In order to include categorical features in your Machine Learning model, you have to encode them numerically using "dummy" or "one-hot" encoding. But how do you do this correctly using scikit-learn? In this video, you'll learn how to use OneHotEncoder and ColumnTransformer to encode your categorical features and prepare your feature matrix in a single step. You'll also learn how to include this step within a Pipeline so that you can cross-validate your model and preprocessing steps simultaneously. Finally, you'll learn why you should use scikit-learn (rather than pandas) for preprocessing your dataset.

Customer churn, also known as customer attrition, occurs when customers stop doing business with a company. The companies are interested in identifying segments of these customers because the price for acquiring a new customer is usually higher than retaining the old one. For example, if Netflix knew a segment of customers who were at risk of churning they could proactively engage them with special offers instead of simply losing them. In this post, we will create a simple customer churn prediction model using Telco Customer Churn dataset. We chose a decision tree to model churned customers, pandas for data crunching and matplotlib for visualizations.

Categorical data are commonplace in many Data Science and Machine Learning problems but are usually more challenging to deal with than numerical data. In particular, many machine learning algorithms require that their input is numerical and therefore categorical features must be transformed into numerical features before we can use any of these algorithms. One of the most common ways to make this transformation is to one-hot encode the categorical features, especially when there does not exist a natural ordering between the categories (e.g. a feature'City' with names of cities such as'London', 'Lisbon', 'Berlin', etc.). For each unique value of a feature (say, 'London') one column is created (say, 'City_London') where the value is 1 if for that instance the original feature takes that value and 0 otherwise. Even though this type of encoding is used very frequently, it can be frustrating to try to implement it using scikit-learn in Python, as there isn't currently a simple transformer to apply, especially if you want to use it as a step of your machine learning pipeline.