nabanitagupta Goto Github PK

07-sqlite

08-etl

09-advanceddata-weather

10-missiontomarschallenge-webscraping

11-ufos-java

12-visualization

16-bigdata-challenge

17-supervisedlearning-challenge

19-neuralnet-challenge

air-pollution-image-dataset-from-india-and-nepal

Air Pollution Image Dataset from India and Nepal

bikesharing

Tableau Visualization

classwork_dataanalytics

cryptocurrencies

Module18-Unsupervised

deep-learning-for-facies-classification

Machine learning contest - October 2016 TLE

deeplearningwithpytorch-code

Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.

election_analysis

Python

facies-visualization-and-classification

A Deep Learning based Artificial Neural Network to classify Facies.

kickstarter-analysis

Performing analysis on Kickstarter data to uncover trends

linkedinarticles

A place to put our LinkdIn articles and notebooks.

lithofacies-classification-using-machine-learning_zhao

Supervised classification to predict rock facies and a T-test flow to evaluate the prediction performance.

log-reader

Node.js application for parsing, viewing and make calculations on .las files

machine-predict-lithologies-using-wireline-logs

To identify lithologies, geoscientists use subsurface data such as wireline logs and petrophysical data. However, this process is often tedious, repetitive, and time-consuming. This project aims to use machine learning techniques to predict lithology from petrophysical logs, which are direct indicators of lithology.

machine-predicted-lithology

FORCE Competition : Create a machine learning model that has the highest accuracy in prediction lithology from a suite of wireline logs

mapping_earthquakes

petrodc

Petroleum Data Collector

pyber_analysis

r_analysis

readme

Initiating Repository with ReadMe

school_district_analysis

Anaconda and Jupyter

school_district_analysis_2

Python and Jupyter