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.

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📝 Table of Contents

• About the Project
• Key Features
• Dataset
• Technologies Used
• Getting Started
• Results
• Contributors
• Acknowledgments

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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.

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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.

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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

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Technologies Used 🛠️

• Programming Language: Python
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
• Tools: Jupyter Notebook

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Getting Started 🚀

Prerequisites

• Python 3.8 or higher
• Required libraries installed (pip install -r requirements.txt)

Installation

1. Clone the repository:

   git clone https://github.com/QuantumCoderrr/Car_Safety_Analysis.git
   

2. Navigate to the project directory:

   cd Car_Safety_Analysis
   

3. Install dependencies:

   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.

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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.

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Output Visualizations

1. Feature Importance Analysis

2. Confusion Matrix

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Contributors 🤝

This project is brought to you by:

• Sandip Ghosh
• Abhirup Raha
• Anushka Goswami

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Contributing

We welcome contributions from everyone! To learn how you can contribute, please see our Contributing Guidelines.

Code of Conduct

Please note that we have a Code of Conduct 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 file for details.