Correlation Analysis
This handles correlation analysis for numerical data, measuring relationships between variables and detecting multicollinearity. You'd reach for it during exploratory data analysis, before building regression models, or when deciding which features to keep. It covers both linear and non-linear associations, which is handy since Pearson correlations alone miss a lot. The skill is part of aj-geddes' useful-ai-prompts collection that's passed security audits from Gen Agent Trust Hub, Socket, and Snyk. Straightforward utility for anyone doing feature engineering or trying to understand their dataset before modeling.
- GitHub stars
- 317
- last updated
- Aug 21, 2026
Install
$ npx -y skills add aj-geddes/useful-ai-prompts --skill 'Correlation Analysis' --agent claude-codeInstalls into .claude/skills of the current project.
Files
Correlation Analysis
Overview
Correlation analysis measures the strength and direction of relationships between variables, helping identify which features are related and detect multicollinearity.
When to Use
- Identifying relationships between numerical variables
- Detecting multicollinearity before regression modeling
- Exploratory data analysis to understand feature dependencies
- Feature selection and dimensionality reduction
- Validating assumptions about variable relationships
- Comparing linear and non-linear associations
Correlation Types
- Pearson: Linear correlation (continuous variables)
- Spearman: Rank-based correlation (ordinal/non-linear)
- Kendall: Rank correlation (robust alternative)
- Cramér's V: Association for categorical variables
- Mutual Information: Non-linear dependencies
Key Concepts
- Correlation Coefficient: Ranges from -1 to +1
- Positive Correlation: Variables move together
- Negative Correlation: Variables move oppositely
- Multicollinearity: High correlations between predictors
Implementation with Python
Sponsors
Promote your product hereReach 125k+ visitors monthlyPromote your product hereReach 125k+ visitors monthly
Agent CaffeineSponsoredClose your lid, come back to finished work
SnugSponsoredThe clipboard manager for the AI eraPromote your product hereReach 125k+ visitors monthly
WaxSponsoredBlock anything with supervision, without erasing your iPhonePromote your product hereReach 125k+ visitors monthly
knobsSponsoredSee your site on any phone, any day, in any language
Agent CaffeineSponsoredClose your lid, come back to finished workSnugSponsoredThe clipboard manager for the AI eraPromote your product hereReach 125k+ visitors monthlyWaxSponsoredBlock anything with supervision, without erasing your iPhonePromote your product hereReach 125k+ visitors monthlyknobsSponsoredSee your site on any phone, any day, in any languageDetails
- Repository
- github.com/aj-geddes/useful-ai-prompts
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
More from aj-geddes/useful-ai-prompts
All 258- Data Cleaning Pipeline
- Data Visualization
- Dimensionality Reduction
- Exploratory Data Analysis
- Feature Engineering
- Funnel Analysis
- ML Model Explanation
- ML Model Training
- ML Pipeline Automation
- Model Deployment
- Model Hyperparameter Tuning
- Model Monitoring
- Natural Language Processing
- Network Analysis
- Neural Network Design
- Recommendation Engine
- Recommendation System
- Regression Modeling
- Sentiment Analysis
- Statistical Hypothesis Testing
- Survival Analysis
- Time Series Analysis
- Nodejs Express Server3.3K
- Code Review Analysis2.4K
Recommended
More AI & Agent Building1
Data Cleaning Pipelineaj-geddes/useful-ai-promptsBuild robust processes for data cleaning, missing value imputation, outlier handling, and data transformation for data preprocessing, data quality, and data pip0skills.sh installs
Data Cleaning Pipelineaj-geddes/useful-ai-promptsBuild robust processes for data cleaning, missing value imputation, outlier handling, and data transformation for data preprocessing, data quality, and data pip0skills.sh installs2Data Visualizationaj-geddes/useful-ai-promptsCreate effective visualizations using matplotlib and seaborn for exploratory analysis, presenting insights, and communicating findings with business stakeholder0skills.sh installs
Data Visualizationaj-geddes/useful-ai-promptsCreate effective visualizations using matplotlib and seaborn for exploratory analysis, presenting insights, and communicating findings with business stakeholder0skills.sh installs3Exploratory Data Analysisaj-geddes/useful-ai-promptsDiscover patterns, distributions, and relationships in data through visualization, summary statistics, and hypothesis generation for exploratory data analysis, 0skills.sh installs
Exploratory Data Analysisaj-geddes/useful-ai-promptsDiscover patterns, distributions, and relationships in data through visualization, summary statistics, and hypothesis generation for exploratory data analysis, 0skills.sh installs4ML Model Explanationaj-geddes/useful-ai-promptsInterpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability0skills.sh installs
ML Model Explanationaj-geddes/useful-ai-promptsInterpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability0skills.sh installs5ML Pipeline Automationaj-geddes/useful-ai-promptsBuild end-to-end ML pipelines with automated data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins0skills.sh installs
ML Pipeline Automationaj-geddes/useful-ai-promptsBuild end-to-end ML pipelines with automated data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins0skills.sh installs6Model Monitoringaj-geddes/useful-ai-promptsMonitor model performance, detect data drift, concept drift, and anomalies in production using Prometheus, Grafana, and MLflow0skills.sh installs
Model Monitoringaj-geddes/useful-ai-promptsMonitor model performance, detect data drift, concept drift, and anomalies in production using Prometheus, Grafana, and MLflow0skills.sh installs