Best Gemini prompts for Credit Analysts

Using a dataset of 10,000 customer records, each containing 50 features such as credit score, income, and payment history, develop a Python script that utilizes the scikit-learn library to train a logistic regression model to predict the likelihood of default. The model should be optimized using a grid search with cross-validation to find the best combination of hyperparameters. The output should include a confusion matrix, ROC curve, and a list of the top 10 most important features contributing to the model's predictions. Assume the data is stored in a Snowflake database and can be accessed using SQL queries.

Design an ETL pipeline using Python and the pandas library to extract credit data from a variety of sources, including CSV files, JSON APIs, and a relational database. The pipeline should handle missing values, data type conversions, and aggregation of data from multiple sources. The output should be a cleaned and transformed dataset stored in a Tableau-compatible format, with detailed documentation of the pipeline's steps and data lineage. Assume the data sources are heterogeneous and require custom parsing and processing.

Using a dataset of historical credit data, perform a statistical analysis to identify trends and correlations between different credit metrics, such as credit score, debt-to-income ratio, and interest rates. The analysis should include hypothesis testing, confidence intervals, and regression analysis to model the relationships between these variables. The output should include a written report summarizing the findings, along with visualizations and tables to support the conclusions. Assume the data is stored in a SQL database and can be queried using SQL.

Develop a data quality assessment framework to evaluate the accuracy and completeness of credit data reports. The framework should include metrics such as data coverage, consistency, and validity, as well as a data profiling process to identify patterns and anomalies in the data. The output should include a detailed report highlighting areas of concern, along with recommendations for data quality improvement and a plan for implementing data validation rules. Assume the data is stored in a Snowflake database and can be accessed using SQL queries.