Introduction to Data Scrubbing
Explore the fundamentals of data scrubbing to prepare clean and efficient datasets for machine learning. Understand key operations such as removing redundant variables, applying one-hot encoding, handling missing data, and reducing dimensionality to improve model performance and avoid common errors.
We'll cover the following...
Why do we need data scrubbing?
Like any Swiss or Japanese watch, a good machine learning model should run smoothly and contain no extra parts. This means avoiding syntax or other errors that prevent the code from executing as well as removing redundant variables that might clog up the model’s decision path.
This bias towards simplicity is just as important for beginners coding their first model. Working with a new algorithm helps create a minimal viable model which can then have ...