This repository contains code for a machine learning model built for the Kaggle Titanic competition. The goal of the competition is to predict which passengers survived the Titanic shipwreck based on various features such as age, sex, ticket class, etc.
The dataset used for training the model is provided by Kaggle and contains information about Titanic passengers. It consists of two CSV files: train.csv (for training) and test.csv (for testing). These files contain features such as PassengerId, Pclass, Sex, Age, SibSp, Parch, Fare, Cabin, Embarked, etc.
The machine learning model is implemented using Python and popular libraries such as pandas, scikit-learn, and XGBoost. Various preprocessing techniques and feature engineering methods have been applied to the data before training the model. The model is then trained on the training dataset and evaluated using cross-validation techniques.
The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The final predictions are submitted to the Kaggle competition to assess the model's performance on the test set.