AI Aviator Predictor bot/robot for aviator Gaming
Budget: $30 – $250 USD
Job Description:
I am looking for a skilled AI/ML developer to build a real-time AI Aviator Predictor that analyzes game data, predicts crash points, and optimizes betting strategies. The system should use machine learning, probability modeling, and real-time data processing to help users make informed betting decisions.
? The AI Predictor should work on both PC (web-based) and Android (mobile app), providing real-time insights, betting recommendations, and automated alerts.
? Budget: $60 – $80
? Deadline: Looking for a working prototype within 4-6 weeks
Project Scope & Features
✅ 1. Data Collection & Processing
Scrape or use APIs (if available) to collect real-time game data (crash multipliers, timestamps, and betting trends).
Store historical data in PostgreSQL, MongoDB, or Firebase for model training.
Implement automated data cleaning & feature engineering to identify useful patterns.
✅ 2. AI-Powered Prediction Model
Use LSTM, GRU, or XGBoost to forecast the next crash multiplier.
Train the model on historical data and optimize for accuracy.
Implement probability-based risk assessments (e.g., 80% chance of crash above 2.0x).
✅ 3. Betting Strategy Optimization
Develop a Monte Carlo Simulation to test different betting strategies.
Implement Kelly Criterion for optimal bet sizing.
Support customizable risk levels:
Conservative (safe cashout)
Moderate (medium risk-reward)
Aggressive (high risk, high reward)
✅ 4. Real-Time Prediction Engine
Build a live dashboard showing AI predictions, probability scores, and recommended cashout points.
Implement WebSockets or Kafka for real-time updates.
Allow users to enable Auto-Bet mode (optional).
✅ 5. Multi-Platform Support (PC & Android)
PC Version (Web-Based):
Built using React.js, Vue.js, or Angular.
Supports real-time betting insights & alerts.
Android App (Mobile Version):
Developed using Flutter or React Native for cross-platform compatibility.
Features push notifications for high-probability bets.
Mobile-friendly UI with real-time updates.
✅ 6. Security & Deployment
Encrypt user data and ensure secure authentication.
Host the system on AWS/GCP/Azure for scalability.
Ensure compliance with gambling regulations.
Required Skills:
✅ AI & Machine Learning: TensorFlow, PyTorch, Scikit-learn
✅ Data Processing: Pandas, NumPy, Web Scraping (BeautifulSoup, Selenium)
✅ Backend Development: Python (Flask, FastAPI) or Node.js
✅ Frontend Development: React.js, Vue.js (PC) & Flutter, React Native (Android)
✅ Database: PostgreSQL, MongoDB, Firebase
✅ Cloud & Deployment: AWS, Google Cloud, Docker, Kubernetes
Budget & Timeline:
? Budget: $60 – $80 (Fixed Price)
⏳ Timeline: Looking to have a working MVP within 4-6 weeks
NB!!! The following videos or picture is a copywrite from someone else as was interested like i can be similar like that, but not exact way.
I am looking for a skilled AI/ML developer to build a real-time AI Aviator Predictor that analyzes game data, predicts crash points, and optimizes betting strategies. The system should use machine learning, probability modeling, and real-time data processing to help users make informed betting decisions.
? The AI Predictor should work on both PC (web-based) and Android (mobile app), providing real-time insights, betting recommendations, and automated alerts.
? Budget: $60 – $80
? Deadline: Looking for a working prototype within 4-6 weeks
Project Scope & Features
✅ 1. Data Collection & Processing
Scrape or use APIs (if available) to collect real-time game data (crash multipliers, timestamps, and betting trends).
Store historical data in PostgreSQL, MongoDB, or Firebase for model training.
Implement automated data cleaning & feature engineering to identify useful patterns.
✅ 2. AI-Powered Prediction Model
Use LSTM, GRU, or XGBoost to forecast the next crash multiplier.
Train the model on historical data and optimize for accuracy.
Implement probability-based risk assessments (e.g., 80% chance of crash above 2.0x).
✅ 3. Betting Strategy Optimization
Develop a Monte Carlo Simulation to test different betting strategies.
Implement Kelly Criterion for optimal bet sizing.
Support customizable risk levels:
Conservative (safe cashout)
Moderate (medium risk-reward)
Aggressive (high risk, high reward)
✅ 4. Real-Time Prediction Engine
Build a live dashboard showing AI predictions, probability scores, and recommended cashout points.
Implement WebSockets or Kafka for real-time updates.
Allow users to enable Auto-Bet mode (optional).
✅ 5. Multi-Platform Support (PC & Android)
PC Version (Web-Based):
Built using React.js, Vue.js, or Angular.
Supports real-time betting insights & alerts.
Android App (Mobile Version):
Developed using Flutter or React Native for cross-platform compatibility.
Features push notifications for high-probability bets.
Mobile-friendly UI with real-time updates.
✅ 6. Security & Deployment
Encrypt user data and ensure secure authentication.
Host the system on AWS/GCP/Azure for scalability.
Ensure compliance with gambling regulations.
Required Skills:
✅ AI & Machine Learning: TensorFlow, PyTorch, Scikit-learn
✅ Data Processing: Pandas, NumPy, Web Scraping (BeautifulSoup, Selenium)
✅ Backend Development: Python (Flask, FastAPI) or Node.js
✅ Frontend Development: React.js, Vue.js (PC) & Flutter, React Native (Android)
✅ Database: PostgreSQL, MongoDB, Firebase
✅ Cloud & Deployment: AWS, Google Cloud, Docker, Kubernetes
Budget & Timeline:
? Budget: $60 – $80 (Fixed Price)
⏳ Timeline: Looking to have a working MVP within 4-6 weeks
NB!!! The following videos or picture is a copywrite from someone else as was interested like i can be similar like that, but not exact way.