Best Jasper prompts for Data Scientists

I have a dataset of 100,000 samples with 50 features, and I'm trying to optimize the hyperparameters for an XGBoost model to predict customer churn. The target variable is binary, and I've already encoded the categorical features. I want to perform a grid search over the following hyperparameters: learning rate (0.01, 0.1, 0.5), max depth (3, 5, 7), and gamma (0, 0.5, 1). Please generate a Python script using the XGBoost library and Scikit-learn's GridSearchCV to find the optimal combination of hyperparameters that results in the highest AUC-ROC score on the validation set. Assume the data is stored in a Pandas DataFrame called 'df' and the target variable is 'churn'. Provide the optimal hyperparameters and the corresponding AUC-ROC score.

I have a time series dataset of daily sales figures for the past 2 years, and I want to use an LSTM network to forecast the next 30 days of sales. The data has strong seasonal and trend components. Please generate a Python script using the Keras library and TensorFlow backend to build and train an LSTM model that takes into account the seasonal and trend components. The script should include data preprocessing, model architecture definition, training, and evaluation on a test set. Assume the data is stored in a Pandas DataFrame called 'sales_data' with a 'date' column and a 'sales' column. Provide the forecasted sales figures for the next 30 days and the mean absolute error (MAE) of the model on the test set.

I have a dataset of customer information with 10 features, including demographic and transactional data. I want to generate a data quality report that includes summary statistics, distribution plots, and data quality metrics such as missing value rates, outlier detection, and data consistency checks. Please generate a Python script using the Pandas library and Matplotlib for visualization to create a comprehensive data quality report. The script should include data loading, data quality checks, and report generation. Assume the data is stored in a CSV file called 'customer_data.csv'. Provide the data quality report as a PDF file.

I have a trained Random Forest model that predicts customer lifetime value based on 20 features, including demographic, behavioral, and transactional data. I want to perform a feature importance analysis to identify the top 5 most important features contributing to the model's predictions. Please generate a Python script using the Scikit-learn library to calculate the feature importance scores using the permutation importance method. The script should include data loading, model loading, feature importance calculation, and visualization of the top 5 features using a bar chart. Assume the data is stored in a Pandas DataFrame called 'customer_data' and the model is stored in a Pickle file called 'rf_model.pkl'. Provide the feature importance scores and the corresponding bar chart.