Model	CV Accuracy	CV F1	CV AUC	Test Accuracy	Test F1	Test AUC
LogisticRegression	0.8020	0.7402	0.8590	0.8045	0.7368	0.8594
RandomForest	0.8231	0.7569	0.8826	0.7877	0.7077	0.8473
GradientBoosting 🏆	0.8104	0.7450	0.8913	0.8101	0.7463	0.8422
XGBoost	0.8146	0.7475	0.8892	0.8045	0.7328	0.8428

Features Used

Engineered features from the raw Titanic dataset:

Passenger class (Pclass)
Age (imputed by title median)
Family features: SibSp, Parch, FamilySize, IsAlone
Fare & FarePerPerson
Cabin info: HasCabin, Deck
Title (extracted from Name: Mr, Mrs, Miss, Master, Rare)
Interactions: Age × Class
Embarked port
Age bins: Child, Teen, Adult, Middle, Senior

Usage

python

import joblib
import numpy as np

model = joblib.load("model.joblib")
scaler = joblib.load("scaler.joblib")
label_encoders = joblib.load("label_encoders.joblib")

# See config.json for feature_columns ordering

Dataset

Trained on phihung/titanic (891 passengers, 80/20 train/test split, stratified).

Hyperion912/titanic-survival-predictor

Titanic Survival Prediction Model

Best Model: GradientBoosting

All Models Compared

Features Used

Usage

Dataset

Hyperion912/
titanic-survival-predictor