Career that Tend to be Unpaid for Motorcycles Sales Loans

摘要

Country A is in Latin America and GDP is lower than average. Prices of Japanese products such as motorcycles are on an upward trend. It was influenced by instability of the world economy. The customer selects loan payment. Among them, some customers cannot pay for the specified payment period. If this situation deteriorates, it will be difficult to recover manufacturing costs. Therefore, we analyze the characteristics of customers who loan bankruptcy. There is a weak positive correlation (0.300) between average income for each province and sales volume. There is a negative correlation (-0.542) to the main income amount and those who go bankrupt. So it can be said that people living in poor countries cannot easily buy a motorbike. Even if they make loans to purchase, they cannot pay. I divided the state data into five. It turned out that there was economic disparity. The southern region is rich and the north region is a poor region. There are many customers who cannot repay the loan to the north region. In this country, 13% of 10 million young people have not enrolled in school and are not working. In order to see the relationship between academic background and other factors, we quantified the academic qualification. There is a negative correlation between academic records and the proportion of unpaid -0.673. Therefore, I can say that the lower the academic background, the more delayed repayment of the loan. I conducted a multiple regression analysis with the loan's unrepayable party as the dependent variable. The standard partial regression coefficients were -0.543 for educational record number and -0.327 for main income. And I learned that educational background is a factor that has a big influence on judging whether it will be unpaid than main income. We analyze logistic regression on the probability that that person will be Bad with these elements. We understood what condition customers are likely to be Bad. My goal is to be able to know the percentage of probabilities that your loan can not be repaid from your profile information.