Principal Component Analysis (PCA) is a useful technique when you want to get a better understanding of a dataset containing a large number of correlated features. For example, in image processing, PCA is used for data compression. The main objective is to reduce the complexity of the data with a minimum loss of information.
New technologies are transforming the insurance industry. Tomorrow’s winners will be those insurers able to convert vast amounts of data into actionable insights about clients and products alike. An increasing demand for machine learning methods, combined with a growing shortage of data scientists able to create, implement and communicate these methods, call for a data pipeline that is as efficient as possible.
This post is about publishing your first Python package on PyPI.
This post features an overview of the development process in Python.
This post features some practical examples on how to parse different date formats from flat text files, using Pandas.