Amazon_superstore_analysis

The Amazon Super Store dataset contains data on order details of customers for orders of a superstore in the US. This includes the state, region, order date, shipping date, product ordered etc. In this blog, we’ll define some use cases for this dataset. A use case for a dataset can be defined as questions that determine what information the dataset could represent. These can be a single question or multiple questions. Questions that we can ask the dataset and get answers from.

Let’s take a look at an example dataset and try to define its use cases. I’ll be taking the US Superstore dataset. Such datasets are huge, but we need to focus mainly on the column names (attributes) and 1 or 2 rows. I have used Python libraries pandas and numpy for analysis here.

objective : to find out the weak areas where the amazons business manager can work to make more profit or increase sales and deriving the business problem by exploring the data.

steps involved

1. defining the problem
2. checking versions of libraries
3. importing libraries into this notebook
4. importing dataset
5. cleaning of data - preprocessing of data
6. summarizing datasets to extract actionable insights
7. visualization of data
8. conclusion EXPLORATORY DATA ANALYSIS

Exploratory Data Analysis is a crucial step before diving into more advanced analysis or building models. It helps you understand the data's characteristics, identify potential challenges, and gather insights that guide your analysis direction.

#Exploratory Data Analysis (EDA) for the "Sample - Superstore" dataset involves a preliminary examination of the data to understand its structure, patterns, and potential insights. Here's a general description

Data Loading: Import the dataset into your preferred data analysis tool (e.g., Tableau, Excel, Python) and ensure that all columns are correctly imported.

RESULT | CONCLUSION

1. profit in south and central is less 2.profit in east and west region is better
2. highest profit is earned in copiers while selling price for chairs and phones are extremely high
3. intresting fact- people don't prefer buying tables and bookcases from superstore
4. the store has wide variety of office supplies
5. there is negative correlation between profit and discount
6. total sum of profit in sale of table is negative
7. profit is more in sales of copiers
8. very less profit in sale of supplies
9. technology segment is more profitable
10. INSIGHTS: Conclusion

#CONCLUSION

We’ve visualised and analysed various use cases in the superstore dataset. We got some insightful results about the Profit and Sales that can be used to improve future policies. We also found a trend over the year so preparations in stores and warehouses for the next year can be made accordingly. We can now confidently pick up more datasets to define use cases for, and visualise them in Tableau.