This project aims to develop a machine learning model that can analyze customer data and predict whether a customer is likely to churn. By identifying potential churners in advance, organizations can take proactive steps to improve customer retention and reduce revenue loss.
Telecom Churn Case Study
To predict if a customer will churn, given the ~170 columns containing customer behavior, usage patterns, payment patterns, and other features that might be relevant. Your target variable is "churn_probability"
To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.
Exploratory Data Analysis on Telecom Customer Churn — uncovering key drivers like contract type, payment method, and tenure to support retention strategies.
Predict churn and derive actionable strategies to retain users in a highly competitive telecom environment.
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