Interesting datasets can make personal machine learning projects more fun and exciting. Here are some of my favorite places to go looking for datasets to hone my data science and ML skills.
ReadDetecting outliers can be important when exploring your data before building any type of machine learning model. Some causes of outliers include data collection issues, measurement errors, and data input errors. Detecting outliers is one step in analyzing data points for potential errors that may need to be removed prior to model training. This helps prevent a machine learning model from learning incorrect relationships and potentially lowering accuracy.
ReadHyperparameter tuning can be used to improve default configurations of many popular machine learning libraries. This article walks through how to apply Optuna - a hyperparameter optimization library - to predict Wine prices!
ReadSliced is a competitive data science game show - participants are given 2 hours to explore and predict data they've just seen! In this article, I walk through my approach building a prediction for the Week 1 data.
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