K-Means clustering is a popular unsupervised machine learning algorithm that is commonly used in the exploratory data analysis phase of a project. It groups data together into clusters based on similarities within the data. In this tutorial, we will go through the basics of running a k-means algorithm on well log data.

My Medium article this video is based on. Check it out as it contains more examples and extra plots.
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Timestamps:
0:00 Introduction
0:53 K-Means Clustering Theory
2:56 Jupyter Notebook Loading Data & Importing Libraries
5:53 Applying a Standard Scaler
8:27 Identifying Optimum Number of Clusters - Elbow Plot
11:20 Appling K-Means Clustering Algorithm
12:55 Plotting K-Means Clustering Results on a Scatter Plot
14:25 Comparing Results from Multiple K Values
18:40 Other Clustering Methods & Outro

DOWNLOAD NOTEBOOK & DATA
Data and notebooks for my entire YouTube series can now be found here:
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REFERENCES & LIBRARIES
Force 2020 Competition Github: Bormann P., Aursand P., Dilib F., Dischington P., Manral S. 2020. FORCE Machine Learning Competition. [ Ссылка ]

Competition Results: [ Ссылка ]

Books I Recommend:
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PYTHON FOR DATA ANALYSIS: Data Wrangling with Pandas, NumPy, and IPython
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FUNDAMENTALS OF PETROPHYSICS
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PETROPHYSICS: Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties
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WELL LOGGING FOR EARTH SCIENTISTS
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GEOLOGICAL INTERPRETATION OF WELL LOGS
UK: [ Ссылка ]
US: [ Ссылка ]

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#petrophysics #python #MachineLearning #unsupervised-learning

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