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A general-purpose framework for solving problems with machine learning applied to predicting customer churn
| Date | Stars |
|---|---|
| 2026-07-31 | 425 |
| 2026-08-03 | 425 |
| 2026-08-06 | 426 |
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# A Machine Learning Framework with an Application to Predicting Customer Churn  This project demonstrates applying a 3 step general-purpose framework to solve problems with machine learning. The purpose of this framework is to provide a scaffolding for rapidly developing machine learning solutions across industries and datasets. The end outcome is a both a specific solution to a customer churn use case, with a reduction in revenue lost to churn of more than 10%, as well as a general approach you can use to solve your own problems with machine learning. # Framework Steps 1. __Prediction engineering__ * State business need * Translate business requirement into machine learning task by specifying problem parameters * Develop set of labels along with cutoff times for supervised machine learning 2. __Feature Engineering__ * Create features - predictor variables - out of raw data * Use cutoff times to make valid features for each label * Apply automated feature engineering to automatically make hundreds of relevant, valid features 3. __Modeling__ * Train a machine learning model to predict labels from features * Use a pre-built solution with common libraries * Optimize model in line with business objectives Machine learning currently is an ad-hoc process requiring a custom solution for each problem. Even for the same dataset, a slightly different prediction problem requires an entirely new pipeline built from scratch. This has made it too difficult for many companies to take advantage of the benefits of machine learning. The standardized procedure presented here will make it easier to solve meaningful problems with machine learning, allowing more companies to harness this transformative technology. # Application to Customer Churn The notebooks in this repository document a step-by-step application of the framework to a real-world use case and dataset - predicting customer churn. This is a critical need for subscription-based businesses and an ideal application of machine learning. The dataset is provided by KKBOX, Asia's largest music streaming service, and can be downloaded [here](https://www.kaggle.com/c/kkbox-churn-prediction-challenge/data). Within the overall scaffolding, several standard data science toolboxes are used to solve the problem: * [Featuretools](https://docs.featuretools.com/#): automated feature engineering * [Pandas](https://pandas.pydata.org): data munging and engineering * [Scikit-Learn](http://scikit-learn.org/stable/documentation.html): standard machine learning algorithms * [Apache Spark](https://spark.apache.org/documentation.html) with [PySpark](https://spark.apache.org/docs/latest/api/python/index.html): Running comptutations in parallel * [TPOT (Tree-based Pipeline Optimization Tool)](https://github.com/EpistasisLab/tpot): model selection optimization using genetic algorithms # Results The final results comparing several models are shown below: | Model | ROC AUC | Recall | Precision | F1 Score | |-------------------------------------------|---------|--------|-----------|----------| | Naive Baseline (no ml) | 0.5 | 3.47% | 1.04% | 0.016 | | Logistic Regression | 0.577 | 0.51% | 2.91% | 0.009 | | Random Forest Default | 0.929 | 65.2% | 14.7% | 0.240 | | Random Forest Tuned for 75% Recall | 0.929 | 75% | 8.31% | 0.150 | | Auto-optimized Model | 0.927 | 2.88% | 64.4% | 0.055 | | Auto-optimized Model Tuned for 75% Recall | 0.927 | 75% | 9.58% | 0.170 | __Final Confusion Matrix__  __Feature Importances__  # Notebooks 1. [Partitioning Data](https://github.com/Featuretools/predict-customer-churn/blob/master/churn/1.%20Partitioning%20Data.ipynb): separate data into independent subsets
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:00cfe28e12c45e52, llm:description: 'A general-purpose framework for solving problems with machine learning applied to predicting customer churn'
matched fp:00cfe28e12c45e52, llm:description: 'A general-purpose framework for solving problems with machine learning applied to predicting customer churn'
matched fp:00cfe28e12c45e52, llm:description: 'A general-purpose framework for solving problems with machine learning applied to predicting customer churn'