Detecting the fraudulent credit card transactions by training Decision Tree using Scikit-learn and SnapML
A real data set is used to train the model, downloaded from Kaggle
Can download here : https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud/
Libraries Used: (except snapml, other libraries are built-in when using Jupyter notebook)
numpy, pandas, warnings, matplotlib, sklearn, time, gc, sys, snapml (library developed by IBM)
Outcomes:
Data preprocessing in Python
Classifying model with Scikit-learn and SnapML
Use Scikit-learn and SnapML to train Decision Tree model
Assess the quality and speed of the trained model in each case (Scikit-learn and SnapMl)