May 27 · 9 min read · This is the fourth and final post in my Google Advanced Data Analytics Certificate series on the TikTok claims classification project. Part 1: data inspection. Part 2: EDA and visualization. Part 3: h
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May 19 · 11 min read · Series: Building an AI-Powered Municipal Complaint Management System from Scratch Where We Left Off In Part 1, we built our dataset from scratch — growing it from 200 raw records to a clean, label-ri
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May 11 · 14 min read · You've trained your decision tree. It fits your training data beautifully — almost suspiciously well. Then you test it on new data, and it falls apart. Sound familiar? This is the classic overfitting
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May 9 · 7 min read · You've shipped a model. It looks great on your training data. You put it in production and it falls apart. Or maybe the opposite, it works sometimes, wildly fails other times, with no pattern you can
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Mar 29 · 18 min read · TLDR: 🌲 Ensemble methods combine multiple "weak" learners to create stronger predictors. Random Forest uses bootstrap sampling + feature randomization. Gradient Boosting sequentially corrects errors.
Join discussionMar 2 · 16 min read · Week 18 of my data science internship at DataraFlow shifted the focus from distance-based and probabilistic classifiers to tree-based methods; a family of algorithms that don't just predict outcomes,
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Feb 25 · 5 min read · I built an employee attrition predictor. Random Forest, 83% accuracy, clean notebooks. Wrapped it in FastAPI, tested it... and it predicted "Stay" for everyone. This is what I learned debugging my way
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Feb 22 · 7 min read · Imagine spotting a money launderer before the wire transaction even settles! Capital One’s ML model just turned a traditional 30 page rulebook into a 200 ms risk score that tells investigators which g
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Feb 17 · 7 min read · Decision Tree & Random Forest Classification Employee attrition is one of the most expensive silent risks in any organization. Replacing talent costs money, time, productivity, and morale. In this project, I built a complete machine learning pipeline...
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Feb 17 · 15 min read · Last week, I got my first real taste of classification algorithms. I worked through K-Nearest Neighbours (KNN), Support Vector Machines (SVM), and Naive Bayes — three very different approaches to the same core problem: teaching a computer to sort thi...
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