This is the initial release of the Car Safety Analysis project. The project focuses on analyzing various car safety features and building predictive models to classify cars based on safety ratings. Key milestones and features in this release include:
Data Preprocessing: Data cleaning and preparation from the Car Safety dataset.
Feature Engineering: Extracted important features for predictive modeling.
Model Building: Implementation of machine learning models to predict safety levels.
Evaluation Metrics: Performance evaluation using accuracy, confusion matrix, and feature importance analysis.
Output Visualizations: Included visualizations like feature importance and confusion matrix for better model interpretation.
🚀 Key Features:
Data analysis and model development using Jupyter Notebooks.
Machine learning models achieving 85%+ accuracy in predicting safety levels.
Feature importance and confusion matrix visualizations included for better model evaluation.
🔧 Setup:
To set up the project locally, clone the repository and follow the instructions in the README for installing dependencies and running the project.
Dependencies: All required packages are listed in requirements.txt.
Jupyter Notebook: Car_Safety_Analysis.ipynb file can be opened and run using Jupyter Notebook to execute the analysis.
🛠 Installation:
Clone the repository.
Install dependencies: pip install -r requirements.txt.
Open the Jupyter Notebook and execute the analysis.