k nearest neighbor python numpy language:

Welcome everyone in python crash course (Machine learning). This is first part of this section,if you want to learn SVM in python then click on it.
 K Nearest Neighbors method also used for data prediction purpose, so in his section we will learn  K Nearest Neighbors predict method.

k nearest neighbor python numpy language

How do you use K nearest neighbor in Python language in smart way?

What is K nearest neighbor used for?

suppose you have been given a classified data set from a any popular company,they give you the data and the target classes and say predicts a class for a new data point based off of the features.
Let’s do it!

What is the Python code for importing k nearest Neighbours?

Import Libraries


import pandas as pd


import seaborn as sns


import matplotlib.pyplot as plt


import numpy as np


%matplotlib inline

Get the Data
Set index_col=0

df = pd.read_csv("Sample Classified Data",index_col=0)

df.head()

WTS PTS EQS SBS LQS QWS FDS PJS HQS NXS TARGETC CLASSES


 0.923917 1.172073 0.467946 0.655464 0.880862 0.252608 0.659697 0.343798 0.979422 1.231409 1


 0.645632 1.033722 0.545342 0.865645 0.934109 0.658450 0.675334 1.213546 0.681552 1.492702 1


 0.5521360 1.201493 0.921990 0.8775595 1.526629 0.720781 1.776351 1.154483 0.957877 1.285597 0


 1.434204 1.386726 0.653046 0.425624 1.142504 0.875128 1.509708 1.380003 1.522692 1.253093 0


 1.579491 0.949750 0.627280 0.768976 1.232537 0.703727 1.815596 0.646691 1.463812 1.519167 1

Standardize the Variables  K Nearest Neighbors method
In the next step we drop the TARGETC CLASSES from the data because this type of data affect on the prediction.

from sklearn.preprocessing import StandardScaler


scaler = StandardScaler()


scaler.fit(df.drop('TARGETC CLASSES',axis=1))


StandardScaler(copy=True, with_mean=True, with_std=True)


scaled_features = scaler.transform(df.drop('TARGETC CLASSES',axis=1))


df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])

df_feat.head()

WTS  PTS EQS SBS LQS QWS FDS PJS HQS NXS


 -0.045232 0.185907 -0.913431 0.3229629 -1.033637 -2.308375 -0.798951 -1.482368 -0.949719 -0.643314


 -1.674836 -0.430348 -1.025313 0.625388 -0.444847 -1.152706 -1.129797 -0.202240 -1.828051 0.636759


 -0.9988702 0.339318 0.301511 0.785873 2.031693 -0.870156 2.5109818 0.285707 -0.682494 -0.377850


 0.992841 1.060193 -0.621399 0.635299 0.452820 -0.267220 1.770208 1.066491 1.241325 -1.026987


 1.149275 -0.640392 -0.709819 -0.557175 0.822886 -0.936773 0.6996782 -1.472352 1.040772

Train Test Split method:

from sklearn.model_selection import train_test_split


X_train, X_test, y_train, y_test = train_test_split(scaled_features,df['TARGETC CLASSES'],


                                                    test_size=0.20)

Using KNN method
Remember we will start with k=1 value.

from sklearn.neighbors import KNeighborsClassifier


knn = KNeighborsClassifier(n_neighbors=1)


knn.fit(X_train,y_train)


KNeighborsClassifier(algorithm='auto', leaf_size=20, metric='minkowski',


           metric_params=None, n_jobs=1, n_neighbors=1, p=2,


           weights='uniform')


pred = knn.predict(X_test)

Predictions and Evaluations using knn
Let’s start to evaluate our KNN model!

from sklearn.metrics import classification_report,confusion_matrix

print(confusions_matrix(y_test,pred))

[[124  19]


 [ 12 145]]

print(classification_report(y_test,pred))

             precisionx    recallx  f1-scorex   supportx


          1       0.92      0.87      0.89       144


          0       0.99      0.92      0.90       158


avg / total       0.94      0.90      0.89       300

Choosing a correct value for K for this
we use the elbow method to pick a correct K Value:

error_rate = []

# wait some time

for i in range(1,40):


    knn = KNeighborsClassifier(n_neighbors=i)


    knn.fit(X_train,y_train)


    pred_i = knn.predict(X_test)


    error_rate.append(np.mean(pred_i != y_test))


plt.figure(figsize=(10,6))


plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', marker='o',


         markerfacecolor='red', markersize=10)


plt.title('Error Rate value vs. K mean Value')


plt.xlabel('K mean Value')


plt.ylabel('Error Rate value')

<matplotlib.text.Text at 0x11ca93ba9>.
After run this program the knn plot is display
Summary:
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sach Pagar

I am the founder of Pythonslearning, a Passionate Educational Blogger and Author, who love to share the informative content on educational resources.