Last month, I received an email from Kaggle inviting me to participate in a beginner-friendly “30 Days of Machine Learning” challenge. It was a timely reminder to continue on my data science learning journey, especially since I haven’t made any progress for quite some time.
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Using a shotgun approach for the Titanic competition
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.
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Getting started with the Titanic competition
The 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.
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