I have sample transaction records that are manually classified as either 'fraudulent' or 'non-fraudulent'.
I want to use these records to train a supervised machine learning algorithm.
After building a model using this training data, the algorithm should be able to classify new records as either fraudulent or non-fraudulent.
Please let me know which of the following algorithms you can leverage to accomplish said task and what software you'll use.
Linear Regression
Logistic Regression
Decision Tree
SVM
Naive Bayes
kNN
K-Means
Random Forest
Dimensionality Reduction Algorithms
Gradient Boosting algorithms
Example Records:
Name,Type,Amount,Where Issued,Where Used,Age,IsFradulent
John,Visa,$2000,USA,RUS,29,Yes
Smith,MasterCard,$2000,USA,FRA,45,No
etc
etc
Thank you!
About the recuiterMember since May 20, 2018 Manish Lal Moha
from Kyonggi-do, South Korea