We have done Exploratory Data Analysis on full data then we have removed outliers using "LocalOutlierFactor", then finally we have used KNN technique to predict to train the data and to predict whether the transaction is Fraud or not. We have also applied T-SNE to visualize the Fraud and genuine transactions in 2-D.
In order to run the project just download the data from above mentioned source then run any file.
You need to have installed following softwares and libraries in your machine before running this project. Python 3 Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy, scipy.
Python 3: https://www.python.org/downloads/ Anaconda: https://www.anaconda.com/download/
Om Krishna - Complete work
Data Source: https://www.kaggle.com/dalpozz/creditcardfraud/data
It is a CSV file, contains 31 features, the last feature is used to classify the transaction whether it is a fraud or not.