Random Forest Regression Machine Learning - Well Log Prediction for Petrophysics

Random Forest Regression Machine Learning - Well Log Prediction for Petrophysics

5.388 Lượt nghe
Random Forest Regression Machine Learning - Well Log Prediction for Petrophysics
Random forest is a very popular machine learning algorithm that can be used for both classification and regression. Within this tutorial, we will see how we can use the Random Forest algorithm to predict a continuous output using well logs as an example. ⭐️ If you haven't already, make sure you subscribe to the channel: https://www.youtube.com/channel/UCn1O_4_ApzbYwrsUdRoMmOg?sub_confirmation=1 ▼ ---- SUPPORT THE CHANNEL ---- ▼ ☕️ BUY ME A COFFEE: https://www.buymeacoffee.com/andymcdonaldgeo ▼ ---- GET THE CODE --- ▼ https://github.com/andymcdgeo/streamlit_tutorial_series ▼ ---- RECOMMENDED BOOKS ---- ▼ As an Amazon Associate I earn from qualifying purchases. By buying through any of the links below I will earn commission at no extra cost to you. PYTHON FOR DATA ANALYSIS: Data Wrangling with Pandas, NumPy, and IPython UK: https://amzn.to/3HNycJ9 US: https://amzn.to/3DL7qPv FUNDAMENTALS OF PETROPHYSICS UK: https://amzn.to/3l1PgSf PETROPHYSICS: Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties UK: https://amzn.to/30UNWZS US: https://amzn.to/3DNqBbd WELL LOGGING FOR EARTH SCIENTISTS UK: https://amzn.to/3FHsbfn US: https://amzn.to/3CILAuE GEOLOGICAL INTERPRETATION OF WELL LOGS UK: https://amzn.to/3l2v2HV US: https://amzn.to/30UOTkU ▼ ---- SOCIAL CHANNELS ---- ▼ Thanks for watching, if you want to connect you can find me at the links below: https://andymcdonaldgeo.medium.com/ https://twitter.com/geoandymcd https://www.linkedin.com/in/andymcdonaldgeo/ https://www.andymcdonald.scot/ Be sure to sign up for my newsletter to be kept updated when I post and share new content on YouTube and Medium. https://www.getrevue.co/profile/andymcdonald #datascience #petrophysics #python #streamlit #eda 00:00 Introduction 01:14 Data Loading and Preparation 07:44 Building the Model 08:11 Verifying the Model Results 10:49 Predicting on the Test Data