Group Members: Zhuohui Wu, Lin Gong, Maggie Liang

Factors Affecting Loans

What it does

Our project is mainly working on :

• How will personal factors influence the loan amount?
• How will external factors influence the loan amount?
• How will a loan be approved by advance by the bank?

How we built it

We use Python code to make visualization to do the main part of our project.

We also use machine learning to train models and predict value.

Challenges we ran into

Messy data can figure out how to deal with this kind of data.

What we learned

• Income has no clear correlation with the loan amount
• property value has a positive correlation with the loan amount
• Sometimes numerical data is not the best choice
• What we assume before the research will not always be the result of the question
• The use of some new python libraries

What's next for Factors Affecting Loans

For more data sets and more up-to-data datasets, we can make more accurate predictions on more different situations. We think borrowers and the bank will both benefit from datasets with more recent data points. Since all the data in our project is from 2019, it is inapplicable to the current situation of the pandemic economy. So with more recent data points, we can enlarge our vision to see how the trend goes by comparing different years instead of fixing on the dataset within a specific year.

Built With

• python