💊 Drug Relapse Prediction App

A Machine Learning-based web application that predicts the likelihood of drug relapse based on various behavioral and lifestyle inputs.

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🚀 Overview

This project uses a Deep Learning (LSTM) model to analyze user inputs and predict whether a person is at Low Risk or High Risk of Relapse.

The app is built using Streamlit for UI and TensorFlow/Keras for prediction.

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🎯 Features

• 🧠 ML-based relapse prediction
• 🎛️ Interactive UI with sliders and dropdowns
• ⚡ Real-time prediction
• 📊 Probability score output
• 💻 Runs locally using Streamlit

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📸 Screenshots

🧾 Input UI

📊 Prediction Output

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🧠 How It Works

1. User enters data
2. Data is preprocessed (encoding + scaling)
3. Input reshaped for LSTM model
4. Model predicts relapse probability
5. Result shown as:
   • ✅ Low Risk
   • ⚠️ High Risk

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🛠️ Tech Stack

• Python
• Streamlit
• TensorFlow / Keras (LSTM)
• Pandas, NumPy
• Scikit-learn

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📂 Project Structure

drug_relapseProject/ │── app.py
│── train.py
│── patient_drug_relapse_dataset.csv
│── drug_relapse_lstm_model.keras
│── scaler.pkl
│── label_encoders.pkl
│── screenshot1.png
│── screenshot2.png
│── README.md

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⚙️ How to Run

streamlit run app.py# drug_relapseProject