AI/ML-Sklearn for Well-log Lithology prediction

Interpreting lithology is critical for achieving a greater understanding of the subsurface. Manual interpretation is a time-consuming activity that might lead to inconsistency, where the most used conventional methods are cross-plotting and statistical analysis-based techniques. Many research papers propose using machine learning (SVM, NNs) and deep learning (CNN, RNN) applications of various types.
Thanks to Olawale Ibrahim for participating in the FORCE 2020 competition, so now we have a solution in Python and sklearn that attains the highest score on the blind test.

Link to the prediction code: https://github.com/bolgebrygg/Force-2020-Machine-Learning-competition/blob/master/lithology_competition/code/OlawaleI/FORCE_Submission_File.ipynb
Link to the writeup: https://docs.google.com/document/d/1VuXv09yvDMz_5vzbBbcfQz9E6gWoSC2FcA9FHTDY-K4/edit
Link to the LinkedIn post