https://github.com/razerxp/footballer-classification
Identifies footballers among a group of 5
https://github.com/razerxp/footballer-classification
classification machine-learning opencv-python sklearn svc-model
Last synced: 5 months ago
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Identifies footballers among a group of 5
- Host: GitHub
- URL: https://github.com/razerxp/footballer-classification
- Owner: RazerXP
- Created: 2024-08-03T20:04:42.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-04T15:25:34.000Z (almost 2 years ago)
- Last Synced: 2025-04-07T14:48:12.024Z (about 1 year ago)
- Topics: classification, machine-learning, opencv-python, sklearn, svc-model
- Language: Jupyter Notebook
- Homepage:
- Size: 14.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Footballer-Classification
Identifies and classifies footballers among a group of 5
## Requirements
OpenCV
pip install opencv-python
Scikit-learn
pip install scikit-learn
OpenCV Haarcascades Github xml files
https://github.com/opencv/opencv/tree/master/data/haarcascades
OpenCV to read images
OpenCV Haarcascades to identify Faces and Eyes
Uses SVC and Pipeline from Scikit-Learn library in Python
## What it does
In this data science and machine learning project, we classify 5 different Players playing for Manchester city football club,
1) Kevin De Bruyne
2) Erling Haaland
3) Phil Foden
4) Bernardo Silva
5) Rodri
## Usage
To test clone this repo and add your image(s) to the "test images" folder.
Then run 'predictor.ipynb' notebook to find out the results.
## Technologies Used
1. Python
2. Numpy and OpenCV for data cleaning
3. Matplotlib for data visualization
4. Sklearn for model building
5. Jupyter notebook, Google Collab as IDE