السلام عليكم,
اولا هذا المشروع هو: Loan Approved Prediction Model.
في هذا المشوع أتمت تطبيق واتباع:
CRISP_DM Methodology :
1.Business Understanding
2.Data Understanding
3.Data Preparation
4.Modeling
5.Evaluation
6.Deployment
التي يتبعها اغلبية كل من يريد بناء machine learning model.
1.Business_Understanding
1.Business Problem
Financial institutions face significant risks from loan defaults, which can impact their profitability and financial stability. This project addresses the need for a reliable, data-driven approach to predict loan default risk and make informed loan approval decisions. By accurately assessing each applicant's likelihood of repayment, institutions can reduce default rates, improve lending decisions, and enhance overall operational efficiency.
2.Business Goal
The business goal is to minimize loan default risk and optimize loan approval decisions through accurate risk assessment and predictive modeling.
Introduction
This is summary about the loan dataset an some information about the data and key attribute:
About part from dataset
AnnualIncome
Age
CreditScore
EmploymentStatus
EducationLevel
LoanAmount
LoanDuration
MonthlyIncome
LoanApproved
RiskScore
Summary of Predictive Analytics for predict loan approval outcomes and credit risk levels Project:
This project aims to apply artificial intelligence and machine learning techniques to analyze applicant data and predict loan approval outcomes and credit risk levels. By utilizing predictive modeling, including classification and regression algorithms, the model estimates an applicant’s likelihood of loan approval and assesses their risk score. Early and accurate identification of high-risk applicants allows financial institutions to make informed decisions, reduce loan defaults, and optimize their approval processes. This project is essential for the financial sector as it enables lenders to manage risk proactively, ensure financial stability, and support responsible lending practices.
Finally, work was done on applying the classification model so that it predicts whether the customer will default on the loan amount or not.
And building an interface with Gradio to enter new data and predict it.
