https://github.com/quantumcoderrr/car_safety_analysis
https://github.com/quantumcoderrr/car_safety_analysis
Last synced: 7 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/quantumcoderrr/car_safety_analysis
- Owner: QuantumCoderrr
- License: mit
- Created: 2024-12-14T04:05:14.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-26T08:08:53.000Z (12 months ago)
- Last Synced: 2025-02-26T09:22:47.514Z (12 months ago)
- Language: Jupyter Notebook
- Size: 131 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Car Safety Analysis 🚗💡
Welcome to the **Car Safety Analysis** project! This initiative is a collaborative effort to explore and analyze the safety features of various car models, providing insights through data visualization and machine learning techniques.
---
## 📝 Table of Contents
- [About the Project](#about-the-project)
- [Key Features](#key-features)
- [Dataset](#dataset)
- [Technologies Used](#technologies-used)
- [Getting Started](#getting-started)
- [Results](#results)
- [Contributors](#contributors)
- [Acknowledgments](#acknowledgments)
---
## About the Project 📚
The **Car Safety Analysis** project evaluates car safety metrics using the dataset provided and uncovers patterns and insights that can assist consumers, manufacturers, and regulators. Through this project, we aim to:
- Perform comprehensive data cleaning and preprocessing.
- Visualize relationships among key safety parameters.
- Build predictive models to classify car safety ratings.
- Share findings through interactive visualizations.
---
## Key Features 🎯
- **Data Exploration**: Uncover trends and distributions of safety parameters.
- **Visualization**: Dynamic and static visualizations to make data accessible.
- **Machine Learning Models**: Predictive models for classifying car safety levels.
- **Insights for Action**: Practical recommendations based on analysis.
---
## Dataset 📂
The dataset for this project, **Car_Safety_Data.csv**, contains information about cars, including their safety features, ratings, and other relevant details.
Key attributes include:
- Safety Ratings (Low, Medium, High)
- Car Features
- Cost-Effectiveness
---
## Technologies Used 🛠️
- **Programming Language**: Python
- **Libraries**: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
- **Tools**: Jupyter Notebook
---
## Getting Started 🚀
### Prerequisites
- Python 3.8 or higher
- Required libraries installed (`pip install -r requirements.txt`)
### Installation
1. Clone the repository:
```bash
git clone https://github.com/QuantumCoderrr/Car_Safety_Analysis.git
2. Navigate to the project directory:
```bash
cd Car_Safety_Analysis
3. Install dependencies:
```bash
pip install -r requirements.txt
### Running the Project 🚀
1. Open the `Car_Safety_Analysis.ipynb` file in Jupyter Notebook.
2. Run the notebook to execute the data analysis and model-building process.
---
## Results 📊
Our analysis yielded the following insights:
1. **Safety Correlations**: Certain features like airbags and stability control showed high positive correlations with safety ratings.
2. **Predictive Accuracy**: Machine learning models achieved over 85% accuracy in classifying safety levels.
---
### Output Visualizations
**1. Feature Importance Analysis**
**2. Confusion Matrix**
---
## Contributors 🤝
This project is brought to you by:
- [Sandip Ghosh](https://github.com/QuantumCoderrr)
- [Abhirup Raha](https://github.com/MesvRon)
- [Anushka Goswami](https://github.com/anushka16-gitt)
---
### Contributing
We welcome contributions from everyone! To learn how you can contribute, please see our [Contributing Guidelines](CONTRIBUTING.md).
### Code of Conduct
Please note that we have a [Code of Conduct](CODE_OF_CONDUCT.md) in place to ensure that all participants can contribute in a respectful and welcoming environment.
### License 📜
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.