A loan is a type of credit where a sum of money is lent to a borrower in exchange for repayment of the principal amount plus interest or other finance charges. Loans can be for a specific amount or available as a line of credit with a maximum limit. There are different types of loans, including secured loans (backed by collateral) and unsecured loans (without collateral), as well as commercial and personal loans.
The loan process involves the borrower applying for a loan and providing relevant information such as their financial history and income. The lender reviews the application and assesses the borrower's creditworthiness before deciding whether to approve or deny the loan. If approved, both parties sign a contract that outlines the terms and conditions of the loan, including the repayment amount, interest rate, and repayment date.
Loans are used for various purposes, such as making major purchases, investing, consolidating debt, or funding business ventures. They contribute to economic growth by providing individuals and businesses with the necessary funds to expand their operations. Lenders earn revenue from loans through interest and fees.
Key components of a loan include the principal amount (the borrowed money), loan term (the repayment period), interest rate (the rate at which the owed amount increases), and loan payments (the amount paid regularly to fulfill the loan obligations). Additional fees may also apply, and collateral may be required for larger loans.
The Loan Analysis machine learning project involves preprocessing steps like data cleaning, encoding, normalization, and feature extraction. Outliers are removed, and optimization is performed using GridSearchCV. Data visualization using Seaborn is used to gain insights. Various models including KNN, Random Forest, and Logistic Regression are employed. The project aims to provide accurate predictions and valuable insights for loan-related decision-making.