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Analyzing Total Sales by Category in Superstore Dataset

Total sales by category

The dataset 'Sample - Superstore.csv' was loaded successfully. The first few rows were inspected to understand its structure.
The total sales were then calculated by category, resulting in the following values:
  • Furniture: $754,747.76
  • Office Supplies: $731,893.31
  • Technology: $839,893.28

Continue the analysis

The analysis involves visualizing total sales by category using a bar chart. The chart is configured to display the total sales for each category with labels and text annotations for clarity.

create a KPI card for month over month change in sales by category

Summary of Analysis

  • Data Preparation: Converted 'Order Date' to datetime format and extracted 'YearMonth'.
  • Sales Calculation: Computed monthly sales by category.
  • MoM Change Calculation: Calculated month-over-month (MoM) change in sales for each category.
  • Visualization: Created a KPI card displaying the latest month's sales and relative change for each category using Plotly.

Create a dashboard for monitoring sales per region

Monthly Sales by Region Dashboard

  • Data Preparation: Grouped sales data by month and region.
  • Visualization: Created a line chart to display monthly sales trends for each region.

Key Findings

  • The chart allows for easy comparison of sales performance across different regions over time.
  • Sales trends can be observed to identify patterns or anomalies in specific regions.

What insights do you have about this dataset?

Dataset Summary

  • Total Rows: 10,194
  • Numerical Columns: Sales, Quantity, Discount, Profit

Numerical Columns Statistics:

  • Sales: Ranges from 0.44 to 22,638.48 with a mean of 228.23
  • Quantity: Ranges from 1 to 14 with a mean of 3.79
  • Discount: Ranges from 0 to 0.8 with a mean of 0.155
  • Profit: Ranges from -6,599.98 to 8,399.98 with a mean of 28.67

Dates

  • Order Date: Ranges from 2020-01-03 to 2023-12-30

What are the biggest factors driving sales?

The correlation analysis reveals the following factors driving sales:
  • Profit: Positive correlation (0.481)
  • Quantity: Positive correlation (0.198)
  • Discount: Slight negative correlation (-0.028)
Profit has the strongest positive correlation with sales, followed by quantity. Discount shows a slight negative correlation.

Continue the analysis

The analysis proceeded with visualizing the correlation matrix of numerical features using a heatmap. The heatmap was displayed to provide insights into the relationships between different numerical variables in the dataset.

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