This APP is committed to showing the relationship between various aspects and whether the user can borrow according to the user's selection conditions. This app is a user page with strong interaction with users. Here's how to use it:
- The page has a sidebar for users to choose according to their own ideas, and this sidebar has three major blocks. The "Navigate" block allows the user to select different graphs (heat maps, bar charts, etc.) to show the relationship between each condition and the success of the loan, for example, clicking on the Plot_bar will show the average personal income of the male and female successful loan (in the data we collect). In the "The size of data" block, the user drags and drops the displayed line to select the cardinality of the dataset, for example, if you select 0.5, the displayed data analysis will be performed in the first half of the dataset we collected. The last block is to let the user select the condition to better query the information, for example, select "true" in "graduate", and the chart will only show the data analysis graph of the graduated population.
- The main page of the page is selected by the "Navigate" block. The "home" page is a brief introduction to the app and the publication of our dataset. The "Plot" page is a display of charts, and there are also blocks on the charts for users to select conditions, so as to show the data analysis graphs that users need in more detail. On the rest of the pages, there will be detailed questions that are of common interest to users, and users can follow the page introduction and get some data sheets.
Home Loan Approval (loan_sanction_train.csv)
https://www.kaggle.com/datasets/rishikeshkonapure/home-loan-approval?select=loan_sanction_train.csv
About the company Dream Housing Finance Corporation. They have a presence in all urban, semi-urban and rural areas. They would like to present the relevant charts based on the details of the customer provided when filling out the online application form. These details include the borrower's gender, marital status, educational background, employment situation, income situation, co-applicant income, loan amount required, repayment time, number of loans, place of residence, etc. Here, they provide a partial data set.
Our team used two versions in the process of building the APP. In order to show the completeness of the team work, we will briefly introduce the two versions of our app before and after. The following is the completed version of our first version:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent