from sklearn.neighbors import KNeighborsClassifier import pandas as pd # Load dataset animals = pd.read_csv('animal_data.csv') # Separate features and classes X = animals.iloc[:, :-1] y = animals['class'] # Create KNeighborsClassifier object knn = KNeighborsClassifier(n_neighbors=3) # Fit the classifier to the data knn.fit(X, y) # Predict the classification of a new animal new_animal = [[4, 2, 5, 3]] predicted_class = knn.predict(new_animal)[0] print(predicted_class) # Output: 'mammal'In this example, we load a dataset of animal data where each animal has 4 features (number of legs, weight, height, length) and a classification (mammal, reptile, bird). We separate the features and classes into X and y. We then create a KNeighborsClassifier object with k=3 (3 nearest neighbors) and fit it to the data. Finally, we predict the classification of a new animal with 4 legs, weight 2, height 5, and length 3. The classifier predicts this animal is a mammal. This code example is using the python sklearn.neighbors library to implement KNeighborsClassifier to predict the classification of animals based on their features.