https://github.com/vyjayanthipolapragada/fraud_detection_creditcard
Detecting the fraudulent credit card transactions by training Decision Tree model using Scikit-learn and SnapML
https://github.com/vyjayanthipolapragada/fraud_detection_creditcard
classification-model data-preprocessing decision-tree-classifier kaggle-dataset machine-learning numpy pandas python scikit-learn snapml time tree-model
Last synced: 2 months ago
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Detecting the fraudulent credit card transactions by training Decision Tree model using Scikit-learn and SnapML
- Host: GitHub
- URL: https://github.com/vyjayanthipolapragada/fraud_detection_creditcard
- Owner: VyjayanthiPolapragada
- Created: 2023-11-20T12:14:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-20T12:19:01.000Z (over 1 year ago)
- Last Synced: 2024-01-29T00:04:56.454Z (over 1 year ago)
- Topics: classification-model, data-preprocessing, decision-tree-classifier, kaggle-dataset, machine-learning, numpy, pandas, python, scikit-learn, snapml, time, tree-model
- Language: Jupyter Notebook
- Homepage:
- Size: 22.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Fraud_Dectection_CreditCard
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)