Life is a Journey
After completing Alexis Cook’s very useful Titanic Tutorial, I couldn’t help myself and spent a couple of days hacking around to try and improve my score without going through the usual data science workflow of EDA, feature engineering, model selection, hyperparameter tuning, train/test iterations.
I know it’s not the proper way of doing data science, but like I said, I just couldn’t help myself.
Continue readingThe first port of call for all Kagglers is the “Titanic: Machine Learning from Disaster” practice competition, where you get to use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.
Continue readingI was planning to sign-up for Kaggle further down in my data science learning journey, only after I had built up sufficient foundational knowledge and skills. However, I decided to take a quick look under the hood, and found that it was surprisingly easy to get started.
Continue readingFinal post on mapping out my data science learning journey, including an overall tracking chart with all the checkpoints.
Continue reading
