Came across the missingno library. Made a couple of Jupiter notebooks to see how it works and what I can use it for at work.

A classification approach to the machine learning Titanic survival challenge on Kaggle.Data visualisation, data preprocessing and different algorithms are tested and explained in form of Jupyter Notebooks

EDA for more than 30K game ratings collected from [IGDB API](https://api-docs.igdb.com/#about) using [igdb-api-v4 for python](https://github.com/twitchtv/igdb-api-python). This notebook explores any common trends for games that have ratings from igdb and external critics.

In the real world, a dataset with no missing values doesn't exist...So in this notebook, we explore different ways of dealing with it.