Hi All,
This small project is to detect the anamoly in credit cards transactions using machine learning.
We will need python, some machine learning libraries like matplotlib.pyplot, scikit-learn , numpy , pandas ,
I will use jupyter notebook for the same but you can use any IDE you are confortable with,. The dataset used in this project can be downloaded from Kaggle - The link to the dataset is - https://www.kaggle.com/mlg-ulb/creditcardfraud/downloads/creditcardfraud.zip/3 Make sure to extract the dataset and move it to the project folder.
We are going to use a local outlier factor to calculate anomaly score An isolation forest algorithm These two algorithm will comb through the dataset of almost around 280,000 transactions and predict which ones are fraudalent