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This issue proposes the development of a new notebook that demonstrates how to perform customer segmentation using K-Means clustering with BigQuery Dataframes. The notebook should cover the following aspects:
Data Preparation
Select an appropriate customer data from a BigQuery public dataset or one dataset from our public GCS bucket, if available.
Perform feature engineering and selection relevant to customer segmentation (e.g., recency, frequency, monetary value - RFM analysis).
Prepare the data for K-Means clustering using BigQuery Dataframes.
Model Training
Use bigframes.ml.cluster.KMeans to train a K-Means clustering model.
Optimize the number of clusters (k) using techniques like the elbow method or silhouette analysis.
Cluster Analysis
Analyze the characteristics of each customer segment.
Visualize the clusters using appropriate techniques (e.g., scatter plots, t-SNE).
Interpretation and Application
Draw insights from the customer segments.
Discuss.potential applications of the segmentation results (e.g., targeted marketing, personalized recommendations).
Instructions for Contributors
Use the existing notebooks in the repository as a template for structure and style.
Ensure the notebook is well-documented and easy to follow.
Include a clear explanation of the concepts and techniques used.
Provide visualizations to illustrate the results.
Use a publicly available dataset or provide instructions on how to generate synthetic data.
Test the notebook thoroughly before submitting a pull request.
Note: Please refer to the contributing guidelines for detailed instructions on how to contribute to this repository.
This notebook will provide a valuable resource for users interested in applying K-Means clustering for customer segmentation using BigQuery Dataframes. We encourage contributions from the community to help develop this notebook.
We appreciate a lot your contribution! :)
The text was updated successfully, but these errors were encountered:
Description
This issue proposes the development of a new notebook that demonstrates how to perform customer segmentation using K-Means clustering with BigQuery Dataframes. The notebook should cover the following aspects:
Data Preparation
Select an appropriate customer data from a BigQuery public dataset or one dataset from our public GCS bucket, if available.
Perform feature engineering and selection relevant to customer segmentation (e.g., recency, frequency, monetary value - RFM analysis).
Prepare the data for K-Means clustering using BigQuery Dataframes.
Model Training
Use bigframes.ml.cluster.KMeans to train a K-Means clustering model.
Optimize the number of clusters (k) using techniques like the elbow method or silhouette analysis.
Cluster Analysis
Analyze the characteristics of each customer segment.
Visualize the clusters using appropriate techniques (e.g., scatter plots, t-SNE).
Interpretation and Application
Draw insights from the customer segments.
Discuss.potential applications of the segmentation results (e.g., targeted marketing, personalized recommendations).
Instructions for Contributors
Use the existing notebooks in the repository as a template for structure and style.
Ensure the notebook is well-documented and easy to follow.
Include a clear explanation of the concepts and techniques used.
Provide visualizations to illustrate the results.
Use a publicly available dataset or provide instructions on how to generate synthetic data.
Test the notebook thoroughly before submitting a pull request.
Resources
BigQuery Dataframes documentation: https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.cluster.KMeans
K-Means Clustering documentation: https://cloud.google.com/bigquery/docs/kmeans-tutorial
Contributing guidelines: CONTRIBUTING.md
Note: Please refer to the contributing guidelines for detailed instructions on how to contribute to this repository.
This notebook will provide a valuable resource for users interested in applying K-Means clustering for customer segmentation using BigQuery Dataframes. We encourage contributions from the community to help develop this notebook.
We appreciate a lot your contribution! :)
The text was updated successfully, but these errors were encountered: