Metadata-Version: 2.2
Name: combined_bukmacherska
Version: 0.9.0
Summary: Tools for analyzing sports statistics and using machine learning to assist in betting strategies.
Home-page: https://github.com/your-repo-url
Author: Twoje Imię
Author-email: your.email@example.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: scikit-learn
Requires-Dist: xgboost
Requires-Dist: catboost
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Combined Bukmacherska

**Combined Bukmacherska** is a comprehensive project that provides tools for analyzing sports statistics and using machine learning to assist in betting strategies. The package offers utilities for training machine learning models, statistical analysis, and data visualization.

## Features

- **Train Machine Learning Models**: A suite of classifiers, including Random Forest, Gradient Boosting, SVM, and more.
- **Statistical Analysis**: Analyze team performance metrics like average goals scored/conceded.
- **Mathematical Utilities**: Tools for Gamma distribution, Beta distribution, and Poisson probabilities.
- **Visualizations**: Generate line, bar, and 3D plots for data analysis.

## Installation

Clone the repository and install dependencies:

```bash
git clone <repository-url>
cd combined_bukmacherska
pip install -r requirements.txt

Usage Examples
Train Machine Learning Models

from combined_bukmacherska.train_models import train_models, predict_with_models

# Example data
X_train, X_test, y_train, y_test = ...  # Replace with your dataset
models = train_models(X_train, y_train)
predictions = predict_with_models(models, X_test)

from combined_bukmacherska.statistics import analiza_statystyczna, oblicz_statystyki_druzyny

druzyna1 = {'zdobyte': 30, 'stracone': 20}
druzyna2 = {'zdobyte': 25, 'stracone': 15}
mecze = 10

statystyki1, statystyki2 = analiza_statystyczna(druzyna1, druzyna2, mecze)

from combined_bukmacherska.visualizations import rysuj_wykresy

rysuj_wykresy(statystyki1['Ĺ›rednia zdobytych'], statystyki1['Ĺ›rednia straconych'], 
              statystyki2['Ĺ›rednia zdobytych'], statystyki2['Ĺ›rednia straconych'])


git clone https://github.com/your-repository/combined_bukmacherska2.git

from combined_bukmacherska2 import train_models

# Example usage
X_train, y_train = ...  # Your training data
models = train_models(X_train, y_train)

from combined_bukmacherska2 import predict_with_models

# Example usage
X_test = ...  # Your test data
predictions = predict_with_models(models, X_test)

from combined_bukmacherska2 import plot_results

# Example usage
plot_results(predictions, team1_lambda, team2_lambda, team1_avg_conceded, team2_avg_conceded)
