Knn classification in python

Knn_classification

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv("/kaggle/input/iris-flower-dataset/IRIS.csv")
dataset.head()
Features=["sepal_length","sepal_width","petal_length","petal_width"]
X=dataset[Features]
y=dataset.species
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)
from sklearn.preprocessing import StandardScaler
scaler= StandardScaler()
caler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
#for k=8
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 8)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
result = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(result)
result1 = classification_report(y_test, y_pred)
print("Classification Report:",)
print (result1)
result2 = accuracy_score(y_test,y_pred)
print("Accuracy:",result2)
#for k=5
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
result = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(result)
result1 = classification_report(y_test, y_pred)
print("Classification Report:",)
print (result1)
result2 = accuracy_score(y_test,y_pred)
print("Accuracy:",result2)