Best Gemini prompts for Data Scientists

I have a dataset of sensor readings from an industrial machine, collected at 1-minute intervals over the course of 6 months. The data is stored in a Google BigQuery table, and I've already applied some basic preprocessing steps like handling missing values and normalizing the data. Using a combination of statistical methods and machine learning algorithms, identify the most anomalous readings in the dataset and provide a detailed report on the possible causes of these anomalies, including any relevant visualizations and summaries of the data. Assume that the anomalies are rare and may be due to equipment failure, human error, or other external factors. Provide a Python script using the Google Cloud AI Platform and scikit-learn library to implement the anomaly detection algorithm.

I'm working on a text classification project using a dataset of customer reviews stored in a Google Cloud Storage bucket. The goal is to predict the sentiment of the reviews (positive, negative, or neutral) based on the text features. Using a combination of techniques like tokenization, stemming, and word embeddings, create a set of relevant features from the text data and evaluate their performance using a machine learning model like logistic regression or random forest. Provide a detailed report on the feature engineering process, including any visualizations of the word embeddings and summaries of the model performance. Assume that the dataset is imbalanced, with a majority of positive reviews, and provide a Python script using the Google Cloud Natural Language API and scikit-learn library to implement the feature engineering and modeling steps.

I'm responsible for monitoring the data quality of a Google BigQuery dataset used for business intelligence reporting. The dataset contains customer demographic information, transactional data, and other relevant metrics. Create a data quality monitoring dashboard using Google Data Studio, including metrics like data completeness, accuracy, and consistency. Identify the most critical data quality issues and provide a detailed report on the root causes and recommended actions to improve the data quality. Assume that the dataset is updated daily, and provide a Python script using the Google Cloud BigQuery API to automate the data quality monitoring and reporting process.

I've trained a machine learning model using the Google Cloud AI Platform and TensorFlow library to predict customer churn based on a set of demographic and behavioral features. However, the model is complex and difficult to interpret, making it challenging to understand the underlying factors driving the predictions. Using techniques like feature importance, partial dependence plots, and SHAP values, create a detailed report on the model interpretability and explainability, including any relevant visualizations and summaries of the results. Assume that the model is a black box, and provide a Python script using the Google Cloud AI Platform and scikit-explain library to implement the model interpretability and explainability steps.