Logistic regression is an algorithm used for binary classification use cases. More information about it can be found here. The below code snippet will help to create a logistic regression model.
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#### 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) ############################################################ ###### Logistic Regression in Python ####### import pandas as pd from sklearn.linear_model import LogisticRegression #choose parameter Penalty='l1' or C=1 clf = LogisticRegression(C=1,penalty='l1') #Printing all the parameters of logistic regression print(clf) #Creating the model on Training Data LOG=clf.fit(X_train,y_train) prediction=LOG.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)) #Printing some sample values of prediction TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors) TestingDataResults['TargetColumn']=y_test TestingDataResults['Prediction']=prediction TestingDataResults.head() |
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