Statistical package in Python based on Pandas

🔗 Methods for Correlation Analysis

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.

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.

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

Statistical standard error estimation tools for correlated data

Abinitio Dynamical Vertex Approximation

Data Mining project 2020/2021 @ University of Pisa

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

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

NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semantic, and taxonomic correlations between EEG electrode locations and brain regions, resulting in improved accuracy. Presented at PAKDD '24.

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

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

Text Mining and Analysis with Biplots.

An R package to explore and quality check data

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

A hub that contains notebooks that perform elementary descriptive statistics of populations and samples and demonstrates 3 hypothesis tests- Welch t-test, Correlation, and Chi-square test. It shows how to run them in python and understand the results

A network model for studying the relation between temporal dynamics and connectivity structure

This repository includes my Liver Disease Machine Learning-Flatiron School Module 3 Project. For this project I used libraries such as Pandas, Matplotlib, and Seaborn for visualizations and Scikit-Learn for the machine learning portion of the project. I implemented various classification algorithms on the data including some hyperparameter tuning.