IEEE-CIS-Fraud-Detection

# IEEE-CIS-Fraud-Detection * This repository consists of deep dive Exploratory Data Analysis(EDA) about IEEE-CIS Fraud Detection Data Set. This competition is a binary classification problem - i.e. our target variable is a binary attribute (Is the user making the click fraudulent or not?) and our goal is to classify users into "fraudulent" or "not fraudulent" as well as possible. * You can read the complete dataset overview and data description found in IEEE-CIS Fraud Detection page. https://www.kaggle.com/c/ieee-fraud-detection/overview * In this repository you will have access to; * EDA.ipynb: Jupyter notebook that contains deep dive analysis * util_data_cleaning.py: Python file that contains extensive data cleaning functions. * util_reporting.py: Python file that contains extensive visualisation and reporting functions. * util_feature_engineering.py: Python file that contains extensive data preparation and wrangling functions. * You can glance at my Kaggle kernel to have an idea how i eased the EDA process: