archd3sai / Customer-Survival-Analysis-and-Churn-Prediction

In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

Date Created 2019-03-20 (5 years ago)

Commits 99 (last one 4 years ago)

Stargazers 182 (0 this week)

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Forks 70

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RepositoryStats indexes 595,856 repositories, of these archd3sai/Customer-Survival-Analysis-and-Churn-Prediction is ranked #193,696 (67th percentile) for total stargazers, and #427,587 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #4,530/17,543.

archd3sai/Customer-Survival-Analysis-and-Churn-Prediction is also tagged with popular topics, for these it's ranked: data-analysis (#309/671), explainable-ai (#52/171)

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archd3sai/Customer-Survival-Analysis-and-Churn-Prediction has 4 open pull requests on Github, 0 pull requests have been merged over the lifetime of the repository.

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Homepage URL: https://churn-prediction-app.herokuapp.com/

All Topics

hazard shap-values data-analysis random-forest explainable-ai flask-application survival-analysis partial-dependence-plot customer-churn-prediction customer-survival-analysis

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updated: 2024-12-13 @ 12:32am, id: 176831583 / R_kgDOCoo8Xw