Best Gemini prompts for Models

Given a set of classification models trained on different datasets, compare their performance using metrics such as accuracy, precision, recall, and F1-score, and provide a detailed analysis of the results, including visualizations and statistical significance tests, using Google Data Studio to create interactive dashboards and Google Sheets for data manipulation.

Using a dataset of monthly sales data from the past five years, perform seasonal decomposition to identify trends, seasonality, and residuals, and then use a combination of Google AutoML and TensorFlow to train a time series forecasting model, evaluating its performance using metrics such as mean absolute error and mean squared error, and provide a report on the results, including visualizations and recommendations for future improvements.

For a given dataset of customer demographic and transactional data, perform feature engineering to extract relevant features, including scaling and encoding categorical variables, and then use Google Colab to train a clustering model, evaluating its performance using metrics such as silhouette score and calinski-harabasz index, and provide a detailed report on the results, including visualizations and insights into customer segments.

Using a trained regression model and a dataset of feature importance values, calculate SHAP values to explain the contribution of each feature to the predicted outcomes, and provide a detailed analysis of the results, including visualizations and insights into feature interactions, using Google Data Studio to create interactive dashboards and Google Sheets for data manipulation, and evaluate the results against a set of quality metrics, including feature correlation and partial dependence plots.