# import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
data = pd.read_csv(‘column_2C_weka.csv’)
data.head()
data[‘class’].unique()
# you split class as abnormal and normal
A = data[data[‘class’] == ‘Abnormal’]
N = data[data[‘class’] == ‘Normal’]
#visualization
plt.scatter(A.pelvic_incidence, A.pelvic_radius, color = ‘purple’, label = ‘Abnormal’,alpha = 0.5)
plt.scatter(N.pelvic_incidence, N.pelvic_radius, color = ‘orange’, label = ‘Normal’, alpha = 0.5)
plt.xlabel(‘pelvic_incidence’)
plt.ylabel(‘pelvic_radius’)
plt.legend()
plt.show()
# abnormal and normal are string. So you transform integer or float.
data[‘class’] = [0 if each == ‘Abnormal’ else 1 for each in data[‘class’]]
# determine feature and feature class.
y = data[‘class’].values
x_data = data.drop([‘class’], axis=1)
# normalization
x = (x_data-np.min(x_data))/(np.max(x_data)-np.min(x_data))
# (x-min(x))/(max(x)-min(x))
#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 = 1)
#knn model
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 4)
knn.fit(x_train,y_train)
prediction = knn.predict(x_test)
print(‘{} nın score: {}’.format(3,knn.score(x_test,y_test)))
# find the most appropriate k value
score_list = []
for each in range(1,15):
knn2 = KNeighborsClassifier(n_neighbors=each)
knn2.fit(x_train, y_train)
score_list.append(knn2.score(x_test,y_test))
plt.plot(range(1,15),score_list,color=’purple’)
plt.xlabel(“k values”)
plt.ylabel(“accuracy”)
plt.show()
