Sales Dataset

Introduction

This report analyzes the X5000 Sales Records dataset to understand sales performance using descriptive analytics and visualization techniques.
The dataset contains both numerical variables (Units Sold, Total Revenue, Total Profit) and categorical variables (Region, Item Type, Sales Channel, Order Priority).

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Load Required Library

library(readr) X5000_Sales_Records <- read_csv(“C:/Users/ASUS/Downloads/5000 Sales Records.csv”) head(X5000_Sales_Records) str(X5000_Sales_Records) summary(X5000_Sales_Records)

Assumptions for the Dataset :

Higher Units Sold leads to higher Total Revenue. Some Item Types generate higher profits. Online sales channel may have more high-priority orders. Revenue distribution may vary across transactions.

Descriptive Analytics:

Histogram – Total Revenue Distribution

hist(X5000_Sales_Records\(`Total Revenue`, breaks = 20, main = "Distribution of Total Revenue", xlab = "Revenue", col = "pink", border = "black") mean(X5000_Sales_Records\)Total Revenue) median(X5000_Sales_Records$Total Revenue)

Insight : Most sales fall within a moderate revenue range, with fewer extremely high-value transactions. Inference : (Central Tendency)

Boxplot – Profit by Item Type

boxplot(Total Profit ~ Item Type, data = X5000_Sales_Records, main = “Profit Distribution by Item Type”, col = “purple”)

Insight : Some item types show higher median profits. Inference : Product categories with higher median profit contribute more to overall revenue.

Scatterplot – Units Sold vs Revenue

plot(X5000_Sales_Records\(`Units Sold`, X5000_Sales_Records\)Total Revenue, main = “Units Sold vs Revenue”, xlab = “Units Sold”, ylab = “Total Revenue”, pch = 19, col = “pink”)

abline(lm(Total Revenue ~ Units Sold, data = X5000_Sales_Records), col = “black”, lwd = 2)

Insight : The scatterplot shows a positive relationship between units sold and revenue. Inference : As the number of units sold increases, total revenue also increases.

Mosaic Plot – Sales Channel vs Order Priority

mosaicplot(table(X5000_Sales_Records\(`Sales Channel`, X5000_Sales_Records\)Order Priority), main = “Sales Channel vs Order Priority”, col = c(“pink”,“grey”,“purple”,“green”)) mean(X5000_Sales_Records\(`Total Profit`) median(X5000_Sales_Records\)Total Profit) sd(X5000_Sales_Records$Total Profit)

Insight : The plot shows the relationship between sales channel and order priority. Inference : Some order priorities appear more frequently in specific sales channels.

Interpretation: Mean represents the average profit. Median represents the middle value of profits. Standard deviation shows how much profits vary.

Story Summary

The X5000 Sales Records dataset provides insights into global sales transactions across different regions, product categories, and sales channels.

The descriptive analysis revealed that:

• Revenue distribution shows that most transactions generate moderate sales values.
• Certain product categories contribute significantly higher profits.
• The scatterplot indicates a strong positive relationship between units sold and revenue.
• The mosaic plot reveals patterns between sales channels and order priorities.

Overall, the analysis indicates that sales performance is strongly influenced by product category and sales volume. Businesses can improve profitability by focusing on high-performing product types and optimizing sales channels.

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