superstore-dataset

Analysis about data of a superstore to make business decisions. Reference link for the data: https://www.kaggle.com/datasets/vivek468/superstore-dataset-final?resource=download

In this Jupyter Notebook I have analysed the dataset of the superstore to answer several questions. For example:

1. what is the best month for sales
2. which products are sold the most
3. which customer segment has received the most discounts
4. relationship between variables: a graphical representation of the relationship between the variables using a scatter plot.
5. Correlation between customer segment and quantity using a heatmap.
6. Total sales and profit per state.

Context

With growing demands and cut-throat competitions in the market, a Superstore Giant is seeking knowledge in understanding what works best for them. They would like to understand which products, regions, categories and customer segments they should target or avoid.

Metadata

• Row ID => Unique ID for each row.
• Order ID => Unique Order ID for each Customer.
• Order Date => Order Date of the product.
• Ship Date => Shipping Date of the Product.
• Ship Mode=> Shipping Mode specified by the Customer.
• Customer ID => Unique ID to identify each Customer.
• Customer Name => Name of the Customer.
• Segment => The segment where the Customer belongs.
• Country => Country of residence of the Customer.
• City => City of residence of of the Customer.
• State => State of residence of the Customer.
• Postal Code => Postal Code of every Customer.
• Region => Region where the Customer belong.
• Product ID => Unique ID of the Product.
• Category => Category of the product ordered.
• Sub-Category => Sub-Category of the product ordered.
• Product Name => Name of the Product
• Sales => Sales of the Product.
• Quantity => Quantity of the Product.
• Discount => Discount provided.
• Profit => Profit/Loss incurred.

Example of code

In this snippet we are going to see how to create barplots about the States and the total sales or profit about a certain state

# Define the comparison variables
comparison_vars = ['Sales', 'Profit']

# Iterate through the list and create scatter plots
for var in comparison_vars:
    # 1. Group by State and calculate total sales
    state_sales = df.groupby('State')[var].sum()

    # 2. Create a bar chart
    plt.figure(figsize=(10, 6))  # Adjust figure size as desired
    state_sales.plot(kind='bar', color='skyblue')
    plt.xlabel('State')
    plt.ylabel('Total Sales [$]')
    plt.title(f'Total {var} per State')
    #plt.xticks(rotationv=45)  # Rotate x-axis labels for better readability (optional)
    plt.tight_layout()  # Adjust layout to prevent overlapping elements
    plt.show()

You can see the full code in the Jupyter notebook above (see main.ipynb)

The result of said code snippet, for the Profit part, is as follows: