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https://github.com/rahulsm20/storedata

A data analysis project aimed at analyzing the sales data of the super store and providing useful insight into customer preferences.
https://github.com/rahulsm20/storedata

data-analysis matplotlib numpy pandas python streamlit

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A data analysis project aimed at analyzing the sales data of the super store and providing useful insight into customer preferences.

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# SuperStore Data Analysis
### [Website](https://rahulsm20-storedata-main-t64r4t.streamlit.app)

### In this project, we have aimed to analyse the sales data of the super store and provide useful insight into customer preferences.

## About the dataset
The dataset used for this analysis is an excel file containing the following columns:
- Row ID
- Order ID
- Order Date
- Ship Date
- Ship Mode
- Customer ID
- Customer Name
- Segment
- Country
- City
- State
- Postal Code
- Region
- Product ID
- Category
- Sub-Category
- Product Name
- Sales
- Quantity
- Discount
- Profit

## Methodology
The analysis was conducted in Python using the following libraries:
* Pandas
* Numpy
* Matplotlib
* Streamlit (for deployment)

## Results
* The stores makes the most revenue in terms of sales from the state of California, followed by New York, Texas, Washington and Pennsylvania in that order.
* Over 50% of the store's revenue comes from consumer goods, followed by corporate and home-office in that order.
* The store offers multiple types of shipping modes, but the standard class is by far the most popular.
* The store has branches all over the country and the sales distribution is well spread out. The West brings in the most though, at 31.6% followed by East, Central and South.
* Copiers are the most profitable sub-category of products, whereas tables are the least profitable, actually producing a net loss.
* Continuing with sub-categories, Phones bring in the most revenue at 14.4% followed closely by Chairs at 14.3%