# verolian-67/retail_profitability_project architecture

> Architecture diagram of the GitHub repository verolian-67/retail_profitability_project, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-28). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/verolian-67/retail_profitability_project
- Repository: https://github.com/verolian-67/retail_profitability_project

## Overview

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: [`notebooks/04_eda.ipynb`](https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/04_eda.ipynb) (Exploratory analysis), [`notebooks/02_data_cleaning.ipynb`](https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/02_data_cleaning.ipynb) (Data cleaning), [`notebooks/01_data_understanding.ipynb`](https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/01_data_understanding.ipynb) (Data understanding), [`notebooks/03_kpi_analysis.ipynb`](https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/03_kpi_analysis.ipynb) (KPI analysis)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Data preparation"]
  node_dataset["Superstore data"]
  node_understanding["Data understanding"]
  node_cleaning["Data cleaning"]
end

subgraph group_python["Python analysis"]
  node_kpis["KPI analysis"]
  node_eda["Exploratory analysis<br/>[04_eda.ipynb]"]
  node_statistics["Statistical analysis"]
end

subgraph group_sql["SQL analysis"]
  node_qualitysql["Data quality SQL<br/>[data_quality.sql]"]
  node_kpissql["KPI SQL<br/>[kpi_analysis.sql]"]
  node_productsql["Product analysis SQL"]
  node_profitabilitysql["Profitability SQL"]
  node_regionsql["Regional analysis SQL<br/>[regions.sql]"]
  node_segmentsql["Segment analysis SQL<br/>[segment.sql]"]
  node_shipsql["Shipping analysis SQL<br/>[ship_mode.sql]"]
end

subgraph group_outcomes["Business reporting"]
  node_recommendations["Management recommendations"]
  node_dashboard["Power BI dashboard"]
end

node_analyst(("Analyst"))

node_analyst -->|"starts analysis"| node_understanding
node_dataset -->|"inspected in"| node_understanding
node_understanding -->|"informs cleaning"| node_cleaning
node_cleaning -->|"precedes KPI analysis"| node_kpis
node_cleaning -->|"precedes exploration"| node_eda
node_analyst -->|"runs queries"| node_qualitysql
node_analyst -->|"runs queries"| node_kpissql
node_analyst -->|"runs queries"| node_productsql
node_analyst -->|"runs queries"| node_profitabilitysql
node_analyst -->|"runs queries"| node_regionsql
node_analyst -->|"runs queries"| node_segmentsql
node_analyst -->|"runs queries"| node_shipsql
node_eda -.->|"investigates relationships"| node_statistics
node_eda -->|"supports findings"| node_recommendations
node_kpis -.->|"informs planned dashboard"| node_dashboard
node_eda -.->|"informs planned dashboard"| node_dashboard
node_recommendations -.->|"summarized in"| node_dashboard

click node_dataset "https://github.com/verolian-67/retail_profitability_project/blob/main/data/raw/SampleSuperstore.csv"
click node_understanding "https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/01_data_understanding.ipynb"
click node_cleaning "https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/02_data_cleaning.ipynb"
click node_kpis "https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/03_kpi_analysis.ipynb"
click node_eda "https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/04_eda.ipynb"
click node_statistics "https://github.com/verolian-67/retail_profitability_project/tree/main/notebooks"
click node_qualitysql "https://github.com/verolian-67/retail_profitability_project/blob/main/sql/data_quality.sql"
click node_kpissql "https://github.com/verolian-67/retail_profitability_project/blob/main/sql/kpi_analysis.sql"
click node_productsql "https://github.com/verolian-67/retail_profitability_project/blob/main/sql/product_analysis.sql"
click node_profitabilitysql "https://github.com/verolian-67/retail_profitability_project/blob/main/sql/profitability_analysis.sql"
click node_regionsql "https://github.com/verolian-67/retail_profitability_project/blob/main/sql/regions.sql"
click node_segmentsql "https://github.com/verolian-67/retail_profitability_project/blob/main/sql/segment.sql"
click node_shipsql "https://github.com/verolian-67/retail_profitability_project/blob/main/sql/ship_mode.sql"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_dataset,node_understanding,node_cleaning toneBlue
class node_kpis,node_eda,node_statistics toneAmber
class node_qualitysql,node_kpissql,node_productsql,node_profitabilitysql,node_regionsql,node_segmentsql,node_shipsql toneMint
class node_recommendations,node_dashboard toneRose
class node_analyst toneIndigo
```

## Components

### Data preparation

- [Superstore data](https://github.com/verolian-67/retail_profitability_project/blob/main/data/raw/SampleSuperstore.csv): `data/raw/SampleSuperstore.csv`
- [Data understanding](https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/01_data_understanding.ipynb): `notebooks/01_data_understanding.ipynb`
- [Data cleaning](https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/02_data_cleaning.ipynb): `notebooks/02_data_cleaning.ipynb`

### Python analysis

- [KPI analysis](https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/03_kpi_analysis.ipynb): `notebooks/03_kpi_analysis.ipynb`
- [Exploratory analysis](https://github.com/verolian-67/retail_profitability_project/blob/main/notebooks/04_eda.ipynb): `notebooks/04_eda.ipynb`
- [Statistical analysis](https://github.com/verolian-67/retail_profitability_project/tree/main/notebooks): `notebooks`

### SQL analysis

- [Data quality SQL](https://github.com/verolian-67/retail_profitability_project/blob/main/sql/data_quality.sql): `sql/data_quality.sql`
- [KPI SQL](https://github.com/verolian-67/retail_profitability_project/blob/main/sql/kpi_analysis.sql): `sql/kpi_analysis.sql`
- [Product analysis SQL](https://github.com/verolian-67/retail_profitability_project/blob/main/sql/product_analysis.sql): `sql/product_analysis.sql`
- [Profitability SQL](https://github.com/verolian-67/retail_profitability_project/blob/main/sql/profitability_analysis.sql): `sql/profitability_analysis.sql`
- [Regional analysis SQL](https://github.com/verolian-67/retail_profitability_project/blob/main/sql/regions.sql): `sql/regions.sql`
- [Segment analysis SQL](https://github.com/verolian-67/retail_profitability_project/blob/main/sql/segment.sql): `sql/segment.sql`
- [Shipping analysis SQL](https://github.com/verolian-67/retail_profitability_project/blob/main/sql/ship_mode.sql): `sql/ship_mode.sql`

### Business reporting

- **Management recommendations**
- **Power BI dashboard**

### Other components

- **Analyst**

## Connections

- 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