# load the iris dataset as an example
from sklearn.datasets import load_iris
iris = load_iris()
 
# store the feature matrix (X) and response vector (y)
X = iris.data
y = iris.target
 
# splitting X and y into training and testing sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1)
 
# training the model on training set
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
 
# making predictions on the testing set
y_pred = knn.predict(X_test)
 
# comparing actual response values (y_test) with predicted response values (y_pred)
from sklearn import metrics
print("kNN model accuracy:", metrics.accuracy_score(y_test, y_pred))
 
# making prediction for out of sample data
sample = [[3, 5, 4, 2], [2, 3, 5, 4]]
preds = knn.predict(sample)
pred_species = [iris.target_names[p] for p in preds]
print("Predictions:", pred_species)

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