Generating a feature set and training data

We will refactor a bit of the code previously developed in our local environment to generate features for training to add to our MLflow project the data pipelineof our MLflow project .

We will now create the feature_set_generation.py file, which will be responsible for generating our features and saving them in the training folder where all the data is valid and ready to be used for ML training. You can look at the contents in the file in the repository https://github.com/PacktPublishing/Machine-Learning-Engineering-with-MLflow/blob/master/Chapter07/psystock-data-features-main/feature_set_generation.py:

1. We need to import the following dependencies:

   import mlflow
   from datetime import date
   from dateutil.relativedelta import relativedelta
   import pprint
   import pandas as pd
   import pandas_datareader
   import pandas_datareader.data as web
   import numpy as np

2. Before delving into the main component of the code, we'll now proceed to...