Best Claude prompts for Statisticians

Given a dataset of monthly sales figures for the past 5 years, with variables including region, product category, and seasonality, develop a robust time series model that accounts for trends, seasonality, and outliers, and provides a 12-month forecast with 95% confidence intervals, using a combination of ARIMA, exponential smoothing, and regression techniques, and evaluate the model's performance using metrics such as MAE, MSE, and RMSE.

For a classification problem involving customer churn prediction, with a dataset containing 20 variables including demographic, behavioral, and transactional data, develop a feature engineering pipeline that extracts relevant features from the data, including interaction terms, polynomial transformations, and encoding schemes, and evaluates the importance of each feature using techniques such as mutual information, correlation analysis, and recursive feature elimination, with the goal of improving the precision of the classification model by at least 10%.

Given a large dataset of customer information, with variables including name, address, phone number, and email, develop a data quality assessment and cleaning pipeline that identifies and corrects errors, inconsistencies, and missing values, using techniques such as data profiling, data validation, and data imputation, and evaluates the quality of the cleaned data using metrics such as data accuracy, completeness, and consistency, with the goal of achieving a data quality score of at least 95%.

For a regression problem involving prediction of continuous outcomes, with a dataset containing 10 variables and a range of candidate models including linear regression, decision trees, random forests, and neural networks, develop a model selection and hyperparameter tuning pipeline that evaluates the performance of each model using metrics such as MSE, MAE, and R-squared, and tunes the hyperparameters of the best-performing model using techniques such as grid search, random search, and Bayesian optimization, with the goal of achieving a model precision of at least 90%.