import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
def load_data(file_path, encoding='latin1'): try: data = pd.read_csv(file_path, encoding=encoding) return data except UnicodeDecodeError: raise ValueError("File encoding issue. Try a different encoding.") except FileNotFoundError: raise ValueError("File not found. Check the file path.")
def preprocess_data(data): # Keep only the relevant columns and rename them data = data[['v1', 'v2']] data.columns = ['label', 'message'] return data
def split_data(data): # Split the data into training 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, stratify=data['label'] ) return X_train, X_test, y_train, y_test
def extract_features(X_train, X_test): # Preprocess the text data using TF-IDF vectorizer = TfidfVectorizer(stop_words='english') X_train_tfidf = vectorizer.fit_transform(X_train) X_test_tfidf = vectorizer.transform(X_test) return X_train_tfidf, X_test_tfidf, vectorizer
def train_model(X_train_tfidf, y_train): # Train a Logistic Regression model model = LogisticRegression() model.fit(X_train_tfidf, y_train) return model
def evaluate_model(model, X_test_tfidf, y_test): # Make predictions on the test set y_pred = model.predict(X_test_tfidf) # Evaluate the model accuracy = accuracy_score(y_test, y_pred) report = classification_report(y_test, y_pred) conf_matrix = confusion_matrix(y_test, y_pred) return accuracy, report, conf_matrix
def main(): file_path = 'path/to/your/spam[1].csv'
# Load and preprocess data
data = load_data(file_path)
data = preprocess_data(data)
# Split the data
X_train, X_test, y_train, y_test = split_data(data)
# Extract features
X_train_tfidf, X_test_tfidf, vectorizer = extract_features(X_train, X_test)
# Train the model
model = train_model(X_train_tfidf, y_train)
# Evaluate the model
accuracy, report, conf_matrix = evaluate_model(model, X_test_tfidf, y_test)
# Print results
print(f'Accuracy: {accuracy}')
print(f'Classification Report:\n{report}')
print(f'Confusion Matrix:\n{conf_matrix}')
if name == "main": main()