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João Paulo Nogueira joaopcnogueira

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  • Fortaleza, Ceará
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joaopcnogueira / how_to_build_regressive_features.R
Last active November 3, 2023 02:35
Piece of code demonstrating how to build regressive features for machine learning modeling, such as the sum of sales for the last 3 months, the mean of sales for the last 6 months and so on.
# Creating a spine table with three columns ----
# customer_id: identifier of the customer, for which we are going to predict the next month sales
# year_month: reference date
# sales: the metric we want to predict
spine_tbl <- tibble(
customer_id = c(rep("João", 24), rep("Denise", 24)),
year_month = c( seq( ymd("2021-11-01"), ymd("2023-10-01"), by = '1 month' ), seq( ymd("2021-11-01"), ymd("2023-10-01"), by = '1 month' ) ),
sales = sample(100:1000, 48, replace = TRUE)
)
@evan-burke
evan-burke / dremio-ubuntu
Last active July 10, 2021 01:01 — forked from jcaristy/info.sh
[DREMIO: Install dremio on Ubuntu] #dremio
Installing Dremio 1.4 on Ubuntu 16
### NOTE: this is significantly out of date since I last edited it in Jan 2018.
# See the comments on the gist for suggested changes for more recent versions.
##Install links / references
https://www.dremio.com/tutorials/recommender-scikit-learn-dremio-postgres-mongodb/
https://www.dremio.com/tutorials/dremio-oracle-aws/
https://docs.dremio.com/deployment/standalone-tarball.html
@ledmaster
ledmaster / MultipleTimeSeriesForecasting.ipynb
Last active April 28, 2024 20:20
How To Predict Multiple Time Series With Scikit-Learn (With a Sales Forecasting Example)
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@stripedpurple
stripedpurple / O'Reilly Free Books
Last active October 4, 2023 14:35
http://www.oreilly.com/data/free/files/2014-data-science-salary-survey.pdf
http://www.oreilly.com/data/free/files/2015-data-science-salary-survey.pdf
http://www.oreilly.com/data/free/files/Data_Analytics_in_Sports.pdf
http://www.oreilly.com/data/free/files/advancing-procurement-analytics.pdf
http://www.oreilly.com/data/free/files/ai-and-medicine.pdf
http://www.oreilly.com/data/free/files/analyzing-data-in-the-internet-of-things.pdf
http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf
http://www.oreilly.com/data/free/files/architecting-data-lakes.pdf
http://www.oreilly.com/data/free/files/being-a-data-skeptic.pdf
http://www.oreilly.com/data/free/files/big-data-analytics-emerging-architecture.pdf
@conormm
conormm / r-to-python-data-wrangling-basics.md
Last active April 24, 2024 18:22
R to Python: Data wrangling with dplyr and pandas

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs: