Build a elegant and customisable machine learning process in Python

I am looking for someone to create a Python machine learning pipeline which I can use for a variety of projects. My own Python skills are fairly developed however not efficient. I have written all the below myself but it is messy and not very well optimised. I am looking for someone to build an elegant pipeline where it is easily adjustable and can work on different datasets. I have multiple datasets that have: - Dataset information columns - Target variable column - Feature columns These are all in pandas dataframs so would like to stick to pandas/scikit learn as much as possible. I would like some functions/code built that takes the input dataframe and does the following: - Pre processing steps (where i can input which column/s are information, target & features) - One hot encodes given features - Scales features (would like to choose different types eg. min/max) - Feature Selection (which are the most important features to use) - Train / Test split / cross validation and get the dataframe ready - Model Selection (both Classifier and regression options). Would like to then choose the best model - HyperParameter tuning - Model Training - Model Evaluation (show scores, errors, which model is best) - Bring back in Information columns to evaluate - Then a function to deploy algorithms on new datasets for future predictions. I have attached a sample dataset which has the following: Columns A to E are information columns Columns F to V are possible target columns Columns W to KE are possible features I would like clearly defined functions and elegant code with comments so that I can easily run the ML process myself. Im hoping there are some people out there who may already have done this on their own projects so it may not take too long for someone with ML experience in python Any questions please let me know!