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Assignment4-Submission

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 ?

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