📌 Post 4: EDA- Exploring the Data Before Modeling In geology, this is called EDA (Exploratory Data Analysis): the process of visualizing, understanding, and detecting patterns or errors in your data before modeling or estimation. 🔍 What does EDA look for in geology? ✅ Grade distribution – Are the values normal, skewed, or multimodal? ➡️ Histograms, boxplots, correlation matrix. ✅ Outliers – Is it an error or a geological anomaly? ➡️ Boxplots, scatter plots ✅ Gaps or unsampled intervals – Is there continuity in the data or zones without information? ➡️ Depth charts, heatmaps ✅ Behavior by geological unit – Do grades change by lithology? ➡️ Boxplots by geological unit ✅ Grades by depth or zone – Are there vertical or spatial trends? ➡️ Scatter plots, grade vs. depth profiles ✅ Comparison between campaigns or laboratories – Are there systematic differences? ➡️ Boxplots by group, comparative scatter plots 📊 A good EDA helps answer key questions: 📌 Where are the rich and poor zones? 📌 How does mineralization vary by rock type? 📌 Is there bias between methods or campaigns? 📌 How continuous is the sampling? 💡 Remember: you can’t estimate what you don’t understand. And EDA is also part of QA/QC, because it helps detect systematic errors or inconsistencies that you wouldn’t notice through geological validation alone. 🎯 In summary: ➡️ EDA is not just about visualization… ➡️ It’s about interpreting, questioning, and preparing your data for estimation with geological criteria. 💬 What charts do you use in your EDA? Have you ever been surprised? Note: Image for illustrative purposes only 😉