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JonathanMitchell1234
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data-science-expert

Perform data cleaning, feature engineering, and statistical modeling with pandas and scikit-learn.

Updated Feb 27, 2026
Search Tags:#analytics#data science#machine learning#feature engineering#time series#visualization#statistics
One-click install
npx skills add https://github.com/JonathanMitchell1234/Stock-Swing-Trading-Bot --skill data-science-expert-jonathanmitchell1234
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-science-expert
Source: https://github.com/JonathanMitchell1234/Stock-Swing-Trading-Bot/tree/main/.agents/skills/data-science-expert
Command: npx skills add https://github.com/JonathanMitchell1234/Stock-Swing-Trading-Bot --skill data-science-expert-jonathanmitchell1234

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, scipy, statsmodels, scikit-learn, and includes scripts (resource) components.

What problem does it solve?

This Skill provides expert guidance and tools for complex data science tasks, enabling users to analyze data, build models, and visualize results effectively.

Core Features & Use Cases

  • Data Cleaning & EDA: Handle missing values, remove duplicates, and perform exploratory data analysis with statistical summaries and visualizations.
  • Feature Engineering: Create new features, encode categorical variables, and scale data for machine learning models.
  • Time Series Analysis: Decompose time series, test for stationarity, and fit ARIMA models.
  • A/B Testing: Conduct statistical tests to compare group performances.
  • Use Case: Analyze customer churn data by cleaning the dataset, engineering relevant features, building a predictive classification model, and evaluating its performance.

Quick Start

Use the data-science-expert skill to clean the provided pandas DataFrame by filling missing values with the median and removing duplicate rows.

Frequently Asked Questions about data-science-expert

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform exploratory data analysis and handle missing values in a pandas DataFrame?▼

Exploratory data analysis in pandas involves handling missing values by filling them with the median and removing duplicates. This Skill provides tools to clean datasets, generate statistical summaries, and create visualizations using matplotlib and seaborn.

What is the best way to build a predictive classification model for customer churn using scikit-learn?▼

Building a predictive classification model for customer churn requires cleaning data, engineering features, and training with scikit-learn. This Skill supports the complete workflow from data preparation to model evaluation for classification tasks.

Can I use statsmodels to fit ARIMA models and test for stationarity in time series analysis?▼

Time series analysis with statsmodels allows you to decompose series, test for stationarity, and fit ARIMA models. This Skill enables time series forecasting and statistical modeling directly using the statsmodels library.

How do I conduct A/B testing and interpret statistical results for comparing group performances?▼

A/B testing compares group performances by conducting statistical tests to evaluate significance. This Skill enables you to conduct A/B testing and interpret statistical results using scipy and statsmodels.

Does this data science skill support feature engineering like encoding categorical variables and scaling data?▼

Feature engineering is fully supported to create new features, encode categorical variables, and scale data for machine learning models. This Skill leverages scikit-learn and pandas to prepare data for model training.

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