Why Data Scientists Keep Saying Not To Use Prophet
I tested Prophet against seasonal naive and ETS on two monthly series to see why a clean forecast chart is not enough to trust the model.
Code by Night Learning to Program Journey Log
Recent content on Code by Night Learning to Program Journey Log
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Churn Prediction Is Easy. Reducing E-Commerce Churn Is Hard.
How to reduce e-commerce churn after building a churn prediction model. A quick and raw look at retention workflows, customer context, discounts, and what happens after the churn score.
ARIMA Is Boring, and That Is Why I Still Like It
ARIMA is old, limited, and a little annoying. I still like it because it makes the assumptions and uncertainty in a forecast harder to ignore.
The More Technical I Get, the Less I Want to Automate Everything
AI and automation save me time but they also make it much easier to build small programmes that make life very difficult for me later.
AI Agent Guardrails Look Simple Until You Actually Code Them
I already understood AI agent guardrails in theory, but writing a tiny path check made me respect the annoying details behind them.
1 Year of Omarchy as My Learning OS, Not Just My Linux Setup
After one year of using Omarchy, the thing I value most is not the Linux setup itself. It is the learning environment I built around it.
What One Year in AI Security and Governance Changed About How I See AI
After one year working around AI security and governance, I trust flashy AI demos less and pay more attention to data, permissions, discovery, and the boring systems around AI.
Churn Is Not a Data Science Problem
Churn prediction looks like a clean data science problem, but useful retention work needs systems, ownership, context, and feedback loops.
My Machine Learning master’s degree made AI feel less magical, and the hype harder to trust
After finishing my data science and machine learning master’s, I feel more confident with AI, but also less willing to accept the vague hype still flowing around.
Marketers Must Evolve Into Marketing Engineers To Survive The AI World
Everyone is building AI-powered marketing workflows right now. Most of them will collect dust. Here is why, and what actually works.
Why You Should NOT Use an AI Browser Today
AI browsers are all about convenience, but behind the scenes, they're filled with serious security, privacy, and trust issues. Here's why you should hold off on using them.
You Can’t Lead in AI If You Don’t Understand the Math
The more abstracted AI becomes, the more valuable it is to understand what’s behind the abstraction. A personal note on why I’m going back to the math behind machine learning.
From Copy‑Paste to First Principles of Machine Learning
Why I’m resisting the urge to copy‑paste ML, relearning the math behind algorithms, and building from scratch to bridge marketing and machine learning.
K-Means vs K-Means++: Smarter Centroids, Better Clusters
K-Means++ improves K-Means by choosing better initial centroids using a distance-aware probability distribution. A couple of thoughts on it as I read through this again today.
I Built an Open Source AI Powered SaaS Market Intelligence Tool for Marketing Teams. Here's How
A full walkthrough of building and deploying a SaaS Market Intelligence tool using Streamlit + FastAPI + GPT-4. I cover prompt design for clarity vs competitor analysis, scraping dynamic websites with Playwright, deployment lessons using Render, and what’s next for the tool - all from a builder-marketer’s perspective.
ARIMA vs. SARIMA: Differences and When to Use Each for Forecasting
ARIMA vs SARIMA explained: the key difference is seasonality. See when to use each model, how their parameters differ, and how to validate your choice.
Full Code Walkthrough - Reducing Churn in E-Commerce with Predictive Modelling
A detailed, step-by-step explanation of the code from my churn prediction project - covering data cleaning, feature engineering, SMOTE, Random Forest model, and lessons from end-to-end implementation.
A Primer to Framing Business Problems for Machine Learning
Here is how to frame vague business goals into clear machine learning problems using classification, regression, and ranking lenses to make real-world impact.
Why ARIMA and SARIMA Still Matter: A Technical Guide to Time Series Forecasting
Despite the hype around deep learning, classical time series models like ARIMA and SARIMA are essential tools for reliable forecasting. Let's take you through their core concepts, practical use, and a hands-on example with wine sales data.
22 Lessons from 1 year in Data Science and Machine Learning
I started data science in 2024, and now 1 year in of masters in DS, building personal projects as well as applying what I learnt into projects for my job I feel there are tons of lessons I can share. These are mainly honest, practical lessons you’ll only learn by doing.
Reducing Churn in E-Commerce: My End-to-End Capstone Project in Predictive Modeling
An in-depth walkthrough of my churn analysis capstone project: from data exploration and clustering to actionable insights for customer retention in a competitive e-commerce landscape.
A Beginner Explains Pointers in Go: The Thing That Made Me Quit Coding (And Why I'm Back Now)
A decade ago, pointers made me quit programming. Now I'm back — still scared of them, but this time with just enough understanding to be dangerous.
Day 5–8: Fine-Tuning AI Models, Learning MLOps, and Structuring My Year of Projects
A learning log covering project planning for AI/ML, Go language practice, deep learning theory, MLflow basics, and fine-tuning open-source LLMs across Day 5 to Day 8.
Day 4 of Learning: Switching Between Golang and Deep Learning
Learning Golang structs and pointers while returning to deep learning with PyTorch and prepping for my second year of masters in data science & ML.
Day 3 of Learning Go: Concurrency & Project Structure Refactor
Refactored my Go project structure and started the concurrency book — a short but insightful Day 3.
Day 2 of Learning Go: The Pointers I Finally Understood (10 Years Later)
A short, honest reflection on finally understanding pointers in Go — a concept that I picked up after a decade.
Learning Golang from Scratch: My Day 1 Log of Backend Development with Golang
Documenting my first day of learning Golang from scratch with real projects and tests.
Why I Chose to Learn Coding Properly After the Age of 30
A reflection on deciding to learn programming properly at 30+ — and why creation, not code, is what brings me back.
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Notes on GTM engineering, AI security, data systems, forecasting, backend work, and the technical decisions behind software people actually have to use.