K-Nearest Neighbour(KNN) algorithm is a supervised machine learning algorithm which can be used for regression as well as classification. More information about it can be found here.
You can learn more about KNN in the below video.
The below code snippet will help you to create a classification model using KNN.
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
#### Create Loan Data for Classification in Python #### import pandas as pd import numpy as np ColumnNames=['CIBIL','AGE', 'SALARY', 'APPROVE_LOAN'] DataValues=[[480, 28, 610000, 'Yes'], [480, 42, 140000, 'No'], [480, 29, 420000, 'No'], [490, 30, 420000, 'No'], [500, 27, 420000, 'No'], [510, 34, 190000, 'No'], [550, 24, 330000, 'Yes'], [560, 34, 160000, 'Yes'], [560, 25, 300000, 'Yes'], [570, 34, 450000, 'Yes'], [590, 30, 140000, 'Yes'], [600, 33, 600000, 'Yes'], [600, 22, 400000, 'Yes'], [600, 25, 490000, 'Yes'], [610, 32, 120000, 'Yes'], [630, 29, 360000, 'Yes'], [630, 30, 480000, 'Yes'], [660, 29, 460000, 'Yes'], [700, 32, 470000, 'Yes'], [740, 28, 400000, 'Yes']] #Create the Data Frame LoanData=pd.DataFrame(data=DataValues,columns=ColumnNames) LoanData.head() #Separate Target Variable and Predictor Variables TargetVariable='APPROVE_LOAN' Predictors=['CIBIL','AGE', 'SALARY'] X=LoanData[Predictors].values y=LoanData[TargetVariable].values #Split the data into training and testing set from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) ################################################################### ###### K-Nearest Neighbour(KNN) Classification in Python ####### import pandas as pd from sklearn.neighbors import KNeighborsClassifier clf = KNeighborsClassifier(n_neighbors=2) #Printing all the parameters of KNN print(clf) #Creating the model on Training Data KNN=clf.fit(X_train,y_train) prediction=KNN.predict(X_test) #Measuring accuracy on Testing Data from sklearn import metrics print(metrics.classification_report(y_test, prediction)) print(metrics.confusion_matrix(y_test, prediction)) #Plotting the feature importance for Top 10 most important columns #There is no built-in method to get feature importance in KNN #Printing some sample values of prediction TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors) TestingDataResults['TargetColumn']=y_test TestingDataResults['Prediction']=prediction TestingDataResults.head() |
Author Details
Lead Data Scientist
Farukh is an innovator in solving industry problems using Artificial intelligence. His expertise is backed with 10 years of industry experience. Being a senior data scientist he is responsible for designing the AI/ML solution to provide maximum gains for the clients. As a thought leader, his focus is on solving the key business problems of the CPG Industry. He has worked across different domains like Telecom, Insurance, and Logistics. He has worked with global tech leaders including Infosys, IBM, and Persistent systems. His passion to teach inspired him to create this website!