How verolian-67/retail_profitability_project works
This repository documents a retail analytics workflow over the Sample Superstore data: inspect and clean transactions, develop KPIs, explore business performance, and use SQL for complementary analyses. The README also describes statistical analysis, management recommendations, and a planned Power BI dashboard. No source excerpts were available, so links reflect documented workflow and analysis scope, not verified notebook or SQL execution details; the dashboard has no implementation path in the supplied tree.
Main entry points: 04_eda.ipynb Exploratory analysis · 02_data_cleaning.ipynb Data cleaning · 01_data_understanding.ipynb Data understanding · 03_kpi_analysis.ipynb KPI analysis
Components 16
Data preparation
- Superstore data
data/raw/SampleSuperstore.csv - Data understanding
notebooks/01_data_understanding.ipynb - Data cleaning
notebooks/02_data_cleaning.ipynb
Python analysis
- KPI analysis
notebooks/03_kpi_analysis.ipynb - Exploratory analysis
notebooks/04_eda.ipynb - Statistical analysis
notebooks
SQL analysis
- Data quality SQL
sql/data_quality.sql - KPI SQL
sql/kpi_analysis.sql - Product analysis SQL
sql/product_analysis.sql - Profitability SQL
sql/profitability_analysis.sql - Regional analysis SQL
sql/regions.sql - Segment analysis SQL
sql/segment.sql - Shipping analysis SQL
sql/ship_mode.sql
Business reporting
- Management recommendations
- Power BI dashboard
Other components
- Analyst
Connections 17
- Analyst → Data understanding: starts analysis
- Superstore data → Data understanding: inspected in
- Data understanding → Data cleaning: informs cleaning
- Data cleaning → KPI analysis: precedes KPI analysis
- Data cleaning → Exploratory analysis: precedes exploration
- Analyst → Data quality SQL: runs queries
- Analyst → KPI SQL: runs queries
- Analyst → Product analysis SQL: runs queries
- Analyst → Profitability SQL: runs queries
- Analyst → Regional analysis SQL: runs queries
- Analyst → Segment analysis SQL: runs queries
- Analyst → Shipping analysis SQL: runs queries
- Exploratory analysis → Statistical analysis: investigates relationships
- Exploratory analysis → Management recommendations: supports findings
- KPI analysis → Power BI dashboard: informs planned dashboard
- Exploratory analysis → Power BI dashboard: informs planned dashboard
- Management recommendations → Power BI dashboard: summarized in