This project aims to predict lottery numbers using various machine learning techniques, specifically Long Short-Term Memory (LSTM) networks. The code scrapes historical lottery data, processes it, and trains a machine learning model to predict future lottery numbers. Table of Contents
Installation
Usage
Features
Model Details
Results
Contributing
License
Installation Prerequisites
Ensure you have Python 3.7+ installed. You will also need the following Python libraries:
BeautifulSoup
requests
numpy
pandas
matplotlib
scikit-learn
tensorflow
You can install the required libraries using pip:
bash
pip install beautifulsoup4 requests numpy pandas matplotlib scikit-learn tensorflow
Usage
Clone this repository:
bash
git clone https://github.com/ProfesseurFalken/Loto_Ai_Prediction.git cd loto-prediction-ai
Run the script:
bash
python main.py
The script will scrape the latest lottery data, process it, train the LSTM model, and output the predicted numbers. Features
Web Scraping: Scrapes historical lottery data from the web.
Feature Engineering: Creates additional features from the raw lottery data to enhance model performance.
LSTM Model: Uses an LSTM network to predict the next set of lottery numbers.
Model Checkpointing: Saves the best model during training.
Prediction: Outputs predicted lottery numbers.
Model Details Data Preprocessing
Scrapes data from loto.akroweb.fr.
Processes the data to include various features such as frequency, mean, median, standard deviation, range, sum of numbers, odd/even ratio, and pair/impair indicators.
LSTM Network
Layers: Two LSTM layers with dropout regularization.
Optimizer: Adam.
Loss Function: Mean Absolute Error (MAE).
Early Stopping: Stops training when the validation loss does not improve for 200 epochs.
Checkpointing: Saves the best model based on validation loss.
Training
Splits the data into training and validation sets.
Uses a batch size of 30 and trains for a maximum of 1000 epochs.
Prediction
Uses the trained model to predict the next set of lottery numbers.
Inverse transforms the scaled predictions to original values.
Results
The model predicts the next set of lottery numbers based on historical data. The prediction includes five main numbers and two luck numbers. Contributing
Contributions are welcome! Please open an issue or submit a pull request. License
This project is licensed under the MIT License. See the LICENSE file for details.
Contact : [email protected]
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