Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)
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Updated
Sep 28, 2017 - R
Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)
Customer Churn Analysis in R: Logistic, Classification Tree, XGBoost, Random Forest.
Materi praktikum Talent Scouting Academy (TSA) Kominfo 2023
Customer Churn project for a telecom firm. The project aims to predict the possibility of a customer to churn by using methods of Data Analysis and Machine Learning with sound accuracy and justifies its result by showing the expected cost-benefit from following their recommendations.
This code provides a glimpse on how to analyse Churn, Appetency and Upselling using R
Telecom Churn prediction Using Logistic Regression and Random Forest in R
An R package for calculating the business value of using predictions from a churn model.
A data-driven communication strategy with Email Engagement Model, Cluster Analysis and Churn Prediction
Client Churn Prediction with Data Mining. Literature Overview and SaaS B2B Case Study thesis repository
Predict Churn in Telecom Industry Using Logistic Regression with R
The dataset I am working with is from finance industry. The dataset contains a total dimensions of 14 variables across 5181 observations.
This project is the Incident driven contract conversion modelling based on Logit probability.There were two datasets one with conversion information and other past incidence information of a company that provides professional services. The proposed model helps business leaders to take informed decisions about chances of contract renewals based o…
In this case study we will predict that whether a particular customer of a telecom company will churn or not based on the demographic data and churn data.
Predict customer churn rate for a mock telecommunications company using explanatory variables such as age, monthly charges, complaints, etc.
Customer churn rate is an important metric for e-commerce businesses. Predicting whether someone will churn or not would be great...
Analyze IBM Telco Customer data to offer valuable insights for data-driven decision-making on customer retention to reduce churn
Churn Prediction: Predicted customer churn based on the past service incidents,
Using the data provided for the past 3 months, I have created a model by classifying the first two months as a customer’s happy phase and the 3rd month as a customer entering the sore phase. Based on this, I created a model which predicts which customers will enter the churn phase next month. By using this model, we will be able to identify and …
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