python-scikit-learnSklearn classifier example
from sklearn import datasets, neighbors, model_selection
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = neighbors.KNeighborsClassifier(3)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)ctrl + c| from sklearn importimport module from scikit-learn | load_irisloads Iris dataset | 
| model_selection.train_test_splitsplits given  | .KNeighborsClassifier(K neighbors classification model | 
| (3)how many neighbors to use | .fit(train model with a given features and target variable dataset | 
| .predict(predict target variable based on given features dataset | y_predtarget variable predicted values by our model (values to evaluate) | 
Usage example
from sklearn import datasets, neighbors, model_selection
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = neighbors.KNeighborsClassifier(3)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(y_pred)output
[1 2 1 2 1 0 1 0 0 2 1 1 2 2 0 0 1 2 2 2 0 1 0 0 0 2 1 2 0 2 0 0 2 0 0 2 0
 0]
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