To implement a K-Nearest Neighbors (KNN) classifier in Python using `scikit-learn`, the `KNeighborsClassifier` can be used. After splitting the dataset into training and testing sets, the model is initialized, fitted to the training data, and then used to make predictions and score its accuracy on the testing data.

```python
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
knnc = KNeighborsClassifier(n_neighbors=5).fit(X_train, y_train)
knnc.predict(X_test)
knnc.score(X_test, y_test)
```

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Scikit-learn enables the creation of classifier objects. For example, 
```python
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5, weights = 'distance')
```
In this example, the classifier object (named knn) will consider 5 neighbors in its kneighbors queries.   Setting the weights parameter to 'distance' means that closer neighbors will be more influential than those farther away  (in terms of detemrining which category the "target" point belongs to).

sklearn.neighbors.KNeighborsClassifier

Useful Parameters for KNN Classifier

- fit(X_train, y_train)
- predict(X_test)
- score(X_test, y_test)

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