### KNeighborsClassifier
Implementing k-Nearest Neighbors is pretty straightforward. We only need to import and use the `KNeighborsClassifier` class.

Once you imported the class and created a class object like this:
```python
from sklearn.neighbors import KNeighborsClassifier # Import the class
knn = KNeighborsClassifier(n_neighbors=3)
```
You need to feed it the training data using the `.fit()` method:
```python
knn.fit(X_scaled, y)
```
And that's it! You can predict new values now.
```python
y_pred = knn.predict(X_new_scaled)
```  


  
### Scaling the data
However, remember that the data **must be scaled**. Let's take a closer look at the `StandardScaler` commonly used for scaling the data.  
`StandardScaler` just subtracts the sample's mean and then divides the result by the sample's standard deviation.

> Note
>
> If the terms *sample mean* and *sample standard deviation* sound unfamiliar, you can check out our [Statistics course](https://codefinity.com/courses/a849660e-ddfa-4033-80a6-94a1b7772e23). But an understanding of what those numbers are for is not mandatory, so you can just ignore the meanings of **x̄** and **s** and move on :)

You should calculate **x̄** and **s** on the training set(using either `.fit()` or `.fit_transform()`) and use the same **x̄** and **s** to preprocess the data you are predicting(using `.transform()`). Here is an example:
```python
from sklearn.preprocessing import StandardScaler # import the class
# X is the training set's features
# X_new is the new instances' features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X) # calculates x̄ and s and scales the X
X_new_scaled = scaler.transform(X_new) # scales the X_new with x̄ and s calculated in the previous line
```

**If you use different x̄ and s for training set and new instances your predictions will likely be worse**.

### Example
So to perform a k-NN classification in Python, you need to use `KNeighborsClassifier` and `StandardScaler`. Your code will look like this:
```python
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler

# Load and preprocess X, y, and X_new here

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_new_scaled = scaler.transform(X_new)

knn = KNeighborsClassifier(n_neighbors=13).fit(X_scaled, y)
y_pred = knn.predict(X_new_scaled)
```
Here is a simple example where we try to predict whether the person will like Star Wars VI based on his ratings for Star Wars IV and V. We will read the data from the URL.

from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_binary.csv')
X = df[['StarWars4_rate', 'StarWars5_rate']] # Store feature columns as X
y = df['StarWars6'] # Store target column as y (contains 1(liked SW 6) or 0(didn't like SW 6)
X_new = np.array([[5, 5], [4.5, 4]]) # 2 insances to predict, ratings [5, 5] and [4.5 for Star Wars IV and 4 for V]
# Scale the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_new_scaled = scaler.transform(X_new)
# Build a model and predict new instances
knn = KNeighborsClassifier(n_neighbors=13).fit(X_scaled, y)
y_pred = knn.predict(X_new_scaled)
print(y_pred)

The data is taken from [The Movies Dataset](https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset) with extra preprocessing. A person considered to like Star Wars VI if he/her rated it more than 4/5.

Classification with Python

Implementing k-NN

KNeighborsClassifier

Scaling the data

Example