Analyzing credit card transactions using machine learning techniques – 2

Principal Component Analysis - Introduction and Data Preperation Principal Component Analysis [PCA] is an unsupervised algorithm which reduces dimensionality and is widely used. A good visual explanation can be found here: http://setosa.io/ev/principal-component-analysis/ As mentioned in our previous article, Correspondence Analysis  works exclusively on categorical data. In contrast, PCA accepts only numerical data. This means our data … Continue reading Analyzing credit card transactions using machine learning techniques – 2

Analyzing credit card transactions using machine learning techniques

Introduction In this 3-part series we'll explore how three machine learning algorithms can help a hypothetical financial analyst explore a real data set of credit card transactions to quickly and easily infer relationships, anomalies and extract useful data. Data Set The data set we'll use in this hypothetical scenario is a real data set released … Continue reading Analyzing credit card transactions using machine learning techniques