This is a machine learning project that uses a decision tree classifier to evaluate the acceptability of cars based on four features: buying price, maintenance cost, number of doors, and luggage capacity. The dataset used contains 1728 instances with 6 attributes. The project goes through the process of exploratory data analysis, model training, and hyperparameter tuning to achieve an accuracy score of 96.53%.
To run this project locally, you will need Python 3 and the following libraries:
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn To install these libraries, you can use pip. For example pip install pandas
The main script is car-evaluation.ipynb. You can run this script in a Jupyter Notebook environment, such as Anaconda, or in a Google Colab notebook.
The notebook contains detailed explanations of each step in the machine learning pipeline, including:
- Exploratory data analysis
- Data cleaning and preprocessing
- Model selection and training
- Hyperparameter tuning
- Prediction and evaluation The final model is saved in the file car_evaluation_model.pkl.