[x] Except the date features, I have analyzed the left features and got the following conclusions
[x] 1.'NewExist','RevLineCr' and 'Sector' are the features which show difference in terms of the number of repayors and defaulters and the differences are as follows:
[x] 1) Companies which already exist are more likely not to repay the loan.
2) Companies who are considered to have no credit value are the most likely group to repay the loan.
3) The companies in sector 11 are the most likely group to repay the loan, and companies in sector 49 are the least likely group to repay the loan.
[x] Things I am going to do next:
[x] 1. Do more EDA about the date features
[x] 2. Tune the tried models and try voting classifier
[ ] 3. Find and try other extra data (such as the data Kondo-san recommended) outside the provided data
[x] Except the date features, I have analyzed the left features and got the following conclusions
[x] 1.'NewExist','RevLineCr' and 'Sector' are the features which show difference in terms of the number of repayors and defaulters and the differences are as follows:
[x] 1) Companies which already exist are more likely not to repay the loan. 2) Companies who are considered to have no credit value are the most likely group to repay the loan. 3) The companies in sector 11 are the most likely group to repay the loan, and companies in sector 49 are the least likely group to repay the loan.
[x] Things I am going to do next:
[x] 1. Do more EDA about the date features
[x] 2. Tune the tried models and try voting classifier
[ ] 3. Find and try other extra data (such as the data Kondo-san recommended) outside the provided data