Churn prediction for banking customers using logistic regression and decision trees, implemented from scratch in R.
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Updated
Sep 9, 2024 - R
Churn prediction for banking customers using logistic regression and decision trees, implemented from scratch in R.
Predicting Churning in a Telecommunications Company
Analyze IBM Telco Customer data to offer valuable insights for data-driven decision-making on customer retention to reduce churn
Developed a desktop application, that uses ML algorithms to accurately predict customer churn based on customer details. The application helps to identify customers at risk of churning using regression models.
Materi praktikum Talent Scouting Academy (TSA) Kominfo 2023
Fun fact: It is more valuable to hold on to an existing customer than to acquire new ones. Source: Trust me
Customer churn rate is an important metric for e-commerce businesses. Predicting whether someone will churn or not would be great...
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.
The dataset I am working with is from finance industry. The dataset contains a total dimensions of 14 variables across 5181 observations.
Client Churn Prediction with Data Mining. Literature Overview and SaaS B2B Case Study thesis repository
Customer Churn Analysis in R: Logistic, Classification Tree, XGBoost, Random Forest.
An R package for calculating the business value of using predictions from a churn model.
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 …
Predict customer churn rate for a mock telecommunications company using explanatory variables such as age, monthly charges, complaints, etc.
Telecom Churn prediction Using Logistic Regression and Random Forest in R
A data-driven communication strategy with Email Engagement Model, Cluster Analysis and Churn Prediction
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