Studi Kasus Heart Disease 5 Fitur Part 2#

Implementasi dengan Menggunakan Model Bagging Classifier , Random forest dan Stacking clasifier

Membaca data#

from scipy.io import arff
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import joblib

data = pd.read_csv('https://raw.githubusercontent.com/soumya-mishra/Heart-Disease_DT/main/heart_v2.csv')
df = data
df.head()
age sex BP cholestrol heart disease
0 70 1 130 322 1
1 67 0 115 564 0
2 57 1 124 261 1
3 64 1 128 263 0
4 74 0 120 269 0

Memisahkan Label#

y = df['heart disease'].values
y[0:5]

array([1, 0, 1, 0, 0])

X = df.drop(columns=['heart disease'])
X
age sex BP cholestrol
0 70 1 130 322
1 67 0 115 564
2 57 1 124 261
3 64 1 128 263
4 74 0 120 269
... ... ... ... ...
265 52 1 172 199
266 44 1 120 263
267 56 0 140 294
268 57 1 140 192
269 67 1 160 286

270 rows × 4 columns

Preprocessing Data (Min-Max)#

scaler = MinMaxScaler()
scaled = scaler.fit_transform(X)
features_names = X.columns.copy()
scaled_features = pd.DataFrame(scaled, columns=features_names)
scaled_features.head(10)
age sex BP cholestrol
0 0.854167 1.0 0.339623 0.447489
1 0.791667 0.0 0.198113 1.000000
2 0.583333 1.0 0.283019 0.308219
3 0.729167 1.0 0.320755 0.312785
4 0.937500 0.0 0.245283 0.326484
5 0.750000 1.0 0.245283 0.116438
6 0.562500 1.0 0.339623 0.296804
7 0.625000 1.0 0.150943 0.257991
8 0.645833 1.0 0.433962 0.381279
9 0.708333 0.0 0.528302 0.641553

Split Data#

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test=train_test_split(scaled_features, y, test_size=0.2, random_state=1)

Eksekusi Pada Model Bagging Clasifier#

Bagging Clasifier Dengan SVC#

Mencari akurasi tertinggi dengan N_estimators dari 2 sampai 100

# import model
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import BaggingClassifier
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
# eksekusi data pada model
X, y = make_classification(n_samples=100, n_features=4,
                           n_informative=2, n_redundant=0,
                           random_state=0, shuffle=False)
# bagging clasifier menggunakan SVC dan Gaussian(naive bayes)
# # SVC
n_estimator = range(2,101)
akurasi_bags_1 = []
for n in n_estimator:
  # inisialisasi model
  clf = BaggingClassifier(base_estimator=SVC(),
                          n_estimators=n, random_state=40).fit(X_train, y_train)
  # predict x_test
  y_pred = clf.predict(X_test)
  # akurasi count
  akurasi_bags_1.append(accuracy_score(y_test,y_pred))

Visualisasi Akurasi Bagging dengan SVC

import matplotlib.pyplot as plt
plt.plot(n_estimator,akurasi_bags_1)
plt.xlabel('Value of N')
plt.ylabel('Testing Accuracy')
plt.show()
_images/notebooks10_16_0.png

Mencari n_estimator dengan Akurasi Tertinggi

akurasi_bags_1.index(max(akurasi_bags_1))+1 , max(akurasi_bags_1)

(5, 0.7592592592592593)

Bagging Clasifier Dengan GaussianNB#

Mencari akurasi tertinggi dengan N_estimators dari 2 sampai 100

akurasi_bags_2= []
for n in n_estimator:
  # inisialisasi model
  clf2 = BaggingClassifier(base_estimator=GaussianNB(),
                          n_estimators=n, random_state=40).fit(X_train, y_train)
  # predict x_test
  y_pred2 = clf2.predict(X_test)
  # akurasi count
  akurasi_bags_2.append(accuracy_score(y_test,y_pred2))

import joblib
clf2 = BaggingClassifier(base_estimator=GaussianNB(),
                          n_estimators=6, random_state=40).fit(X_train, y_train)
filenameBCG = '/content/drive/MyDrive/datamining/tugas/model/bagginggaussian.pkl'
joblib.dump(clf2,filenameBCG)

['/content/drive/MyDrive/datamining/tugas/model/bagginggaussian.pkl']

Visualisasi Hasil Akurasi

plt.plot(n_estimator,akurasi_bags_2)
plt.xlabel('Value of N')
plt.ylabel('Testing Accuracy')
plt.show()
_images/notebooks10_24_0.png

Mencari Akurasi Tertinggi

akurasi_bags_2.index(max(akurasi_bags_2))+1 , max(akurasi_bags_2)

(7, 0.7777777777777778)

Eksekusi Pada Model Random Forest#

Mencari akurasi tertinggi dengan N_estimators dari 2 sampai 100

from sklearn.ensemble import RandomForestClassifier
akurasirf= []
for n in n_estimator:
  # inisialisasi model
  rf = RandomForestClassifier(
                          n_estimators=n,max_depth=2, random_state=40).fit(X_train, y_train)
  # predict x_test
  y_predrf = rf.predict(X_test)
  # akurasi count
  akurasirf.append(accuracy_score(y_test,y_predrf))

rf = RandomForestClassifier(
                          n_estimators=13,max_depth=2, random_state=40).fit(X_train, y_train)
filenameRF = '/content/drive/MyDrive/datamining/tugas/model/randomforest.pkl'
joblib.dump(rf,filenameRF)

['/content/drive/MyDrive/datamining/tugas/model/randomforest.pkl']

Visualisasi Hasil Akurasi

plt.plot(n_estimator,akurasirf)
plt.xlabel('Value of N')
plt.ylabel('Testing Accuracy')
plt.show()
_images/notebooks10_32_0.png

Mencari Akurasi Tertinggi dari N

akurasirf.index(max(akurasirf))+1 , max(akurasirf)

(14, 0.7222222222222222)

Eksekusi Pada Model Stacking clasifier#

from sklearn.ensemble import StackingClassifier
from sklearn.tree import DecisionTreeClassifier

# estimator menggunakan Random Forest, SVC GaussianNB
## untuk n_estimators menggunakan n dengan akurasi tertinggi 
estimators = [
    ('rf', RandomForestClassifier(n_estimators=38, max_depth=2, random_state=40)),
    ('svc', SVC()),
     ('gnb', GaussianNB()),
     ('bagsvc', BaggingClassifier(base_estimator=SVC(),
                          n_estimators=14, random_state=40)),
    ('baggnb' ,BaggingClassifier(base_estimator=GaussianNB(),
                          n_estimators=9, random_state=40))
]

sc = StackingClassifier(
      estimators=estimators, final_estimator=SVC()).fit(X_train, y_train)

y_predsc = sc.predict(X_test)
akurasi = accuracy_score(y_test,y_predsc)

Hasil Akurasi Dan Score dari Stacking Clasifier#

print(f'Akurasi Untuk Stacking Clasifier =  {akurasi}')

Akurasi Untuk Stacking Clasifier =  0.6666666666666666