PI202202-alako-data VS feature-engineering-tutorials

Compare PI202202-alako-data vs feature-engineering-tutorials and see what are their differences.

Our great sponsors
  • WorkOS - The modern identity platform for B2B SaaS
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • SaaSHub - Software Alternatives and Reviews
PI202202-alako-data feature-engineering-tutorials
1 1
4 263
- 1.5%
10.0 0.0
over 1 year ago 28 days ago
Jupyter Notebook Jupyter Notebook
- GNU Affero General Public License v3.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

PI202202-alako-data

Posts with mentions or reviews of PI202202-alako-data. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

feature-engineering-tutorials

Posts with mentions or reviews of feature-engineering-tutorials. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-08.
  • How to balance multiple time series data?
    2 projects | /r/datascience | 8 Mar 2022
    I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.

What are some alternatives?

When comparing PI202202-alako-data and feature-engineering-tutorials you can also consider the following projects:

ydata-quality - Data Quality assessment with one line of code

intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists

gastrodon - Visualize RDF data in Jupyter with Pandas

dtreeviz - A python library for decision tree visualization and model interpretation.

jupyter-notebook-chatcompletion - Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks!

PRML - PRML algorithms implemented in Python

sizedwaitgroup - SizedWaitGroup has the same role and close to the same API as the Golang sync.WaitGroup but it adds a limit on the amount of goroutines started concurrently.

ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

MachineLearningNotebooks - Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft

PyImpetus - PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features

jupytemplate - Templates for jupyter notebooks

reinforcement_learning_course_materials - Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University