Machine-Learning

"Banking Credit Analysis" based on "Machine Learning" to predict whether the next Loan Applicant is a good customer or he will end up in doing fraud with the bank.

Used various Python libraries such as

1. Numpy
2. Matplotlib(Graph Plotting)
3. Panadas(Data Manipulation and Analysis)
4. Seaborn(Data Visualization)
5. Scikit-Learn(Machine Learning)

ML Model used :

1. Linear and Logistic Regression
2. k-Nearest
3. Random Forest
4. Gradient Boosting Algorithm
5. SVM

Objective

Objectives: Machine learning is a critical step in the discovery of information that includes theories, methodologies and instruments to reveal patterns in the data.

Explanation

It is essential to know the rationale behind the techniques so that instruments and techniques fit the data and pattern recognition goal properly. There may be several choices available for a dataset.

When a bank gets a loan application, the bank must decide whether or not to go ahead with the loan process based on the applicant's profile. In this scenario, two types of risks are associated with the bank’s decision – • If the applicant is a good credit risk, i.e. is likely to repay the loan, then not approving the loan to the person results in a loss of business to the bank. And, • If the applicant is a bad credit risk, i.e. is not likely to repay the loan, then approving the loan to the person results in a financial loss to the bank.

To minimize losses from the bank's perspective, the bank requires a decision framework as to who and who should not be approved the loan.

The Nordic Kredit contains data on 20 variables and the classification whether an applicant is considered a Good or a Bad credit risk for 1000 loan applicants. A predictive model developed on this data is expected to provide bank manager guidance for making a decision whether to approve a loan to a prospective applicant based on his/her profiles.