employment = {'Salaried': 0, 'Self employed': 1, ' ': 1}
#importing dataset
dataset = pd.readcsv('CreditCardData.csv')
dataset = dataset.dropna()
#creating dummy variable for column EmploymentType
dataset.EmploymentType = [
employment[number] for number in dataset.EmploymentType
]
loanDefaulter = dataset[['loandefault']]
factors = dataset[[
'disbursedamount', 'assetcost', 'EmploymentType', 'PRI.CURRENT.BALANCE',
'PRI.SANCTIONED.AMOUNT', 'PRIMARY.INSTAL.AMT'
]]
#creating training and testing set and scaling the data
factorsTrain, factorsTest, loanTrain, loanTest = sl.traintestsplit(
factors, loanDefaulter, testsize=0.2, shuffle=True)
scaler = StandardScaler()
factorsTrain = scaler.fittransform(factorsTrain)
factorsTest = scaler.fittransform(factorsTest)
#creating regression object
LogisticRegressor = LogisticRegression()
LogisticRegressor.fit(factorsTrain, loanTrain)
predictedResult = LogisticRegressor.predict(factorsTest)
print('Confusion matrix using solver: ')
print(metrics.confusionmatrix(loanTest, predictedResult))
print('Accuracy using solver: ')
print('Accuracy:', metrics.accuracy_score(loanTest, predictedResult))
