13_RandomForest.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report

from sklearn.impute import SimpleImputer

df = pd.read_csv("D:/GCET/Machine Learning/Datasets/framingham.csv")

X = df.iloc[:, :-1]   # all columns except last
y = df.iloc[:, -1]    # target column

imputer = SimpleImputer(strategy='mean') 
X = imputer.fit_transform(X)

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 5. Split dataset into training and testing
X_train, X_test, y_train, y_test = train_test_split(
    X_scaled, y, test_size=0.3, random_state=42
)

rf = RandomForestClassifier(
    n_estimators=100,     # number of trees
    max_depth=5,       # tree depth
    random_state=42
)

rf.fit(X_train, y_train)

# Predict
y_pred = rf.predict(X_test)


#Importances
importances = rf.feature_importances_
print(importances)

#Evaluation Metrics
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')
f1 = f1_score(y_test, y_pred, average='weighted')

print("\nModel Performance")
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)

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

# 11. Confusion Matrix
cm = confusion_matrix(y_test, y_pred)

disp = ConfusionMatrixDisplay(confusion_matrix=cm)
disp.plot()

plt.title("Confusion Matrix - Random Forest")
plt.show()