Projects

Titanic Survival Prediction using Scikit with PyQT5 GUI

As a Software Engineer, I've mostly dealt with Web Applications. But recently, I felt the pull of uncharted territory: machine learning. Intrigued by the possibilities, I decided to embark on a project that combined my existing skills with this exciting new field. This minor hobby project aimed to predict the survival of passengers aboard the Titanic.


Why this topic?


The Titanic dataset is one of the most popular datasets available on Kaggle. It is often the first project of many ML enthusiasts try their hands on similar to "Hello World". The dataset is clean and clear with minimal modifications required in terms of missing values.


Why PyQt5?


I chose PyQt5 to create a user-friendly interface for my project. PyQt5 allowed me to build a graphical interface where users could input passenger details and receive a survival prediction. This not only enhanced the user experience but also provided a visual representation of the model's output.

Learnings from this project


I learned the basics of data cleaning, feature engineering, feature selection, model training and much more from my first elementary ML project

Completing this project gave me a sense of accomplishment and a newfound appreciation for the power of machine learning. It allowed me to merge my desktop app development skills with ML to create innovative applications.



Started on Jan. 24, 2025, 3:56 a.m.
Updated on Jan. 24, 2025, 4:01 a.m.
Status:
Tags: Python

Project Link : https://github.com/Apfirebolt/titanic_survival_prediction

Technologies Used : Pythion, Scikit-Learn, pyQt5


Images:
Gui1
Prediction result