Code Program

import pandas as pd
from sklearn import datasets

iris = datasets.load_iris()

x = iris.data
y = iris.target

x = pd.DataFrame(x, columns=iris.feature_names)
df_y = pd.DataFrame(y, columns=['target'])

df_x

df_x.shape

df_y

df = pd.concat([df_x, df_y], axis=1)

df.head(10)

df.info

df['target'].unique()

df.describe()

from sklearn.model_selection import train_test_split

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

round(150*0.2)

round(150*0.8)

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(x_train, y_train)

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

y_pred = model.predict(x_test)
accuracy = accuracy_score(y_test, y_pred)
print("Laporan Klasifikasi:")
print(f'Accuracy: {accuracy * 100:.1f}%')

import seaborn as sns
import matplotlib.pyplot as plt

cm = confusion_matrix(y_test, y_pred)

plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=iris.target_names, yticklabels=iris.target_names)
plt.xlabel('Prediksi')
plt.ylabel('Aktual')
plt.title('Confusion Matrix')
plt.show()