satz2000 / End-to-end-project---Customer-churnLinks
End to end projects-- Customer Churning prediction using Gradient Boost Classifier Algorithm perform pre-processing steps then fit data into the Algorithm and Hyper Parameter Tunning to reduce TN & FN value to perform our model to works with a new data. Finally deploying the model using Flask API
☆21Updated last year
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