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Statistical package in Python based on Pandas
:link: Methods for Correlation Analysis
Python package to generate Gaussian (1/f)**beta noise (e.g. pink noise)
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.
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
Codes written in the course of a data science workshop at KIT in cooperation with FZI
A Python utility for Cramer's V Correlation Analysis for Categorical Features in Pandas Dataframes.
Text Mining and Analysis with Biplots.
An R package to explore and quality check data
Util library to provide R-like dataframes and statistical functions over Parquet DataSet from parquet-dotnet
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.
A network model for studying the relation between temporal dynamics and connectivity structure
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
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.
Code repository for New J. Phys. 20, 043034 (2018) [arXiv:1708.06363]
A web scraper and some ML analysis scripts for recipe data
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
This contains R Script to calculate Top-down Correlation Iman & Conover Tecnometrics 1987
Predicting price housing using a small data set. A project to understand the whole ML workflow.
Web scraping additional data to building a model to predict football coaches' salaries
Colección de notebooks tutoriales para bajar series de tiempo financieras y analizarlas sus correlaciones.
Visualization project of diabetes rates along Age, Income, Food Security, and Urban/Rural settings.
Binary classification of residential utility problems in NYC; Capstone project for the IBM Certificate in Data Science
The project aims to better understand the company's customers and make more effective marketing strategies when modifying products to customers in different segments.
Correlations of polarization and depolarization fields in ferroelectrics
Movie Insights: Exploring film industry trends and analytics through data analysis techniques.
Quickly uncover potential relationships in a CSV dataset by getting an overview of correlation coefficients between several pairs of metrics.
Predicts the red and white wine qualities, given their physicochemical attributes