https://github.com/batthulavinay/ev-population
This repository contains a Jupyter Notebook focused on analyzing Electric Vehicle (EV) population data. The notebook includes data visualizations, exploratory analysis, and key insights.
https://github.com/batthulavinay/ev-population
data-science datacleaning datapreprocessing datavisualization jupyter-notebook matplotlib numpy pandas seaborn
Last synced: 10 months ago
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This repository contains a Jupyter Notebook focused on analyzing Electric Vehicle (EV) population data. The notebook includes data visualizations, exploratory analysis, and key insights.
- Host: GitHub
- URL: https://github.com/batthulavinay/ev-population
- Owner: BatthulaVinay
- Created: 2025-01-27T17:22:37.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-15T12:14:44.000Z (12 months ago)
- Last Synced: 2025-02-15T13:18:21.768Z (12 months ago)
- Topics: data-science, datacleaning, datapreprocessing, datavisualization, jupyter-notebook, matplotlib, numpy, pandas, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 843 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# EV Population Analysis
## 📌 Project Description
This project analyzes the Electric Vehicle (EV) population dataset to identify trends, adoption rates, and other key insights. The analysis is performed using Python in a Jupyter Notebook and a standalone Python script.
## 📂 Dataset
- The dataset contains information on the EV population, including make, model, battery capacity, location, and more.
## 🔧 Installation
To run this project, you need to install Python and Jupyter Notebook. Follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/your-username/ev-population-analysis.git
cd ev-population-analysis
```
2. Install dependencies:
```bash
pip install pandas matplotlib seaborn notebook
```
3. Open the Jupyter Notebook:
```bash
jupyter notebook
```
4. Open the `EV Population.ipynb` file and run the cells.
## 🚀 Running the Python Script
For quick analysis, run the standalone Python script:
```bash
python ev_analysis.py
```
This will generate visualizations and save a processed dataset (`processed_ev_population.csv`).
## 📊 Features & Analysis
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
- EV Adoption Trends Over Time
- Regional Distribution of EVs
- Battery Performance & Range Insights
- Visualizations and Statistical Analysis
## 📈 Results
- **EV Growth Trends**: Insights into evolving EV adoption.
- **Top Manufacturers**: Identifying major players in the EV market.
- **Battery Performance**: Analyzing trends in battery capacity and range.
- **Geographical Distribution**: Understanding where EV adoption is highest.
## 🛠 Technologies Used
- Python 🐍
- Jupyter Notebook 📓
- Pandas 🏷
- Matplotlib 📊
- Seaborn 🎨
## 📜 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🤝 Contributing
Feel free to fork this repository, make improvements, and submit a pull request! Contributions are welcome. 🚀