{"id":48442755,"url":"https://github.com/bautipelossi/mydatalab","last_synced_at":"2026-04-06T16:04:29.325Z","repository":{"id":243742582,"uuid":"809925253","full_name":"bautipelossi/mydatalab","owner":"bautipelossi","description":"This library for Phyton is the final assignment in the \"Data Science III\" course of the Data Science Career at Universidad Nacional del 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","statistics"],"created_at":"2026-04-06T16:04:00.326Z","updated_at":"2026-04-06T16:04:29.310Z","avatar_url":"https://github.com/bautipelossi.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## About(Spanish)\nMyDataLab es un proyecto final de la cátedra \"Ciencia de Datos III\" de la Facultad de Ingeniería Química de la Universidad Nacional del Litoral (Santa Fe, Argentina). \n\nAutores: Bautista Pelossi y Franco Vismara\n\n## Descripción\n\nEsta librería proporciona funciones simples para realizar Regresión Lineal Simple y Múltiple, Regresión Logística, Test de Bondad para Estadístico Chi-cuadrado y generar histogramas.\n\n## Características\n\n- Clase ResumenNumerico: Procede a realizar resumen numérico con principales estadísticos (media, mediana, desvío estandar y maximo y minimo)\n- Clase ResumenGrafico: Consta de tres posibles resumenes gráficos ampliamente utilizados en estadística: el histograma, los nucleos Kernel y QQ-Plot\n- Clase Regresion: Ajusta el modelo de regresión, ya sea lineal (simple o múltiple) o logístico.\n- Clase RegresionLinealSimple: Predice un valor de respuesta ante un nuevo valor de la variable explicativa\n  mediante el modelo de regresion lineal simple ajustado en la clase Regresión.\n- Clase RegresionLinealMultiple: Predice un valor de respuesta ante un nuevo vector de las variables explicativas\n  mediante el modelo de regresion lineal múltiple ajustado en la clase Regresión.\n- Clase RegresionLogistica: Predice un valor de respuesta ante un nuevo valor de la variable explicativa\n  mediante el modelo de regresion logístico ajustado en la clase Regresión.\n- Clase Cualitativas: Test de bondad para estadístico Chi-Cuadrado\n\n## Instalación\n\n1. Clonar este repositorio:\n\n2. Ejecutar el archivo mydatalab.py\n\n\n## About (English)\nMyDataLab is a final project for the \"Data Science III\" course at the Universidad Nacional del Litoral (Santa Fe, Argentina).\nAuthors: Bautista Pelossi and Franco Vismara\n\n## Description\nThis library provides simple functions to perform Simple and Multiple Linear Regression, Logistic Regression, Chi-Square Goodness-of-Fit Test, and generate histograms.\n\nFeatures\n- Class ResumenNumerico: Performs a numeric summary with key statistics (mean, median, standard deviation, maximum, and minimum).\n- Class ResumenGrafico: Includes three widely used graphical summaries in statistics: histogram, kernel density estimation, and QQ-Plot.\n- Class Regresion: Fits regression models, whether linear (simple or multiple) or logistic.\n- Class RegresionLinealSimple: Predicts a response value for a new explanatory variable using the simple linear regression model fitted in the Regresion Class.\n- Class RegresionLinealMultiple: Predicts a response value for a new vector of explanatory variables using the multiple linear regression model fitted in the Regresion Class.\n- Class RegresionLogistica: Predicts a response value for a new explanatory variable using the logistic regression model fitted in the Regresion Class.\n- Class Cualitativas: Goodness-of-fit test for Chi-Square statistic.\n\n## Installation\n1. Clone this repository:\n\n2. Run the mydatalab.py file\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbautipelossi%2Fmydatalab","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbautipelossi%2Fmydatalab","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbautipelossi%2Fmydatalab/lists"}