https://github.com/allanotieno254/epl_match_outcome_predictor
A machine learning project predicting English Premier League (EPL) match outcomes (win, lose, or draw) based on match statistics, using a Random Forest classifier. Includes an interactive Python script for users to input match details and obtain predicted outcomes.
https://github.com/allanotieno254/epl_match_outcome_predictor
exploratory-data-analysis ipynb-jupyter-notebook modeling python visualization
Last synced: 7 months ago
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A machine learning project predicting English Premier League (EPL) match outcomes (win, lose, or draw) based on match statistics, using a Random Forest classifier. Includes an interactive Python script for users to input match details and obtain predicted outcomes.
- Host: GitHub
- URL: https://github.com/allanotieno254/epl_match_outcome_predictor
- Owner: AllanOtieno254
- Created: 2024-11-13T13:24:25.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-11-13T13:33:05.000Z (11 months ago)
- Last Synced: 2025-01-14T01:44:36.887Z (9 months ago)
- Topics: exploratory-data-analysis, ipynb-jupyter-notebook, modeling, python, visualization
- Language: Jupyter Notebook
- Homepage: https://colab.research.google.com/drive/1gfJhEjKHJiB_p9Y5vLoOgkWRLeUm_V3F?authuser=0#scrollTo=8TfzCl_uNlx7
- Size: 529 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# EPL Match Outcome Predictor
A machine learning-based project for predicting the outcomes of English Premier League (EPL) matches. Using match statistics such as shots, possession, fouls, yellow/red cards, and corners, this project predicts whether the home team will win, lose, or draw. The project is built with a Random Forest classifier and includes a command-line interface for prediction.
## Features
- Predicts EPL match outcomes based on match statistics
- Includes interactive input for team details and match statistics
- Uses a pre-trained Random Forest model for predictions
- Scaled data with StandardScaler for consistent input/output values
## Technologies Used
- Python
- Scikit-Learn (RandomForestClassifier, StandardScaler)
- Pandas
- Joblib for model saving/loading
## Getting Started
### Prerequisites
1. Install Python 3.x
2. Install necessary libraries:
```bash
pip install pandas scikit-learn joblib
```
### Repository Structure
The project is organized as follows:
```plaintext
epl_match_outcome_predictor/
├── data/
│ ├── epl_dataset.csv # Training dataset for EPL matches
├── models/
│ ├── best_rf_model.pkl # Pre-trained Random Forest model
│ ├── scaler.pkl # Scaler for input data
├── src/
│ ├── predict_match.py # Main script for predicting match outcomes
│ ├── train_model.py # Script to train the Random Forest model
│ ├── utils.py # Utility functions for data processing and feature engineering
├── tests/
│ ├── test_predict_match.py # Unit tests for the prediction functionality
│ ├── test_train_model.py # Unit tests for model training functions
├── README.md # Project README file
├── requirements.txt # List of dependencies for the project