shivamkc01 / handling_imbalanced_dataset

This project is about how you can deal with imbalanced data and which performance metrics' particularly important compared to usual practices with fairly balanced data.

Handling_Imbalanced_dataset

This repository is IPython Notebook for Kaggle Dataset, https://www.kaggle.com/mlg-ulb/creditcardfraud

• The aim of this project is detecting fraudulent or non-fraudulent transactions while dealing with imbalanced data. To achieve this, various supervised learning algorithms will be used and the results will be compared.

• Imbalanced data refers to classification problems based on the binary class inequality. There are several methods for dealing with this problem like Re-Sampling, Generate Synthetic Samples, Anomaly Detection Methods or performance metrics instead of accuracy results.

• In this project, the undersampling method will be implemented to the majority class and performance metrics such as Precision, Recall, F1 Score and AUC and some anomaly detection methods like one-class SVM and Neural Network will be used to find the best algorithm which highly predicted fraudulent or non-fraudulent transactions.

Dataset can be found in below link

https://www.kaggle.com/mlg-ulb/creditcardfraud

The project has 4 main topics:

1. Data Exploration
2. Hyperparameter Optimisation
3. Model Building
4. Comparing Performance Metrics

Dependencies
1.Pandas
2.NumPy
3.SciKit-Learn
4.Seaborn
5.Matplotlib

If you want to know more about How to Handle Imbalanced dataset then here is my link to my blog:

https://shivamkc01.medium.com/handling-imbalanced-dataset-in-machine-learning-9ac075787e07