How ramisrkit/car_evaluation_prediction works
This repository is a notebook-based car-acceptability classification project. Its documented workflow uses the categorical car dataset for preprocessing and train/test splitting, trains and tunes a Decision Tree, evaluates its performance, and supports predictions. PCA is a separate visualization path, not a model-training step. The notebook is not excerpted, so relationships below rely on README statements; the README also describes a saved model, but no such file appears in the repository tree.
Main entry points: Car_Evaluation_prediction.ipynb Encode and split · car_evaluation.csv Car evaluation data
Components 7
Data preparation
- Car evaluation data
car_evaluation.csv - Encode and split
Car_Evaluation_prediction.ipynb
Analysis and modeling
- PCA visualization
Car_Evaluation_prediction.ipynb - Decision Tree tuning
Car_Evaluation_prediction.ipynb - Performance evaluation
Car_Evaluation_prediction.ipynb
Model use
- Class prediction
Car_Evaluation_prediction.ipynb
Other components
- Project user
Connections 6
- Project user → Encode and split: runs notebook
README.md - Car evaluation data → Encode and split: provides records
README.md - Encode and split → PCA visualization: prepares visualization
README.md - Encode and split → Decision Tree tuning: provides training data
README.md - Decision Tree tuning → Performance evaluation: evaluated on split
README.md - Project user → Class prediction: requests prediction
README.md