voskresenskiianton
Data Scientist. Time-series forecasting and geospatial analysis
Saudi AramcoSaudi Arabia
voskresenskiianton's Stars
solegalli/feature-selection-for-machine-learning
Code repository for the online course Feature Selection for Machine Learning
fastai/fastbook
The fastai book, published as Jupyter Notebooks
catboost/tutorials
CatBoost tutorials repository
imranfadhil/logASCII_viewer
Upload well LAS (log ASCII) files and view the raw logs interactively in streamlit
antoniodagnino/Oil-Gas-Drilling-Activity-Prediction
Drilling Activity Prediction: Oil and Gas operations are dramatically affected by supply, demand and several other factors that compromise the operational planning of resources. To overcome this challenge, predictive analytics could be applied to forecast rotary rig count inside United States using time-series data.
antoniodagnino/GeothermalDatathon
The U.S Department of Energy is developing Enhanced Geothermal Systems as a solution for renewable energies. The Utah Forge Project is currently searching for the optimum well placement of a production well that enables the maximum possible net energy for 20 years. ‘GeotherML’ team participated in this challenge, analyzed data provided by SPE - PIVOT, and delivered a solution that includes the use of Deep Learning. The development of this initiative covers Exploratory Data Analysis with feature engineering, data modeling and evaluation metrics.
yohanesnuwara/ML-and-DS-Geoscience-Papers
A collection of published papers, abstracts, and introductory articles of Machine Learning and Data Science related to applications on Geoscience and Hydrocarbon exploration.
yohanesnuwara/GeostatsPy_Intro_Course
Introduction to spatial data analytics and machine learning with GeostatsPy Python package
yohanesnuwara/pyreservoir
Python utilities for reservoir engineering calculations
Skoltech-CHR/DeepField
Machine learning framework for reservoir simulation
yohanesnuwara/pyresim
Reservoir simulator in Python language
salmansust/Machine-Learning-TSF-Petroleum-Production
Time series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time-consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this research, we applied machine learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions and showed comparison of different machine learning models. For evaluation purpose, a case study from the petroleum industry domain is carried out using the production data of an actual gas field of Bangladesh. Toward a fair evaluation, the performance of the models were evaluated by measuring the goodness of fit through the coefficient of determination (R2 ) and Root Mean Square Error (RMSE), Mean Squared Error (MSE) , Mean Absolute Error(MAE) and model Accuracy
petrobras/WPRAutoencoders
This is one of Petrobras' open repositories on GitHub. It contains the WPRAutoencoders project which encompasses a wellbore pressure response generator, a dataset of 20.000 synthetic pressure responses and an autoencoder neural network capable of clustering this data based on transmissibility and reservoir geometry.
scikit-learn-contrib/MAPIE
A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.
thiyangt/fforms
Feature-based forecast model selection
open-data-science/awesome
Cherry picked DS educational materials
a-milenkin/Competitive_Data_Science
Материалы по курсу анализу данных
trent-b/iterative-stratification
scikit-learn cross validators for iterative stratification of multilabel data
GeoStat-Framework/PyKrige
Kriging Toolkit for Python
DataTalksClub/mlops-zoomcamp
Free MLOps course from DataTalks.Club
n3moy/RN_Challenge
esp failure prediction competition
facebookresearch/balance
The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.
bhulston/Time-Series-Prediction-with-LSTM-and-XGB
Build an algorithm that can predict multiple future states of Limit Order Books using high-frequency, multi-variate, short time-frame data
google-research/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
anticensority/runet-censorship-bypass
Web-extension for bypassing censorship in Russia
FUlyankin/neural_nets_prob
Понимаем как работают нейросетки на ручных задачках :)
FUlyankin/yet_another_matstat_course
Yet another matstat course (ru)
christianversloot/machine-learning-articles
🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
scikit-garden/scikit-garden
A garden for scikit-learn compatible trees
zillow/quantile-forest
Quantile Regression Forests compatible with scikit-learn.