Introduction to Machine learning

 

Machine Learning is a branch of AI where computers learn from data to make decisions or predictions without explicit programming. It is the process by which systems improve their performance on specific tasks by gaining experience from data.

 

Let’s explore what machine learning is and why it’s essential, using a simple example below:

 

1. Collecting Data (Experience): The bank gathers historical data from thousands of past loan applications. Each application in this dataset includes details or features that help determine whether the loan was approved or disapproved. These features might include:

   • Income: How much the applicant earns annually
   • Credit Score: A rating that indicates how reliably the applicant repays debt
   • Employment Status: Whether the applicant is employed full-time, part-time, or is unemployed
   • Loan Amount: The amount of money the applicant wants to borrow
   • Existing Debts: How much the applicant already owes
   • Loan History: Whether the applicant has repaid loans on time in the past
   • Loan status: whether each application was approved or disapproved

2. Training the Machine Learning Model: Using this data, a machine learning algorithm is trained. The algorithm learns patterns and relationships between the features (income, credit score, etc.) and the final decision (approved or disapproved). For example:

   • If applicants with high incomes and good credit scores were often approved, the model learns that these are favorable characteristics.
   • If applicants with low incomes and poor credit histories were usually disapproved, the model learns that these are risk factors.

   This learning process is called training, where the model essentially gains experience from past applications to understand which patterns are likely to lead to an approval or disapproval.

3. Making Predictions (Performance): Once trained, the model can evaluate new loan applications by examining their features. Here’s how it works in a simple scenario:

   • A new applicant requests a loan.
   • The model checks the applicant’s data—income, credit score, employment, etc.—and compares these to the patterns it learned during training.
   • Based on these patterns, the model predicts whether the application is likely to be approved or disapproved.

   The model’s performance improves as it processes more applications and adapts to new patterns in the data.

   

Example Prediction Process:

Suppose an applicant has the following features:

• Income: ₹50,000 per month
• Credit Score: 750
• Employment Status: Full-time
• Loan Amount: ₹2,00,000
• Existing Debts: None
• Loan History: Excellent repayment record

The ML model examines these features and sees they align well with other applicants who were approved in the past. Therefore, it predicts an approval.

On the other hand, if the applicant had a low income, a credit score of 500, and high existing debt, the model might predict a disapproval based on patterns observed in similar past cases.

Benefits of ML in Loan Approval:

• Consistency: Machine learning can make quick, consistent decisions by following patterns in data, reducing human error and bias.
• Efficiency: The bank can process thousands of applications in a fraction of the time, giving customers faster responses.
• Adaptability: As more data is collected, the model can be retrained to improve accuracy and adapt to new patterns in applicant data.

In this way, machine learning helps banks make more informed, data-driven decisions about loan approvals, reducing the time and effort traditionally required while maintaining accuracy.