This is the 1xbet Fishing Game Pattern Recognizer tool, developed using Python and the Tkinter library. This tool provides an easy-to-use graphical interface for analyzing patterns in CSV data, allowing users to detect frequently occurring patterns in the selected columns.
This application is not financial advice or betting advice. The predictions generated by this application are for informational and educational purposes only. Betting involves risk, and you should never bet more than you can afford to lose. Use this tool responsibly and at your own risk.
I initially created this tool for personal use while playing a fishing game. However, I've decided to share it with the community to receive feedback and enhance its features in future updates. Please note, this tool is strictly for educational purposes and is not intended to promote gambling or any related activities.
- CSV File Handling: Easily upload CSV files and analyze the data.
- Pattern Detection: Identify patterns of different lengths within your data.
- Customizable Settings: Choose columns, define pattern lengths, and set sorting preferences.
- Automatic Reload: Automatically reload data at regular intervals to keep your analysis up-to-date.
- Modern User Interface: Designed with a responsive, modern interface using
ttkbootstrap
.
Creating a pattern recognizer is not overly complex; however, I have accumulated a dataset of over 10,000 real historical values specifically for this tool. The dataset is updated regularly to ensure its relevance and accuracy.
I cannot share the data-gathering tool publicly due to ethical reasons, but rest assured that all data is collected responsibly and is intended purely for educational use.
To get started with the 1xbet Fishing Game Pattern Recognizer:
-
Clone this repository:
git clone https://github.com/dinethlive/fishing-game-patterns-recognizer.git
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Install the required dependencies:
pip install -r requirements.txt
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Run the application:
python pattern_recognizer.py
Your feedback is vital to improving this tool! If you have any suggestions, spot a bug, or want to contribute to this project, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.