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CONTEXT
In the competitive landscape of customer service, understanding churn is vital for improving retention strategies. This analysis aims to explore customer data to identify patterns of churn.
OBJECTIVE
To conduct a comprehensive analysis of customer churn and determine the significant predictors affecting retention rates.
FORMAT
The analysis should include data cleansing, exploratory data analysis (EDA), visualization of churn factors, and building a predictive model using machine learning algorithms.
EXAMPLES
For example, you might analyze the relationship between customer service interactions and churn rates, or assess the predictive power of various features through logistic regression or decision trees.
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