Titanic Survival Prediction Model

Project Overview

This repository contains a machine learning model designed to predict passenger survival outcomes from the Titanic disaster. The model utilizes the XGBoost classification algorithm with optimized hyperparameters to achieve high prediction accuracy.

Dataset

The analysis is based on the famous Titanic dataset from Kaggle, which includes passenger information such as:

• Age
• Sex
• Passenger class
• Fare

Methodology

The notebook implements a comprehensive machine learning workflow:

1. Data Exploration and Visualization

   • Correlation analysis between numerical features
   • Examination of survival rates by gender

2. Data Preprocessing

   • Removal of less relevant features (Embarked, Cabin, Ticket, etc.)
   • Encoding categorical variables
   • Handling missing values through mean imputation
   • Class imbalance correction using SMOTE (Synthetic Minority Over-sampling Technique)

3. Model Development

   • Implementation of XGBoost classifier
   • Hyperparameter optimization through GridSearchCV
   • Model evaluation using accuracy metrics

4. Prediction Generation

   • Application of the trained model to the test dataset
   • Creation of submission file for Kaggle competition

Results

The model achieves strong predictive performance through careful feature selection and parameter tuning. Hyperparameter optimization significantly improved the model's accuracy compared to the baseline implementation.