Data Analysis Nodes
Comprehensive data analysis toolkit for statistical analysis, outlier detection, correlation analysis, and data preprocessing for machine learning workflows.
Node Reference
Detailed documentation for each data analysis node available in VERA.
Statistical Summary
Generate comprehensive statistical summaries of numerical data
Key Features
- •Mean, median, mode calculations
- •Standard deviation and variance
- •Min/max values and range
- •Skewness and kurtosis
- •Percentiles and quartiles
Input Ports
DataFrame with numerical columns
Output Ports
Statistical summary DataFrame
Detailed descriptive statistics
Correlation Analysis
Compute correlation matrices and analyze relationships between variables
Key Features
- •Pearson correlation coefficient
- •Spearman rank correlation
- •Correlation significance testing
- •Heatmap visualization
- •Threshold-based filtering
Input Ports
DataFrame with numerical columns
Output Ports
Correlation coefficient matrix
Heatmap visualization
Highly correlated variable pairs
Outlier Detection
Identify and handle outliers using multiple statistical methods
Key Features
- •Z-score method
- •IQR (Interquartile Range) method
- •Isolation Forest algorithm
- •Local Outlier Factor (LOF)
- •Multiple outlier handling options
Input Ports
DataFrame with numerical columns
Output Ports
Data with outliers removed/replaced
Indices of detected outliers
Detailed outlier analysis report
Data Normalization
Normalize and scale data using various transformation methods
Key Features
- •Min-Max scaling (0-1 range)
- •Standard scaling (z-score)
- •Robust scaling (median/IQR based)
- •Log transformation
- •Box-Cox transformation
Input Ports
DataFrame with numerical columns
Output Ports
Normalized data
Scaling parameters for inverse transform
Normalization method report
Feature Engineering
Create new features through mathematical transformations
Key Features
- •Polynomial features
- •Interaction features
- •Mathematical transformations
- •Binning/discretization
- •Feature selection methods
Input Ports
DataFrame with numerical columns
Output Ports
Data with new features
Feature importance scores
Applied transformations log
Workflow Examples
Common data analysis workflows you can build with these nodes.
Data Quality Assessment
Complete workflow for analyzing data quality and preparing for ML
- 1Load dataset using CSV Reader node
- 2Generate statistical summary
- 3Detect and handle outliers
- 4Check for missing values
- 5Analyze feature correlations
- 6Normalize numerical features
- 7Create derived features
- 8Export clean dataset
Exploratory Data Analysis
Comprehensive EDA workflow for understanding dataset characteristics
- 1Import data from multiple sources
- 2Generate statistical summaries
- 3Create correlation heatmaps
- 4Plot distribution histograms
- 5Identify data patterns
- 6Detect anomalies and outliers
- 7Generate automated insights
- 8Create summary report
Feature Selection Pipeline
Automated feature selection and engineering workflow
- 1Load training dataset
- 2Calculate feature correlations
- 3Remove highly correlated features
- 4Apply feature scaling
- 5Generate polynomial features
- 6Select top features
- 7Validate with cross-validation
- 8Export feature set