π Best Model: Random Forest
π― Target: loan_approved
π Classes: No, Yes
β Best Accuracy: 1.0000
π Training Samples: 100
β±οΈ Training Time: 352.58s
π’ Numeric Features: age, annual_income, academic_score, credit_score, co_applicant_income
π·οΈ Categorical Features: education_level, student_type, program_type, college_city, is_listed_university, has_collateral, co_applicant
π‘ Note: All fields are optional. Missing values will be handled automatically by the AutoML pipeline.
| Model | Accuracy | AUC | F1-Score | CV Score | Time (s) |
|---|---|---|---|---|---|
| Random Forestπ | 1.0000 | 1.0000 | 1.0000 | 0.9375Β±0.000 | 293.29 |
| Gradient Boosting | 0.9000 | 1.0000 | 0.5000 | 0.9250Β±0.073 | 56.29 |
| Decision Tree | 0.8500 | 0.9211 | 0.4000 | 0.9125Β±0.050 | 0.87 |
| Logistic Regression | 0.8500 | 1.0000 | 0.4000 | 0.9625Β±0.031 | 0.55 |