# KNN P.3 – Implementation

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For the official SkLearn KNN documentation click here.

## Training a KNN Classifier

Creating a KNN Classifier is almost identical to how we created the linear regression model. The only difference is we can specify how many neighbors to look for as the argument n_neighbors.

`from sklearn.neighbors import KNeighborsClassifier model = KNeighborsClassifier(n_neighbors=9)`

To train our model we follow precisely the same steps as outlined earlier.

`model.fit(x_train, y_train)`

And once again to score our model we will do the following.

`acc = model.score(x_test, y_test) print(acc)`

## Testing Our Model

If we'd like to see how our model is performing on the unique elements of our test data we can do the following.

`predicted = model.predict(x_test) names = ["unacc", "acc", "good", "vgood"] for x in range(len(predicted)): print("Predicted: ", names[predicted[x]], "Data: ", x_test[x], "Actual: ", names[y_test[x]]) # This will display the predicted class, our data and the actual class # We create a names list so that we can convert our integer predictions into # their string representation`

Our output should look like the following.

## Looking at Neighbors

The KNN model has a unique method that allows for us to see the neighbors of a given data point. We can use this information to plot our data and get a better idea of where our model may lack accuracy. We can use **model.neighbors** to do this.

Note: the .neighbors method takes 2D as input, this means if we want to pass one data point we need surround it with [] so that it is in the right shape. Parameters: The parameters for .neighbors are as follows: data(2D array), # of neighbors(int), distance(True or False) Return: This will return to us an array with the index in our data of each neighbor. If distance=True then it will also return the distance to each neighbor from our data point.

`predicted = model.predict(x_test) names = ["unacc", "acc", "good", "vgood"] for x in range(len(predicted)): print("Predicted: ", names[predicted[x]], "Data: ", x_test[x], "Actual: ", names[y_test[x]]) # Now we will we see the neighbors of each point in our testing data n = model.kneighbors([x_test[x]], 9, True) print("N: ", n)`

Our output should now be a mess that looks like this.

##Full Code

`import sklearn from sklearn.utils import shuffle from sklearn.neighbors import KNeighborsClassifier import pandas as pd import numpy as np from sklearn import linear_model, preprocessing data = pd.read_csv("car.data") le = preprocessing.LabelEncoder() buying = le.fit_transform(list(data["buying"])) maint = le.fit_transform(list(data["maint"])) door = le.fit_transform(list(data["door"])) persons = le.fit_transform(list(data["persons"])) lug_boot = le.fit_transform(list(data["lug_boot"])) safety = le.fit_transform(list(data["safety"])) cls = le.fit_transform(list(data["class"])) predict = "class" X = list(zip(buying, maint, door, persons, lug_boot, safety)) y = list(cls) x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.1) model = KNeighborsClassifier(n_neighbors=9) model.fit(x_train, y_train) acc = model.score(x_test, y_test) print(acc) predicted = model.predict(x_test) names = ["unacc", "acc", "good", "vgood"] for x in range(len(predicted)): print("Predicted: ", names[predicted[x]], "Data: ", x_test[x], "Actual: ", names[y_test[x]]) n = model.kneighbors([x_test[x]], 9, True) print("N: ", n)`