from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
import joblib

iris = load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(
   X, y, test_size = 0.4, random_state=1
)

classifier_knn = KNeighborsClassifier(n_neighbors = 3)
classifier_knn.fit(X_train, y_train)
y_pred = classifier_knn.predict(X_test)

# Finding accuracy by comparing actual response values(y_test)with predicted response value(y_pred)
print("Model Accuracy:", metrics.accuracy_score(y_test, y_pred))
# Providing sample data and the model will make prediction out of that data

# save the trained model file
model_file_name = "iris_classifier_knn.joblib"
joblib.dump(classifier_knn, model_file_name)
print("Model file {} saved successfully".format(model_file_name))