Xente Fraud Detection Challenge
$4,500 USD
Accurately classify the fraudulent transactions from Xente's e-commerce platform
1201 data scientists enrolled, 547 on the leaderboard
Financial ServicesClassificationStructured
Uganda
20 May 2019—23 September 2019
Any tips and tricks to handle imbalance data?
published 28 Jun 2019, 15:09

I am new in data science I want to handle imbalance data how do i do that?

Try SMOTE.

i tried smote but result is so bad.

I tried SMOTE and didn't work !!!

On the anomaly detection w/ autoenconder front i am looking at this document : https://www.cs.ru.nl/bachelors-theses/2018/Tom_Sweers___4584325___Autoencoding_credit_card_fraude.pdf

I am trying just for kicks because i think this is a very signal poor dataset.

Hi jayesh! There are quite a number of ways to deal with imbalanced datasets. Some of these are:

1. Use of SMOTE

2. Use of the NearMiss algorithm

3. Random Sampling (Undersampling and/or Oversampling)

4. Anomaly Detection

5. Use of certain ensemble learning models.

Since the results you obtained on using SMOTE gave you low scores, I will advise you to check your features very well...something may be fishy.