Correlation Matrix Generator

Pro v1.0.0 1 view

Generate correlation matrices and annotated heatmaps from CSV datasets. Identifies strong correlations, flags multicollinearity issues, and produces professional visualizations for regression preparation.

What You Get

Automate correlation analysis with professional heatmaps and multicollinearity detection in minutes instead of hours of manual Excel work

The Problem

Computing correlations between all numeric variables in a dataset is tedious and error-prone. Without systematic analysis and professional visualizations, you miss strong relationships and potential multicollinearity issues that could derail regression models and lead to incorrect business conclusions.

The Solution

This skill automates complete correlation analysis by computing Pearson correlation coefficients between all numeric variables, creating professional annotated heatmaps with diverging color schemes, and identifying potential issues. It validates your dataset, calculates correlations with 3-decimal precision, automatically finds and ranks all correlations with absolute value greater than 0.7, flags multicollinearity concerns (correlations above 0.8) with specific recommendations, and generates downloadable outputs including correlation matrices as CSV and heatmaps as high-resolution PNG (300 DPI). Perfect for exploratory data analysis, feature selection, regression preparation, and understanding how key metrics relate to each other.

How It Works

  1. 1 Validate dataset by reading CSV, identifying numeric columns, and confirming minimum 2 numeric variables with missing value reporting
  2. 2 Compute Pearson correlation coefficients between all numeric variable pairs with 3-decimal precision using pairwise deletion for missing values
  3. 3 Identify and rank strong relationships with absolute correlation values greater than 0.7, distinguishing positive from negative correlations
  4. 4 Flag multicollinearity concerns for correlations above 0.8 absolute value with specific recommendations for variable removal or combination
  5. 5 Generate professional annotated heatmap using diverging coolwarm color scheme with correlation values displayed in cells
  6. 6 Produce downloadable outputs including correlation matrix CSV and heatmap PNG at 300 DPI resolution

What You'll Need

  • CSV file with at least 2 numeric columns
  • Python 3 with pandas, seaborn, matplotlib, numpy packages

Get This Skill

Requires Pro subscription ($9/month)

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