Correlation
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About Correlation
Find relationships between variables (Pearson Correlation).
This tool computes a Pearson correlation coefficient between every pair of numeric columns in your CSV and lays the results out as a color-coded matrix, so you can spot strong positive or negative relationships at a glance.
It's built for the moment right before modeling or feature selection, when you need to know which variables move together — and which ones are redundant — before you build a regression or scale your inputs.
Category: Analysis & VisualizationCommon Use Cases
- Checking which product metrics move together before building a regression model
- Spotting redundant features before feeding data into machine learning
- Validating that a marketing spend column actually tracks with revenue
Key Features
- Pairwise Pearson Coefficients
- Color-Coded Correlation Matrix
- Numeric Column Auto-Detection
- Exportable Matrix
Run Correlation to identify which numeric columns are related, then use Regression to model the strongest relationships, or Scaling first if your columns are on very different numeric ranges.
Frequently Asked Questions
Pearson correlation, which measures the strength and direction of a linear relationship between two numeric variables.
It means the two variables show little to no linear relationship with each other.
A quick sanity check for anyone about to build a model or report and needing to know which numbers are secretly telling the same story.
Read the related guide →