NBA-Predict | Predicts Daily NBA Games Using a Logistic Regression Model | Machine Learning library

by JakeKandell Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | NBA-Predict Summary

NBA-Predict is a Python library typically used in Manufacturing, Utilities, Machinery, Process, Artificial Intelligence, Machine Learning, Pandas applications. NBA-Predict has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Predicts Daily NBA Games Using a Logistic Regression Model

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Quality

Security

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Support

NBA-Predict has a low active ecosystem.

It has 104 star(s) with 43 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 15 have been closed. On average issues are closed in 40 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of NBA-Predict is current.

Quality

NBA-Predict has 0 bugs and 0 code smells.

Security

NBA-Predict has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

NBA-Predict code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

NBA-Predict does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

NBA-Predict releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

NBA-Predict saves you 150 person hours of effort in developing the same functionality from scratch.

It has 375 lines of code, 23 functions and 10 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed NBA-Predict and discovered the below as its top functions. This is intended to give you an instant insight into NBA-Predict implemented functionality, and help decide if they suit your requirements.

Creates a model for training data
Performs logistic regression
Change the working directory
Save a model
Make predictions for a given date
Predict daily games
Obtains the daily matchups present in a given date
Interpret the results of the prediction
Make predictions for the prediction set
Create a dataframe from a list of games
Creates a csv file from training set
Gets predictions from a pickle model

Get all kandi verified functions for this library.

NBA-Predict Key Features

No Key Features are available at this moment for NBA-Predict.

NBA-Predict Examples and Code Snippets

No Code Snippets are available at this moment for NBA-Predict.

Community Discussions

Trending Discussions on NBA-Predict

Python 'int' object is not iterable when using len on a list from beautifulsoup

QUESTION

Python 'int' object is not iterable when using len on a list from beautifulsoup

Asked 2020-Jan-28 at 00:40

So far I have the following code, I'm including all of it in case it helps

...

ANSWER

Answered 2020-Jan-28 at 00:29

len(list(todays_games.children)) evaluates to an integer - 5, 6, etc. You can't iterate directly over an integer with a for loop.

You can use the built-in function range to loop a set number of times if you'd need, but you should iterator directly over todays_games.children.

A big part of Python is that it simplifies iterator usage. Instead of using indices to access things like an array, (asking for the 5th element, etc) you can directly access the element with the for loop.

Source https://stackoverflow.com/questions/59940722

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install NBA-Predict

You can download it from GitHub.
You can use NBA-Predict like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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