Modul 5 Praktikum Sains Data

Modul 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.

#import modul
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline

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)
0.32

Membuat 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)
0.31

Mencari 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_acc
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)
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