FRA Assignment Group 3.pdf - Assignment on Finance Risk Analy3cs From Group 3 Jai Jangid Abhay sharma Vikram sharma Mridul Tiwari The given dataset is

# FRA Assignment Group 3.pdf - Assignment on Finance Risk...

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Assignment on Finance & Risk Analy3cs From Group 3: Jai Jangid Abhay sharma Vikram sharma Mridul Tiwari Subscribe to view the full document.

The given dataset is given for developing the India Credit Risk Model. The given dataset has 52 rows which capture various financial parameters for the given dataset. We are working on the dataset to predict using the logisAc regression. The “net worth for next year” in the given dataset need to be converted into the binary form so that the logisAc regression can modelled on the given dataset. The given dataset has very high skewness in the data set which need to be removed before modelling. Also, the outlier in the given dataset need the treatment so that model gives us beIer output. The first step we took was to convert the net worth next year to binary figure so that we can run logisAc regression on the dataset. The logic we use for reducing the binary form is: If the net worth for the given parameter is less than zero or negaAve (company making losses) it is treated as zero & if the net worth is posiAve (company making profit) the value is considered as 1. We have used R & excel to deduce the iniAal acAons. With this we come to know that 234 Sample values are there which can default. They are rated as 0. The default rate comes out to be 234/3541 as 6.4%. The R code for calculaAon is aIached below: #reading the data from the file Raw-Data data_raw = read_excel("C:/Users/MRIDUL TIWARI/Desktop/R/raw-data.xlsx") #reading the test data from the valildaAon file data_validaAon = read_excel("C:/Users/MRIDUL TIWARI/Desktop/R/validaAon_data.xlsx") data_training = data_raw[ !(data_raw\$Num %in% data_validaAon\$Num), ] View(credit_risk_data_raw) dim(data_validaAon) dim(data_training) # Add a column of Default based on the value of NetWorthNextYear. # 0 if NetWorthNextYear is posiAve, #1 if NetWorthNextYear is negaAve. #Then remove the NetWorthNextYear column data_training\$Default <- ifelse(data_training\$`Networth Next Year`>=0 ,0,1) #data_training = data_training[,c(1,3,4:53)] #Reordering the columns so making the details easy to find data_training = data_training[,order(names(data_training))] data_validaAon = data_validaAon[,order(names(data_validaAon))] data_training = data_training[,c(29,1:28,30:52)] data_validaAon = data_validaAon[,c(29,1:28,30:52)] aIach(data_training) #Default Rate for the data set default_rate = (sum(data_training\$Default)/(nrow(data_training)))*100 paste("Default Rate for the dataset is ", default_rate, "%") Missing value treatment: The Outlier has been treated with the capping logic. The highest value has been treated to the 99% value & the lowest with the 1% value. So that we can shrink the data & outlier can be removed. By doing this we have kept in mind regarding the skewness factor. The missing values has been treated with the average value. For treaAng he missing value we got the missing vs. observed graph ready which shared that the 7% of the value are not available for the given dataset. R Code for finding the missing values: missing_values = lapply(data_training,funcAon(x) sum(is.na(x))) missing_values missmap(data_training, main = "Missing values vs observed") Output: > missing_values Subscribe to view the full document. • Summer '17
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