Description
This issue proposes the development of a new notebook that demonstrates how to predict customer churn using BigFrames.
The notebook should cover the following aspects:
Data Preparation
Select an the customer data from the BigQuery public dataset: firebase-public-project.analytics_153293282.events_*
Perform feature engineering and selection relevant to customer churn (e.g., recency, frequency, monetary value, customer demographics, service usage patterns, customer support interactions).
Prepare the data for machine learning model training using BigQuery ML.
Model Training
Use BigQuery ML to train a classification model for churn prediction. Consider using logistic regression, XGBoost, or other suitable models available in BigQuery ML.
Optimize model hyperparameters using BigQuery ML's built-in features.
Split the data into training and testing sets for model evaluation.
Model Evaluation
Evaluate the model's performance using metrics such as accuracy, precision, recall, F1-score, and AUC.
Analyze the model's predictions and identify potential areas for improvement.
Interpretation and Application
Draw insights from the model's predictions and identify factors contributing to customer churn.
Discuss potential applications of the churn prediction model (e.g., targeted retention campaigns, proactive customer support).
Resources
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 BigQuery Dataframes, Gemini. We encourage contributions from the community to help develop this notebook.
We appreciate a lot your contribution! :)
Description
This issue proposes the development of a new notebook that demonstrates how to predict customer churn using BigFrames.
The notebook should cover the following aspects:
Data Preparation
Select an the customer data from the BigQuery public dataset: firebase-public-project.analytics_153293282.events_*
Perform feature engineering and selection relevant to customer churn (e.g., recency, frequency, monetary value, customer demographics, service usage patterns, customer support interactions).
Prepare the data for machine learning model training using BigQuery ML.
Model Training
Use BigQuery ML to train a classification model for churn prediction. Consider using logistic regression, XGBoost, or other suitable models available in BigQuery ML.
Optimize model hyperparameters using BigQuery ML's built-in features.
Split the data into training and testing sets for model evaluation.
Model Evaluation
Evaluate the model's performance using metrics such as accuracy, precision, recall, F1-score, and AUC.
Analyze the model's predictions and identify potential areas for improvement.
Interpretation and Application
Draw insights from the model's predictions and identify factors contributing to customer churn.
Discuss potential applications of the churn prediction model (e.g., targeted retention campaigns, proactive customer support).
Resources
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 BigQuery Dataframes, Gemini. We encourage contributions from the community to help develop this notebook.
We appreciate a lot your contribution! :)