import pandas as pd import string from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score, confusion_matrix, classification_report from joblib import dump import nltk from nltk.corpus import stopwords from nltk.stem import PorterStemmer # Download necessary NLTK resources nltk.download('stopwords') # Initialize stopwords and stemmer stop_words = set(stopwords.words('english')) stemmer = PorterStemmer() def preprocess(text): """Clean and preprocess text for model input.""" # Lowercase and remove punctuation text = text.lower() text = ''.join([char for char in text if char not in string.punctuation]) # Remove stopwords and apply stemming words = text.split() return ' '.join([stemmer.stem(word) for word in words if word not in stop_words]) # Load your dataset data = pd.read_csv('spam.csv', encoding='latin-1') data = data[['v1', 'v2']] data.columns = ['label', 'message'] # Convert labels to binary data['label'] = data['label'].map({'ham': 'LABEL_0', 'spam': 'LABEL_1'}) # Split data into train and test sets X_train, X_test, y_train, y_test = train_test_split(data['message'], data['label'], test_size=0.2, random_state=42) # Create a pipeline that includes preprocessing, vectorization, and classification model_pipeline = Pipeline([ ('vectorizer', CountVectorizer(preprocessor=preprocess)), ('classifier', MultinomialNB()) ]) # Train the model using the pipeline model_pipeline.fit(X_train, y_train) # Evaluate the model y_pred = model_pipeline.predict(X_test) print('Accuracy:', accuracy_score(y_test, y_pred)) print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred)) print('Classification Report:\n', classification_report(y_test, y_pred)) # Serialize the model pipeline dump(model_pipeline, 'spam_classifier_pipeline.joblib')