Kaggle Titanic: Improving Survival Prediction with Random Forest Age Imputation

Kaggle Practice 1: Setting Up a Local Environment for the Kaggle Titanic Competition
Kaggle Practice 2: First Submission
Kaggle Practice 3: Feature Engineering for Cabin
Kaggle Practice 4: Feature Engineering (Imputing Age with Random Forest)

https://www.kaggle.com/c/titanic

Available on GitHub

Abstract

• Changed age imputation from median values by title to predictive imputation using RandomForestRegressor.
• The best 5-Fold CV (Cross-Validation) score improved from 0.8507 to 0.8519 (Logistic Regression).
• Kaggle Public Score increased from 0.78708 to 0.78947.
• Validation code is committed to GitHub: titanic_eda_20260702_2031_age_imputation.ipynb.

Overview

In the Kaggle Titanic: Machine Learning from Disaster competition, passenger Age is a critical factor for predicting survival.
Previously, we filled missing values with the median age of each passenger title (Mr, Miss, Mrs, Master, Rare). This time, we tried a more advanced approach: predicting the missing ages using a machine learning model (RandomForestRegressor) based on other features (Pclass, Sex, SibSp, Parch, Fare, Embarked, Deck).

Implementation

Here is the preprocessing code for the imputation. We trained RandomForestRegressor on passengers with known ages and predicted the missing values.

from sklearn.ensemble import RandomForestRegressor

# Features used for predicting Age
age_features = ['Pclass', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Title', 'Deck', 'FamilySize', 'IsAlone', 'Age']
df_age_prep = df_all[age_features].copy()

# One-Hot Encoding for categorical features
cat_cols_for_age = ['Sex', 'Embarked', 'Title', 'Deck']
df_age_encoded = pd.get_dummies(df_age_prep, columns=cat_cols_for_age, drop_first=True)

# Split into known and unknown age datasets
train_age = df_age_encoded[df_age_encoded['Age'].notnull()]
test_age = df_age_encoded[df_age_encoded['Age'].isnull()]

X_train_age = train_age.drop(columns=['Age'])
y_train_age = train_age['Age']
X_test_age = test_age.drop(columns=['Age'])

# Train regressor and predict missing age
age_regressor = RandomForestRegressor(n_estimators=100, random_state=42)
age_regressor.fit(X_train_age, y_train_age)
predicted_ages = age_regressor.predict(X_test_age)

# Impute missing values in the original dataframe
df_all.loc[df_all['Age'].isnull(), 'Age'] = predicted_ages

Validation Results

Comparison of 5-Fold CV (Cross-Validation) accuracy across different models:

Model	Before (Median by Title)	After (Random Forest Imputation)	Difference
Logistic Regression	0.8507 +/- 0.0104	0.8519 +/- 0.0115	+0.0012
Random Forest	0.8204 +/- 0.0193	0.8249 +/- 0.0348	+0.0045
XGBoost	0.8215 +/- 0.0241	0.8226 +/- 0.0244	+0.0011
LightGBM	0.8496 +/- 0.0211	0.8485 +/- 0.0147	-0.0011

Logistic Regression achieved our personal best 5-Fold CV accuracy of 0.8519.
We also observed accuracy improvements in tree-based models like Random Forest and XGBoost.

Kaggle Submission Score

We predicted the test dataset using the updated Logistic Regression model and submitted it to Kaggle.
Our Public Score successfully improved from 0.78708 to 0.78947!
It is encouraging to see that the local CV score improvement translated directly to the Kaggle Public Score.

Summary & Next Steps

By estimating passenger age from other relevant features instead of using simple median values, the model could learn a more realistic passenger representation.
For our next attempt, we will target hyperparameter tuning (using Optuna) and model ensembling to achieve further improvements.

Hope this helps!