🌊 SafeVoyage AI: Smart Survival Predictor for Maritime Emergencies

Created by Harsh Gahlawat
Powered by Random Forest & Real-World Data

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SafeVoyage AI is a machine learning-based decision support tool designed to assist in real-time survivability analysis during maritime emergencies such as cruise ship evacuations or ferry accidents.
Leveraging historical passenger data and a Random Forest Classifier, the tool can estimate the likelihood of survival based on key personal and situational attributes.

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📌 Overview

• 📁 Dataset: Simulated maritime passenger manifest (tested.csv)
• 🤖 Model: Random Forest Classifier
• 🌐 Interface: Gradio-based Web UI
• 🛠 Tech Stack: Python, Pandas, Scikit-learn, Gradio

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🚀 Features

✅ Cleaned and preprocessed passenger data
✅ Encoded categorical variables for ML compatibility.
✅ Trained and saved a robust Random Forest model. ✅ Gradio web interface for real-time prediction
✅ Modular, maintainable code structure (training + UI)
✅ Real-life applicability for marine rescue strategy planning

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🧠 Model Insights

• Algorithm: Random Forest Classifier.
• Target Variable: Survived (0 = No, 1 = Yes)
• Input Features:
  • Pclass — Passenger Class (1st, 2nd, 3rd)
  • Sex — Gender
  • Age — Passenger Age
  • SibSp — Siblings/Spouses Aboard
  • Parch — Parents/Children Aboard
  • Fare — Ticket Fare
  • Embarked — Port of Embarkation
    (C = Cherbourg, Q = Queenstown, S = Southampton)

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🌍 Real-World Relevance

While based on Titanic-era data, this project demonstrates how AI can enhance disaster response systems. Emergency responders and maritime authorities could use similar models to:

• Optimize rescue strategies
• Simulate emergency scenarios
• Understand demographic vulnerabilities in disasters

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📎 Tags

Machine Learning Random Forest Disaster Prediction Gradio Python Maritime Safety

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⚓ “Because every second counts in a crisis. SafeVoyage AI helps make smarter, faster decisions when it matters most.”