Although there are tons of great books and papers outside to practice machine learning, I always wanted to see something short, simple, and with a descriptive manuscript. I always wanted to see an example with an appropriate explanation of the procedure accompanied by detailed results interpretation. Model evaluation metrics should also need to be elaborated clearly. In this work, I will try to include all important steps of ML modeling (even though some are not necessary for this dataset) to make a consistent and tangible example, especially for geoscientists. Eight important ML algorithms will be examined and results will be compared. I will try to have an argumentative model evaluation discussion. I will not go deep into the algorithm's fundamentals. This tutorial has four parts: This tutorial has four parts: Part.1: Exploratory Data Analysis, Part.2: Build Model & Validate, Part.3: Model Evaluation-1, Part.4: Model Evaluation-2. In the final part, we will examine model performances on blind well data that we kept out of models exposure.
You may find full elaboration in thses links:
1- https://towardsdatascience.com/practical-machine-learning-tutorial-part-1-data-exploratory-analysis-c13d39b8f33b
2- https://towardsdatascience.com/practical-machine-learning-tutorial-part-2-build-model-validate-c98c2ddad744
3- https://towardsdatascience.com/practical-machine-learning-tutorial-part-3-model-evaluation-1-5eefae18ec98
4- https://towardsdatascience.com/practical-machine-learning-tutorial-part-4-model-evaluation-2-764d69f792a5