Overfitting is everywhere in machine learning, easy to define, but harder to conceptualize. So we cook up tiny datasets, throw a lot of models at them, animate, and toss in an interactive plot for good measure. All in the name of a single goal: get a feel for what a well fitted model looks like.
What do you get when you mix a century-old shipwreck, a Kaggle leaderboard, and a stubborn data analyst? A machine learning journey that begins with raw CSVs and climbs to 0.82 accuracy, passing through group survival logic, spatial deck patterns, careful hyperparameter tuning, and a detour into SHAP values and model interpretation. And at the end of it all: a quiet tribute to the 2,208 passengers who did not ask to become training data.
I assumed Python's range was a function, until a friend's correction launched me down a rabbit hole. Join me as we dissect iterables, low level C implementations, and how being wrong about range taught me more than being right ever could. Spoiler: It ends lazy evaluation, and newfound awe for Python's greatest magic trick : making hard things appear simple.
When a "simple" binary fought back. This seven years old CTF writeup, preserved for posterity, captures the struggle of a low-level beginner: humbled by Linux, lost in assembly, saved by decompilers, and almost outwitted by self-deleting code. And beyond the flag, a timeless lesson about knowledge.