# 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.