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.
- About the Project
- Key Features
- Dataset
- Technologies Used
- Getting Started
- Results
- Contributors
- Acknowledgments
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.
- 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.
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
- Programming Language: Python
- Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
- Tools: Jupyter Notebook
- Python 3.8 or higher
- Required libraries installed (
pip install -r requirements.txt)
- Clone the repository:
git clone https://github.com/QuantumCoderrr/Car_Safety_Analysis.git
- Navigate to the project directory:
cd Car_Safety_Analysis - Install dependencies:
pip install -r requirements.txt
- Open the
Car_Safety_Analysis.ipynbfile in Jupyter Notebook. - Run the notebook to execute the data analysis and model-building process.
Our analysis yielded the following insights:
- Safety Correlations: Certain features like airbags and stability control showed high positive correlations with safety ratings.
- Predictive Accuracy: Machine learning models achieved over 85% accuracy in classifying safety levels.
1. Feature Importance Analysis
2. Confusion Matrix
This project is brought to you by:
We welcome contributions from everyone! To learn how you can contribute, please see our Contributing Guidelines.
Please note that we have a Code of Conduct in place to ensure that all participants can contribute in a respectful and welcoming environment.
This project is licensed under the MIT License. See the LICENSE file for details.