# lakshmangnanasambandan/understanding-ev-adoption architecture

> Architecture diagram of the GitHub repository lakshmangnanasambandan/understanding-ev-adoption, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-08-13). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/lakshmangnanasambandan/understanding-ev-adoption
- Repository: https://github.com/lakshmangnanasambandan/understanding-ev-adoption

## Overview

#### 1. Purpose and deliverable
This is a completed EV consumer-survey analytics project. It identifies factors associated with EV purchase intention—especially range anxiety, environmental concern, income, home charging, and subsidies—and turns findings into business segmentation and dashboard reporting. Primary consumer-facing artifact: `Power BI/Ev_Adoption project.pbix`.

#### 2. Source-data boundary
The workflow begins with a survey workbook stored as both the original project-level file and raw data copy:
- `EV_Adoption_and_Range_Anxiety_Dataset.xlsx`
- `data/raw/EV_Adoption_and_Range_Anxiety_Dataset.xlsx`

Excel is the stated cleaning/preparation environment. The governed analytical dataset is the CSV at `data/cleaned/EV_Adoption_Cleaned.csv`, which is the intended handoff from preparation into analysis, SQL, and visualization.

#### 3. Exploratory analysis stage
Python-based exploratory profiling validates and investigates the cleaned survey data before reporting:
- `eda/01_Data_Profiling.ipynb`
- `data/cleaned/EV_Adoption_Cleaned.csv`

This stage examines relationships between purchase intention and survey attributes, informing the downstream business questions and segments rather than operating as a deployed runtime.

#### 4. SQL Server analytical layer
Microsoft SQL Server / SSMS provides the structured business-analysis path. Its scripts establish the database, define/load the analysis table, and execute adoption and segmentation queries in order:
- `SQL/01_Database_Setup.sql`
- `SQL/02_Table_Creation.sql`
- `SQL/03_Business_Queries.sql`

The material control flow is setup → schema/table creation and data loading → business queries. SQL is a separate analysis boundary from the Power BI file; the repository does not establish an automated pipeline between them.

#### 5. Business-question and insight layer
The analytical contract is defined by purchase-intention questions around environmental concern, range anxiety, income, charging, and subsidies:
- `Business_Questions.md`
- `SQL/03_Business_Queries.sql`
- `reports/Insights_Register.md`

Query results and dashboard analysis are translated into decision-ready findings and recommendations, including target-segment prioritization. The insight register is the durable narrative output rather than an application API.

#### 6. Power BI reporting and segmentation
The Power BI Desktop artifact is the interactive reporting endpoint:
- `Power BI/Ev_Adoption project.pbix`
- `data/cleaned/EV_Adoption_Cleaned.csv`

It presents adoption KPIs and breakdowns by range anxiety, environmental concern, income, demographics, and customer type. Power BI’s Key Influencers, Top Segments, and decomposition capabilities form the main segmentation/driver-analysis layer. Report filters support slicing by age group, gender, city type, and income group.

#### 7. Runtime and infrastructure profile
There is no application server, web service, CI workflow, or cloud infrastructure represented. Execution occurs locally through Excel, Python notebook tooling, SQL Server/SSMS, and Power BI Desktop. External dependencies are limited to those desktop/data platforms; repository artifacts are files and SQL scripts rather than deployable services.

Main entry points: [`data/cleaned/EV_Adoption_Cleaned.csv`](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/data/cleaned/EV_Adoption_Cleaned.csv) (Governed Survey Dataset), [`SQL/02_Table_Creation.sql`](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/SQL/02_Table_Creation.sql) (Table Creation & Load), [`SQL/03_Business_Queries.sql`](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/SQL/03_Business_Queries.sql) (Adoption & Segment Queries), [`Power BI/Ev_Adoption project.pbix`](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/Power%20BI/Ev_Adoption%20project.pbix) (EV Adoption Dashboard)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_preparation["Data Preparation"]
  node_source_workbook["Survey Workbook<br/>Excel source"]
  node_raw_workbook["Raw Data Copy<br/>Excel raw data"]
  node_excel_preparation["Excel Cleaning<br/>manual preparation"]
  node_cleaned_csv[("Governed Survey Dataset<br/>cleaned CSV")]
end

subgraph group_sql["SQL Server Analysis"]
  node_db_setup["Database Setup<br/>SQL script"]
  node_table_creation["Table Creation &amp; Load<br/>SQL script"]
  node_sql_analysis_table[("Analysis Table<br/>SQL Server table")]
  node_business_queries["Adoption &amp; Segment Queries<br/>SQL script"]
end

subgraph group_reporting["Reporting &amp; Insights"]
  node_business_questions["Business Questions<br/>analysis contract"]
  node_power_bi["EV Adoption Dashboard<br/>Power BI report"]
  node_segmentation["Driver &amp; Segment Analysis<br/>Power BI analytics"]
  node_insight_register["Insights Register<br/>narrative report"]
end

node_eda_notebook["Data Profiling<br/>Python notebook"]

node_source_workbook -.->|"raw copy"| node_raw_workbook
node_raw_workbook -->|"prepared in"| node_excel_preparation
node_excel_preparation -->|"exports"| node_cleaned_csv
node_cleaned_csv -->|"profiles"| node_eda_notebook
node_db_setup -->|"setup → schema/load"| node_table_creation
node_cleaned_csv -->|"loads data"| node_table_creation
node_table_creation -->|"creates"| node_sql_analysis_table
node_sql_analysis_table -->|"queried by"| node_business_queries
node_business_questions -->|"guides"| node_business_queries
node_business_questions -->|"defines reporting focus"| node_power_bi
node_cleaned_csv -->|"report data source"| node_power_bi
node_power_bi -->|"provides"| node_segmentation
node_eda_notebook -.->|"informs"| node_insight_register
node_business_queries -->|"findings"| node_insight_register
node_segmentation -->|"dashboard findings"| node_insight_register

click node_source_workbook "https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/EV_Adoption_and_Range_Anxiety_Dataset.xlsx"
click node_raw_workbook "https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/data/raw/EV_Adoption_and_Range_Anxiety_Dataset.xlsx"
click node_cleaned_csv "https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/data/cleaned/EV_Adoption_Cleaned.csv"
click node_eda_notebook "https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/eda/01_Data_Profiling.ipynb"
click node_db_setup "https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/SQL/01_Database_Setup.sql"
click node_table_creation "https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/SQL/02_Table_Creation.sql"
click node_business_queries "https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/SQL/03_Business_Queries.sql"
click node_business_questions "https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/Business_Questions.md"
click node_power_bi "https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/Power%20BI/Ev_Adoption%20project.pbix"
click node_insight_register "https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/reports/Insights_Register.md"

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classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_source_workbook,node_raw_workbook,node_excel_preparation,node_cleaned_csv toneBlue
class node_db_setup,node_table_creation,node_sql_analysis_table,node_business_queries toneAmber
class node_business_questions,node_power_bi,node_segmentation,node_insight_register toneMint
class node_eda_notebook toneNeutral
```

## Components

### Data Preparation

Local Excel-based survey cleaning.

- [Survey Workbook](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/EV_Adoption_and_Range_Anxiety_Dataset.xlsx): Excel source · Original project-level survey workbook. · `EV_Adoption_and_Range_Anxiety_Dataset.xlsx`
- [Raw Data Copy](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/data/raw/EV_Adoption_and_Range_Anxiety_Dataset.xlsx): Excel raw data · `data/raw/EV_Adoption_and_Range_Anxiety_Dataset.xlsx`
- **Excel Cleaning**: manual preparation · Local cleaning and preparation environment.
- [Governed Survey Dataset](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/data/cleaned/EV_Adoption_Cleaned.csv): cleaned CSV · Shared analytical handoff dataset. · `data/cleaned/EV_Adoption_Cleaned.csv`

### SQL Server Analysis

Structured SSMS analysis workflow.

- [Database Setup](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/SQL/01_Database_Setup.sql): SQL script · `SQL/01_Database_Setup.sql`
- [Table Creation & Load](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/SQL/02_Table_Creation.sql): SQL script · Defines and loads the analytical table. · `SQL/02_Table_Creation.sql`
- **Analysis Table**: SQL Server table
- [Adoption & Segment Queries](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/SQL/03_Business_Queries.sql): SQL script · Answers purchase-intention and segment questions. · `SQL/03_Business_Queries.sql`

### Reporting & Insights

Decision-facing analysis artifacts.

- [Business Questions](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/Business_Questions.md): analysis contract · Defines drivers and segmentation questions. · `Business_Questions.md`
- [EV Adoption Dashboard](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/Power%20BI/Ev_Adoption%20project.pbix): Power BI report · Interactive KPI, driver, and segmentation reporting. · `Power BI/Ev_Adoption project.pbix`
- **Driver & Segment Analysis**: Power BI analytics · Uses Key Influencers, Top Segments, and decomposition.
- [Insights Register](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/reports/Insights_Register.md): narrative report · Durable findings and recommendations. · `reports/Insights_Register.md`

### Other components

- [Data Profiling](https://github.com/lakshmangnanasambandan/understanding-ev-adoption/blob/main/eda/01_Data_Profiling.ipynb): Python notebook · Explores purchase-intention relationships before reporting. · `eda/01_Data_Profiling.ipynb`

## Connections

- Survey Workbook → Raw Data Copy: raw copy
- Raw Data Copy → Excel Cleaning: prepared in
- Excel Cleaning → Governed Survey Dataset: exports
- Governed Survey Dataset → Data Profiling: profiles
- Database Setup → Table Creation & Load: setup → schema/load
- Governed Survey Dataset → Table Creation & Load: loads data
- Table Creation & Load → Analysis Table: creates
- Analysis Table → Adoption & Segment Queries: queried by
- Business Questions → Adoption & Segment Queries: guides
- Business Questions → EV Adoption Dashboard: defines reporting focus
- Governed Survey Dataset → EV Adoption Dashboard: report data source
- EV Adoption Dashboard → Driver & Segment Analysis: provides
- Data Profiling → Insights Register: informs
- Adoption & Segment Queries → Insights Register: findings
- Driver & Segment Analysis → Insights Register: dashboard findings