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.

Portfolio credit risk modeling

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

Create beautiful tiles of scatterplots between variables in MATLAB

correlationMatrix is a Python powered library for the statistical analysis and visualization of correlations

Code for calculating correlations between clade indicators.

Instant Polychoric and Polyserial Correlation

A model that utilizes text data preprocessing, feature engineering, correlation reduction using PCA, and training a Decision Tree Classifier to classify languages based on input text data.

Beautiful correlation plots for the terminal

Examples demonstrating the NAG Numerical Library for Java

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

The goal of this research is to better understand the relationships between cryptocurrencies and stock indexes, including how cryptocurrencies are interconnected. Preliminary visualization revealed a trend of market movement across all cryptocurrencies, indicating a substantial correlation. Initial analysis focusses on finding the correlation be…

Showing how to identify multicollinearity in a regression problem using the OLS(Ordiniary Least Square Method) and correlation chart adn finaly eradicating it.

Python library for Random Matrix Theory, cleaning schemes for correlation matrices, and portfolio optimization

This repository contains Exploratory Data Analysis in Python on Autism Behavioural Challenges on children(0-18 years) dataset

The objective is to use the dataset to explore the relationship between cardiac disease, body measurements, blood markers, and lifestyle choices.

Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.

In this Notebook, I analyze the following five semiconductor stocks: HD, INTC, AMD, MU, NVDA, and TSM. Then, I choose the stock with the least correlation to JNJ in order to diversify a portfolio. The data was generated using the GOOGLEFINANCE historical market data script.

This project aims to build a machine learning model using K-Nearest Neighbor, LogisticRegression, RandomForestClassifier to classify whether or not a person has heart disease based upon his medical attributes. (accuracy achieved : 88.52%)