import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from knn import KNearestNeighbors
iris = load_iris()
data = iris.data
target = iris.target
X_train, X_test, y_train, y_test = train_test_split(data,
target,
test_size=0.2,
random_state=5656)
clf = KNearestNeighbors(K=3)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print('Accuracy:', accuracy_score(y_test, predictions))
print(dataset.head())
X = dataset.drop('label', axis=1)
y = dataset['label']
from sklearn.preprocessing import MinMaxScaler
x_scaler = MinMaxScaler()
X = x_scaler.fit_transform(X)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.25,
random_state=2)
from knn import KNearestNeighbors
knn = KNearestNeighbors(k=3)
knn.fit(X_train, y_train)
predict = knn.predict(X_test)
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
print(accuracy_score(y_test, predict))
print(confusion_matrix(y_test, predict))
print(classification_report(y_test, predict))