Sales Inventory Analysis
In this project, I imported and cleaned the data, created interactive visualizations, and answered key objective questions to make decisions based on the dataset.
Dataset Summary
This dataset includes three interconnected tables: Sales, Product, and Customer.
The Sales table is the core of the dataset, containing transaction-level details about the quantity of products sold, their unit price, discounts applied, and the total amount earned from each sale.
The Product table provides additional details about the items sold, including their names, categories, and standard unit prices.
The Customer table offers demographic and geographic information about the buyers, such as their names, locations, gender, and birthdates.
A few insights possible from this dataset are:
Most Quantity Purchased by Customer
Quantity, Total Amount and Unit Price of Products by their Name and Category
Unit Price by Product Name and Product Category
Amount by Gender and Birth Year
Top 3 Least Sold Products
Top 3 Most Sold Products
Top 3 Most Expensive Products
Data
Cleansing
To cleanse the dataset, I utilized the Power Query Editor and performed the following steps:
Merged Queries: I combined the product and customer tables with the sales table, consolidating all relevant data into a single table for streamlined visualization development.
Column Expansion: I expanded each of the queries to retain only the columns necessary for the analysis, ensuring a more efficient dataset.
Column Renaming: After merging the tables, I renamed the columns to provide more descriptive titles, enhancing clarity and ease of use during visualization creation.
Removal of Redundant Columns: I eliminated unnecessary columns that were created during the query merge process to avoid data duplication and improve dataset clarity.
Value Replacement: I standardized the gender values by replacing "M" and "F" with "Male" and "Female" for consistency and ease of interpretation.
