Description

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

# Load dataset
data = pd.read_csv('Iris.csv')  # pastikan file sudah unzip dan di path yg benar

# Tampilkan 5 baris pertama
print(data.head())

# Pisahkan fitur dan target
X = data.drop(['Species', 'Id'], axis=1)
y = data['Species']

# Bagi data train dan test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standarisasi fitur (opsional tapi direkomendasikan)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Buat model KNN
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

# Prediksi
y_pred = knn.predict(X_test)

# Evaluasi model
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test, y_pred))
 

1. Instalasi

Kalau kamu belum punya Python, bisa download di python.org atau langsung pakai Google Colab yang online dan gratis.

2. Contoh kode dasar Python

a. Cetak tulisan ke layar

print("Halo, dunia!")

b. Variabel dan tipe data

nama = "Andi"     # string   umur = 25         # integer   tinggi = 170.5    # float   

c. Percabangan (if-else)

if umur > 18:      print("Dewasa")   else:      print("Belum dewasa")  

d. Perulangan (loop)

for i in range(5):      print(i)  

e. Fungsi

def sapa(nama):      print(f"Halo, {nama}!")   sapa("Andi")  

3. Tools bantu belajar Python

Jupyter Notebook: Cocok buat eksplorasi data dan belajar interaktif.

Google Colab: Mirip Jupyter tapi online, gampang dipakai tanpa instalasi.

IDE seperti VS Code, PyCharm: Untuk coding lebih serius dan projek besar.