Enhancing `ggplot2` plots with statistical analysis 📊🎨📣

Beautiful correlation plots for the terminal

Python for Random Matrix Theory: cleaning schemes for noisy correlation matrices.

Parallel correlation calculation of big numpy arrays or pandas dataframes with NaNs and infs.

Сorrelation matrix UI component for Swing

My fictitious firm, GDSMC Global, is a security consultancy focusing on supporting governments around the world in understanding, predicting, and stopping terrorism attacks. Our goal is to allow individual nation states to better deploy security resources to reduce the likelihood of successful terrorism in the future, and to understand what are the likely coming costs of terrorism so that resources can be set aside, in advance, to rebuild after inevitable and unfortunate attack.Although governments can submit their own internal security data to us for study, our models are constructed using the Global Terrorism Database (GTD) maintained by the National Consortium for the Study of Terrorism and Responses to Terrorism at the University of Maryland ( http://start.umd.edu/gtd/ ).

CryptoIntel is a one stop dashboard which gives all the information about cryptocurrencies. All the inquisitive users can get their answers related to cryptocurrencies from cryptointel.

A Matlab utility for plotting correlation matrices, with similar appearance to Seaborn in Python.

Some code related to our paper Per,Duc,Nes. Detection (2019). The objective is to detect block-exchangeable structures in correlation matrices. For any help, please contact me or leave a comment somewhere. I will be glad to help you.

This repository focuses on the projects that I would be doing on "Linear Regression". Feel free to make any improvements. Thanks

Finding Covariance Matrix, Correlation Coefficient, Euclidean and Mahalanobis Distance

Algorithms for feature selection based on covariance matrix.

This repository should help people that would like to code in R and work with the National Health and Nutrition Examination Survey (NHANES). Some topics corved are SQL , logistic regression.... etc

Making use of R programming, the analysis is focussed on the problem which insurance providers are facing today to define their target market and plan their sale strategies which helps them increase their market share and thereby, maximize their profitability. The analysis techniques used in the project are learnt through Data Analysis and Decision Making course at Rutgers University.

Comparing results of SVM and decision trees in stone classification

Represent convolution/correlation matrices in Python 2.7 without the need to actually compute them.

A New Parametrization of Correlation Matrices

Interactive data visualizations for Kaggle Brooklyn Home Sales data, built using D3.js

CART, K-Means, Apriori, Adaboost, RFE; models using Anti-cancer peptides vs Human proteins

Baseball repository for my original research

A handbook exemplifying Data Mining concepts in effortless code snippets.

Matlab scripts for analyzing calcium imaging data presented in "Neural Coding of Leg Proprioception in Drosophila" (Mamiya, Gurung, and Tuthill (2018) Neuron: DOI:https://doi.org/10.1016/j.neuron.2018.09.009)

Repositório para os trabalhos práticos da disciplina de Processamento Digital de Imagens 2019.2 UFPI

JED is a program for performing Essential Dynamics of protein trajectories written in Java. JED is a powerful tool for examining the dynamics of proteins from trajectories derived from MD or Geometric simulations. Currently, there are two types of PCA: distance-pair and Cartesian, and three models: COV, CORR, and PCORR.

The below analysis is for the estimation of relative performance values using multiple ML models. See their article (pp 308-313) for more details on how the relative performance values were set.

Looking at the probability of being accepted in a graduate program using a machine learning model

Price prediction model

Creating customer segments using unsupervised learning algorithms

A Whale Off the Port(folio): DIY portfolio to compare with performances of whale portfolios based on rolling moving averages and standard deviations. Correlation heatmaps are generated for visually assessing co-movements on returns.