Model Type: XGBoost Classifier
Task: Binary Classification (Survived vs Died)
Dataset: lewtun/titanic (Kaggle Titanic dataset)
Features: Pclass, Sex, Age, SibSp, Parch, Fare, Embarked, FamilySize, IsAlone, Title, AgeBin, FareBin

Performance Metrics (Validation Set)

Metric	Value
Accuracy	0.8156
F1 Score	0.7481
Precision	0.7903
Recall	0.7101

Feature Importance

The most important features for prediction are:

Sex (26.1%) - Gender is the strongest predictor
Title (20.2%) - Social status from name
Pclass (11.1%) - Passenger class
SibSp (8.6%) - Siblings/spouses aboard

How to Use

python

import pickle

with open("titanic_xgb_model.pkl", "rb") as f:
    data = pickle.load(f)
    model = data["model"]
    le_sex = data["le_sex"]
    le_embarked = data["le_embarked"]
    le_title = data["le_title"]

# See inference.py for full prediction function

Limitations

Model trained on historical Titanic data only
May not generalize to other maritime disasters
Missing values are imputed with median/mode values

Citation

Dataset: https://huggingface.co/datasets/lewtun/titanic

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muhammadmaaz724/titanic-survival-predictor

Titanic Survival Predictor

Model Details

Performance Metrics (Validation Set)

Feature Importance

How to Use

Limitations

Citation

Generated by ML Intern

muhammadmaaz724/
titanic-survival-predictor