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smart-env-monitor-models
satwikshreshth1/smart-env-monitor-models
smart-env-monitor-models is a machine learning model from satwikshreshth1. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
These are the trained ML models for the smart-env-monitor project — a Raspberry Pi based indoor environment monitoring system that measures temperature, humidity, and air quality (CO₂) and predicts air quality labels…
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Public
Updated Apr 11, 2026
Repo size
289 MB
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.pkl289 MB · 100%
At a glance
- Access
- Public
- Created
- Apr 11, 2026
- Updated
- Apr 11, 2026
- SHA
- d6fafa58
Model card
From the Hugging Face model README
Smart Environment Monitor — ML Models
These are the trained ML models for the smart-env-monitor project — a Raspberry Pi based indoor environment monitoring system that measures temperature, humidity, and air quality (CO₂) and predicts air quality labels in real-time.
Models
| File | Type | Purpose |
|---|---|---|
models/rf_model.pkl | Random Forest | Predicts air quality label (Good / Moderate / Poor / Hazardous) |
models/kmeans_model.pkl | K-Means (k=4) | Clusters readings into 4 groups for borderline detection |
models/isoforest_model.pkl | Isolation Forest | Detects anomalous sensor readings |
models/label_encoder.pkl | Label Encoder | Encodes/decodes air quality labels |
models/scaler.pkl | Standard Scaler | Scales features for K-Means input |
Input Features
| Feature | Description |
|---|---|
temp_c | Temperature in °C |
humidity | Relative humidity in % |
co2_ppm | CO₂ in ppm (derived from MQ135 raw ADC reading) |
Labels
| Label | Meaning |
|---|---|
| Good | Clean air |
| Moderate | Acceptable air quality |
| Poor | Ventilation recommended |
| Hazardous | Immediate action required |
K-Means Cluster Mapping
| Cluster | Label |
|---|---|
| 0 | Good |
| 1 | Poor |
| 2 | Hazardous |
| 3 | Moderate |
Usage
from huggingface_hub import hf_hub_download
import joblib
import os
REPO = "satwikshreshth1/smart-env-monitor-models"
FILES = [
"models/rf_model.pkl",
"models/kmeans_model.pkl",
"models/isoforest_model.pkl",
"models/label_encoder.pkl",
"models/scaler.pkl",
]
os.makedirs("models", exist_ok=True)
for f in FILES:
hf_hub_download(repo_id=REPO, filename=f, local_dir=".")
rf = joblib.load("models/rf_model.pkl")
scaler = joblib.load("models/scaler.pkl")
Hardware
- Microcontroller: Arduino (serial output)
- Sensors: DHT11/DHT22 (temp & humidity), MQ135 (air quality)
- Host: Raspberry Pi (runs inference)
License
MIT
- Created
- Apr 11, 2026
- Updated
- Apr 11, 2026