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TELCO Churn Prediction

Telecom Churn Prediction

Companies usually have a greater focus on customer acquisition and keep retention as a secondary priority. However, it can cost five times more to attract a new customer than it does to retain an existing one. Increasing customer retention rates by 5% can increase profits by 25% to 95%, according to research done by Bain & Company.

Churn is a metric that shows customers who stop doing business with a company or a particular service, also known as customer attrition. By following this metric, what most businesses could do was try to understand the reason behind churn numbers and tackle those factors, with reactive action plans

The main goal is to develop a machine learning model capable to predict customer churn based on the customer’s data available.

Data Set:

The telecom customer churn dataset from kaggle

Data Processing Workflow:

  • Data Inspection
  • Data Validation
  • Missing value imputation
  • Numerical and Categorical data
  • Feature encoding
  • Train Test split of data

Machine Learning:

  • Decision Tree model
  • Hyperparameter tuning
  • Tree viualization
  • Preformance analysis

Thank You!