Just a little example on how to use the K-Nearest Neighbor model in Python. In this example we’re utilizing an anonymous dataset with unknown context. All we know is that there is a TARGET CLASS of 0/1 and we want to predict if a row belongs to that class or not. K-Nearest Neighbor functions by comparing a point to a certain number of points around it. It’s pretty simple to implement. The dataset can be found on my github here.

Check it out on github Last updated: 29/08/2021 03:36:51

K-Nearest Neighbor - Simple Example¶

I.E. predict value based on clustering of values surrounding a point¶

Utilizes anon data, unknown columns or context. We want to find out if a value should exist within the TARGET CLASS or not.

In [1]:

#Imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

In [2]:

#Read in the data
df = pd.read_csv('Classified Data',index_col=0)

In [3]:

df.head()

Out[3]:

	WTT	PTI	EQW	SBI	LQE	QWG	FDJ	PJF	HQE	NXJ	TARGET CLASS
0	0.913917	1.162073	0.567946	0.755464	0.780862	0.352608	0.759697	0.643798	0.879422	1.231409	1
1	0.635632	1.003722	0.535342	0.825645	0.924109	0.648450	0.675334	1.013546	0.621552	1.492702	0
2	0.721360	1.201493	0.921990	0.855595	1.526629	0.720781	1.626351	1.154483	0.957877	1.285597	0
3	1.234204	1.386726	0.653046	0.825624	1.142504	0.875128	1.409708	1.380003	1.522692	1.153093	1
4	1.279491	0.949750	0.627280	0.668976	1.232537	0.703727	1.115596	0.646691	1.463812	1.419167	1

Scale Standardization of dataset¶

Tranform data such that its distribution will have a mean value of 0, and standard deviation of 1 Essentially it it "standardizes" the dataset so that the scales of every metric are common, useful when comparing data of different units

In [4]:

#Import Sci-kit learn
from sklearn.preprocessing import StandardScaler

In [5]:

#Scaler Object
scaler = StandardScaler()

In [6]:

#Fit data to everything except Target Class column first (before transforming)
scaler.fit(df.drop('TARGET CLASS',axis=1))

Out[6]:

StandardScaler()

In [9]:

#Scale the dataset, minus the TARGET ClASS column
scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))

In [12]:

#Put the new scaled dataset into a dataframe
#Get all column names except TARGET CLASS (the last one)
df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])

In [14]:

#Review "re-scaled" dataset
df_feat.head()

Out[14]:

	WTT	PTI	EQW	SBI	LQE	QWG	FDJ	PJF	HQE	NXJ
0	-0.123542	0.185907	-0.913431	0.319629	-1.033637	-2.308375	-0.798951	-1.482368	-0.949719	-0.643314
1	-1.084836	-0.430348	-1.025313	0.625388	-0.444847	-1.152706	-1.129797	-0.202240	-1.828051	0.636759
2	-0.788702	0.339318	0.301511	0.755873	2.031693	-0.870156	2.599818	0.285707	-0.682494	-0.377850
3	0.982841	1.060193	-0.621399	0.625299	0.452820	-0.267220	1.750208	1.066491	1.241325	-1.026987
4	1.139275	-0.640392	-0.709819	-0.057175	0.822886	-0.936773	0.596782	-1.472352	1.040772	0.276510

Split data into test/train and predict¶

In [17]:

#Import splitting library
from sklearn.model_selection import train_test_split

In [20]:

#Set X,Y
X = df_feat
y = df['TARGET CLASS']

In [21]:

#Choose the test size
#Test size = % of dataset allocated for testing (.3 = 30%)
#Random state = # of random splits
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)

In [22]:

#Import KNN classifier library
from sklearn.neighbors import KNeighborsClassifier

In [23]:

#Build model object
knn = KNeighborsClassifier(n_neighbors=1)

In [24]:

#Fit model
knn.fit(X_train,y_train)

Out[24]:

KNeighborsClassifier(n_neighbors=1)

In [30]:

#Get predictions
predictions = knn.predict(X_test)

In [27]:

#See if the model worked, print reports (overall it worked very well)
from sklearn.metrics import classification_report, confusion_matrix

In [28]:

print(confusion_matrix(y_test,predictions))

[[151   8]
 [ 15 126]]

In [29]:

print(classification_report(y_test,predictions))

              precision    recall  f1-score   support

           0       0.91      0.95      0.93       159
           1       0.94      0.89      0.92       141

    accuracy                           0.92       300
   macro avg       0.92      0.92      0.92       300
weighted avg       0.92      0.92      0.92       300

Determining a better K-value¶

How exactly can we determine what K-value produces the least error rate?

In [34]:

#First let's see what error_rate we get by looping k-values

error_rate = [] #Empty

#Choose the max k-value to test (40 this time), start at 1
for i in range(1,40):
    knn = KNeighborsClassifier(n_neighbors=i)
    knn.fit(X_train,y_train)
    predictions_i = knn.predict(X_test)
    error_rate.append(np.mean(predictions_i != y_test)) #Avg times the prediction is NOT correct

In [38]:

#Plot out the error rates
plt.figure(figsize=(10,5))
plt.plot(range(1,40),error_rate,color='blue',linestyle='--',marker='o',markerfacecolor='red',markersize=10)
plt.title('Error Rate depending on K-value')
plt.xlabel('K-value')
plt.ylabel('Error Rate')

Out[38]:

Text(0, 0.5, 'Error Rate')