Kaggle - Titanic(4)

4.4.2 Binning : Binning/Converting Numerical Age to Categorical Variable

• feature vector map:
• child: 0
• young: 1
• adult: 2
• mid-age: 3
• senior: 4

4.5 Embarked

4.5.1 filling missing values

>>more than 50% of 1st class are from S embark
>>more than 50% of 2nd class are from S embark
>>more than 50% of 3rd class are from S embark

 

fill out missing embark with S embark

4.6 Fare

4.7 Cabin

4.8 FamilySize

5. Modelling

6.2 Cross Validation (K-fold)

6.2.1 kNN

6.2.2 Decision Tree

6.2.3 Ramdom Forest

6.2.4 Naive Bayes

6.2.5 SVM

7. Testing

References

This notebook is created by learning from the following notebooks:

• Mukesh ChapagainTitanic Solution: A Beginner's Guide
• How to score 0.8134 in Titanic Kaggle Challenge
• Titanic: factors to survive
• Titanic Survivors Dataset and Data Wrangling

출처 : https://github.com/minsuk-heo/kaggle-titanic/blob/master/titanic-solution.ipynb