Acycle: Time-series analysis software for research and education

Code release for "STMask: Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation"(CVPR2021)

vasco: Discover hidden patterns in your Postgres data

Efficient ways to compute Pearson's correlation between columns of two matrices in various scientific computing languages

Routines for exploratory data analysis.

A two-stage predictive machine learning engine that forecasts the on-time performance of flights for 15 different airports in the USA based on data collected in 2016 and 2017.

A system to recognize hand gestures by applying feature extraction, feature selection (PCA) and classification (SVM, decision tree, Neural Network) on the raw data captured by the sensors while performing the gestures.

Robustness of DWT vs DCT is graded based on the quality of extracted watermark. The measure used is the Correlation coefficient (0-100%).

10 Days of Statistics Challenges at HackerRank

Correlation coefficients with uncertainties

A package that calculates correlation between two arrays. Simple, with no dependencies

Multiple ways to select feature from data

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

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

Python package to simplify plotting of common evaluation metrics for regression models. Metrics included are pearson correlation coefficient (r), coefficient of determination (r-squared), mean squared error (mse), root mean squared error(rmse), root mean squared relative error (rmsre), mean absolute error (mae), mean absolute percentage error (m…

Functions and analyses to illustrate the performance of correction methods that allow averaging correlation coefficients.

Regression with Python & R.

Covariance and correlation matrix via Rhadoop (rmr2 and HDFS)

Machine learning functions written in goLang:

Parses apart a PDF file into separate documents and then uses Natural Language Processing, Machine Learning models, and statistics to rank the documents by similarity to a single document.