https://github.com/meitarbach/nba-individual-predictions-lstm

NBA players individual performance perdictions using Neural Networks. Comparison between LSTM and Feed Forward Architectures. Created by Meitar Bach, Mai Elenberg and Lior Ben-Ami
https://github.com/meitarbach/nba-individual-predictions-lstm

fantasy-basketball ipynb-notebook lstm-model nba-prediction neural-networks

Last synced: about 1 month ago
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NBA players individual performance perdictions using Neural Networks. Comparison between LSTM and Feed Forward Architectures. Created by Meitar Bach, Mai Elenberg and Lior Ben-Ami

• Host: GitHub
• URL: https://github.com/meitarbach/nba-individual-predictions-lstm
• Owner: MeitarBach
• Created: 2020-04-03T10:30:12.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-04-03T10:42:52.000Z (over 4 years ago)
• Last Synced: 2024-10-11T15:44:05.478Z (about 1 month ago)
• Topics: fantasy-basketball, ipynb-notebook, lstm-model, nba-prediction, neural-networks
• Language: Jupyter Notebook
• Size: 40 KB
• Stars: 6
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Predicting NBA Players Individual Performance Using LSTM - Fantasy Basketball has never been Easier!

Fantasy Basketball is a simple game, each user plays the role of a general manager and fills out a roster by drafting actual basketball players. The measure of success in the game is based on the players’ performance in real life. As fantasy basketball enphusiasts while playing the game and going through players statistics, the idea of using this data for predicting players' performances in order to construct a winning team at the available budget arose.

In this notebook you'll find a step by step process of building training and experimenting with a LSTM Neural Network, designed to predict NBA Players Efficiency Rating (PER) scores based on previous games data.

We hope you enjoy!

Meitar Bach,

Mai Elenberg,

Lior Ben-Ami