Spotify_Song_Recommender
feature-engineering-tutorials
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Spotify_Song_Recommender | feature-engineering-tutorials | |
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3 | 1 | |
28 | 266 | |
- | 2.3% | |
0.0 | 0.0 | |
almost 2 years ago | 22 days ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | GNU Affero General Public License v3.0 |
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Spotify_Song_Recommender
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Spotify Song Recommender that uses Data Science Modeling
You can find the github project Here. To run the code, download the notebook file (.ipynb) and load it in google colab. Once you have it loaded, there are step by step instructions in the notebook. The code is pretty easy to run and just requires some link copy and pasting, so I would so programming experience is not required.
- Spotify Song Recommender
feature-engineering-tutorials
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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.
What are some alternatives?
Machine-Learning-Specialization-Coursera - Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
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handson-ml - ⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists
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mango - Parallel Hyperparameter Tuning in Python
ydata-quality - Data Quality assessment with one line of code
StravaKudos - :running: :dart: Predicting Strava Kudos on my own activities using the given activity's attributes.
gastrodon - Visualize RDF data in Jupyter with Pandas
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
PRML - PRML algorithms implemented in Python