Now that we have started preparing our data for our clustering analysis, we need to preprocess it. Specifically, we need to transform our categorical variables into integers via Label Encoder. A label encoder is a tool used in machine learning to convert categorical data or data that can be divided into categories, into numerical values. This is useful because many machine learning algorithms require numerical input and cannot process categorical data directly. The label encoder assigns a unique integer value to each category in the data, allowing the data to be used in machine learning models.

Methods description

• sklearn.preprocessing: This is a module from the scikit-learn (sklearn) library, which provides various tools for data preprocessing. It includes methods for scaling, normalization, encoding categorical variables, and more;
• LabelEncoder(): LabelEncoder is a class within the sklearn.preprocessing module. It is used to encode categorical labels into numerical labels. This is particularly useful when dealing with categorical variables in machine learning algorithms, as many algorithms require numerical input;
  • fit_transform(): This method of the LabelEncoder class fits the encoder to the input data and transforms it. It learns the encoding for the input data and applies it, converting categorical labels into numerical labels. In this specific case, it encodes the "status_type" column of the input data (X) into numerical labels;
  • transform(): This method of the LabelEncoder class transforms the input data based on the encoding learned during fitting. It applies the learned encoding to new data without re-learning it. Here, it transforms the target variable (y) into numerical labels using the encoding learned during the fit_transform step.