https://github.com/avrabyt/technble-demo
Preditcs football player's playing position
https://github.com/avrabyt/technble-demo
demo-app football-data k-nearest-neighbors-k-nn machine-learning-algorithms python streamlit
Last synced: about 2 months ago
JSON representation
Preditcs football player's playing position
- Host: GitHub
- URL: https://github.com/avrabyt/technble-demo
- Owner: avrabyt
- License: mit
- Created: 2022-09-03T08:25:38.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-09-03T21:44:01.000Z (almost 4 years ago)
- Last Synced: 2025-01-04T04:16:03.523Z (over 1 year ago)
- Topics: demo-app, football-data, k-nearest-neighbors-k-nn, machine-learning-algorithms, python, streamlit
- Language: Jupyter Notebook
- Homepage: https://avrabyt-technble-demo-demo-4t709i.streamlitapp.com
- Size: 13.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Live Master Class - Demo app
Preditcs player's playing position - Minimalistic app build on trained model with [k-Nearest Neighbors algorithm](https://www.ibm.com/topics/knn).
```yaml
Charlie Jackson:
- K-Nearest Neighbors is a clustering algorithm used to classify new data based upon its 'closeness' to other laballed data points.
- As it uses labeled data to make predictions on new data, it is a supervised learning technique.
- The features for k-Nearest Neighbor algorithm must be continous rather than categorical.
```
For example, clustering based on centroids position, figure below self-explains the idea behind, (*however, not from the current example tried here*)
> **Note**
> Try the app [here](https://avrabyt-technble-demo-demo-4t709i.streamlitapp.com/) [app is under development]
------------------------
### Resources
1. https://charlieojackson.co.uk/python/predicting-football-positions.php?fbclid=IwAR34jqESq_86XzCuVtn7E9SgN4t3nQHhQMWscpjvrthagEG0fufHOCazFjs
2. https://www.kaggle.com/code/bennyf/player-position-classification/notebook?fbclid=IwAR34d_eC313oW0rkNQL3jTb0F_Oozs7zGuVwCCWatZf-gGjsWtu4mlEltN8
### Data
- https://www.kaggle.com/datasets/stefanoleone992/fifa-22-complete-player-dataset?select=players_22.csv
- https://www.kaggle.com/datasets/stefanoleone992/fifa-20-complete-player-dataset
-----------------------------------------
> **Note**
> ### Schedule
> 