#import modul
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inlineModul 5 Praktikum Sains Data
K-Nearest Neighbor
K-Nearest neighbor adalah salah satu jenis algoritma supervised learning. Biasanya, algoritma ini digunakan untuk masalah klasifikasi. Kelas dari data tersebut ditentukan dari sejumlah k titik yang berperan “tetangga”. Pada gambar di atas, ketika k = 3, bintang akan diklasifikasikan sebagai kelas ungu, sebab mayoritas dari tetangganya adalah ungu. Sedangkan, ketika k = 6, bintang akan diklasifikasikan sebagai kelas kuning.
Data
Pada module kali ini, akan digunakan data csv teleCust1000t (teleCust1000t.csv) yang bisa didownload dari: - Kaggle
#membaca dataset
df = pd.read_csv('./teleCust1000t.csv')
df.head()| region | tenure | age | marital | address | income | ed | employ | retire | gender | reside | custcat | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 13 | 44 | 1 | 9 | 64.0 | 4 | 5 | 0.0 | 0 | 2 | 1 |
| 1 | 3 | 11 | 33 | 1 | 7 | 136.0 | 5 | 5 | 0.0 | 0 | 6 | 4 |
| 2 | 3 | 68 | 52 | 1 | 24 | 116.0 | 1 | 29 | 0.0 | 1 | 2 | 3 |
| 3 | 2 | 33 | 33 | 0 | 12 | 33.0 | 2 | 0 | 0.0 | 1 | 1 | 1 |
| 4 | 2 | 23 | 30 | 1 | 9 | 30.0 | 1 | 2 | 0.0 | 0 | 4 | 3 |
#menghitung jumlah anggota tiap kelas
df['custcat'].value_counts()custcat
3 281
1 266
4 236
2 217
Name: count, dtype: int64 #melihat sebaran income dengan histogram
df.hist(column='income')array([[<Axes: title={'center': 'income'}>]], dtype=object)
#melihat 4 row pertama
X = df[['region', 'tenure', 'age', 'marital', 'address', 'income', 'ed', 'employ', 'retire','gender', 'reside']].values
X[0:4]array([[ 2., 13., 44., 1., 9., 64., 4., 5., 0., 0., 2.],
[ 3., 11., 33., 1., 7., 136., 5., 5., 0., 0., 6.],
[ 3., 68., 52., 1., 24., 116., 1., 29., 0., 1., 2.],
[ 2., 33., 33., 0., 12., 33., 2., 0., 0., 1., 1.]])#melihat kelas dari 4 row pertama
Y = df[['custcat']].values
Y[0:4]array([[1],
[4],
[3],
[1]], dtype=int64)Preprocessing
from sklearn import preprocessing#normalize data
X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))
X[0:4]array([[-0.02696767, -1.055125 , 0.18450456, 1.0100505 , -0.25303431,
-0.12650641, 1.0877526 , -0.5941226 , -0.22207644, -1.03459817,
-0.23065004],
[ 1.19883553, -1.14880563, -0.69181243, 1.0100505 , -0.4514148 ,
0.54644972, 1.9062271 , -0.5941226 , -0.22207644, -1.03459817,
2.55666158],
[ 1.19883553, 1.52109247, 0.82182601, 1.0100505 , 1.23481934,
0.35951747, -1.36767088, 1.78752803, -0.22207644, 0.96655883,
-0.23065004],
[-0.02696767, -0.11831864, -0.69181243, -0.9900495 , 0.04453642,
-0.41625141, -0.54919639, -1.09029981, -0.22207644, 0.96655883,
-0.92747794]])Train test split
#train test split
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 = 4)print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)(800, 11)
(800, 1)
(200, 11)
(200, 1)Membuat model
#membuat model dengan k = 4
from sklearn.neighbors import KNeighborsClassifier
k = 4
neighbor = KNeighborsClassifier(n_neighbors = k).fit(X_train, Y_train)c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)Prediksi
#hasil prediksi
ypredict = neighbor.predict(X_test)
ypredict[0:5]array([1, 1, 3, 2, 4], dtype=int64)#kelas sebenarnya
Y_test[0:5]array([[4],
[1],
[1],
[2],
[4]], dtype=int64)Evaluasi Model
#menghitung akurasi
from sklearn import metrics
metrics.accuracy_score(Y_test,ypredict)0.32Membuat model dengan k lainnya
#membuat model dengan k = 6
k = 6
neighbor_6 = KNeighborsClassifier(n_neighbors = k).fit(X_train, Y_train)c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)#hasil prediksi
ypredict6 = neighbor_6.predict(X_test)
ypredict6[0:5]array([3, 3, 3, 4, 4], dtype=int64)#kelas sebenarnya
Y_test[0:5]array([[4],
[1],
[1],
[2],
[4]], dtype=int64)#akurasi
metrics.accuracy_score(Y_test,ypredict6)0.31Mencari k terbaik
Kinerja model K-NN sangat bergantung pada jumlah k yang dipilih. Kita dapat menentukan k terbaik dengan menggunakan loop.
#mencari k terbaik diantara 1<=k<=10
nk = 10
mean_acc= np.zeros((nk))
std_acc = np.zeros((nk))
for n in range(1,nk+1):
neighbor_k = KNeighborsClassifier(n_neighbors= n).fit(X_train,Y_train)
ypredict = neighbor_k.predict(X_test)
mean_acc[n-1] = metrics.accuracy_score(Y_test, ypredict)
std_acc[n-1]= np.std(ypredict==Y_test)/np.sqrt(ypredict.shape[0])
mean_accc:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)
c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)
c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)
c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)
c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)
c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)
c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)
c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)
c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)
c:\Users\ACER\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\neighbors\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
return self._fit(X, y)array([0.3 , 0.29 , 0.315, 0.32 , 0.315, 0.31 , 0.335, 0.325, 0.34 ,
0.33 ])#plot akurasi dari beberapa k
plt.plot(range(1,nk+1),mean_acc,'g')
plt.fill_between(range(1,nk+1),mean_acc-1*std_acc,mean_acc+1*std_acc,alpha = 0.10)
plt.fill_between(range(1,nk+1),mean_acc-3*std_acc,mean_acc+3*std_acc,alpha = 0.10, color = "red")
plt.legend(('Accuracy', '+-1xstd', '+-3xstd'))
plt.ylabel('Accuracy')
plt.xlabel('Jumlah neighbor')
plt.tight_layout()
plt.show()
#k terbaik beserta hasilnya
print("akurasi terbaik model adalah", mean_acc.max(), "dengan jumlah k=", mean_acc.argmax()+1)akurasi terbaik model adalah 0.34 dengan jumlah k= 9