Titanic is a very basic and beginner competition in Kaggle. It is also the best, first challenge for you to dive into ML competitions and familiarize yourself with how the Kaggle platform works.
There are many tutorials on implementing ML techniques to solve this problem. I would like to explain the whole process how I build the model from the very scratch. I will focus on how I choose the features for the ML model by visualisation and basic calculation as well as introducing multiple algorithms which can be applied in this problem.
The Jupyter notebook illustrates the step-by-step explanation of building ML model for Titanic problem. It also includes a sample of the simple ML model (Random Forest).
For more algorithms/ models, please have a look on the folder models and use main.py file.
Enjoy!!!
on going project