Different methods for churn prediction
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Updated
Apr 4, 2019 - Jupyter Notebook
Different methods for churn prediction
deep learning model
Determining the churn rate of a bank and predicting which of their customers are at high risk of leaving the bank.
Predict Churn in Telecom Industry Using Logistic Regression with R
A simple deep neural network using python's deep learning package Keras to predict if a customer with specific characteristics will churn as a client using an artificial neural network
A model to predict customer churn using Spark
Churn prediction for sparkify music streaming service
Churn prediction for bank customers
Churn prediction based on bank customers
Performed a churn analysis on a Kaggle competition - Customer Churn Prediction 2020 to predict whether a customer will change telco provider
predicting churn on a Telecom company
Challenge Data-Science 1ª Edição
The purpose of this project is to use SQL to transform multiple datasets relating to customer phone calls over a four month period, to engineer new features, and to combine the datasets into a suitable case table in order to use Machine Learning techniques to predict the likelihood of a customer churning in any given month.
[124th Place] Repository for Challenge 05 - SONDA of the IBM Maratona Behind the Code 2021
Churn rate analysis with artificial neural network.
Predicting customer churn for the music app, Sparkify, using PySpark on AWS EMR clusters
telco dataset 85% precision, 81% accuracy
This project is an attempt to create a Churn Customer Classification models, resulting in balanced Logistic Regression model with 80% recall and 73% precision value, also Decision Tree with high recall value (91%). Web application included.
Uma empresa de telecomunicações que fornece serviços está preocupada em reduzir a taxa de retenção de seus clientes. Portanto, o gerente de CRM me contratou para que eu desenvolva um modelo de previsão de clientes que provavelmente irão parar de utilizar os serviços da empresa.
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