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Descriptive Analysis of Retail Sales Dataset

retail_sales_dataset.csv

Missing file

Do descriptive analysis from this dataset through excel pivot table and gives inferences also.

To perform a descriptive analysis similar to what you would do with an Excel pivot table, we can use Python's pandas library to summarize and analyze the data. Here's a step-by-step approach:
  1. Summarize Sales by Product Category:
    • Calculate total sales and average sales per product category.
  2. Analyze Sales by Gender:
    • Compare total sales and average sales between male and female customers.
  3. Age Group Analysis:
    • Group customers into age brackets and analyze sales within each bracket.
  4. Time-based Analysis:
    • Analyze sales trends over time (e.g., daily or monthly sales).
Let's start by summarizing the sales by product category.

Sales Summary by Product Category

  • Beauty:
    • Total Sales: $142,015
    • Average Sales: $464.10
    • Total Quantity Sold: 768
  • Clothing:
    • Total Sales: $155,580
    • Average Sales: $443.25
    • Total Quantity Sold: 894
  • Electronics:
    • Total Sales: $156,875
    • Average Sales: $460.04
    • Total Quantity Sold: 848
From this summary, we can infer that while Clothing has the highest total sales, Beauty products have the highest average sales per transaction. Electronics also show strong sales figures, indicating a balanced performance across categories.
Let's proceed to analyze sales by gender.

Sales Summary by Gender

  • Female:
    • Total Sales: $232,840
    • Average Sales: $456.55
    • Total Quantity Sold: 1,298
  • Male:
    • Total Sales: $221,630
    • Average Sales: $454.16
    • Total Quantity Sold: 1,212
From this analysis, we observe that female customers contribute slightly more to the total sales and quantity sold compared to male customers. However, the average sales per transaction are quite similar between the genders.
Next, let's analyze sales by age groups.

Can you analyze sales trends over time?

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