Loan Status Prediction
Machine learning
Support Vector Machine
Python
Scikit-learn
Seaborn
Pandas
The Loan Approval Prediction Model, powered by Support Vector Machine (SVM), determines loan eligibility for customers based on essential parameters. Utilizing features such as property area, income, credit history, and number-of-dependents, the model evaluates the likelihood of a customer's loan approval. Employing SVM, the model creates a decision boundary to segregate loan approval categories, leveraging a kernel-based approach to classify customers into distinct loan approval classes.
Github
DatasetFeature Description
- Gender: The gender of the customer
- Married: To know if the customer is married or not
- Dependents: The number of people dependent on the customer.
- Education: Is the customer a graduate or not
- Self_Employed: Is the customer self employed or not
- ApplicantIncome: The income of the applicant
- CoapplicantIncome: The income of the people living with the customer
- LoanAmount: The amount requested for loan
- Loan_Amount_Term: The time for which the loan is requested
- Credit_History: Does the customer have his/her credit history or not
- Property_Area: The region in which the property of the customer is localted
Files
Try it out
Click Run for Results
Most Used Packages
Other Projects
View Project
