Datasets:
ArXiv:
License:
| { | |
| "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": [ | |
| 0 | |
| ], | |
| "criteria": "Text preprocessing is performed, including removing stop words and punctuation in `src/data_loader.py`.", | |
| "category": "Data preprocessing and postprocessing", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 2, | |
| "prerequisites": [ | |
| 0, | |
| 1 | |
| ], | |
| "criteria": "\"TF-IDF\" features are used 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": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "Hyperparameter tuning is performed using \"GridSearchCV\" in `src/train.py`.", | |
| "category": "Machine Learning Method", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 5, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| 4 | |
| ], | |
| "criteria": "The confusion matrix is saved as `results/figures/confusion_matrix.png`.", | |
| "category": "Visualization", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 6, | |
| "prerequisites": [ | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| 4, | |
| 5 | |
| ], | |
| "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.", | |
| "satisfied": null | |
| } | |
| ], | |
| "is_kaggle_api_needed": false, | |
| "is_training_needed": true, | |
| "is_web_navigation_needed": false | |
| } |