Feature engineering

Articles tagged Feature engineering

How to engineer new features using Decision Tree models

One interesting technique in feature engineering is the use of Decision Trees (and other models) to create or derive new features using combinations of features from the original dataset. Here,...

How to segment customers using RFM and ABC

While the Recency, Frequency, Monetary value or RFM approach to customer segmentation might be old, it’s based on sound science, so no matter what customer model you’re building, it’s generally...

How to calculate CLV using BG/NBD and Gamma-Gamma

Calculating Customer Lifetime Value or CLV is considered a really important thing in marketing and ecommerce, yet most companies can’t do it properly. This clever metric tells you the predicted...

How to engineer customer purchase latency features

Purchase latency or customer latency is a measure of the number of days between a customer’s orders and is one of the most powerful features in many propensity and churn...

How to use Category Encoders to encode categorical variables

Most datasets you’ll encounter will probably contain categorical variables. They are often highly informative, but the downside is that they’re based on object or datetime data types such as text...

How to use mean encoding in your machine learning models

When you’re building a machine learning model, the feature engineering step is often the most important. From your initial small batch of features, the clever use of maths and stats...

How to engineer date features using Pandas

When dealing with temporal or time series data, the dates themselves often yield information that can vastly improve the performance of your model. However, to get the best from these...