https://github.com/kelvinjuliusarmandoh/loan-approval-prediction

Implementing Machine Learning for predicting loan approval
https://github.com/kelvinjuliusarmandoh/loan-approval-prediction

classification loan-default-prediction machine-learning matplotlib-pyplot numpy pandas scikit-learn seaborn

Last synced: 11 days ago
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Implementing Machine Learning for predicting loan approval

• Host: GitHub
• URL: https://github.com/kelvinjuliusarmandoh/loan-approval-prediction
• Owner: kelvinjuliusarmandoh
• Created: 2023-06-18T04:18:04.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-06-18T10:01:11.000Z (over 1 year ago)
• Last Synced: 2024-11-05T20:18:55.622Z (about 2 months ago)
• Topics: classification, loan-default-prediction, machine-learning, matplotlib-pyplot, numpy, pandas, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 3.59 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Loan Approval Prediction

# Predicting Approval of the loan using machine learning

![Loan Defaults Picture](https://github.com/KelvinJulius/loan-approval-prediction/assets/108222785/4de788ae-76df-450e-8675-9af21c6b076e)

This notebook using python for machine learning and data science in attempt to predict the approval of the loan based on the attributes.

We're going to take the following approach:

1. Problem definition

2. Data

3. Evaluation

4. Features

5. Modelling

6. Experimentation

## 1. Problem definition

We assume as a loan officer who is deciding whether to approve a loan to a small business. In this case, we predict the approval (**Approved** or **not approved**) based on the attributes.

## 2. Data

This dataset can be looked at this link: https://www.kaggle.com/datasets/mirbektoktogaraev/should-this-loan-be-approved-or-denied

## 3. Evaluation

Evaluation metrics are:

* Accuracy (90%)

* ROC curve and AUC score

* Confusion matrix

* Precision

* Recall

* Classification report

* F1-score

* Cross-validation

## 4. Features

Information about the description of features

**Create data dictionary:**

* LoanNr_ChkDgt = Identifier Primary key

* Name = Borrower name

* City = Borrower city

* State = Borrower state

* Zip	= Borrower zip code

* Bank = Bank name

* BankState = Bank state

* NAICS = North American industry classification system code

* ApprovalDate = Date SBA commitment issued

* ApprovalFY = Fiscal year of commitment

* Term = Loan term in months

* NoEmp = Number of business employees

* NewExist  -  1 = Existing business (0) , 2 = New business (1)

* CreateJob = Number of jobs created

* RetainedJob	= Number of jobs retained

* FranchiseCode =Franchise code, (00000 or 00001) = No * franchise

* UrbanRural - 1 = Urban, 2 = rural, 0 = undefined

* RevLineCr = Revolving line of credit: Y = Yes, N = No

* LowDoc = LowDoc Loan Program: Y = Yes, N = No

* ChgOffDate = The date when a loan is declared to be in * default

* DisbursementDate = Disbursement date

* DisbursementGross = Amount disbursed

* BalanceGross = Gross amount outstanding

* MIS_Status - Loan status charged off = CHGOFF (1) , Paid in full = PIF (0) (Target)

* ChgOffPrinGr = Charged-off amount

* GrAppv = Gross amount of loan approved by bank

* SBA_Appv = SBA’s guaranteed amount of approved loan

## Preparing the tools

We're going to use pandas, Matplotlib , seaborn, and NumPy for data analysis and manipulation. We're using scikit-learn for machine learning