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100-days-of-ml-code

100 Days of ML Coding

2020_force_lithology_prediction

a-fast-and-compact-3-d-cnn-for-hsic

The code is associated with the following paper "A Fast and Compact 3-D CNN for Hyperspectral Image Classification". IEEE Geoscience and Remote Sensing Letters

altair-used-to-visualize-and-interrogate-well-by-well-production-data-from-volve-field

The objective of this project is to interactively interrogate oil field production data using Altair

arbwellfail

A python Program to calculate the rock strength required for an arbitrary wellbore

awesome-open-geoscience

Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome

awesome-remote-sensing-change-detection

List of datasets, codes, and contests related to remote sensing change detection

bmi_and_adiposity_index_calculator

this python project uses a series of else if loops to calculate 9 different body measurements and also provides the healthy ranges for most of them

bookartgeneratorpy

Python version of BookArtGenerator with improved support for transparency, smoothing and an additional proportionate preview picture. Requires Pillow and Python 2.7. More info about usage and outcome can also be found at http://www.instructables.com/id/How-to-make-folded-book-art-easier-using-your-comp/ .

car-price-prediction-highly-comprehensive-linear-regression-project-

A Linear Regression model to predict the car prices for the U.S market to help a new entrant understand important pricing variables in the U.S automobile industry. A highly comprehensive analysis with detailed explanation of all steps; data cleaning, exploration, visualization, feature selection, model building, evaluation & MLR assumptions validity.

change-detection-in-multispectral-satellite-images

Vegetation change detection in multispectral satellite images using remote sensing

cider

This repository contains the dataset and the pytorch implementations of the models from the paper CIDER: Commonsense Inference for Dialogue Explanation and Reasoning. CIDER has been accepted to appear at SIGDIAL 2021.

classification-on-sentinel-2-satellite-image-using-rasterio-machine-learning-and-neural-networks

cloths-length-measurement

The model is able to predict long and short sleeves t-shirts, jackets, tops , shirt as well as trouser, jeans, skirts etc. Then length is measured in pixels using OpenCV.

coderbyte-challenges

Javascript and SQL

computational-geophysics

All geophysics method: gravity, magnetic, seismic, electrical, electromagnetic

credit-card-fraud-detection-using-machine-learning-with-python

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification. Update (03/05/2021) A simulator for transaction data has been released as part of the practical handbook on Machine Learning for Credit Card Fraud Detection - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html. We invite all practitioners interested in fraud detection datasets to also check out this data simulator, and the methodologies for credit card fraud detection presented in the book. Acknowledgements The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project Please cite the following works: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019 Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019 Yann-Aël Le Borgne, Gianluca Bontempi Machine Learning for Credit Card Fraud Detection - Practical Handbook

credit-modeling

Modeling a borrower's credit risk Problem Statement Can we build a machine learning model that can accurately predict if a borrower will pay off their loan on time or not? I'll be working with financial lending data from Lending Club. Lending Club is a marketplace for personal loans that matches borrowers who are seeking a loan with investors looking to lend money and make a return. Each borrower fills out a comprehensive application, providing their past financial history, the reason for the loan, and more. Lending Club evaluates each borrower's credit score using past historical data (and their own data science process!) and assign an interest rate to the borrower. A higher interest rate means that the borrower is riskier and more unlikely to pay back the loan while a lower interest rate means that the borrower has a good credit history is more likely to pay back the loan. The interest rates range from 5.32% all the way to 30.99% and each borrower is given a grade according to the interest rate they were assigned. If the borrower accepts the interest rate, then the loan is listed on the Lending Club marketplace. Investors are primarily interested in receiving a return on their investments. Approved loans are listed on the Lending Club website, where qualified investors can browse recently approved loans, the borrower's credit score, the purpose for the loan, and other information from the application. Once they're ready to back a loan, they select the amount of money they want to fund. Once a loan's requested amount is fully funded, the borrower receives the money they requested minus the origination fee that Lending Club charges. The borrower then makes monthly payments back to Lending Club either over 36 months or over 60 months. Lending Club redistributes these payments to the investors.

credit_score_prediction

cyphercrescent_test_exercise

This repo contains codes and files used to attempting Cyphercrescent internship Test Exercises

data-science-tools-petroleum-exploration-and-production

Python and R notebooks accompanying a talk at the 2018 CSEG / CSPG Geoconvention in Calgary - and a lot MORE!

decline_curve_analysis_tool

A DCA (Decline Curve Analysis) Tool for Petroleum Engineers.

deep-learning-cnn-for-image-recognition

Google TensorFlow project for classification using images or video input.

deep-neural-networks-healthcare

Tangible and Practical Deep Learning Projects Repository for Healthcare such as Cancer, Drug Discovery, Genomic and More

deep_learning

detection-of-dents-and-scratches-on-a-card-body

differential-equations-earth-system

Course material for the lecture "Differential equations in the earth system" (WS 2019/2020) @ CAU Kiel

digital-forensics-lab

Free hands-on digital forensics labs for students and faculty

drilling

Drilling Models, Data, and Case Studies

ere-251_pvt-simulator

PVT and fluid two-phase behavior simulator