scikit-learn VS Prophet

Compare scikit-learn vs Prophet and see what are their differences.


scikit-learn: machine learning in Python (by scikit-learn)


Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. (by facebook)
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scikit-learn Prophet
80 220
57,552 17,536
0.6% 0.7%
9.8 7.7
1 day ago 22 days ago
Python Python
BSD 3-clause "New" or "Revised" License MIT License
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.


Posts with mentions or reviews of scikit-learn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-08.
  • Polars
    11 projects | | 8 Jan 2024
    sklearn is adding support through the dataframe interchange protocol ( scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.
  • [D] Major bug in Scikit-Learn's implementation of F-1 score
    2 projects | /r/MachineLearning | 8 Dec 2023
    Here is the (as of posting time) still un-merged fix to the bug in question. Note that this bug also affects sklearn.metrics.classification_report. I think you you can temporarily get around this by using sklearn version 1.2.2. Anyway, if you use Scikit-Learn's metrics for evaluation, go double check your scores!
    2 projects | /r/MachineLearning | 8 Dec 2023
    Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!
  • Ask HN: Learning new coding patterns – how to start?
    3 projects | | 10 Nov 2023
    I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.

    I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.

    You can also look into the source code of popular data science libraries such as sklearn ( and see how a lot of them have Base classes to define shared functionality between object of the same nature.

    As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?

  • How to Build and Deploy a Machine Learning model using Docker
    5 projects | | 30 Jul 2023
    Scikit-learn Documentation
  • Link Prediction With node2vec in Physics Collaboration Network
    4 projects | | 16 Jun 2023
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.
  • List of AI-Models
    14 projects | /r/GPT_do_dah | 16 May 2023
    Click to Learn more...
  • PSA: You don't need fancy stuff to do good work.
    10 projects | /r/datascience | 9 May 2023
    Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive documentation and community support, making it easy to learn and apply new techniques without needing specialized training or expensive software licenses.
  • Mastering Data Science: Top 10 GitHub Repos You Need to Know
    10 projects | | 24 Apr 2023
    1. Scikit-learn Scikit-learn is a must-know Python library for any data scientist. It offers a wide range of machine learning algorithms, data preprocessing tools, and model evaluation metrics that are easy to use and highly efficient. Whether you’re working on regression, classification, or clustering tasks, Scikit-learn has got you covered.
  • We are the developers behind pandas, currently preparing for the 2.0 release :) AMA
    9 projects | /r/Python | 1 Mar 2023
    There's an issue here about that


Posts with mentions or reviews of Prophet. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-26.

What are some alternatives?

When comparing scikit-learn and Prophet you can also consider the following projects:

tensorflow - An Open Source Machine Learning Framework for Everyone

darts - A python library for user-friendly forecasting and anomaly detection on time series.

xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

Surprise - A Python scikit for building and analyzing recommender systems

Keras - Deep Learning for humans

greykite - A flexible, intuitive and fast forecasting library

MLflow - Open source platform for the machine learning lifecycle

gensim - Topic Modelling for Humans

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

sktime - A unified framework for machine learning with time series