Jwalitsolanki's starred repositories
ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
PythonNumericalDemos
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
Petrophysics-Python-Series
A series of Jupyter notebooks showing how to load well log and petrophysical data in python.
reservoir-engineering
Python worked examples and problems from Reservoir Engineering textbooks (Brian Towler SPE Textbook Vol. 8, etc.)
Force-2020-Machine-Learning-competition
the results, code and the data for the Force 2020 Machine learning competition after the completion of the competition in October 2020.
ExcelNumericalDemos
A set of numerical demonstrations in Excel to assist with teaching / learning concepts in probability, statistics, spatial data analytics and geostatistics. I hope these resources are helpful, Prof. Michael Pyrcz
tutorials-2016
Geophysical Tutorials for 2016
PEG_Python
Petroleum Engineering and Geosciences exercises in Python
Machine-Learning-Competition-2020
SPWLA PDDA’s 1st Petrophysical Data-Driven Analytics Contest -- Sonic Log Synthesis
Monograph-20-Examples
Worked examples from Monograph 20 'Phase Behavior' - Appendices B and C
synthetic_well-log_polynomial_regression
This project attempts to construct a missing well log from other available well logs, more specifically an NMR well log from the measured Gamma Ray (GR), Caliper, Resistivity logs and the interpreted porosity from a well.
Pima-Indians-Diabetes-Dataset-Classification
Predicting if a patient is suffering from Diabetes or not using Machine Learning in Python. Give the repo a star if you found it informative.
Machine_Learning
1 Day Machine Learning Course
onepetro_scrapping
Web scrapping for papers on OnePetro website
Data-Science-Workshop-PU
Covid - 19 Data analysis using pandas
git-test
Hello there! This repository is created by Shashank Tiwari (Pre-final year student, Btech in applied petroleum engineering with specialization in Upstream from U.P.E.S). It contains application of machine learning and other analysis on the Petroleum topics such as reservoir engnn, drilling, geology and geophysics, etc.