lifetimes
statsmodels
| lifetimes | statsmodels | |
|---|---|---|
| 1 | 8 | |
| 1,433 | 9,557 | |
| - | 1.0% | |
| 0.0 | 9.4 | |
| over 1 year ago | 5 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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.
lifetimes
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Customer lifetime value
I've you haven't come across it, I recommend this straightforward library as a starting point https://github.com/CamDavidsonPilon/lifetimes
statsmodels
- statsmodels Release Candidate 0.14.0rc0 tagged
- How to generate Errors using Scipy Minimize with Powell Method
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[P] statsmodels.tsa.holtwinters.ExponentialSmoothing results in NaN forecasts and parameters when fitting on entire dataset using known parameters from training model.
I reckon you're more likely to get a good response on their Github page than here. Unless a dev happens to see this post.
- Statsmodels 0.13.3 released with Python 3.11 support
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First Year UG here, can someone offer any coding advice?
The method they use for computing the parameter covariance (in the code here, around line 330) involves some linear algebra, as they use the Moore-Penrose pseudo-inverse of the outputs.
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How do you usually build your models?
Since you are using python, pandas, scikit-learn, scipy, and statsmodels are what you are looking for
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Advice required to choose appropriate software for an assignment
Can't you get a student discount for Stata? R would definitely be able to handle everything. For Python, have a look through the statsmodel package https://github.com/statsmodels/statsmodels
- [C] I have an MS in Statistics - how can I get better at coding?
What are some alternatives?
popmon - Monitor the stability of a Pandas or Spark dataframe ⚙︎
SciPy - SciPy library main repository
scikit-learn - scikit-learn: machine learning in Python
Numba - NumPy aware dynamic Python compiler using LLVM
PyMC - Bayesian Modeling and Probabilistic Programming in Python
Dask - Parallel computing with task scheduling
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
SymPy - A computer algebra system written in pure Python
NumPy - The fundamental package for scientific computing with Python.
NetworkX - Network Analysis in Python
Biopython - Official git repository for Biopython (originally converted from CVS)