What pre-processing is done to my data prior to training a model?

A dataset that is mostly ready for machine learning is required. However, we do apply some basic pre-processing steps to the data before building models. 

1. Imputation of nulls
2. Encoding categorical features (a.k.a. creating "dummy variables")
3. Feature scaling (a.k.a. normalization)
4. Five-fold cross validation and automatic holdout
   1. Extract 20% of your dataset to be used for final model evaluation in what's known as automatic holdout.
   2. Randomly sample the training dataset for five fold cross validation.

All of these pre-processing steps are performed given different thresholds set in our pre-processing pipeline.