Getting started with feature engineering
When it comes to a machine learning algorithm, the first question to ask is usually what features are available or what the predictive variables are.
The driving factors that are used to predict future prices of NASDAQ, the close prices, include historical and current open prices as well as historical performance (high, low, and volume). Note that current or same-day performance (high, low, and volume) shouldn’t be included because we simply can’t foresee the highest and lowest prices at which the stock traded, or the total number of shares traded before the market closed on that day.
Predicting the close price with only those preceding four indicators doesn’t seem promising and might lead to underfitting. So, we need to think of ways to generate more features in order to increase predictive power. In machine learning, feature engineering is the process of creating features in order to improve the performance of...
