Product overview

Lotto Forecaster AI is an advanced AI-assisted lottery analytics and forecasting platform engineered to analyse historical draw data, identify recurring statistical and numerical patterns, evaluate forecasting models, and generate lottery-type-specific next-draw forecasts. The platform combines machine learning, historical data analysis, automated retraining, ensemble forecasting, pattern recognition, backtesting, confidence scoring, pattern-drift detection, and continuous performance evaluation. Each supported lottery type maintains its own isolated learning environment, helping prevent unrelated draw histories from contaminating another lottery type's forecasting model. The system is being developed as an intelligent, self-improving analytical platform capable of learning from every newly recorded draw and continually evaluating the effectiveness of previous forecasts.

The problem

Turning lottery-type-specific historical draw records into reproducible analysis and clearly qualified forecasts requires disciplined data ingestion, isolated datasets, retraining, traceability and responsible presentation.

The solution

Implemented a Flutter Android client and a FastAPI service backed by Supabase. The workflow stages imported CSV and OCR data before commit, keeps each lottery type in an isolated learning context, trains and evaluates forecasting models, and returns ranked candidates with structured reasoning and confidence information.

Already implemented

✓Advanced historical draw analysis
✓AI-assisted pattern recognition
✓Lottery-type-specific forecasting models
✓Candidate number scoring and ranking
✓Five-number next-draw forecasting
✓Confidence scoring
✓Forecast strength analysis
✓Machine learning model training
✓Automatic retraining after new draw data
✓Self-learning from newly recorded draws
✓Pattern drift detection
✓Ensemble forecasting

Technology stack

FlutterDartPythonFastAPISupabasePostgreSQLSupabase Edge FunctionsMachine LearningOCRDocker

My role and responsibilities

✓Designed and developed the Flutter Android client for import, preview, analysis and predictor workflows
✓Implemented FastAPI endpoints for authenticated imports, OCR, staging, commit, analysis and forecast training
✓Designed the Supabase PostgreSQL data model, row-level security boundaries and supporting Edge Functions
✓Implemented traceable model artifacts, training manifests, evaluation gates and versioned release controls
✓Built and verified the signed Android production release

Engineering challenges

✓Keeping unrelated lottery-type histories isolated from one another
✓Staging and validating imported rows before any committed-data mutation
✓Presenting probabilistic analysis without implying guaranteed outcomes
✓Keeping the public release version, build, filename and binary integrity metadata synchronized

Verifiable outcomes

Verified Android release: Version 1.0.12, build 13; The signed universal APK reports package com.lottoforecasterai.mobile, version 1.0.12 and build 13.
Release integrity: SHA-256 verified; The published 63,762,582-byte APK matches SHA-256 95b82115ff70909d3f2655d3b75b269155b7c1e3885d4995656fdae0bc0a33dd.