Update README.md

README.md

@@ -14,4 +14,74 @@ 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 **HydraSense**.  
+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("HydraSense/waterpotabler-v1", "water_potability_pipeline.pkl")
+pipeline = 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 = pipeline.predict(sample_df)
+
+print("Prediction:", "💧 Potable" if prediction[0] == 1 else "⚠️ Not Potable")
+```
+
+# Developed With ❤️ By DarkNeuronAI