Titanic Survival Detector

A React web application that predicts the survival chances of Titanic passengers using a Python machine learning model. Flask is used for communication between the Python backend and the React app.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• How It Works
• Backend Details
• Contributing
• License
• Contact

Introduction

Titanic Survival Detector is a web application built with React for the frontend and a Python machine learning model for the backend. It predicts the survival chances of passengers based on their personal details.

Features

• User-friendly Interface: Simple interface to input passenger details and predict survival chances.
• Real-time Predictions: Quickly processes input to provide survival predictions.
• Machine Learning: Utilizes a trained machine learning model for accurate predictions.

Installation

Prerequisites

• Node.js
• Python 3.x
• pip (Python package installer)

Frontend Setup

1. Clone the repository:

   git clone https://github.com/LavKalsi/TitanicSurvivalDetector.git
   cd TitanicSurvivalDetector

2. Navigate to the frontend directory and install dependencies:

   cd frontend
   npm install

3. Start the React application:

   npm start

Backend Setup

1. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

2. Install the required Python packages:

   pip install -r res/requirements.txt

3. Run the backend server:

   python res/Server.py

Usage

1. Ensure both the frontend and backend servers are running.
2. Open your browser and navigate to http://localhost:3000.
3. Enter the passenger details (such as age, gender, class, etc.).
4. Click the "Predict" button to receive the survival prediction.

How It Works

The Titanic Survival Detector web app allows users to predict the survival chances of Titanic passengers. Here's how you can use it:

1. Input Details: Users can input passenger details such as age, gender, and class into the provided fields on the web app.
2. Submit for Prediction: After entering the details, users click the "Predict" button to submit the information for analysis.
3. Backend Processing: The frontend sends the passenger details to the backend Python server, where the machine learning model processes them.
4. Receive Results: The backend returns the prediction result (survival probability) to the frontend, which is then displayed to the user.

Backend Details

The backend is a Python Flask application that serves a machine learning model trained to predict the survival chances of Titanic passengers. The backend files, including the model and Flask app, are located in the res folder.

Files in res Folder

• Server.py: The Flask application that handles HTTP requests from the frontend.
• titanic_survival_prediction_model.pkl: The trained machine learning model.
• requirements.txt: The dependencies required for the Python backend.

Contributing

Contributions are welcome! Please open an issue or submit a pull request if you have any improvements or suggestions.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -am 'Add your feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

LavKalsi - GitHub

Feel free to contact me if you have any questions or suggestions!