RFM segmentation is one of the oldest and most effective ways to segment customers. RFM models are based on three simple values - recency, frequency, and monetary value - which...
There are hundreds of excellent Python data science libraries and packages that you’ll encounter when working on data science projects. However, there are four of them that you’ll probably use...
There are loads of different ways to convert categorical variables into numeric features so they can be used within machine learning models. While you can perform this process manually on...
Selecting, filtering and subsetting data is probably the most common task you’ll undertake if you work with data. It allows you to extract subsets of data where row or column...
The predictive response models used to help identify customers in marketing can also be used to help outbound sales teams improve their call conversion rate by targeting the best people...
While there are many open source datasets available for you to use when learning new data science techniques, sometimes you may struggle to find a data set to use to...
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...
As a practical demonstration of how the confusion matrix works, lets load up the Wisconsin Breast Cancer dataset, create a classification model and examine the confusion matrix to see how...
As models require numeric data and don’t like NaN, null, or inf values, if you find these within your dataset you’ll need to deal with them before passing the data...
