Gbolahan I’ve been working on the Kaggle Disaster Tweets classification project, and for a while, progress felt good. I built a baseline model using TF-IDF and Logistic Regression and managed to get an F1 score of 0.82 without using a pipeline. Then I decided to “do things properly” and refactor everything into a scikit-learn pipeline — and … Read more
Introduction After completing my first Kaggle competition on Housing Prices, I decided to tackle the Spaceship Titanic dataset. The goal is to predict whether passengers were transported to another dimension during the voyage. This competition has been a great opportunity to improve my workflow, learn about pipelines, and practice model evaluation. In this post, I’ll walk through … Read more
In my previous post, I built a simple baseline model for the House Prices Kaggle competition using only numerical features, scaling, and a linear model. Since then, I’ve iterated on that baseline by introducing a proper preprocessing pipeline, adding categorical features through one-hot encoding, and applying feature engineering, cross-validation, and hyperparameter tuning. The goal wasn’t … Read more
I recently submitted my first baseline model to the Kaggle Housing Prices competition. This post is not about a perfect solution—it’s about documenting my learning process as I work through the problem step by step. I plan to make 3–4 submissions in total, each one improving on the previous version. This post covers the absolute … Read more