Statistical package in Python based on Pandas

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

🔗 Methods for Correlation Analysis

Python package to generate Gaussian (1/f)**beta noise (e.g. pink noise)

Compute interstation correlations of seismic ambient noise, including fast implementations of the standard, 1-bit and phase cross-correlations.

🔎Data Understanding, Visualization , Preparation & Cleaning - Clustering algorithms (unsupervised learning) - Classification algorithms (supervised learning) - Sequential Pattern Mining

A Python package to calculate, visualize and analyze correlation maps of proteins.

Statistical standard error estimation tools for correlated data

Util library to provide R-like dataframes and statistical functions over Parquet DataSet from parquet-dotnet

Codes written in the course of a data science workshop at KIT in cooperation with FZI

Fast and flexible two- and three-point correlation analysis for time series using spectral methods.

Data Mining project 2020/2021 @ University of Pisa

A basic analysis of poverty with a special emphasis on the United States.

An R package to explore and quality check data

A Python utility for Cramer's V Correlation Analysis for Categorical Features in Pandas Dataframes.

Using Python, R, and SQL with the 2014-15 NBA season data set. Our project imports the data set, merges with other files for cleaning & processing then puts the material into a machine learning algorithm

Code repository for New J. Phys. 20, 043034 (2018) [arXiv:1708.06363]

Visualization project of diabetes rates along Age, Income, Food Security, and Urban/Rural settings.

Global sensitivity analysis that takes into account correlations and dependencies in the LCA model during uncertainty propagation with Monte Carlo approach.

Predicts the red and white wine qualities, given their physicochemical attributes