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Visual Interpretation

Companion to: [[General EDA Guidebook]] and [[Eda Advanced Guidebook]]

🎯 Purpose

This guide builds on the basics with advanced techniques to deepen your understanding of data through EDA. It focuses on practical applications and actionable insights to help you handle complex data scenarios effectively.

📈 1. Advanced Distribution Analysis

Used for:

  • Detecting subtle distribution features
  • Identifying outliers and anomalies
  • Understanding data shape beyond basics

Practical Tips

  • Use QQ plots to check how closely data follows a theoretical distribution.
  • Apply transformation tests (e.g., Box-Cox) to improve normality.
  • Consider robust statistics like median and MAD for skewed data.

📦 2. Outlier Detection Techniques

Used for:

  • Identifying unusual observations
  • Improving model robustness

Practical Tips

  • Use Mahalanobis distance to find multivariate outliers in a simple way.
  • Apply Isolation Forest for scalable anomaly detection in large datasets.
  • Always review flagged points contextually before removal.

🔄 3. Feature Relationships and Interaction

Used for:

  • Uncovering complex dependencies
  • Identifying non-linear relationships

Practical Tips

  • Explore partial dependence plots to understand feature effects on predictions.
  • Use interaction plots to visualize combined effects of features.
  • Employ correlation ratios for mixed data types.

🔍 4. Dimensionality Reduction

Used for:

  • Simplifying data visualization
  • Reducing noise and redundancy

Practical Tips

  • Use PCA to capture main variance directions.
  • Apply t-SNE or UMAP for visualizing clusters in high-dimensional data.
  • Remember to scale data before applying these methods.

🛠 5. Handling Missing Data

Used for:

  • Understanding missingness patterns
  • Choosing appropriate imputation strategies

Practical Tips

  • Visualize missing data with heatmaps or matrix plots.
  • Use simple imputations (mean, median) for small gaps.
  • For complex missingness, consider model-based imputation or flagging missingness as a feature.

📐 6. Advanced Visualization Techniques

Used for:

  • Revealing hidden patterns
  • Communicating complex insights clearly

Practical Tips

  • Use heatmaps with clustering to spot groups.
  • Apply parallel coordinate plots for multivariate data.
  • Leverage interactive plots for deeper exploration.

🧭 Summary Reference Table

Technique Purpose When to Use Key Action
Advanced Distribution Detailed shape and outliers Subtle distribution features Use QQ plots, transformations
Outlier Detection Identify unusual points Multivariate or large datasets Apply Mahalanobis, Isolation Forest
Feature Interaction Understand complex relations Non-linear or combined effects Use partial dependence, interaction plots
Dimensionality Reduction Simplify and visualize data High-dimensional data Apply PCA, t-SNE, UMAP
Missing Data Handling Manage incomplete data Various missingness patterns Visualize, impute, or flag missing
Advanced Visualization Reveal and communicate patterns Complex or multivariate data Use heatmaps, parallel coords, interactive plots

Let me know when you’re ready for the next steps or additional resources!