4_Logistic_Regression_spam_email.py

# Build a logistic regression model to classify emails as spam or not spam.
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import StandardScaler

# Load the dataset
df = pd.read_csv('spambase (week 4).data', header=None)
df.head()

X = df.iloc[:, :-1]
y = df.iloc[:, -1] 
print("Features:")
print(X.head())
print("Target:")
print(y.head())

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Logistic Regression
model = LogisticRegression()
model.fit(X_train_scaled, y_train)

y_pred = model.predict(X_test_scaled)

# Accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')

# Confusion Matrix
conf_matrix = confusion_matrix(y_test, y_pred)
print(f'Confusion Matrix:\n{conf_matrix}')

# Classification Report
print("\nClassification Report:")
print(classification_report(y_test, y_pred, target_names=['Not Spam', 'Spam']))