main.py

import pandas as pd
import numpy as np

import math

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score

dataset = pd.read_csv('diabetes.csv')
#print(len(dataset))
#print(dataset.head())

zero_not_accepted = ['Glucose', 'BloodPressure', 'SkinThickness', 'BMI', 'Insulin']
for column in zero_not_accepted:
    dataset[column] = dataset[column].replace(0, np.NAN)
    mean = int(dataset[column].mean(skipna=True))
    dataset[column] = dataset[column].replace(np.NAN, mean)

#print(dataset['Glucose'])

#split dataset
X = dataset.iloc[:, 0:8]
y = dataset.iloc[:, 8]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)

#Feature scaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

print(math.sqrt(len(y_test)))

#Define the model: Init K-NN
classifier = KNeighborsClassifier(n_neighbors=11, p=2, metric='euclidean')

#Fit model
classifier.fit(X_train, y_train)

#Predict the test set results
y_pred = classifier.predict(X_test)
print(y_pred)

cm = confusion_matrix(y_test, y_pred)
print(cm)
print(f1_score(y_test, y_pred))
print(accuracy_score(y_test, y_pred))