5.1 explore realtions for survivle

1. Survived The first attribute reported if a traveler lived or died. A comparison revealed that more than 61% of the passengers had died.

code

table(as.factor(train$Survived))
prop.table(table(as.factor(train$Survived)))

2. Pclass This attribute renders the passenger division. The passengers could opt from three distinct sections, namely class-1, class-2, class-3. The third class had the highest number of commuters, followed by class-2 and class-1. The number of passengers in the third class was more than the number of passengers in the first and second class combined. The survival chances of a class-1 traveler were higher than a class-2 and class-3 traveler. 完成数据的基本探索后，在建立模型之前，我们还需要对数据进行清洗，并且对数据集中缺失的数据进行补全。

首先了解数据的缺失情况：

train.info() print(‘-’*30) test.info()

训练集中有891条数据，而测试集中有418条数据。

训练集缺失值：Age，Cabin，Embarked，其中Cabin字段缺失数量较多； 测试集缺失值：Age，Cabin，Fare，其中Cabin字段缺失数量较多。

差早错误 Analyze by pivoting attributes