Encoding categorical columns I: LabelEncoder

Now that you've seen what will need to be done to get the housing data ready for XGBoost, let's go through the process step-by-step.

First, you will need to fill in missing values - as you saw previously, the column LotFrontage has many missing values. Then, you will need to encode any categorical columns in the dataset using one-hot encoding so that they are encoded numerically. You can watch this video from Supervised Learning with scikit-learn for a refresher on the idea.

The data has five categorical columns: MSZoning, PavedDrive, Neighborhood, BldgType, and HouseStyle. Scikit-learn has a LabelEncoder function that converts the values in each categorical column into integers. You'll practice using this here.

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Extreme Gradient Boosting with XGBoost

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Instrucciones del ejercicio

Import LabelEncoder from sklearn.preprocessing.
Fill in missing values in the LotFrontage column with 0 using .fillna().
Create a boolean mask for categorical columns. You can do this by checking for whether df.dtypes equals object.
Create a LabelEncoder object. You can do this in the same way you instantiate any scikit-learn estimator.
Encode all of the categorical columns into integers using LabelEncoder(). To do this, use the .fit_transform() method of le in the provided lambda function.

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# Import LabelEncoder
____

# Fill missing values with 0
df.LotFrontage = ____

# Create a boolean mask for categorical columns
categorical_mask = (____ == ____)

# Get list of categorical column names
categorical_columns = df.columns[categorical_mask].tolist()

# Print the head of the categorical columns
print(df[categorical_columns].head())

# Create LabelEncoder object: le
le = ____

# Apply LabelEncoder to categorical columns
df[categorical_columns] = df[categorical_columns].apply(lambda x: ____(x))

# Print the head of the LabelEncoded categorical columns
print(df[categorical_columns].head())

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