Understanding the meaning of data and Processing.

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

• The company wants to understand the driving factors (or driver variables) behind loan default,i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.

• You work for a consumer finance company which specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision: If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company

• Data understanding:

• Data Analysis:

• Data Cleaning:

Points derived Using Univariate analysis people who owns home took less loans Most of the loan terms are for 36 months and the most approved loan amount is 5,000 It is identified that people from the state with code CA have took more loans and also most of the loan applicants are people who have income of 40000 to 60000 Points derived Using Bivariate analysis it is analysed from the Interest Rate to Installment Ratio vs. Loan Amount' that the interest to installment ratio is less for higher loan amounts From Relationship between Loan amount and terms (Filtered by Interest Rate) it is identified that for higher amount of loan the interest rate is less From Scatter Plot: Loan amount and Verification ratio it is clear that people who have a verification badge got more loan amount From Income vs Loan chart from this it is identified that people who have income between 30000 to 40000 took more loanand the most approved loan amount in between 0 to 10000 From Committed amount and Amount committed by investors it is identified that most part of the committed amount has been funded by the investors

• Pandas - version 2.0.0
• Matplotlib - version 3.8.0
• Seaborn - version 0.13.0

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