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from sklearn.datasets import load_breast_cancer
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import f1_score, classification_report, accuracy_score, mean_squared_error
data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.20, random_state=42)
clf = KNeighborsClassifier()
gridsearch = GridSearchCV(clf, {"n_neighbors": [1, 3, 5, 7, 9, 11], "weights": ['uniform', 'distance'],
'p': [1, 2, 3]}, scoring='f1'), y_train)
print("Best Params: {}".format(gridsearch.best_params_))
y_pred_train = gridsearch.predict(X_train)
print("Train F1: {}".format(f1_score(y_train, y_pred_train)))
print("Test Classification Report:")
y_pred_test = gridsearch.predict(X_test)
print(classification_report(y_test, y_pred_test))
print("Train Accuracy: {}\tTest accuracy: {}".format(accuracy_score(y_train, y_pred_train),
accuracy_score(y_test, y_pred_test)))
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