- feature-engineering-tutorials VS ydata-quality
- feature-engineering-tutorials VS gastrodon
- feature-engineering-tutorials VS intro-to-python
- feature-engineering-tutorials VS jupyter-notebook-chatcompletion
- feature-engineering-tutorials VS dtreeviz
- feature-engineering-tutorials VS PRML
- feature-engineering-tutorials VS FeatureHub
- feature-engineering-tutorials VS code
- feature-engineering-tutorials VS Desbordante
- feature-engineering-tutorials VS MachineLearningNotebooks
Feature-engineering-tutorials Alternatives
Similar projects and alternatives to feature-engineering-tutorials
-
ydata-quality
Data Quality assessment with one line of code
-
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
intro-to-python
[READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists
-
jupyter-notebook-chatcompletion
Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks!
-
dtreeviz
A python library for decision tree visualization and model interpretation.
-
-
FeatureHub
The most comprehensive library of AI/ML features across multiple domains. Our goal is to create a dataset that serves as a valuable resource for researchers and data scientists worldwide (by FeatureHub-AI)
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
code
Compilation of R and Python programming codes on the Data Professor YouTube channel. (by dataprofessor)
-
Desbordante
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
-
RasgoQL
Write python locally, execute SQL in your data warehouse
-
MachineLearningNotebooks
Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
feature-engineering-tutorials reviews and mentions
-
How to balance multiple time series data?
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.
Stats
rasgointelligence/feature-engineering-tutorials is an open source project licensed under GNU Affero General Public License v3.0 which is an OSI approved license.
The primary programming language of feature-engineering-tutorials is Jupyter Notebook.