Datasets:
ArXiv:
License:
| { | |
| "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 | |
| } |