Automate data cleaning, missing-value handling, outlier detection, and correlation analysis.
npx skills add https://github.com/Best6668/AMIS --skill data-preprocessing
Please help me install this Agent Skill. Skill: data-preprocessing Source: https://github.com/Best6668/AMIS/tree/main/skills/data-preprocessing Command: npx skills add https://github.com/Best6668/AMIS --skill data-preprocessing
数据预处理在数据科学和建模工作流中常常耗时且易出错。本 Skill 提供端到端的数据清洗、缺失值处理、异常值检测、分布与相关性分析等能力，帮助用户快速从原始数据获得可直接用于建模的高质量数据和可解释的质量评估结果。
将数据集放置在 data/ 目录后，执行数据预处理技能以生成清洗后的数据和质量分析报告。
High-intent search queries and answers about installing and using this skill.
You can automate data cleaning by using this skill to detect missing values, classify them as MCAR, MAR, or MNAR, and apply type-specific imputation. It outputs a cleaned dataset and a quality report ready for modeling pipelines.
Detect outliers by applying both Z-score and IQR standards simultaneously to identify strong and weak anomalies. This skill provides classification and decision guidance on whether to retain or correct the detected outliers.
Correlation analysis for modeling works by calculating Pearson and Spearman matrices and flagging highly correlated variable pairs. This skill computes skewness and kurtosis to evaluate distributions before feeding data into a model.
No, you do not need to manually specify file encoding. The skill automatically identifies data files and their encodings to ensure stable ingestion into the analytics pipeline without manual intervention.
Generating data quality reports and visualizations requires pandas, numpy, and visualization libraries. These dependencies power distribution plotting, correlation matrices, and standardized artifact generation for modeling pipelines.
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erlebachAutomate data cleaning workflows for missing values, outliers, and validation.
