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---
license: mit
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
- confusion_matrix
pipeline_tag: tabular-classification
library_name: sklearn
tags:
- Water Potability
- Random Forest
- Standard Scaler
---
# 💧 HydraSense - Water Potability Classifier Model (v1.0)
A lightweight **Random Forest + StandardScaler** based water potability prediction model developed by **DarkNeuronAI**.
It classifies water as **Potable (1)** or **Not Potable (0)** based on chemical and physical features — ideal for simple tabular classification tasks.
---
## 🚀 Features
- Fast and efficient — runs easily on standard laptops
- Trained with real-world water quality datasets
- Predicts potability from features like **pH, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes, Turbidity**
- Uses a **pipeline** to automatically scale and preprocess input data
- Easy to use and integrate
---
## 🚀 Model Overview
- **Algorithm:** Random Forest Classifier
- **Preprocessing:** StandardScaler (automatic feature scaling)
- **Goal:** Predict whether water is safe to drink (Potable) or unsafe (Not Potable)
- **Performance:** Accurate classification on real-world datasets
---
## 🧩 Files Included
- `water_potability_model.pkl` → Trained Random Forest pipeline (scaler + model)
- `example_usage.py` → Example code to use the model
- `requirements.txt` → Dependencies list
---
## 🏷️ Prediction Labels (Binary)
- **0:** Not Potable (Unsafe to drink)
- **1:** Potable (Safe to drink)
---
## 💡 How to Use (Example Code)
```python
from huggingface_hub import hf_hub_download
import joblib
import pandas as pd
# Download and load the trained pipeline
pipeline_path = hf_hub_download("DarkNeuron-AI/darkneuron-hydrasense-v1", "water_potability_model.pkl")
model = joblib.load(pipeline_path)
# Example water sample
sample_data = {
'ph': [7.2],
'Hardness': [180],
'Solids': [15000],
'Chloramines': [8.3],
'Sulfate': [350],
'Conductivity': [450],
'Organic_carbon': [10],
'Trihalomethanes': [70],
'Turbidity': [3]
}
sample_df = pd.DataFrame(sample_data)
# Predict potability
prediction = model.predict(sample_df)
print("Prediction:", "💧 Potable" if prediction[0] == 1 else "⚠️ Not Potable")
```
# Developed With ❤️ By DarkNeuronAI |