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

<https://repo.masters-masters.com/repos/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction>

[Accueil](https://repo.masters-masters.com/) › [archd3sai](https://repo.masters-masters.com/owners/archd3sai) › [Customer-Survival-Analysis-and-Churn-Prediction](https://repo.masters-masters.com/repos/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction)

archd3sai/Customer-Survival-Analysis-and-Churn-Prediction, dépôt open source à 275 étoiles sur GitHub. Carte Alpha de RepoMasters. Langage : Jupyter Notebook.

| Champ | Valeur |
| --- | --- |
| Propriétaire | [archd3sai](https://repo.masters-masters.com/owners/archd3sai) |
| Étoiles | 275 |
| Forks | 84 |
| Langage | [Jupyter Notebook](https://repo.masters-masters.com/languages/jupyter-notebook) |
| Licence | MIT |
| Création | 2019 |
| Sujets | customer-survival-analysis, random-forest, explainable-ai, data-analysis, flask-application, survival-analysis, shap-values, partial-dependence-plot, hazard, customer-churn-prediction |
| Rang | [Alpha (250 à 699 ★)](https://repo.masters-masters.com/ranks/b_movie) |
| Voir sur GitHub | [archd3sai/Customer-Survival-Analysis-and-Churn-Prediction](https://github.com/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction) |

## Fiche du dépôt

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.

Rang des dépôts qui ont 250 à 699 ★ sur GitHub. Probabilité publiée par carte tirée : 20 %. Rareté : Peu commune.

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Données : GitHub · Illustration dessinée par RepoMasters à partir de l’identifiant du dépôt et de la couleur de son langage.
