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# Imputing Missing Class Labels Using k-Nearest Neighbors

## Preliminaries

```
# Load libraries
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
```

## Create Feature Matrix

```
# Create feature matrix with categorical feature
X = np.array([[0, 2.10, 1.45],
[1, 1.18, 1.33],
[0, 1.22, 1.27],
[1, -0.21, -1.19]])
```

## Create Feature Matrix With Missing Values

```
# Create feature matrix with missing values in the categorical feature
X_with_nan = np.array([[np.nan, 0.87, 1.31],
[np.nan, -0.67, -0.22]])
```

## Train k-Nearest Neighbor Classifier

```
# Train KNN learner
clf = KNeighborsClassifier(3, weights='distance')
trained_model = clf.fit(X[:,1:], X[:,0])
```

## Predict Missing Values’ Class

```
# Predict missing values' class
imputed_values = trained_model.predict(X_with_nan[:,1:])
# Join column of predicted class with their other features
X_with_imputed = np.hstack((imputed_values.reshape(-1,1), X_with_nan[:,1:]))
# Join two feature matrices
np.vstack((X_with_imputed, X))
```

```
array([[ 0. , 0.87, 1.31],
[ 1. , -0.67, -0.22],
[ 0. , 2.1 , 1.45],
[ 1. , 1.18, 1.33],
[ 0. , 1.22, 1.27],
[ 1. , -0.21, -1.19]])
```

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