Now in this video he will be discussing about the case study and the data set that we will be using to apply legislation in SAS software. So in order to do logistic regressions or SaaS software we will be doing credit risk analytics where our response variable will be calculating the probability for Y equals to event and here probability right equals true event is that the loan will be repaid by the customer on right time probability y equals three event in our model is that loan will be repaid by the customer at writing and gravity y equals to zero that is probability y equals to non event his loan will not be repaid by the customer, right? So now let's read the case study. Our credit score is a numerical expression based on a statistical analysis of a person's credit files to represent the credit worthiness of that person.

A credit score is primarily based on credit report information typically so strong credit bureaus, lenders, such as banks and credit card companies use credit scores to evaluate the potential risk posed by lending money to customers that is lending money to consumers and to initiate losses due to bad lenders use credit scores to determine who qualifies for a loan at what interest rate and what credit limits lenders also use credit scores to determine which customers are likely to bring in the most revenue. At the same time credit scoring is not limited to bands other organizations such as mobile phone companies, insurance companies, landlords and government departments employ the same techniques in India In India there are four direct information companies licensed by Reserve Bank of India, the credit information bureau limited that has been has been functioning as a credit information company from January 2001. Subsequently in 2010, Syrian Equifax and Hi Mark will give It licenses by Reserve Bank of India to operate as credit information companies in India here we have the credit information of thousand German individuals from pre euro era they applied for bank loan for various purposes some of the individuals defaulted after certain period the bank wants to create a decision support system to help the loan officer use this data.

So, the loan officer basically wants to understand that which customer is going to repay the loan or writing or which customer has highest chance to repay the loan and like even which customer will not repay the loan and right therefore, we will be using logistic regression where our dependent being able to calculate for viable to even that is the customer is going to repay the loan or correct now let's see the data set. Let's see the data set description. These are my variables that are present in my data set first is OBS that is observation number check account duration history new car used car furniture Radio TV education retraining among saving account employment installed rate meal division meal single meal married or widow co applicant guarantor present resident real estate property unknown non each other installments rent onerous number of credit cards job number of dipping dependence telephone foreign response out of so many variables my response variable is the dependent variable and the rest of the variables from check account to foreign are my independently independent variables can be categorical in nature as well as continuous in nature in case of logistic regression as we know and the dependent variable is binary nature dichotomous in nature OBS is observation number now in our next video, we will be opening SAS and we will be doing the practical session for now.

Let's end the video over here. Goodbye. Thank you see you all for the next video for the practical session of logistic regression.