How to predict the value in KNN?
Here is one example for you to consider.
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

url = &quot;https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data&quot;

# Assign colum names to the dataset
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']

# Read dataset to pandas dataframe
dataset = pd.read_csv(url, names=names)

dataset.head()

X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 4].values

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)

X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors=5)
classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))


classifier = KNeighborsClassifier(n_neighbors=3)
classifier.fit(X, y)
testSet = [[5.1, 3.5, 1.4, .2]]
test = pd.DataFrame(testSet)
y_pred = classifier.predict(testSet)
print(y_pred)

The very last line of code will print this:  ['Iris-setosa']
Let's look at the very first row in the test set:  testSet = [[5.1, 3.5, 1.4, .2]]
Look at: dataset.head()
Do you see how y_pred is the class, given [5.1, 3.5, 1.4, .2]?

Hope that makes sense!  Now, let's change the testSet just a bit, and check the outcome.
classifier = KNeighborsClassifier(n_neighbors=3)
classifier.fit(X, y)
testSet = [[5.1, 3.5, 4.9, .2]]
test = pd.DataFrame(testSet)
y_pred = classifier.predict(testSet)
print(y_pred)

Result: ['Iris-versicolor']
The variable ('petal-length') changed from 1.4 to 4.9, and now we have a different 'Class'!!  Does it make sense?  If you look at the raw data, you will see that the testSet doesn't even exist, but nevertheless, the model can make a prediction about what the Class should be!
K-Nearest Neighbors Algorithm in Python and Scikit-Learn