/kaggle-spaceship-titanic

Kaggle's data science problem to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly.

Primary LanguageJupyter Notebook

Kaggle's Spaceship Titanic

This repo contains notebooks I used in Kaggle's Spaceship Titanic: Predict which passengers are transported to an alternate dimension.

Dataset Description

In this competition your task is to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly. To help you make these predictions, you're given a set of personal records recovered from the ship's damaged computer system.

File and Data Field Descriptions

================================

  • train.csv - Personal records for about two-thirds (~8700) of the passengers, to be used as training data.
    • PassengerId - A unique Id for each passenger. Each Id takes the form gggg_pp where gggg indicates a group the passenger is travelling with and pp is their number within the group. People in a group are often family members, but not always.
    • HomePlanet - The planet the passenger departed from, typically their planet of permanent residence.
    • CryoSleep - Indicates whether the passenger elected to be put into suspended animation for the duration of the voyage. Passengers in cryosleep are confined to their cabins.
    • Cabin - The cabin number where the passenger is staying. Takes the form deck/num/side, where side can be either P for Port or S for Starboard.
    • Destination - The planet the passenger will be debarking to.
    • Age - The age of the passenger.
    • VIP - Whether the passenger has paid for special VIP service during the voyage.
    • RoomService, FoodCourt, ShoppingMall, Spa, VRDeck - Amount the passenger has billed at each of the Spaceship Titanic's many luxury amenities.
    • Name - The first and last names of the passenger.
    • Transported - Whether the passenger was transported to another dimension. This is the target, the column you are trying to predict.
  • test.csv - Personal records for the remaining one-third (~4300) of the passengers, to be used as test data. Your task is to predict the value of Transported for the passengers in this set.
  • sample_submission.csv - A submission file in the correct format.
    • PassengerId - Id for each passenger in the test set.
    • Transported - The target. For each passenger, predict either True or False.