Skip to content

nikosOik99/Fraud-Detection

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 

Repository files navigation

Fraud-Detection

In this notebook we have a Fraudulent transactions dataset from Kaggle We will try to classify the data according to class(Fraud or no fraud) using the columns V1-V28 and the column 'Class' ('Fraud/No fraud') Also we will try to use DecisionTree and RandomForest models using Pipeline.

Models used: Logistic Regression,
Decision Tree,
Random Forest,

OverSampling Algorithms used: SMOTE

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published