Using a regression or another simple model to predict the values of missing variables

This is the approach that we will use for the Age feature of the Titanic example. The Age feature is an important step towards predicting the survival of passengers, and applying the previous approach by taking the mean will make us lose some information.

In order to predict the missing values, you need to use a supervised learning algorithm that takes the available features as input and the available values of the feature that you want to predict for its missing value as output. In the following code snippet, we are using the random forest classifier to predict the missing values of the Age feature:

# Define a helper function that can use RandomForestClassifier for handling the missing values of the age variable
def set_missing_ages():
    global df_titanic_data

    age_data = df_titanic_data[
        ['Age', 'Embarked', 'Fare', 'Parch', 'SibSp', 'Title_id', 'Pclass', 'Names', 'CabinLetter']]
    input_values_RF = age_data.loc[(df_titanic_data.Age.notnull())].values[:, 1::]
    target_values_RF = age_data.loc[(df_titanic_data.Age.notnull())].values[:, 0]

    # Creating an object from the random forest regression function of sklearn<use the documentation for more details>
    regressor = RandomForestRegressor(n_estimators=2000, n_jobs=-1)

    # building the model based on the input values and target values above
    regressor.fit(input_values_RF, target_values_RF)

    # using the trained model to predict the missing values
    predicted_ages = regressor.predict(age_data.loc[(df_titanic_data.Age.isnull())].values[:, 1::])

    # Filling the predicted ages in the original titanic dataframe
    age_data.loc[(age_data.Age.isnull()), 'Age'] = predicted_ages