PandaDock: Physics based Molecular Docking with GNN Scoring

In this repository, four famous correlation algorithms have been implemented. Pearson, spearman, Chatterjee, and MIC correlation algorithm implemented

資料科學的日常研究議題

MediaEval challenge 2019 - to predict the memorability of the Videos

Calculating pairwise euclidean distance matrix for horizontally partitioned data in federated learning environment

10 Days of Statistics Hackerrank Solutions

Currently, there are 26.2 million COVID-19 cases in the US due to people taking lower precautions to reduce transmission at public venues. This project aims to create a tool that provides individuals with information to make a responsible decision about visiting an establishment to prevent unnecessary SARS-CoV-2 cases. A probabilistic model that…

A machine learning project where we first detected and removed the outliers and then checked correlation among features and then applied different ML algorithms to check if the person might get a heart attack or not.

This repository contains statistical analyses conducted on various datasets related to medical and health factors. The analyses include Spearman correlation, Chi-square test, and Linear Regression to explore relationships and predictive models related to heart attacks and other medical conditions.

lambda_corr — Repeated-Average-Rank Correlation Λ (Lambda)

De gebruikte code bij mijn masterproef aan de VUB (Bedrijfskunde)

"Explore and understand various correlation measures such as Pearson, Spearman, and Kendall through detailed explanations, mathematical derivations, and practical examples.

hyper_corr — Hyper-fast, numba-accelerated correlation coefficients with SciPy-compatible results.

Singals applications

Implementation of Spearman and Kendall correlation coefficient for MS Excel (VBA)

A Study of Health Disparity of Dental Care Across London Boroughs in Relation to Average Household Income

SECmp is a system for collecting and comparing search engine rankings, using Google Search results as a baseline to evaluate relevance overlap and ranking correlation across engines.

My personal repository where I can keep files associated with my learning of Statistics

Continuation to Data Analysis using more mathematical approach.

Feature importance refers to a measure of how important each feature/variable is in a dataset to the target variable or the model performance. It can be used to understand the relationships between variables and can also be used for feature selection to optimize the performance of machine learning models.