Skip to content

Latest commit

 

History

History
48 lines (36 loc) · 3.35 KB

README.md

File metadata and controls

48 lines (36 loc) · 3.35 KB
Raw

Superstore Analytics: Power BI Star Schema, Statistical Analysis, and Machine Learning Insights

Explore our GitHub repository where we leverage Power BI to create a star schema for a Superstore dataset, conduct statistical analysis for key insights, and employ machine learning techniques to provide data-driven answers to critical business questions.

Phases of the Project

Phase 1: Data Preparation and Creating Star schema

Delve into our repository featuring a Superstore dataset, where we embarked on a data preprocessing journey, followed by the creation of a robust data warehouse structure.

DataBase

Our mission encompassed key tasks, including initial data retrieval, preprocessing, and cleansing. We meticulously designed a data warehouse schema, established relationships among tables, and seamlessly imported them into Power BI, ensuring impeccable table connections. All operations were meticulously executed using Power BI.

Star

Phase 2: Statistical Analysis

Investigating the Impact of Discounts on Sales Behavior: A Statistical Analysis

  • Explore the hypothesis surrounding the influence of discounts on consumer behavior in retail environments. Utilizing provided dataset, examine whether discounts significantly alter sales patterns. The project involves categorizing data into discount and non-discount groups, analyzing the distribution of sold items within each category, and employing statistical methods to determine the presence of any meaningful differences between the two groups.

Phase 3: Machine Learning Insights

During this stage, we utilized machine learning techniques to address the following inquiries:

  1. Profit Estimation Model for Sales Data Using Machine Learning
  2. Predicting Shipping Modes for Online Orders Using Machine Learning

Phase 4: Creating Dashboard in Power BI

  • Incorporating Statistical and Machine Learning Insights from previous parts into Power BI: Enhancing Reporting with Extracted Data
  • Market Analysis and Investment Strategy based on Average and Total Sales
  • Relationship between Order Amount and Shipping Cost: Analyzing Correlation
  • Average Order Delivery Times by Shipping Method Across Different Countries
  • Day of the Week Analysis: Identifying the Most Profitable Sales Day
  • Maximizing Profits: Product Analysis for Highest-Earning Categories and Items in a Store
  • Profit Deviation Analysis: Identifying High-Performing Regions in Retail Stores
  • Customer-Specific Product Expenditure Breakdown
  • Analysis of Returned Products by Category and Subcategory
  • Customer Purchase Frequency Analysis: Exploring Buying Patterns and Trends
  • Analyzing Returns by Country: A Study of Return Orders by Geographic Segmentation
  • Total Order Costs by Country for Different Products
  • Order Volume Analysis by Shipping Priority and Method
  • Shipping Cost Analysis Based on Delivery Priority: A Breakdown by Country and Region

Authors