Federal Trade Commission (FTC)

Loan Status Prediction: Predict Approval for Property Loans

This project aims to build a machine learning model predicting loan approval for property loan applicants. We analyze data from past applicants, considering factors like:

Applicant demographics: Gender, Marital status, Dependents, Education
Employment: Self-employed status, Applicant and Co-applicant income
Loan details: Loan amount, term, property area
Financial history: Credit history
Goal: Build a model to accurately classify applicants as approved or rejected based on these features.

Dataset: loan_data.csv contains:

Unique identifiers: Loan_ID
Applicant information: Gender, Married, Dependents, Education, Self_Employed, ApplicantIncome, CoapplicantIncome
Loan details: LoanAmount, Loan_Amount_Term, Property_Area
Credit history: Credit_History
Target variable: Loan_Status (approved = Y, rejected = N)
Benefits: This model can help the bank:

Improve loan approval efficiency
Reduce risk of bad loans
Offer personalized loan options
Next steps:

Explore the data: Analyze distributions, correlations, and missing values.
Preprocess the data: Handle missing values, encode categorical features, and scale numerical features.
Train and evaluate models: Experiment with different models (e.g., logistic regression, decision trees, random forests) and evaluate performance metrics like accuracy, precision, recall, and F1-score.
Select the best model: Choose the model with the best performance and interpretability.
deploy and use the model: Integrate the model into the bank's loan processing system for real-time predictions.