https://github.com/lparham2/factors-driving-ev-adoption-charging-station-deployment

This project explores factors driving EV adoption and charging station deployment using Python-based data analysis. It examines sales trends, infrastructure growth, and socioeconomic influences to uncover key insights. The goal is to aid policymakers and businesses in optimizing EV infrastructure and accelerating sustainable transportation.
https://github.com/lparham2/factors-driving-ev-adoption-charging-station-deployment

data-analysis data-visualization electric-vehicle-charging-station electric-vehicles powerpoint-presentations python

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This project explores factors driving EV adoption and charging station deployment using Python-based data analysis. It examines sales trends, infrastructure growth, and socioeconomic influences to uncover key insights. The goal is to aid policymakers and businesses in optimizing EV infrastructure and accelerating sustainable transportation.

• Host: GitHub
• URL: https://github.com/lparham2/factors-driving-ev-adoption-charging-station-deployment
• Owner: lparham2
• Created: 2025-02-05T16:48:27.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-02-07T01:01:53.000Z (10 months ago)
• Last Synced: 2025-03-01T16:18:40.816Z (9 months ago)
• Topics: data-analysis, data-visualization, electric-vehicle-charging-station, electric-vehicles, powerpoint-presentations, python
• Language: Jupyter Notebook
• Homepage:
• Size: 24.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🚗 Factors Driving EV Adoption & Charging Station Deployment

## 📌 Overview  

Electric Vehicle (EV) adoption is rapidly increasing, but the availability of charging infrastructure remains a key challenge. This project analyzes the factors influencing EV adoption and charging station deployment using Python-based data analytics.

📚 **This project was completed for academic use as part of MSA 8010: Data Programming* under **Professor Ugur Kursuncu** at **Georgia State University** for the **Master of Science in Analytics (MSA) program**.  

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## 🎯 Objective  

- Examine **key drivers** of EV adoption trends.  

- Analyze the **availability and distribution** of charging stations.  

- Identify **correlations** between EV growth and socioeconomic factors.  

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## 📊 Dataset  

This analysis is based on multiple datasets, including:  

- **EV Sales Data** – Yearly sales categorized by model and region.  

- **Charging Station Data** – Locations, types, and station capacity.  

- **Demographics & Economic Data** – Income levels, urbanization, and policies.  

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## 🔍 Methodology  

1. **Data Collection & Cleaning**  

   - Standardized data formats and handled missing values.  

2. **Exploratory Data Analysis (EDA)**  

   - Analyzed EV sales trends and charging station growth.  

   - Identified correlations using **statistical methods**.  

3. **Data Visualization**  

   - Used **Matplotlib & Seaborn** for visual storytelling.  

   - Created **heatmaps, line charts, and scatter plots** for insights.  

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## 📈 Key Findings  

- **EV Adoption Trends**  

  - EV sales **increase in high-income, urban areas** with incentives.  

  - Charging availability **positively impacts EV growth**.  

- **Charging Station Deployment**  

  - **Fast chargers** are concentrated in **high-demand regions**.  

  - Rural areas lag behind in **infrastructure expansion**.  

- **Economic & Policy Impact**  

  - **Subsidies & tax rebates** accelerate EV adoption.  

  - Lower **electricity costs** drive consumer preference for EVs.  

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## 🛠 Tech Stack  

- **Python**: Data processing & analysis  

- **Pandas**: Data manipulation  

- **Matplotlib & Seaborn**: Data visualization  

- **Scikit-learn**: Statistical analysis

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## 🎯 Contributors  

👩‍💻 **Lilly Parham**  

👩‍💻 **Gracie Rehberg**  

👩‍💻 **Pamela Alvarado-Zarate** 

📚 **Georgia State University - Master of Science in Analytics**  

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### *📌 Note:*  

This project is for **academic purposes** and is based on public data regarding electric vehicle registrations, demographics, and charging station infrastructure.