Credit Risk Analysis -Janani Prakash

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    Credit Risk Analysis 08.09.2019  ─    Janani Prakash PGPBABI-Online GreatLearning, Great Lakes Institute of Management    Project Objective3 Directory and dataset creation4 Install necessary Packages and Invoke Libraries4 Set up working Directory4 Import and Read the Dataset4 Exploratory Data Analysis4 Importing Dataset4 Missing value treatment7 Outlier treatment12 Univariate and Multivariate Analysis13 Variable Creation23 Modelling25 Logistic Regression25  Analysis28 Model Performance and Measure29  Model performance on Train and Test data29 Deciling31 Source Code35    1.Project Objective TheobjectiveoftheprojectistocreateIndiacreditrisk(default)modelusingthegiven trainingdatasetandvalidateitontheholdoutdataset.Logisticregressionframeworkistobe used to develop the credit default model.   The data provided in raw-data comprises of financial data. Major data points or variables are Networthnextyear,Totalassets,Networth,Totalincome,Totalexpenses,Profitaftertax, PBDITA,PBT(ProfitBeforeTax),Cashprofit,PBDITAas%oftotalincome,PBTas%of  totalincome,Cashprofitas%oftotalincome,PATas%ofnetworth,Sales,Totalcapital, Reservesandfunds,Borrowings,Currentliabilities&provisions,Capitalemployed,Net fixedassets,Investments,Networkingcapital,Debttoequityratio(times),Cashtocurrent liabilities (times), Total liabilities. Inadditiontotheabovevariablesthereareotherfinancialparameterswhichdefinethe financial strength of the organization taking the total tally of variables to 51. The below process is to be followed: 1.Exploratory Data Analysis(EDA) a.Outlier treatment has to be done b.Missing value treatment has to be done c.New variables for Profitability, leverage and liquidity has to be created d.Univariate and Bivariate analysis has to be done 2.Modelling a.Logistic Regression Model has to be built on important variables b.Coefficients of important variables have to be analysed 3.Model Performance Measures a.Theaccuracyofthemodelhastobepredictedonthetrainingand holdout dataset b.Thedatahastobesortedindescendingorderbasedonprobabilityof  default and then divided into 10 deciles based on probability   2.Directory and dataset creation 2.1.1.Install necessary Packages and Invoke Libraries  Thenecessarypackageswereinstalledandtheassociatedlibrarieswere invoked.Havingallthepackagesatthesameplacesincreasescode readability. Please refer Appendix A for Source Code. 2.1.2.Set up working Directory SettingaworkingdirectoryonstartingoftheRsessionmakesimporting andexportingdatafilesandcodefileseasier.Basically,workingdirectory isthelocation/folderonthePCwhereyouhavethedata,codesetc. related to the project. Please refer Appendix A for Source Code. 2.1.3.Import and Read the Dataset Thegivendatasetisin.xlsxformat.Hence,thecommand‘read.xlsx’is used for importing the file. Please refer Appendix A for Source Code. 3.Exploratory Data Analysis 3.1.Importing Dataset Therearetwodatasets:TrainingandTestingdatasetwithsimilarvariables.The datasetconsistsofOrganisationdetailssuchasNetworthnextyear,Total assets,Networth,Totalincome,Totalexpenses,Profitaftertax,PBDITA,PBT (ProfitBeforeTax),Cashprofit,PBDITAas%oftotalincome,PBTas%oftotal income,Cashprofitas%oftotalincome,PATas%ofnetworth,Sales,Total capital,Reservesandfunds,Borrowings,Currentliabilities&provisions,Capital employed,Netfixedassets,Investments,Networkingcapital,Debttoequity ratio (times), Cash to current liabilities (times), Total liabilities The dataset is imported for further analysis >train <- read_excel( C:/Users/Janani Prakash/Desktop/R/raw.xlsx ) >test <- read_excel( C:/Users/Janani Prakash/Desktop/R/valid.xlsx ) > dim(train)
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