👩‍🔧 Hi There!

Welcome to the Data Analysis Repository, where you can explore projects applying data analysis techniques essential for autonomous driving and smart mobility engineering.

As part of my journey to become an Autonomous Driving and Smart Mobility Engineer, I have been exploring and developing data analysis skills in this field. Python is my primary tool, and I also plan to conduct data analysis using SQL soon.

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📂 Repository Structure

├── Project1_Name/                   # Data Analysis Project 1
│   ├── Data/                        # Raw datasets
│   ├── Output_Data_Analysis/        # Analysis results
│   │   ├── Jupyter_Notebook/        # Jupyter notebooks for data analysis
│   └── Output_Paper/                # Result Reports and Documentation (.pdf)
│                
├── Project2_Name/                   # Data Analysis Project 2
│
├── README.md                        # Documentation (this file)

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📊 Latest Data Analysis Project

Analysis of EV Adoption Rates and Air Quality (PM2.5)

• Objective: Explore the relationship between regional EV adoption rates and PM2.5 levels to understand the impact of EVs on air quality.
• Dataset: Monthly regional air pollution data (PM2.5, PM10, etc.) and EV ownership statistics for 2023.
• Key Methods:
  • Data Preprocessing: Handling missing values, removing outliers, and unifying datasets.
  • Analysis: Using Pearson and Spearman correlation to identify trends.
  • Visualization: Interactive heatmaps and scatter plots for regional insights.
• 📈 Key Results:
  • Scatter Plot: Demonstrates the relationship between EV ownership ratio and PM2.5 levels.
  • Heatmap: Interactive visualization showcasing regional variations in pollution and EV adoption.
• 🌐 Future Directions
  • Integrating real-time air quality monitoring data with autonomous vehicle route planning.
  • Exploring traffic optimization models using environmental and mobility data.
  • Extending analysis to predict future trends in sustainable urban mobility.

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📋 Other Data Analysis Projects

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🛠️ Tools and Technologies

• Programming Language: Python
• Libraries: pandas, matplotlib, seaborn, folium, geopy, scipy
• Environment: Jupyter Notebook

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🚀 How to Use

Prerequisites

• Python 3.8 or higher
• Required libraries in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/Arielle0222/Data_Analysis.git
   cd Data_Analysis

2. Install dependencies:

   pip install -r requirements.txt

Run Notebooks and Scripts

• Use Jupyter Notebook for interactive exploration:

  jupyter notebook

• Execute Python scripts for specific tasks:

  python scripts/<script_name>.py

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🤝 Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

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📧 Contact

• GitHub: Arielle0222
• Email: seohee70428@gmail.com