The article outlines a process to segment customers using the k-means clustering algorithm within a Spark environment, highlighting the importance of handling missing data.
A necessary first step in processing the dataset is addressing missing values. We employ a basic imputation strategy using column means to ensure data completeness.
Interaction count serves as a crucial metric in customer segmentation, indicating engagement levels. Increased interaction may reveal loyal customers, while lower figures necessitate re-engagement efforts.
The final step involves storing the k-means output within BigQuery for enhanced visualization in Looker Studio, demonstrating a seamless workflow from data processing to analytics.
Collection
[
|
...
]