| # SMS Spam Detection: Combined Model Card | |
| ## Models | |
| ### 1. Multinomial Naive Bayes | |
| - **Type:** MultinomialNB | |
| - **Library:** scikit-learn | |
| - **Description:** A Naive Bayes classifier for multinomially distributed data, commonly used for text classification tasks. | |
| - **Training Data:** SMS Spam Collection dataset (`train.csv`), preprocessed and vectorized using CountVectorizer. | |
| - **Features:** Bag-of-words (unigrams), stopwords removed. | |
| - **Target:** `label` (0: ham, 1: spam) | |
| - **Accuracy:** `{{ accuracy_score(tahmin, y_test) }}` | |
| - **Date Trained:** `{{ datetime.now().strftime("%Y-%m-%d") }}` | |
| ### 2. Decision Tree Classifier | |
| - **Type:** DecisionTreeClassifier | |
| - **Library:** scikit-learn | |
| - **Description:** A decision tree classifier for binary classification of SMS messages. | |
| - **Training Data:** SMS Spam Collection dataset (`train.csv`), preprocessed and vectorized using CountVectorizer. | |
| - **Features:** Bag-of-words (unigrams), stopwords removed. | |
| - **Target:** `label` (0: ham, 1: spam) | |
| - **Accuracy:** `{{ accuracy_score(tahmin3, y_test) }}` | |
| - **Date Trained:** `{{ datetime.now().strftime("%Y-%m-%d") }}` | |
| ## Preprocessing | |
| - Lowercasing all text | |
| - Removing punctuation, digits, and newlines | |
| - Stopwords removed during vectorization | |
| ## Evaluation Metric | |
| - Accuracy on test set | |
| ## Notes | |
| - Models saved using joblib. | |
| - For further evaluation, consider precision, recall, and F1-score. |