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

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

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.

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…

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.

資料科學的日常研究議題

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

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

Singals applications

Movie Recommendation System based on the Spearman's rank correlation 🎞️

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

SmoothTrend is a comprehensive time series analysis tool that utilizes Holt-Winters, Holt, and Simple Exponential Smoothing methods, as well as ARIMA/SARIMA modeling, to perform advanced trend analysis, stationarity testing, residual analysis, and forecasting.

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.

Telecom Customer Churn Prediction with 9 Different Alghoritms

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

Film recommendation system based on Spearman's rank correlation