Show me the steps to solve :

Problem Statement:

This dataset contains data on sleep habits for $25$ randomly selected mammals. Each mammal is categorized as an omnivore, herbivore, carnivore, or insectivore.

REM sleep cycles of gray seals average $1\mathrm{.}5$ hours. Gray seals are awake on average $17\mathrm{.}8$ hours per day.

Use the kneighbors$()$ method to find the instances in the training data that are closest to gray seals. Assign the instances, but not the distances, to neighbors.

The code contains all imports, loads the dataset, initializes the model, and applies the model to a test dataset.

Code Snippet:

# Import packages and functions

import numpy as np

import pandas as pd

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model$\_$selection import train$\_$test$\_$split

# Import dataset

sleep $=$ pd$.$read$\_$csv$(\text{'}$sleep$.$csv$\text{'})$

# Create input matrix X and output matrix y

X $=$ sleep$[[\text{'}$sleep$\_$rem', 'awake'$]]$

y $=$ sleep$[[\text{'}$vore$\text{'}]]$

knnModel $=$ KNeighborsClassifier$($n$\_$neighbors$=3)$

knnModel $=$ knnModel.fit$($X$.$values, np$.$ravel$($y$.$values$))$

# Your code goes here

# Print neighbors

print$($neighbors$)$