# 1a] Naive Bayes Classifier import pandas as pd from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB # Load data df = pd.read_csv("spam1.csv", encoding="latin-1")[["Message", "Category"]] df.columns = ["SMS", "Type"] # Vectorize text data vectorizer = CountVectorizer(stop_words="english") X = vectorizer.fit_transform(df["SMS"]) y = df["Type"].values # Train model model = MultinomialNB() model.fit(X, y) # Predict on new messages messages = ["Free gifts for all", "We will go for lunch"] predictions = model.predict(vectorizer.transform(messages)) # Print predictions for msg, pred in zip(messages, predictions): print(f"Message: {msg} -> Prediction: {pred}") # spam1.csv (example content, as original not fully provided) """ Message,Category "Free gifts for all",Spam "We will go for l...