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+water_potability_model.pkl filter=lfs diff=lfs merge=lfs -text

README.md

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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

example_usage.py

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+import pandas as pd
+import joblib
+
+# Load the saved pipeline
+try:
+    model = joblib.load('water_potability_model.pkl')
+    print("Model loaded successfully!")
+except FileNotFoundError:
+    print("Error: The file 'water_potability_model.pkl' was not found.")
+    exit()
+except Exception as e:
+    print("An unexpected error occurred while loading the pipeline:", e)
+    exit()
+
+# Create a new water sample
+# IMPORTANT: Use the same feature order and names as in training
+sample_data = {
+    'ph': [7.2],
+    'Hardness': [180],
+    'Solids': [15000],
+    'Chloramines': [8.3],
+    'Sulfate': [350],
+    'Conductivity': [450],
+    'Organic_carbon': [10],
+    'Trihalomethanes': [70],
+    'Turbidity': [3]
+}
+
+# Convert to DataFrame with proper column names
+sample_df = pd.DataFrame(sample_data)
+
+# Make prediction
+try:
+    prediction = model.predict(sample_df)
+    result = "Potable" if prediction[0] == 1 else "Not Potable"
+    print("Sample Prediction:", result)
+except ValueError as e:
+    print("Error: Sample input has incorrect shape or type.", e)
+except Exception as e:
+    print("An unexpected error occurred during prediction:", e)

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requirements.txt

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+pandas
+scikit-learn
+joblib

water_potability_model.pkl

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