#103 - Time-series classification of power demand comparison between Sktime and Sklearn

Using the public dataset that represents the power consumption in Italy in 1997, the task is to classify whether it corresponds to winter (from October to March) or summer (from April to September). This will be done using different techniques from both sklearn and sktime. 

#102 - Public time-series datasets

The following website http://timeseriesclassification.com/ has a great amount of public time-series datasets for several applications such as data from sensors, motion, image, audio, among others. In this post is shown how to use these datasets in a python framework.

#101 - Time-series forecast with sktime and python

Several applications in production, from finance to industrial systems, have the data being recorded and indexed by timestamps. In this post we are presenting a great open source library for python called sktime, which  was recently launched and has been developed and optimized for machine learning time-series classification, regression and forecasting. 

#87 - Machine Learning classification comparison between GridSearchCV and RandomizedSearchCV

In a dataset with 13 features that represent different wine properties, such as color intensity and alcohol content, a machine learning model is created for a classification task. The goal is to predict in which class these wines belong, that could be class 0, class 1 or class 2. 

#85 - Using Pandas to transform categorical data for machine learning models

When creating machine learning models in python, using for instance the libraries numpy, pandas and sklearn, depending on the algorithms selected, the input data should be on the numerical format.

#65 - Chat with Mário André de Deus

(English version at the bottom)

Tive o prazer de bater um papo com o Mário André de Deus, engenheiro mecânico que possui uma pós-graduação em Indústria 4.0 (I4.0), além de uma especialização em ciência de dados. Conversamos sobre sua trajetória profissional e, em particular, seu trabalho atual em que aplica conceitos de I4.0 para a prestação de serviços.

#64 - Chat with Felipe Pereira Finamor

(English version at the bottom)

Tive o prazer de bater um papo com Felipe Pereira Finamor, engenheiro e mestre em metalurgia e materiais que possui uma pós-especialização em ciência de dados. Conversamos sobre sua trajetória profissional e o trabalho que ele tem desenvolvido aplicando Machine Learning para resolver problemas relacionados às predições das propriedades dos materiais. Esse trabalho é realizado em uma das maiores produtoras de aço nacional.