1. Introduction

This post consists of, how basic machine learning and python libraries can be tailored for most of Finance Data Science like in here we are using Random Forest for directional forecasting of a cryptocurrency data consisting of features 'open', 'high', 'low', 'close', 'volume', 'taker_buy_volume_ratio' etc.. with their given 'timestamp's'(useful for extracting month, day, date, hour, minutes) of minutes' basis, containing 2.1 million rows.