Discovering the Power of FeatClus: Simplifying Feature Selection for Clustering Models

Feature selection plays a crucial role in developing effective machine learning models. While it is commonly used in supervised learning to identify features that predict the target variable, have you ever explored feature selection for clustering models? In unsupervised learning, where there is no target variable, determining relevant features can be challenging.

Traditional tutorials often overlook the importance of feature selection in clustering scenarios, especially when dealing with a large number of variables. This is where “featclus,” a Python library I’ve developed, comes into play. It simplifies the process of feature selection for clustering models, making your modeling tasks more efficient and effective.

Before delving deeper, let’s understand the primary application of clustering: customer segmentation. By grouping data into clusters based on similarities, businesses can uncover patterns in customer behavior, enabling targeted strategies tailored to specific customer groups. For instance, customers who purchase expensive products may respond well to high-end promotions, while inactive customers might need incentives to make a purchase.

The FeatClus library employs a data-shifting approach to evaluate feature importance. By shifting feature columns and measuring the impact on a baseline metric, it identifies the most significant features for clustering. This method eliminates the need to manually determine the optimal number of clusters, as it leverages DBSCAN for automatic cluster detection.

Now, let’s walk through a simple case study using a dataset from Kaggle that includes information on customers in a mall, such as gender, age, annual income, and spending score.

pip install featclus

Before initiating any modeling, data preprocessing is essential. The “featclus” library requires numerical data, hence categorical columns like gender must be encoded. Additionally, irrelevant columns like customer IDs should be removed.

# deleting the id column
df = df.drop(["CustomerID"], axis=1)

# encoding the gender column
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
df["Gender"] = encoder.fit_transform(df["Gender"])

Next, we create an instance of the FeatureSelection class and specify the shifts to apply across all columns, along with the number of CPU cores for processing.

from featclus import FeatureSelection
fs = FeatureSelection(df, shifts=[25, 50, 75, 100], n_jobs=-1)

By leveraging the get_metrics() method, we can rank the features and identify the most important ones for clustering. The results provide valuable insights for model building.

Featuring an interactive Plotly chart, the plot_results() method allows us to visualize and filter the top-ranked features, enhancing the understanding of feature importance.

If you’re interested in exploring more, you can generate toy datasets and perform feature selection to gain hands-on experience with the FeatClus library.

By mastering feature selection for clustering models, you can enhance the performance and interpretability of your machine learning models. Thank you for reading, and feel free to connect with me on LinkedIn or explore the GitHub repository for this project.