Filling Missing Age and Embarked Data for Titanic Analysis

Now we'll address the missing age values in our dataset using mean imputation. While this approach isn't without limitations—roughly 20% of our age data will be generic averages—it provides a pragmatic solution that enables us to proceed with analysis while maintaining data integrity for the complete records.

Our strategy involves calculating gender-specific mean ages, recognizing that age distributions often vary significantly between demographic groups. This targeted approach yields more accurate imputations than using a single dataset-wide average.

First, we'll create a filtered DataFrame containing only female passengers. This subset allows us to calculate a representative mean age for women specifically. We extract the Titanic data where the sex column equals 'female', creating a focused view of our female passenger population.

Next, we isolate the age column from our women's DataFrame to create a numeric series. With this series in hand, we calculate the mean and round it to one decimal place for practical application. Our analysis reveals that the mean age for women passengers is 27.9 years—a figure that will serve as our imputation value for missing female ages.

Following the same methodology for male passengers, we replicate this process with appropriate variable adjustments. The calculated mean age for men is 30.7 years, slightly higher than their female counterparts, which validates our decision to use gender-specific imputation rather than a blanket average.

With our target values established, we'll implement the imputation using pandas' apply function. This requires defining a custom function—let's call it `fill_mean_age_by_gender`—that processes each passenger record individually.

Our function employs conditional logic to handle three scenarios efficiently. First, if a passenger already has an age value (using the double-negative check `not pd.isna(passenger['Age'])`), we preserve the original data. For missing values, we apply gender-specific imputation: male passengers receive the mean male age (30.7), while female passengers receive the mean female age (27.9).

The implementation involves applying this function across our entire DataFrame using the `axis='columns'` parameter, which ensures row-wise processing. This approach maintains data relationships while systematically addressing missing values.

To validate our imputation, we perform several verification checks. First, we confirm that no NA values remain in the age column—a critical quality assurance step. Then we examine specific subsets, such as male passengers with the mean age value, to verify correct application. Our validation reveals 124 male passengers received the imputed age value, confirming successful execution.

Moving forward, we'll address the two missing values in the 'embarked' column using mode imputation. Since 'S' represents the most frequent embarkation point in our dataset, we'll use this value for the missing entries. This decision reflects standard practice in data preprocessing where mode imputation is appropriate for categorical variables with clear dominant categories.

The implementation is straightforward: `titanic_data['embarked'].fillna('S')` replaces the missing values efficiently. A final verification check confirms that only cabin data remains incomplete—a common limitation in historical datasets that we'll accept rather than attempt problematic imputation.

With our data preprocessing complete, we're now positioned to conduct meaningful feature analysis. The next phase involves evaluating which variables demonstrate sufficient predictive power to warrant inclusion in our machine learning model—a crucial step that will determine our model's effectiveness and interpretability.