GitHub / KUSHALKUMARD / Customer-Churn-Prediction-App-Project
In this project, I utilized survival analysis models to assess how the likelihood of customer churn changes over time and to calculate customer Lifetime Value (LTV). Additionally, I implemented a Random Forest model to predict customer churn and deployed this model using a Flask web application.
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PURL: pkg:github/KUSHALKUMARD/Customer-Churn-Prediction-App-Project
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Forks: 0
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License: mit
Language: Jupyter Notebook
Size: 24.5 MB
Dependencies parsed at: Pending
Created at: about 1 year ago
Updated at: about 1 year ago
Pushed at: about 1 year ago
Last synced at: about 1 year ago
Topics: customer-churn-prediction, customer-churn-prediction-with-machine-learning, dataanalysis, explainable-ai, explainable-ml, flask-application, hazard, partial-dependence-plots, random-forest, shape-values, survival-analysis