$($

The function should be named create$\_$and$\_$train$\_$knn$\_$model.

Parameters:

x$\_$train : A $2$D array of the training features.

y$\_$train : A $1$D array of the training labels.

$k$ : An integer representing the number of neighbors to consider. The default value should be $1.$

Return:

The function should return the trained KNN model, which is trained onn the input training data and with the input value of $k.$

In $[]$: def create$\_$and$\_$train$\_$knn$\_$model$($x$\_$train, y$\_$train, $k=1)$:

Creates and trains a K$-$Nearest Neighbors $($KNN$)$ model.

Parameters:

X$\_$train: $2$D array of training features.

y$\_$train: $1$D array of training labels.

k: Integer, number of neighbors $($default: $1).$

Returns:

Trained KNN model.

$"""$

# Initialize the KNN model with $k$ neighbors

# Train the model with $x_{{?}_t\text{r}\text{a}\text{i}\text{n}}$ and $y_{-}$train

# Return the trained model

return model

# Example usage:

# model $=$ create$\_$and$\_$train$\_$knn$\_$model $(x\_$train, $y_{-}$train, $k=5)$Do it in python$)$

$1$ K$-$Nearest Neighbors $($KNN$)$ Classifier

$1\mathrm{.}1$ Create and Train K$-$NN Model#

You are tasked with creating and training a K$-$Nearest Neighbors $($KNN$)$ model. Your goal is to understand how to implement a KNN classifier and train your model on a given dataset.

Objective:

Create and train a KNN model using the input training set.

Return the trained model.

Requirements:

The function should be named create$\_$and$\_$train$\_$knn$\_$model $.$

Parameters:

x$\_$train : A $2$D array of the training features.

y$\_$train : A $1$D array of the training labels.

$k$ : An integer representing the number of neighbors to consider. The default value should be $1.$

Return:

The function should return the trained KNN model, which is trained onn the input training data and with the input value of $k.$