💧 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)

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

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