# Find Nearest Neighbors

## Preliminaries

``````
/* Load libraries */
from sklearn.neighbors import NearestNeighbors
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np``````

## Load Iris Dataset

``````
/* Load data */
X = iris.data
y = iris.target``````

## Standardize Iris Data

It is important to standardize our data before we calculate any distances.

``````
/* Create standardizer */
standardizer = StandardScaler()

/* Standardize features */
X_std = standardizer.fit_transform(X)``````

## Find Each Observation’s Two Nearest Neighbors

``````
/* Find three nearest neighbors based on euclidean distance (including itself) */
nn_euclidean = NearestNeighbors(n_neighbors=3, metric='euclidean').fit(X)

/* List of lists indicating each observation's 3 nearest neighors */
nearest_neighbors_with_self = nn_euclidean.kneighbors_graph(X).toarray()

/* Remove 1's marking an observation is nearest to itself  */
for i, x in enumerate(nearest_neighbors_with_self):
x[i] = 0``````

## Show nearest neighbors

``````
/* View first observation's two nearest neighbors */
nearest_neighbors_with_self[0]``````
``````array([ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.,  0.,  0.,  0.,  0.])``````

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