This file includes application of algorithms like logistic regression, Gaussian Naive Bayes and KNN and comparing their classification performances on their test dataset. The project and dataset description is included in the file titled 'Supervised Learning Problem Statement.pdf' The dataset is named 'Bank_Personal_Loan_Modelling.csv'
Results
| Model | Condition | Precision | Recall | Accuracy |
|---|---|---|---|---|
| Logistic Regression | with outliers | 0.90 | 0.79 | 0.95 |
| Gaussian NB | with outliers | 0.68 | 0.74 | 0.87 |
| Kneighbors classifier | with outliers | 0.96 | 0.81 | 0.96 |
| Logistic Regression | without outliers | 0.90 | 0.81 | 0.95 |
| Gaussian NB | without outliers | 0.67 | 0.75 | 0.87 |
| Kneighbors classifier | without outliers | 0.94 | 0.79 | 0.95 |
| Logistic Regression | with polynomial features | 0.96 | 0.90 | 0.98 |
| Gaussian NB | with polynomial features | 0.68 | 0.72 | 0.87 |
| Kneighbors classifier | with polynomial features | 0.96 | 0.79 | 0.96 |