Machine Learning Exercise: Exploring categorical plots, LabelEncoder, pipelines and GridSearchCV using Telco Customer Churn data from Kaggle

Jackknife and Bootstrap resampling and estimation in R

PhD Thesis (TeX) on Data Mining Temporal and Indefinite Relations using Numerical Dependencies. University College London

⚖⚡ Experimental evaluation of ensemble classifiers for imbalance in Big Data.

This model predicts furniture sales on account of 4 year sales record.

Statistical Methods using R Programming

Build and evaluate several machine-learning models to predict credit risk using free data from LendingClub.

This project applies supervised machine learning models to predict credit risk, and compare algorithm effectiveness in an unbalanced classification problem

Nonparametric Bootstrap Test with Pooled Resampling Card Game

A header only ready to include mirror of the HIIR library by Laurent De Soras, an oversampling and Hilbert transform library in C++, with additional support for double precision on ARM AArch64 using Neon.

conanfile https://www.conan.io/ for rubberband http://breakfastquay.com/rubberband/

R function that resamples randomly and without replacement counts, and calculates rarefied richness (N0), diversity (N1, N2) and evenness (N2/N0) (after Hill (1973) and Birks et al. (2016))

Stock Analysis Hands On Stocks Data Retrieving from Yahoo Finance(Advanced Micro Devices, Inc. (AMD), IBM, NIVIDIA ,QualComm and Intel Corporation) From 31-Dec-2013 to 03-Sept-2019(Historical Data)

The SpectCube package aims to provide a fast and flexible tool for spectral resampling.

Heavily imbalanced credit dataset was resampled, six machine learning models were fit to training data to predict credit risk. A suggestion was made based on the accuracy of predictions from each model.

In this assignment, we will generate panoramic images by stitching 3 images. Panoramic images will be created by using registering, warping, resampling and blending algorithms.

A combination of codes developed for the calculation of the cross-correlation confidence intervals, making use of a pair of light-curves. The code conducts a bootstrap random sampling with replacement method to generate artificial light-curves. The code determines the cross-correlation of the artificial light-curves, and uses them for significance.

Image Processing Sem 8