This is a practice on the competition on Kaggle, and I refer to the material from this repo
This competition provides three csv files(train.csv, test.csv, gender_submission.csv(sample))
The Titanic passengers data (train.csv & test.csv) tell us:
| Attribute | Definition |
|---|---|
| Age | Age in years |
| Cabin | Cabin number |
| Embarked | Port of Embarkation(C = Cherbourg, Q = Queenstown, S = Southampton) |
| Fare | Passenger fare |
| Name | |
| Parch | # of parents / children aboard the Titanic |
| PassengerId | |
| Pclass | Ticket class(1 = 1st, 2 = 2nd, 3 = 3rd) |
| Sex | Gender |
| SibSp | # of siblings / spouses aboard the Titanic |
| Survived | Survival(0 = No, 1 = Yes) |
| Ticket | Ticket number |
We need to give a prediction data (submission.csv) that includes:
| PassengerID | Survived |
|---|---|
| # | 0 or 1 |
Features: Feature engineering, Supervised Learning.
Reference: [資料分析&機器學習] 第4.1講 鐵達尼號災難生存預測