Create README.md

README.md

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+# 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.