Interpret K-means Clusters on Credit Card Dataset - Hands-On
In this video, I’ll walk you through a complete, hands-on implementation of K-Means clustering on a credit card dataset. We’ll explore how clustering can reveal hidden patterns in customer behavior, helping us segment customers based on their spending habits. From data preprocessing, handling outliers, to visualizing and interpreting clusters, I’ll guide you through each step of the process.
We’ll dive deep into practical aspects like imputation, scaling, and dimensionality reduction with PCA to ensure the data is ready for clustering. I’ll also demonstrate how to evaluate the model with metrics like the Silhouette Score and use visualizations to make sense of the clusters we’ve created.
📑 Chapters:
00:35 Introduction to K-Means Clustering
02:24 Importing Libraries
04:37 Loading and Exploring the Dataset
06:03 Preprocessing
08:44 Training the Model
10:01 Evaluating the Model
11:27 Interpreting the Results
15:50 Conclusion
🔗 Link to the notebook: https://www.kaggle.com/code/halflingw...
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#DataScience #KMeansClustering #MachineLearning #CreditCardData #ClusteringAnalysis