Please review my sketch of the Machine Learning process It's amazingly difficult to find an outline of the end-to-end machine learning process. As a total beginner, this lack of information is frustrating, so I decided to try scraping together my own process by looking at a lot of tutorials that all do it a slightly different way. 

I would like to have a standard process to go by, and once I am comfortable with it, I can choose to deviate. I'd like some input from you pillars of the industry. Is this a good routine for a beginner to follow?


Get Data
Clean Data
Split data into Training and Test Data ~(80/20)
Separately, for training and test sets:


Normalize Data (continuous features):


standardize (divide by std. deviation)
center (subtract mean)

Impute missing values
Feature Engineering
Encode Categorical Variables:


Integer Encoding
One Hot Encoding
Target Encoding
Weight of Evidence


Separate labels from Test set if classification problem. Keep aside.
Choose a few models.
for each model, using k-fold cross-validaton:


Train base model on "training set".
Tune and test hyper parameters on "validation set"
Save best scores and parameters

Compare each model's final scores on the never touched test data
Choose the model with highest scores.


Edit: Thank you for the overwhelming number of responses. Lots of times my questions get a single answer or none at all. I appreciate the time taken to help out a beginner. 

I have edited the steps above to reflect the wonderful answers below. I hope that this helps another beginner somewhere else.
This process will result in data leaks. The split needs to happen earlier. Normalizing data before the split means that your training data contains information about your test data. I would put the split at 3. in your flow chart.

A common step I think you have missed is imputation of missing values. I would put that before feature engineering.

Overall I think this is a good rough outline for a beginner to follow. It is overly simplistic and leaves a lot out, but I think you know that and you have to start somewhere.