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Exercise

Cleaning and Formatting your Data

Before you can begin constructing your trees you need to get your hands dirty and clean the data so that you can use all the features available to you. In the first chapter, we saw that the Age variable had some missing value. Missingness is a whole subject with and in itself, but we will use a simple imputation technique where we substitute each missing value with the median of the all present values.

train["Age"] = train["Age"].fillna(train["Age"].median())

Another problem is that the Sex and Embarked variables are categorical but in a non-numeric format. Thus, we will need to assign each class a unique integer so that Python can handle the information. Embarked also has some missing values which you should impute witht the most common class of embarkation, which is "S".

Instructions

100 XP
  • Assign the integer 1 to all females
  • Impute missing values in Embarked with class S. Use .fillna() method.
  • Replace each class of Embarked with a uniques integer. 0 for S, 1 for C, and 2 for Q.
  • Print the Sex and Embarked columns