lakshmangnanasambandan/understanding-ev-adoption

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.