Data

Design a comprehensive data analysis plan for a customer churn dataset, including data preprocessing, exploratory data analysis, feature engineering, model selection, and evaluation metrics, to identify key factors contributing to customer churn and provide actionable insights for business stakeholders, assuming the dataset contains demographic, transactional, and behavioral data for a telecommunications company with 10,000 customers, and the goal is to predict the likelihood of a customer churning within the next 6 months, using a combination of statistical and machine learning techniques, such as logistic regression, decision trees, random forests, and clustering analysis, and provide a written report with visualizations, recommendations, and limitations of the analysis.