Automate Data Exploration and Treatment
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Updated
Jan 24, 2024 - R
Automate Data Exploration and Treatment
R package that makes basic data exploration radically simple (interactive data exploration, reproducible data science)
Enjoy your transcriptomic data and analysis responsibly - like sipping a cocktail
An User-Friendly Application for Exploratory Factor Analysis
Using R and machine learning to build a classifier that can detect credit card fraudulent transactions.
Interactive Shiny application for tidyproteomics
Exploring the NTSB Aviation Accident Database
mde: Missing Data Explorer
Cognifyz Technologies Data Science Internship project on Restaurant Data Analysis to explore insights and build predictive models.
Multivariate analysis and statistical modeling (with dimensional reduction) of NYC urban life pathologies
The goal of tabulate is to help you create tabular data in long format.
The web ui (R-Shiny application) for Pheno-Ranker, a tool designed for performing semantic similarity analysis on phenotypic data structured in JSON format, such as Beacon v2 Models or Phenopackets v2
Materi praktikum Talent Scouting Academy (TSA) Kominfo 2023
The given dataset, "Energy20.txt", can be used to create models of energy use of appliances in a energy-efficient house. The dataset provides the Energy use of appliances (denoted as Y) using 671 samples. It is a modified version of data used in the study [1]. The dataset includes 5 variables, denoted as X1, X2, X3, X4, X5, and Y, described as f…
Customer Segmentation using R
Commonly used functions for working with data.
This repository is about analyzing the data of Inside Airbnb for the Team Assignment of the course "Data Prep.&Workflow Mgt at Tilburg University ". Analysis to understand the amount of bedrooms per person has a significant effect on ratings or not.
An overall Analysis of World Cup 2022 using R and Tidyverse
This project focuses on analyzing fitness data collected from various tracking devices to gain insights into users' activity levels, sleep patterns, calorie expenditure, and heart rate. The dataset used in this project consists of multiple CSV files, each containing different aspects of fitness-related data.
Home to the development of the TREND-DB application
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