This repository is dedicated to studying how we can prevent customers from switching to other telecommunications services, a practice commonly referred to as 'churn.' By analyzing customer data, we aim to develop a machine learning model capable of identifying which customers are most likely to leave for competitors. This is crucial for helping companies tailor personalized offers and services to retain at-risk customers.
A telecommunications company is particularly concerned about the number of clients switching from their fixed-line services to cable competitors. The goal is to understand who is leaving and why. As a data analyst for this company, your task is to identify these customers and uncover the reasons behind their migration, enabling the business to take proactive measures to win them back and improve customer retention.
For this machine learning algorithm, we used the Decision Tree. This approach aims to find rules that can determine whether a customer is likely to switch to other telecommunications services.
For this project, we used the database called WA_Fn-UseC_-Telco-Customer-Churn avaliable on Kaggle.
- WA_Fn-UseC_-Telco-Customer-Churn. Kaggle, 2018. Disponível em: https://www.kaggle.com/datasets/palashfendarkar/wa-fnusec-telcocustomerchurn. Acesso em: 17 de set. de 2024.
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