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{
"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
} |