aviator-ai-predictor

Aviator AI Predictor

Project Overview

This project presents an AI model designed to predict outcomes in the Aviator game, focusing on forecasting multiplier values. The repository includes the model implementation, training scripts, data generation utilities, and a comprehensive set of visual evaluations to demonstrate its performance and trustworthiness. The goal is to provide an open-source, transparent, and well-documented solution for understanding and potentially predicting Aviator game dynamics.

Visual Assets

Aviator AI Model Trust Evaluation

Aviator Prediction Pipeline Architecture

Model Performance: Actual Multipliers vs. Prediction Confidence

Model Training Convergence

Prediction Accuracy across Multiplier Thresholds

Model Architecture

The core of the prediction system is an LSTM (Long Short-Term Memory) neural network, a type of recurrent neural network well-suited for sequence prediction tasks. The model processes historical multiplier data to learn patterns and predict the probability of the next multiplier reaching a certain threshold.

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://huggingface.co/your-username/aviator-ai-predictor
   cd aviator-ai-predictor
   

2. Install dependencies:

   pip install -r requirements.txt
   

Usage

Data Generation

To generate synthetic data for training and evaluation:

python generate_aviator_data.py

This will create an aviator_data.csv file in the project directory.

Model Training

To train the Aviator AI model:

python train_aviator_model.py

This script will train the LSTM model and save the trained weights to aviator_model.pth.

Prediction

To use the trained model for predictions:

import torch
from aviator_predictor import AviatorLSTM, predict_next_multiplier

# Load the model
model = AviatorLSTM()
model.load_state_dict(torch.load("aviator_model.pth"))

# Example sequence of past multipliers
sample_sequence = [1.2, 2.5, 1.1, 3.4, 1.5]

# Make a prediction for the next round with a threshold of 1.5x
will_reach, confidence = predict_next_multiplier(model, sample_sequence, threshold=1.5)

print(f"Prediction: {"High Probability" if will_reach else "Low Probability"} (Confidence: {confidence:.2f}%) ")

Performance Metrics and Evaluation

The visual assets provided in the assets/ folder offer a detailed overview of the model's performance, stability, and trust scores. Key metrics include:

• Live Accuracy: Measures the model's accuracy on real-time data streams.
• Data Integrity: Assesses the quality and consistency of the input data.
• Model Stability: Evaluates the model's consistency in predictions over time.
• Fairness Check: Ensures the model does not exhibit biases.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Citation

If you use this model or any part of this project in your research or work, please cite it as:

@misc{aviator_ai_predictor_2026,
    author = {Manus AI},
    title = {Aviator AI Predictor: An Open-Source AI Model for Aviator Game Forecasting},
    year = {2026},
    publisher = {Hugging Face},
    journal = {Hugging Face Hub},
    howpublished = {\url{https://huggingface.co/your-username/aviator-ai-predictor}}
}

Acknowledgments

Special thanks to the open-source community for providing invaluable tools and resources that made this project possible.