Welcome to the Titanic Survival Prediction Project Portfolio Website! This website is a detailed showcase of my machine learning journey with the Titanic dataset from Kaggle. Through this project, I've explored various machine learning models and techniques to predict the survival of passengers aboard the Titanic.
The project consists of several HTML pages, each dedicated to a different aspect of machine learning:
- Home Page (
index.html): An introduction to the project with links to all sections. - Neural Networks (
Neural_Network.html): Delving into deep learning techniques, this section covers the architecture, data processing, training, and evaluation of neural network models. - Gradient Boosted Trees (
Gradient_Boosted.html): This page explores gradient boosting, a powerful ensemble technique for improving prediction accuracy. - Random Forests (
Random_Forest.html): A look at Random Forests, discussing their mechanics, advantages, implementation, and performance in the context of the Titanic dataset.
- Comprehensive Analysis: Each model is thoroughly analyzed, with insights into its workings, advantages, and implementation specifics.
- Interactive Design: The website is built for easy navigation, allowing users to explore different machine learning concepts and models seamlessly.
- Visualization: Visual aids and figures are used throughout to enhance understanding and engagement.
To explore the project, start with the Home Page and navigate through the different sections using the provided links.
For a more in-depth view and access to the Jupyter notebooks used in this project, visit the GitHub Repository.
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