This repository contains the code and files for my submission to the Kaggle Titanic Disaster Challenge. In this challenge, the goal was to predict whether passengers on the Titanic survived based on various features such as passenger class, age, gender, and more.
My submission achieved a Kaggle score of 0.74401, and this README provides an overview of the project, the approach I took, and the files included in this repository.
Data Loading: I used the provided training and test datasets (train.csv and test.csv) to train and evaluate my model.
Missing Data: I handled missing data by imputing missing values in features like 'Age'and 'Fare'.
Categorical Encoding: I converted categorical features like 'Sex' into numerical values.
train.csv: The training dataset used to train the model.
test.csv: The test dataset used to make predictions for submission.
submission.csv: The CSV file containing the predictions for the test dataset.
Titanic_prediction.ipynb: Jupyter Notebook containing the code, data preprocessing, model building, and evaluation.
README.md: This README file provides an overview of the project.
My submission achieved a Kaggle score of 0.74401, representing my model's accuracy in predicting whether passengers survived the Titanic disaster.