import pandas as pd

# Step 1: Load the dataset
df = pd.read_csv('SMSSpamCollection', sep='\t', header=None, names=['label', 'message'])

# Optional: check dataset
print(df.head())
print(df['label'].value_counts())

  label                                            message
0   ham  Go until jurong point, crazy.. Available only ...
1   ham                      Ok lar... Joking wif u oni...
2  spam  Free entry in 2 a wkly comp to win FA Cup fina...
3   ham  U dun say so early hor... U c already then say...
4   ham  Nah I don't think he goes to usf, he lives aro...
label
ham     4825
spam     747
Name: count, dtype: int64

# Step 2: Encode the labels
from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
df['label_encoded'] = le.fit_transform(df['label'])  # ham=0, spam=1

# Step 3: Train-test split
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(df['message'], df['label_encoded'], test_size=0.2, random_state=42)

Vectorization

In machine learning, algorithms cannot work directly with raw text. Therefore, text must be converted into numerical representations. One common approach is Count Vectorization.

CountVectorizer converts a collection of text documents into a matrix of token counts. Each row represents a document (message), and each column represents a unique word from the corpus. The cell values indicate how many times each word appears in each document.

For example, for 3 messages:

['I love spam', 'Spam is bad', 'I love ham']

The vectorized form (simplified):

Word	I	love	spam	is	bad	ham
Doc 1	1	1	1	0	0	0
Doc 2	0	0	1	1	1	0
Doc 3	1	1	0	0	0	1

This matrix is used as input to the machine learning model.

# Step 4: Convert text to numeric features
from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer()
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)

# Step 5: Train a Naïve Bayes classifier
from sklearn.naive_bayes import MultinomialNB

model = MultinomialNB()
model.fit(X_train_vec, y_train)

MultinomialNB()

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 # Step 6: Evaluate the model
from sklearn.metrics import accuracy_score, classification_report

y_pred = model.predict(X_test_vec)

print(f"Accuracy: {accuracy_score(y_test, y_pred):.4f}")
print("\nClassification Report:\n")
print(classification_report(y_test, y_pred, target_names=le.classes_))

Accuracy: 0.9919

Classification Report:

              precision    recall  f1-score   support

         ham       0.99      1.00      1.00       966
        spam       1.00      0.94      0.97       149

    accuracy                           0.99      1115
   macro avg       1.00      0.97      0.98      1115
weighted avg       0.99      0.99      0.99      1115