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{
"name": "12_Spam_Detection_SVM_Enron_ML",
"query": "Hello. I need you to build a project to detect spam emails using the Support Vector Machine (SVM) classifier on the Enron-Spam dataset. The project should preprocess the text by removing stop words and punctuation, employ TF-IDF features, perform hyperparameter tuning using GridSearchCV, and save the confusion matrix to `results/figures/confusion_matrix.png`. I also need to write and save a comprehensive report, including precision, recall, F1-score, and the confusion matrix (to be generated as `results/figures/confusion_matrix.png`), under `results/classification_report.pdf`. The Enron-Spam dataset should be loaded in `src/data_loader.py`. Text preprocessing, including removing stop words and punctuation, and calculating TF-IDF features should be performed in `src/data_loader.py`. The SVM classifier should be implemented in `src/model.py`. Hyperparameter tuning should be performed using GridSearchCV in `src/train.py`. It would be helpful if the text preprocessing step is optimized to handle a large number of emails efficiently.",
"tags": [
"Classification",
"Natural Language Processing",
"Supervised Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"Enron-Spam\" dataset is loaded in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
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"criteria": "Text preprocessing is performed, including removing stop words and punctuation in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
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},
{
"requirement_id": 2,
"prerequisites": [
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],
"criteria": "\"TF-IDF\" features are used in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
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},
{
"requirement_id": 3,
"prerequisites": [],
"criteria": "The \"SVM classifier\" is implemented in `src/model.py`.",
"category": "Machine Learning Method",
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"requirement_id": 4,
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"criteria": "Hyperparameter tuning is performed using \"GridSearchCV\" in `src/train.py`.",
"category": "Machine Learning Method",
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"requirement_id": 5,
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"criteria": "The confusion matrix is saved as `results/figures/confusion_matrix.png`.",
"category": "Visualization",
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"requirement_id": 6,
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"criteria": "A classification report, including \"precision,\" \"recall,\" \"F1-score,\" and the figure `results/figures/confusion_matrix.png`, is saved as `results/classification_report.pdf`.",
"category": "Performance Metrics",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The text preprocessing step should be optimized to handle a large number of emails efficiently.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The classification report should be comprehensive.",
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}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false
} |