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

An R package to explore and quality check data

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

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.

A New Parametrization of Correlation Matrices

App para calcular la ecuación de regresión lineal simple, leyendo los datos desde un archivo CSV

Abinitio Dynamical Vertex Approximation

To explore the given dataset for all basic statistics such as the distributions, correlations, outliers, missing values, etc.

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.

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

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

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

Data Mining project 2020/2021 @ University of Pisa

Text Mining and Analysis with Biplots.

A web scraper and some ML analysis scripts for recipe data

Identifying relationships between Spotify popularity score and audio features, webscrape Billboards, test classification algorithms

Web scraping additional data to building a model to predict football coaches' salaries

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