Elisa Ribeiro's Projects
Repo for the Capstone Project of the Data Science Specialization - Coursera John Hopkins University
This repo contains all projects from the courses within the Data Science Specialization from Johns Hopkins University, on Coursera. The projects were required to achieve the certificate and were all evaluated on each course.
Repo for the course content - Developing Data Products - Data Science Specialiation Coursera JHU
repo for the coursera data science specialization
R scripts for benchmarking next word prediction algorithms developed for the Coursera Data Science Capstone Project.
Sobre mim
Plotting Assignment 1 for Exploratory Data Analysis
Repo for the Final Assignment of the course "Getting and Cleaning Data Course Project"
to learn more git
Resumos, experimentos e resolução de exercicios do livro: Introduction to Statistical Learning v2
This project is based on a fictional situation in which the objective was to determine which houses were good real estate oportunities. The dataset was obtained from Kaggle and the business questions were answered based on the 'Seja um Data Scientist' blog.
Badges for your Profile and Projects.
This repo contains some codes I use for the medium posts I write
an end-to-end mlops project
This repository contains the scripts used in the analysis presented at the paper "Effect of Environmental Factors on Plectranthus neochilus Volatile Composition: a GC-MS-Based Metabolomics Approach."
This repo was created as final project for the course "Practical Machine Learning" of John Hopkins University at Coursera
Repository for Programming Assignment 2 for R Programming on Coursera
This repo contains all the code for the creation of the medicinal plants quality control application on streamlit
Using R and Python in a Streamlit app
This repo contain R scripts for metabolomic data analysis. The scripts were developed based on the research at LabMetaMass. The scripts contain detailed comments, as their use was also intended for members of the LabMetaMass with no experience in R.
Peer Assessment 1 for Reproducible Research