Churn prediction

Last update: Sep 28, 2022

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Deep Learning Churn-prediction

Overview

Churn-prediction

Data preprocessing:: Label encoder is used to normalize the categorical variable

Data Transformation:: For each data transformation sepeate fiels are used Base line (RAW) is used for Original dataset without any data transformation

Feature selection:: Univarient feature selection is used for feature selection

Paremeter tunning:: Grid Search CV is used for parameter tuning for the classifiers

Dataset:: Three datasets are used in our study

data set 1 Telecom_customer churn (10000).rar
churn-5000.csv
churn-data-333.csv

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