Update README.md

README.md

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----
 title: Insulin Dependency Predictor
-emoji: 📊
-colorFrom: gray
-colorTo: yellow
+emoji: 💉
+colorFrom: indigo
+colorTo: green
 sdk: gradio
 sdk_version: 5.30.0
 app_file: app.py
-pinned: false
-short_description: Predict diabetes patients' Insulin dependency (Experimental)
----
+pinned: true
+short_description: Predict insulin dependency in diabetic patients using clinical data.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Insulin Dependency Predictor
+
+This interactive tool predicts whether a diabetic patient is likely to require **insulin therapy** based on their clinical information. The prediction model is trained using a **Random Forest classifier**, and the interface is built with **Gradio**.
+
+# Features
+
+- Input patient data (e.g., age, BMI, HbA1c, glucose levels)
+- Real-time prediction of insulin dependency (Yes / No)
+- Built using Scikit-learn and hosted on Hugging Face Spaces
+- Experimental integration with ensemble models (LightGBM, XGBoost)
+
+# Usage
+
+Enter the patient details and click **Submit**. The model will return a prediction for insulin dependency.
+
+# Model
+
+The model was trained on a real-world diabetes dataset with features such as:
+
+- Age, Gender, Height, Weight, BMI  
+- Fasting Blood Sugar (FBS), Postprandial Blood Sugar (PPBS)  
+- HbA1c, Smoking and Alcohol use, Diabetes duration  
+
+Model type: RandomForestClassifier  
+Saved as: `random_forest_model.pkl`
+
+# Future Work
+
+- Deploying ensemble models (Random Forest + LightGBM)
+- More feature engineering and model optimization
+- Clinical testing and validation
+
+# Disclaimer
+
+This is an **experimental tool** and should not be used for medical diagnosis. Always consult a licensed healthcare provider for medical advice.