This repository is IPython Notebook for Kaggle Dataset, https://www.kaggle.com/mlg-ulb/creditcardfraud
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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.
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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.
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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:
- Data Exploration
- Hyperparameter Optimisation
- Model Building
- 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