 2 years, 17% from 3 years
• More users are Single as compare to Married
• Most of the users are Male
• 60% Single, 40% Married
• Total of 20 product categories are there
• More users belong to B City_Category
• There are 20 different types of Occupation and Product_Category
• 75% of the users are Male and 25% are Female 36
• ~ 80% of the users are between the age 18-50 (40%: 26-35, 18%: 18-25, 20%: 36-45)
• Average amount spend by Male customers: 925344.40
• Average amount spend by Female customers: 712024.39

Comments for each univariate and bivariate plots

Confidence Interval by Marital_Status

• Married confidence interval of means: (806668.83, 880384.76)
• Unmarried confidence interval of means: (848741.18, 912410.38)

Confidence Interval by Age

• For age 26-35 –> confidence interval of means: (945034.42, 1034284.21)
• For age 36-45 –> confidence interval of means: (823347.80, 935983.62)
• For age 18-25 –> confidence interval of means: (801632.78, 908093.46)
• For age 46-50 –> confidence interval of means: (713505.63, 871591.93)
• For age 51-55 –> confidence interval of means: (692392.43, 834009.42)
• For age 55+ –> confidence interval of means: (476948.26, 602446.23)
• For age 0-17 –> confidence interval of means: (527662.46, 710073.17)

Comments on different variables when generalizing it for Population

Confidence Interval by Gender Now using the Central Limit Theorem for the population:

• Average amount spend by male customers is 9,26,341.86
• Average amount spend by female customers is 7,11,704.09

Now we can infer about the population that, 95% of the times:

• Average amount spend by male customer will lie in between: (895617.83, 955070.97)
• Average amount spend by female customer will lie in between: (673254.77, 750794.02)

Recommendations: Based on my insights, I recommend that Walmart focus on the following areas:

• Men spent more money than women, Hence Focus more on Men products
• Product_Category - 1, 5, 8, & 11 have highest purchasing frequency. it means these are the products in these categories are liked more by customers. Company can focus on selling more of these products or selling more of the products which are purchased less.
• Unmarried customers spend more money than married customers, So walmart should focus on acquisition of Unmarried customers.
• Customers in the age 18-45 spend more money than the others, So company should focus on acquisition of customers who are in the age 18-45
• Male customers living in City_Category C spend more money than other male customers living in B or C, Selling more products in the City_Category C will help the company increase the revenue.

Installations

Requirements

• Python 3.9
• Pandas
• Numpy
• Scikit-Learn
• Seaborn
• Matplotlib

Contributors

• Peshimaam Mohammed Muzammil