cocalc tree

# Load libraries
# Atau: Panggil guru-guru yang relevan: pandas, sklearn
import pandas as pd
from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier
from sklearn.model_selection import train_test_split # Import train_test_split function
from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation

# Beri nama kolom yang sesuai dengan data.csv yang kita gunakan juga kemarin
col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']
# load dataset, mirip dengan yang sebelumnya
pima = pd.read_csv("data.csv", header=None, names=col_names)

# split dataset in features and target variable
# kita pisahkan berdasarkan nama kolom, pada sebelumnya kita pisahkan antara 8 kolom di kiri dan 1 kolom di kanan
# kali ini kita pisahkan berdasarkan nama. Ini merupakan pendekatan yang berbeda dalam menyiapkan data
feature_cols = ['pregnant', 'insulin', 'bmi', 'age','glucose','bp','pedigree']
X = pima[feature_cols] # Features
y = pima.label # Target variable

# Split dataset into training set and test set
# kali ini kita memisahkan data yang banyak menjadi Training dan Test. Sebelumnya tidak kita lakukan
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test

# Create Decision Tree classifer object without Pre-Pruning above and with Pre-Pruning below, comment one of them
clf = DecisionTreeClassifier() # without Pruning
# clf = DecisionTreeClassifier(criterion="entropy", max_depth=3) # with Pruning

# Train the Decision Tree
clf = clf.fit(X_train,y_train)

#Predict the response for test dataset
y_pred = clf.predict(X_test)

# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred)*100,"%")

# Prepare the Plotter as Graph
# Untuk menampilkan Grafik-nya
from matplotlib import pyplot as plt
from sklearn import tree

fig = plt.figure(figsize=(25,20))

_ = tree.plot_tree(clf,feature_names = feature_cols,class_names=['0','1'],filled=True)