🕷️️ Job Radar • SCRAPING

freelancer.com 🟡 2026-05-15

🔹 Lottery Prediction & Betting Automation System
👤 Client: 🇪🇸 Getxo, Spain Member since 2026-02-10
💰 Price: $1387 Average bid
🚩 Problem: Automate the collection of historical lottery data, generate predictions using advanced AI models, and execute bets while ensuring security and compliance.
📦 Existing: Not specified

Specifications:

[Target] Historical Data Collection: [Method] Automated scraping from official sources. [UI/UX] N/A. [Stack] Python (BeautifulSoup/Scrapy), PostgreSQL/MongoDB. [Security] Secure user authentication, no credential storage. [Format] Structured data.
[Target] Number Prediction: [Method] Machine Learning ensembles, Deep Learning RNN/LSTM, Neuro-Symbolic AI, Evolutionary Algorithms, Bayesian Networks. [UI/UX] Web interface for predictions and betting. [Stack] Python (Scikit-learn, TensorFlow), Matplotlib/Plotly. [Security] Random delays, CAPTCHA handling. [Format] JSON.
[Target] Bet Cost Calculation: [Method] Auto-calculation based on user inputs. [UI/UX] Web interface for cost and EV simulations. [Stack] Python (NumPy). [Security] Secure masked fields for credentials. [Format] HTML.
[Target] Login and Bet Execution Automation: [Method] Selenium automation with headless browsers. [UI/UX] N/A. [Stack] Python (Selenium), PostgreSQL/MongoDB. [Security] No credential storage, user confirmation required. [Format] JSON.
[Target] Simulations and Reports: [Method] Historical backtesting, post-draw reports. [UI/UX] Web interface for simulations and reports. [Stack] Python (Matplotlib/Plotly), Flask/Django. [Security] Secure session handling. [Format] HTML/JSON.
[Target] User Interface: [Method] Web-based interfaces for predictions, betting, and reporting. [UI/UX] Intuitive dashboards with heatmaps. [Stack] React.js, Flask/Django. [Security] Secure user authentication. [Format] JSON.

Workflow:

1. Set up data collection scripts using BeautifulSoup or Scrapy to scrape historical lottery data from official sources.
2. Store scraped data in PostgreSQL or MongoDB for structured and flexible storage.
3. Train AI models (ML ensembles, RNN/LSTM, Neuro-Symbolic AI) on the full history of lottery draws.
4. Implement bet cost calculation based on user inputs and current jackpots.
5. Develop login process using Selenium with headless browsers for secure automation.
6. Create betting interface to allow users to place bets and view predictions.
7. Set up backtesting scripts to simulate bets from the first historical draw.
8. Generate reports on performance, ROI, and patterns via LSTM/Bayesian models.