I Analyzed 26 ML Libraries and Found a Gap Nobody Fills - So I Built It

How I built dissectml, the missing middle layer between EDA and AutoML. Every data science project starts the same way. You load your dataset. You run df.describe(). You open YData Profiling for a quick report. Then you switch to PyCaret or LazyPredict to screen a bunch of models. Then you pull in SHAP for explainability. Then matplotlib for custom comparison plots. By the time you actually understand your data and your models, you've imported five libraries, written 200 lines of glue code, and it's been three hours. I kept asking myself: why isn't there one library that does the full journey? So I researched every tool in the space. Thoroughly. And then I built the one that was missing. The Research That Started Everything I spent weeks doing deep market research on two categories: Auto-EDA tools (libraries that explore your data) and AutoML/model comparison tools (libraries that train and compare models). Auto-EDA landscape (10+ libraries): YData Profiling (13K+ GitHub stars) — the k