Datasets:
ArXiv:
License:
| { | |
| "name": "23_Wine_Quality_Prediction_DecisionTree_WineQuality_ML", | |
| "query": "Build a wine quality prediction system using a Decision Tree model with the Wine Quality dataset from UCI. Preprocess the data in `src/data_loader.py`, including handling missing values and feature scaling. Use cross-validation to evaluate the model in `src/train.py`. Implement the Decision Tree regression model in `src/model.py`.Save the mean squared error in `results/metrics/mean_squared_error.txt`. Visualize and save feature importance as `results/figures/feature_importance.png`. Create a Jupyter Notebook with results and visualizations, and summarize your observations. The Notebook should thoroughly document the preprocessing steps to ensure reproducibility. Convert the Notebook to a PDF report and save it as `results/report.pdf`. The PDF report should also include a brief discussion on potential improvements of the model.", | |
| "tags": [ | |
| "Classification", | |
| "Supervised Learning" | |
| ], | |
| "requirements": [ | |
| { | |
| "requirement_id": 0, | |
| "prerequisites": [], | |
| "criteria": "The \"Wine Quality\" dataset from \"UCI\" is used.", | |
| "category": "Dataset or Environment", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 1, | |
| "prerequisites": [ | |
| 0 | |
| ], | |
| "criteria": "Data preprocessing is performed in `src/data_loader.py`, including handling missing values and feature scaling.", | |
| "category": "Data preprocessing and postprocessing", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 2, | |
| "prerequisites": [], | |
| "criteria": "The \"Decision Tree\" regression model is implemented in `src/model.py`.", | |
| "category": "Machine Learning Method", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 3, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2 | |
| ], | |
| "criteria": "Cross-validation is used to evaluate the model in `src/train.py`.", | |
| "category": "Performance Metrics", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 4, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "The Mean Squared Error (MSE) is saved in `results/metrics/mean_squared_error.txt`.", | |
| "category": "Performance Metrics", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 5, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "The feature importance plot is generated and saved as `results/figures/feature_importance.png`.", | |
| "category": "Visualization", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 6, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| 4, | |
| 5 | |
| ], | |
| "criteria": "A Jupyter Notebook containing preprocessing steps, results and visualizations is generated with observations summarized. The Notebook is converted to a PDF report and saved as `results/report.pdf`.", | |
| "category": "Visualization", | |
| "satisfied": null | |
| } | |
| ], | |
| "preferences": [ | |
| { | |
| "preference_id": 0, | |
| "criteria": "The feature importance plot should clearly highlight the top influential features.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 1, | |
| "criteria": "The final PDF report should include a brief discussion on potential improvements of the model.", | |
| "satisfied": null | |
| } | |
| ], | |
| "is_kaggle_api_needed": false, | |
| "is_training_needed": true, | |
| "is_web_navigation_needed": false | |
| } |