regression2

Logistic regression code :

# Import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Load dataset
df = pd.read_csv("heart_failure_clinical_records_dataset.csv")

# Feature and target
X = df.drop("DEATH_EVENT", axis=1)
y = df["DEATH_EVENT"]

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Feature scaling
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Model
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

# Prediction
y_pred = model.predict(X_test)

# Accuracy
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy Score:", accuracy)

# Classification report
print("\nClassification Report:\n", classification_report(y_test, y_pred))

# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
print("\nConfusion Matrix:\n", cm)

# Heatmap
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
            xticklabels=["Alive", "Dead"],
            yticklabels=["Alive", "Dead"])