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{
"name": "21_Iris_Classification_SVM_Iris_ML",
"query": "I request a project to classify iris species utilizing the Iris dataset with a Support Vector Machine (SVM) classifier implemented in `src/model.py`. The project should standardize the data in and perform feature selection in `src/data_loader.py`. It will document the classification accuracy and save it as `results/metrics/classification_accuracy.txt`, and generate and save a confusion matrix as `results/figures/confusion_matrix.png`. It will further create an interactive web application in `src/app.py` using Streamlit to showcase classification results and model performance, with the figures stored in `results/figures/`. The web page should be user-friendly, with a brief explanation of the model to help users understand how the SVM classifier works.",
"tags": [
"Classification",
"Supervised Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"Iris\" dataset is used.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Data is standardized to ensure feature values are within the same range in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
0
],
"criteria": "Feature selection is performed to identify important features in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [],
"criteria": "The \"SVM classifier\" is implemented in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
1,
2,
3
],
"criteria": "Classification accuracy is saved in `results/metrics/classification_accuracy.txt`.",
"category": "Performance Metrics",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
1,
2,
3
],
"criteria": "A confusion matrix is generated and saved as `results/figures/confusion_matrix.png`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
2,
3,
4,
5
],
"criteria": "An interactive web application `src/app.py` is created using \"Streamlit\"` to showcase classification results and model performance in results/figures/.",
"category": "Human Computer Interaction",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The Streamlit web page should be user-friendly, allowing users to easily explore different aspects of the model's performance.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "A brief model explanation should be included on the web page, helping users understand how the SVM classifier works.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false
} |