Using dataset_model.csv on Loan Application, build a Classification model to predict Loan Status(Target Variable) :
-> Use innovative methods for Outlier handling & Missing Values Imputation.
------Use Label Encoder as encoding technique on features , predict using below algorithms------
1.Using Logistic Regression - Perform the parameter tuning and list your best performance metrics on -
Precision ,Recall & F1 Score ,AUROC
Refer : https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
2.Using RandomForest Classifier - Perform the parameter tuning and list your best performance metrics on
Precision ,Recall & F1 Score ,AUROC
Refer : https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
----- Use One hot Encoder as encoding technique on feature , predict using the below algorithms ----
3.Using Logistic Regression - Perform the parameter tuning and list your best performance metrics on -
Precision ,Recall & F1 Score ,AUROC
4.Using RandomForest Classifier - Perform the parameter tuning and list your best performance metrics on -
Precision ,Recall & F1 Score ,AUROC
Which approach between Label Encoding & One Hot Encoding gave better results in case of both the algorithms used ?