The PyBites team repo for kaggle competitions
This repository contains all work that is related to Kaggle competitions and challenges.
The idea is that we, as a team, take part in competitions and work together to come up with good solutions. That is why we will begin with the most fundamental challenges and only after a while proceed to more challenging ones.
- Create your own Kaggle user account on https://www.kaggle.com.
- Enter the current challenge. You will find the current challenge further down. To do so click on the "Join Competition" Button on the linked challenge.
- Send me your Kaggle user name and I will invite you to our team! So far I am not aware of a way where you can join our team on your side.
-
Join the Competition
Read about the challenge description, accept the Competition Rules and gain access to the competition dataset.
-
Get to Work
Download the data, build models on it locally or on Kaggle Kernels (our no-setup, customizable Jupyter Notebooks environment with free GPUs) and generate a prediction file.
-
Make a Submission
Upload your prediction as a submission on Kaggle and receive an accuracy score.
-
Check the Leaderboard
See how your model ranks against other Kagglers on our leaderboard.
-
Improve Your Score
Check out the discussion forum to find lots of tutorials and insights from other competitors.
Kaggle Link: https://www.kaggle.com/c/titanic
Repo folder: titanic
data: kaggle competitions download -c titanic
The Challenge
The sinking of the Titanic is one of the most infamous shipwrecks in history.
On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough >lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.
While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.
In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, >age, gender, socio-economic class, etc).
None so far, but we will get there!