In this i have analysed and visualized the loan dataset and converted its categorical feature like purpose of taking the loan to the dummies and trained the dataset using Decision Tree Model and Random Forest model then i have perform prediction on test data for both Decision tree model and Random forest model separately In Random forest model i have no. of estimators(Trees) as 350 on comparison of classification report it was observed that Random forest did it better
Decision Tree:- classification report-------------
precision recall f1-score support
0 0.86 0.82 0.84 2431
1 0.20 0.25 0.22 443
avg / total 0.75
0.73
0.74
2874
Random Forest:- classification report--------------
precision recall f1-score support
0 0.85 1.00 0.92 2431
1 0.47 0.02 0.03 443
avg / total 0.79
0.85
0.78
2874