Overall this app determines if you qualify for a loan by iterating through data and performing numerous calculation.This is made easy for the user and companies.Clients can see which banks are right for them once applications is filled out digitally.
New updates allowed the ability to prompt the user to save qualifying loans as a new csv file enhancing user experience..
As high priority feature request emerge more updates will continuously arise enhancing the user experience
Iterating to create an approval list
This app is built and excuted using Python version 3.10
Iterating through the csv file allows this program to
gather and store data.
credit_score_approval_list = []
for bank in bank_list:
if credit_score >= int(bank[4]):
credit_score_approval_list.append(bank)
return credit_score_approval_list Using the For condiational statement it allow us to iterate through the csv file known as bank_list nested in the For statement is a If statement which tells the computer if the credit score is greater or equal to the bank min approval credit score then append the bank to a list called credit_score_approval_list.
During this process the computer is giving more specific instruction when iterating
int(bank[4])
This specifically tells the computer to iterate bank[4] which is all the bank min credit score this information is givin through the csv file
int is a function in python because we are getting information through a csv file the number is technically a `string` using int() its coverted to a integer If iteration contains decimals such as 0.67 use a
float()to convert string to float.
Created Four approval list
This uses the same iteration that specifically target each of them and create a seperate list for each of them that make up bank_data_filter
Libraries
Through out this project several Python library are use to access different function whithin its library these libraries are
Fire version
0.4.0and Questionary version1.10.0was used in this project
All libraries are up to date working with Python version
3.10
Modules
Through out this project several Modules are use to access different functions organized in folders
The filter folder contain four modules known as the approval_list each containing there own specific iteration to store specific data
The utils folder contain two modules one being for calculation and the other is to load csv
each module is imported to main py file
Easier to pinpoint problems and more organized being more modularize
To install this project clone repository click here for instruction to clone repository
If you cant clone you can download a zip file
Project can be access on github click here to access github
click here for zip file
After Installation
You need libraries use for this project installed refer to libraries in Technology section above for installation
Gather a Csv containing banks qualification such as Lender, Max Loan Amount, Max LTV, Max DTI, Min Credit Score , Interest Rate format your csv identical to example file rob.csv
rob.csv file containins specifc banks qualification click here to see csv of banks qualifications
Once csv is upload in your code editor
Navigate to main app.py
Navigate to main app.py file
Make sure all modules is
imported
import sys
from pathlib import Path
import fire
import questionary
import csv
from qualifier.utils.fileio import load_csv
from qualifier.utils.calculators import (
calculate_monthly_debt_ratio,
calculate_loan_to_value_ratio,
)
from qualifier.filters.max_loan_size import filter_max_loan_size
from qualifier.filters.credit_score import filter_credit_score
from qualifier.filters.debt_to_income import filter_debt_to_income
from qualifier.filters.loan_to_value import filter_loan_to_valueYou are now ready to run project
Dont worry if you click no you will have a
optionto save results or opt out
If you choose to save you will be promted to enter a file path to save results
If you choose not to save you will be prompted to view list of qualifying loans
Author: Robert Smith
Credits-UC Berkley Fintech Bootcamp
This project is open anyone can use modify for any purposes for collaboration email [email protected]