Perform structured exploratory data analysis on CSV inputs using a CRISP-DM workflow.
npx skills add https://github.com/Phife726/ds_agent --skill ds-eda-process
Please help me install this Agent Skill. Skill: ds-eda-process Source: https://github.com/Phife726/ds_agent/tree/main/ds-eda-process Command: npx skills add https://github.com/Phife726/ds_agent --skill ds-eda-process
Guides a disciplined, reproducible approach to exploratory data analysis that helps you quickly identify data quality issues, understand distributions, and surface relationships before modeling.
Provide an input dataset or path to a CSV and receive an end-to-end CRISP-DM guided EDA, including summaries, visualizations, and a data quality report.
High-intent search queries and answers about installing and using this skill.
Exploratory data analysis on a CSV file involves loading the dataset into a Python dataframe to inspect distributions, identify missing values, and prepare data for modeling. A CRISP-DM guided workflow structures this process by generating data summaries, visualizations, and a final data-quality assessment report.
To identify data quality issues before modeling, you should perform missing value audits, uniqueness checks, descriptive statistics, and duplicate checks. Applying a CRISP-DM structured workflow helps systematically assess data readiness and outline missing-value strategies and type corrections.
CRISP-DM applies to data cleaning by structuring the workflow through Business Understanding, Data Understanding, and Data Preparation phases. This framework guides the identification of distributions and outliers, type corrections, and feature engineering opportunities to deliver an end-to-end preparation plan.
Yes, you can generate visualizations like histograms, box plots, heatmaps, and scatter plots during EDA. A structured Python workflow provides guidance on appropriate plots and a consistent plotting setup to reveal patterns, distributions, and outliers in your dataset.
A data quality assessment report for a dataframe includes shape, types, missing value audits, descriptive statistics, and uniqueness checks. It outlines missing-value strategies, outlier notes, type corrections, duplicate checks, and feature engineering opportunities to assess overall data readiness.
You do not need to clean your dataset before running a CRISP-DM guided EDA workflow. The process is designed to uncover data quality issues, identify missing values, and surface relationships first, ultimately delivering an end-to-end preparation plan to guide your subsequent data cleaning steps.
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