This project builds a supervised Machine Learning model to predict whether a loan application should be Approved (1) or Rejected (0) based on applicant financial, personal, and credit-related information.
The goal is to automate the loan screening process, reduce manual bias, and improve decision consistency in banking systems.
- Predict loan approval status using historical applicant data
- Reduce financial risk by identifying high-risk applicants
- Improve efficiency compared to manual verification processes
Each row represents a loan applicant with features such as:
- Applicant_Income
- Coapplicant_Income
- Employment_Status
- Age
- Marital_Status
- Dependents
- Credit_Score
- Existing_Loans
- DTI_Ratio (Debt-to-Income Ratio)
- Savings
- Collateral_Value
- Loan_Amount
- Loan_Term
- Loan_Purpose
- Property_Area
- Education_Level
- Gender
- Employer_Category
Target Variable:
Loan_Approved→ 1 (Approved), 0 (Rejected)
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib / Seaborn
- Data Cleaning & Preprocessing
- Handling Missing Values
- Encoding Categorical Variables
- Feature Scaling (StandardScaler)
- Train-Test Split
- Model Training (e.g., Logistic Regression / Random Forest)
- Model Evaluation using:
- Accuracy
- Precision
- Recall
- F1-Score
- Confusion Matrix
The trained classification model successfully predicts loan approval status with strong performance metrics, demonstrating the effectiveness of supervised learning for financial risk assessment.