Loan Approval Case Study

About this project

Loan-Status-Prediction-Case-Study

In this Notebook, we are going to solve the Loan Approval Prediction. This is a Classification problem in which we need to classify whether the loan will be approved or not.

Problem Statement:-

Automate the loan eligibility process (real-time) based on customer details provided while filling out the online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History, and others.

The major aim of this notebook is to predict which of the customers will have their loan approved.

Features of our data:-

LoanID = Unique Loan ID

Gender = Male/ Female

Married = Applicant married (Y/N)

Dependents = Number of dependents

Education = Applicant Education (Graduate/ Undergraduate) SelfEmployed = Self-employed (Y/N)

ApplicantIncome = Applicant income

CoapplicantIncome = Coapplicant income

LoanAmount = Loan amount in thousands

LoanAmountTerm = Term of the loan in months

CreditHistory = Credit history

PropertyArea= Urban/ Semi-Urban/ Rural

LoanStatus = (Target) Loan approved (Y/N)

Certainly, based on the analysis conducted in the provided code:

Process Involved:

Data Handling:
- Null Values: The missing values in the dataset were dealt with using mode and median imputations based on the data type.
- Outliers: Outliers in features like Applicant Income, Coapplicant Income, and Loan Amount were detected and appropriately handled to ensure model robustness.
- Data Transformation: Log transformation was applied to numeric features to normalize their distributions for better model performance.
Categorical Analysis:
- Loan Approval Rates: Married individuals tend to have higher loan approval rates compared to unmarried applicants.
- Education Impact: Graduates have higher chances of loan approval compared to non-graduates.
- Property Area Influence: Applicants from semi-urban areas have higher chances of loan approval compared to urban and rural areas.
- Self-Employment Factor: Self-employed applicants seem to have slightly lower loan approval rates than non-self-employed individuals.
Model Development:
- Imbalanced Data: The SMOTE technique was used to handle the imbalance in the target variable ('Loan_Status') by oversampling the minority class.
- Model Comparison: Various classifiers like K-Nearest Neighbors, Support Vector Machine, Decision Tree, Naive Bayes, and Random Forest were compared. Logistic Regression emerged as the most effective model with good accuracy for this dataset.

Recommendations:

Further Feature Engineering: Explore additional features or create new features from existing ones that might enhance the predictive power of the model.
Exploration of Other Algorithms: Though Logistic Regression performed well, testing more complex algorithms or ensemble methods could potentially yield better performance.
Feature Importance: Conduct feature importance analysis to identify key features driving loan approvals, which might help in focusing on crucial factors during applicant evaluations.
Data Collection & Quality: Ensure ongoing data quality checks and consider expanding the dataset to improve model robustness and generalizability.
Deployment and Monitoring: Once the model is ready, deploy it in a production environment and continuously monitor its performance for any drift or degradation in accuracy.
Regulatory Compliance: Ensure the model complies with legal and regulatory frameworks governing the financial domain, especially in loan approval scenarios.

By implementing these recommendations, the loan approval prediction model can be enhanced for more accurate and reliable real-time predictions.