#classification model

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import Random ForestClassifier

from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

from sklearn.datasets import load iris

#Load Dataset

data load_iris()

df pd.DataFrame(data.data, columns=data.feature_names)

df[Target']= data.target

#Split Data into Training and Testing Sets

Xdf.drop(columns=['Target'])

y= df ['Target']

x_train, x_test, y_train, y_test train_test_split(x, y, test_size=0.2, random_state=42)

#Initialize and Train Classifier

classifier Random ForestClassifier(n_estimators=100, random_state=42)

classifier.fit(X_train, y_train)

#Make Predictions

y_pred classifier.predict(X_test)

#Evaluate the Model

accuracy accuracy_score(y_test, y_pred)

conf_matrix confusion_matrix(y_test, y_pred)

report classification_report(y_test, y_pred)

#Print Results

print("Accuracy:", accuracy)

print("Confusion Matrix:\n", conf_matrix)

print("Classification Report:\n", report)