knearest

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
iris_data = datasets.load_iris()
iris_df = pd.DataFrame(data=np.c_[iris_data['data'],iris_data['target']],columns=
iris_data['feature_names']+['target'])
iris_df
X=iris_df.drop(columns=['target'])
y=iris_df['target']
X_train,X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=1)
knn = KNeighborsClassifier(n_neighbors=6)
knn = knn.fit(X_train,y_train)
y_pred=knn.predict(X_test)
print("Accuracy of model:", accuracy_score(y_pred,y_test))
i=0
print('Actual label Predicted Label Correct/wrong prediction')
for label in y_test:
  print('%-25s %-25s' %(label,y_pred[i]), end="")
if (label== y_pred[i]):
  print('%-25s' %('correct'));
else:
  print('%-25s' % ('Wrong'));
i=i+1