Pinned Repositories
asn-python
asn_r_intro
asn_yochi
Este trabalho tem o objetivo de ajudar a empresa a melhorar a aprovação de crédito .
cefet
COVID-Alura
Cursos
Este repositório abriga todos os arquivos de código dos cursos do site Computer Science Master.
data-science-blogs
A curated list of data science blogs
data_science
Aqui você encontra os notebooks dos meus projetos na área de Data Science, Machine Learning e Deep Learning.
data_science-1
Notebooks de análises e projetos de Data Science em Python
DeepLearningBook
Repositório do Deep Learning Book - www.deeplearningbook.com.br
ecordeiro's Repositories
ecordeiro/asn-python
ecordeiro/asn_r_intro
ecordeiro/asn_yochi
Este trabalho tem o objetivo de ajudar a empresa a melhorar a aprovação de crédito .
ecordeiro/cefet
ecordeiro/COVID-Alura
ecordeiro/Cursos
Este repositório abriga todos os arquivos de código dos cursos do site Computer Science Master.
ecordeiro/data_science
Aqui você encontra os notebooks dos meus projetos na área de Data Science, Machine Learning e Deep Learning.
ecordeiro/data_science-1
Notebooks de análises e projetos de Data Science em Python
ecordeiro/DeepLearningBook
Repositório do Deep Learning Book - www.deeplearningbook.com.br
ecordeiro/Desafio
ecordeiro/docker-project-template
Template of docker and docker-compose for simple django projects
ecordeiro/files
Repositorio de Arquivos
ecordeiro/Guide-To-Any-Classification-Problem-
ecordeiro/Kaggle--Titanic-Surival-Prediction
<b> The competition is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. </b> Website link: https://www.kaggle.com/c/titanic/ Using Neural Networks got an accuracy of 0.77 on test data. <b> The Challenge <b> On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc). <b> Goal </b> It is your job to predict if a passenger survived the sinking of the Titanic or not. For each in the test set, you must predict a 0 or 1 value for the variable. <b> Metric <b> Your score is the percentage of passengers you correctly predict. This is known as accuracy. <b> Submission File </b> The file should have exactly 2 columns: PassengerId (sorted in any order) Survived (contains your binary predictions: 1 for survived, 0 for deceased)
ecordeiro/Kaggle-Titanic-Solution
How I scored in the top 1% of Kaggle's Titanic Machine Learning Challenge
ecordeiro/machine-learning-imbalanced-data
Code repository for the online course Machine Learning with Imbalanced Data
ecordeiro/ordinalencoder-vs-onehotencoder
Comparing sklearns OrdinalEncoder to OneHotEncoder
ecordeiro/pacote-desafios-pythonicos
Pacote Desafios Pythônicos
ecordeiro/PowerBI-DataScience
Repositório do Curso Microsoft Power BI Para Data Science
ecordeiro/projetoFlores
What the Package Does (One Line, Title Case)
ecordeiro/public-apis
A collective list of free APIs
ecordeiro/PySUS
Library to download, clean and analyze openly available datasets from Brazilian Universal health system, SUS.
ecordeiro/PythonFundamentos
Repositório do Curso Online Python Fundamentos Para Análise de Dados.
ecordeiro/regressao_linear_R
ecordeiro/sql-asn-1
ecordeiro/stock-prediction
ecordeiro/talks
Conjunto de palestras das edições do evento Python Brasil
ecordeiro/template_portfolio
Template para portfólio de Data Science
ecordeiro/teomerefs
ecordeiro/Time-Series
Projetos envolvendo a modelagem de séries temporais