random_feats_knn.py

X = X[random_cols]

# Train-test-split
X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                    test_size=0.25,
                                                    random_state=42)

# Perform feature scaling
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Fit k-NN classifier and make predictions
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred_train = knn.predict(X_train)
y_pred_test = knn.predict(X_test)
    
print(f'Train f1 Score: {f1_score(y_train, y_pred_train)}')
print(f'Test f1 Score: {f1_score(y_test, y_pred_test)}')
print(classification_report(y_test, y_pred_test))