codex-data

Automating data analysis workflows involves using pandas for data cleaning and scikit-learn for modeling, which this skill orchestrates end-to-end. It cleans datasets, performs exploratory analysis, builds models, and generates reports automatically.

An eval-driven loop in exploratory data analysis is a scoring mechanism that iteratively evaluates and improves analysis quality. This skill creates a scoring script to assess models and outputs, reducing manual intervention and enhancing reporting accuracy.

Yes, you can use statsmodels and scikit-learn for automated model building within Codex. This skill requires these modeling libraries to construct models, evaluate them iteratively, and save outputs to an outputs directory with iteration logs.

To generate reports from datasets, this skill imports data, performs cleaning, executes exploratory analysis, and builds models using Python libraries. It saves the final outputs and iteration logs to a dedicated directory for reporting projects.

Yes, this data analysis skill handles repetitive dataset reporting tasks by integrating data cleaning, exploratory analysis, and model building into a reusable workflow. It is specifically applicable for repetitive data-analysis tasks across multiple datasets.

You need an outputs directory for data cleaning and modeling to store generated reports and iteration logs. This skill saves all analysis outputs and evaluation logs there, enabling eval-driven improvements and tracking the analysis cycle.