Collection of sports betting AI tools.

Overview

Travis Codecov CircleCI ReadTheDocs PythonVersion Pypi Conda

sports-betting

sports-betting is a collection of tools that makes it easy to create machine learning models for sports betting and evaluate their performance. It is compatible with scikit-learn.

Documentation

Installation documentation, API documentation, and examples can be found on the documentation.

Dependencies

sports-betting is tested to work under Python 3.6+. The dependencies are the following:

  • pandas(>=1.1.0)
  • rich(>=4.28)

Installation

sports-betting is currently available on the PyPi's repository and you can install it via pip:

pip install -U sports-betting

The package is released also in Anaconda Cloud platform:

conda install -c algowit sports-betting

If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:

git clone https://github.com/AlgoWit/sports-betting.git
cd sports-betting
pip install .

Or install using pip and GitHub:

pip install -U git+https://github.com/AlgoWit/sports-betting.git

Testing

After installation, you can use pytest to run the test suite:

make test
Owner
George Douzas
Physicist
George Douzas
SOTR: Segmenting Objects with Transformers [ICCV 2021]

SOTR: Segmenting Objects with Transformers [ICCV 2021] By Ruohao Guo, Dantong Niu, Liao Qu, Zhenbo Li Introduction This is the official implementation

186 Dec 20, 2022
Code for CVPR 2021 paper TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search.

TransNAS-Bench-101 This repository contains the publishable code for CVPR 2021 paper TransNAS-Bench-101: Improving Transferrability and Generalizabili

Yawen Duan 17 Nov 20, 2022
This repository contains the code for the binaural-detection model used in the publication arXiv:2111.04637

This repository contains the code for the binaural-detection model used in the publication arXiv:2111.04637 Dependencies The model depends on the foll

Jörg Encke 2 Oct 14, 2022
A Survey on Deep Learning Technique for Video Segmentation

A Survey on Deep Learning Technique for Video Segmentation A Survey on Deep Learning Technique for Video Segmentation Wenguan Wang, Tianfei Zhou, Fati

Tianfei Zhou 112 Dec 12, 2022
C3d-pytorch - Pytorch porting of C3D network, with Sports1M weights

C3D for pytorch This is a pytorch porting of the network presented in the paper Learning Spatiotemporal Features with 3D Convolutional Networks How to

Davide Abati 311 Jan 06, 2023
🔪 Elimination based Lightweight Neural Net with Pretrained Weights

ELimNet ELimNet: Eliminating Layers in a Neural Network Pretrained with Large Dataset for Downstream Task Removed top layers from pretrained Efficient

snoop2head 4 Jul 12, 2022
Neural network pruning for finding a sparse computational model for controlling a biological motor task.

MothPruning Scientific Overview Originally inspired by biological nervous systems, deep neural networks (DNNs) are powerful computational tools for mo

Olivia Thomas 0 Dec 14, 2022
A "gym" style toolkit for building lightweight Neural Architecture Search systems

A "gym" style toolkit for building lightweight Neural Architecture Search systems

Jack Turner 12 Nov 05, 2022
For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

LongScientificFormer For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training. Some code

Athar Sefid 6 Nov 02, 2022
NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.

NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.

Xintao 593 Jan 03, 2023
Diabet Feature Engineering - Predict whether people have diabetes when their characteristics are specified

Diabet Feature Engineering - Predict whether people have diabetes when their characteristics are specified

Şebnem 6 Jan 18, 2022
Attentional Focus Modulates Automatic Finger‑tapping Movements

"Attentional Focus Modulates Automatic Finger‑tapping Movements", in Scientific Reports

Xingxun Jiang 1 Dec 02, 2021
Shuwa Gesture Toolkit is a framework that detects and classifies arbitrary gestures in short videos

Shuwa Gesture Toolkit is a framework that detects and classifies arbitrary gestures in short videos

Google 89 Dec 22, 2022
a Lightweight library for sequential learning agents, including reinforcement learning

SaLinA: SaLinA - A Flexible and Simple Library for Learning Sequential Agents (including Reinforcement Learning) TL;DR salina is a lightweight library

Facebook Research 405 Dec 17, 2022
Open-sourcing the Slates Dataset for recommender systems research

FINN.no Recommender Systems Slate Dataset This repository accompany the paper "Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sa

FINN.no 48 Nov 28, 2022
optimization routines for hyperparameter tuning

Hyperopt: Distributed Hyperparameter Optimization Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which

Marc Claesen 398 Nov 09, 2022
A hue shift helper for OBS

obs-hue-shift A hue shift helper for OBS This is a repo based on the really nice script Hegemege made. The original script can be found https://gist.g

Alexis Tyler 1 Jan 10, 2022
Examples of how to create colorful, annotated equations in Latex using Tikz.

The file "eqn_annotate.tex" is the main latex file. This repository provides four examples of annotated equations: [example_prob.tex] A simple one ins

SyNeRCyS Research Lab 3.2k Jan 05, 2023
Official git for "CTAB-GAN: Effective Table Data Synthesizing"

CTAB-GAN This is the official git paper CTAB-GAN: Effective Table Data Synthesizing. The paper is published on Asian Conference on Machine Learning (A

30 Dec 26, 2022
A PyTorch implementation: "LASAFT-Net-v2: Listen, Attend and Separate by Attentively aggregating Frequency Transformation"

LASAFT-Net-v2 Listen, Attend and Separate by Attentively aggregating Frequency Transformation Woosung Choi, Yeong-Seok Jeong, Jinsung Kim, Jaehwa Chun

Woosung Choi 29 Jun 04, 2022