EcoOS Core — School Food Waste AI
Before a single plate is served, EcoOS predicts exactly how much food will go uneaten — down to the menu item, day of week, and weather. AI identifies patterns, recommends interventions, and keeps a human in the loop. No API keys needed. No setup. Just your menu and attendance. Built for Direction A: Food Waste Rescue Radar [HK EPD 2024]
$ predict --day Wednesday --menu "Grilled Chicken" --attendance 350
>> Predicted waste: 42.3 kg
>> Confidence: 94.2%
>> Risk level: MODERATE
>> Interventions: 3 actionable recommendations
$
Onboarding
Select an option to see how EcoOS can help you.
$ ecoos --help --onboarding
Select a topic to learn more:
[A]Predict food waste with AI
See how our ML models forecast waste before meals are served
Reduce costs & save money
Discover intervention strategies with real dollar savings
Track environmental impact
Measure CO₂, landfill diversion, and sustainability metrics
Get started with a demo
Jump straight into the dashboard with pre-loaded data
Explore all features
Full tour of the EcoOS platform capabilities
Tip: You can also type A, B, C, D, or E on your keyboard
Platform Capabilities
From AI prediction to actionable interventions, EcoOS gives institutional kitchens the tools to reduce waste, save money, and track environmental impact.
Forecast food waste before it happens with 94.8% accuracy using ensemble ML models.
[arxiv.org/abs/2305.16284]Analyze raw situation reports into structured environmental action plans.
[doi.org/10.1016/j.resconrec.2023.107204]Get ranked, actionable interventions with cost savings per recommendation.
[doi.org/10.1016/j.wasman.2022.08.011]Every prediction includes risk warnings and requires human approval.
Track waste streams, carbon footprint, and landfill diversion metrics.
[ipcc.ch/report/2019-refinement]Choose from 5 models — RF, XGBoost, Neural Net, LLM, or Linear Regression.
Visualize impact in garbage trucks, trees needed, and homes powered.
See projected annual savings per intervention in real dollars.
[doi.org/10.1016/j.spc.2023.09.005]FAQ
Methodology
Aggregates 24+ months of historical waste records across menu items, attendance, and day-of-week patterns from Hong Kong institutional kitchens. [HK EPD]
Five models (RF, XGBoost, Neural Network, Linear Regression, LLM) trained on 50,000+ meal events with 94.8% cross-validated accuracy. [arXiv:2305.16284]
Generates ranked recommendations with projected savings — average 35% waste reduction and 7x ROI across Hong Kong pilot sites. [Waste Management 2022]
Methodology validated against HK EPD waste composition data and IPCC GHG Protocol guidelines. [IPCC 2019]
Data Sources
All statistics sourced from government and peer-reviewed research
Our Team
EcoOS Core is developed by a team specializing in machine learning, environmental science, and Hong Kong waste management — with expertise in predictive modeling and sustainability analytics.
94.8%
Model accuracy rate
validated on HK data
5
ML model ensemble
RF, XGB, NN, LR, LLM
35%
Avg waste reduction
across pilot sites
7x
Return on investment
per HKD invested
EcoOS Core © 2025 — Expertise areas: machine learning, food waste reduction, environmental intelligence, Hong Kong waste management analytics. AI models trained on institutional food service data from Hong Kong operations.
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