8000 GitHub - parthp7/CreditCardFraudDetection: Various machine learning algorithms to classify fraud in famous credit card fraud detection dataset
[go: up one dir, main page]

Skip to content

Various machine learning algorithms to classify fraud in famous credit card fraud detection dataset

Notifications You must be signed in to change notification settings

parthp7/CreditCardFraudDetection

Repository files navigation

CreditCardFraudDetection

Requirements to run the source are:

  1. Python3 should be installed - if not visit https://www.python.org/downloads/
  2. Libraries required include: a. Pandas - for dataset manipulation - https://pandas.pydata.org/pandas-docs/stable/install.html b. sklearn - for machine learning algorithms - http://scikit-learn.org/stable/install.html c. matplotlib - for plotting result graphs - https://matplotlib.org/users/installing.html

For executing source code:

  1. Download dataset from https://www.kaggle.com/mlg-ulb/creditcardfraud and unzip it.
  2. In the folder where creditcardfraud.csv is present, make a folder with name source.
  3. Copy all source file in source folder.
  4. Each source file can run independently. For executing use any IDE, or for command line use command python3 filename.py
  5. For obtaining different models in same domain change parameters of model initialization in source files as given. E.g. Change model = svm.SVC(kernel='rbf', C=1, gamma=10) to model = svm.SVC(kernel='linear', C=1) to change SVM model from RBF to Linear.

The source files can take anywhere between 3s to 15min to execute for given dataset.

About

Various machine learning algorithms to classify fraud in famous credit card fraud detection dataset

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

0