Engineering Features for Machine Learning

In this course we will introduce a number of techniques used to engineer your features. Accompanying each enrichment method description is a brief outline of their relevance for behavioural predictions. 

Example run-through of concatenating features

There are limitless feature engineering option possibilities to choose from. With ecosystem.Ai, there are a handful of default options that come with the workbench. However, any number of custom feature engineering options can be included in your personalised version of the product. This course will provide you with information about many of the options, as well as how best to use them to improve your data for machine learning.

Through this course and experience, your skills will naturally improve.

What is Feature Engineering

Feature Engineering is the art of data science. It is the process of creating new features from existing ones in your data. 

Most raw data is messy, cleaning allows you to organise and subtract redundant documents, while feature engineering is the addition of new documents. This process  helps to highlight important data for your algorithms.

In light of improving model performance, the isolation and addition of polished data ensures the algorithm will generate more accurate predictions. 

Feature Engineering options

Useful engineering options include being able to merge two or more features into one, adding additional date information to an existing transaction, amongst others. In lesson two, we will outline and describe the default list provided in the ecosystem workbench.

The reason behind engineering data 

The need for high quality model creation informs the need for well defined data inputs. Initial data preparation is vital for success when attempting to achieve the best machine learning results. 

Learn more about the Fundamentals of Data Science and Machine Learning:

Data Science and Feature Engineering