This repository contains my solution for the Kaggle competition Spaceship Titanic. The goal of the project is to predict which passengers aboard the Spaceship Titanic were affected by a spacetime anomaly and transported to an alternate dimension. This project has been recommended based on my participation in Titanic - Machine Learning from Disaster.
- sample_submission.csv : A sample submission file provided by Kaggle to demonstrate the expected format for submissions.
- my_analysis.ipynb : Jupyter notebook containing in-depth analysis and exploration of the Spaceship dataset. This notebook covers data exploration, visualizations, and any insights gained from the analysis.
- predictions.csv : The file containing the predictions generated by the machine learning model on the test dataset.
- test.csv : The test dataset for the competition, used for evaluating the machine learning model.
- train.csv : The training dataset for the competition, used for building and training the machine learning model.
The primary analysis is documented in the my_analysis.ipynb notebook. Explore this notebook for detailed insights into the dataset, visualizations, and any preprocessing steps taken.
The machine learning model's predictions on the test dataset can be found in the predictions.csv file.
The machine learning model achieved an accuracy of 80.476% on the test set, showcasing its effectiveness in predicting outcomes. Further details about model evaluation metrics.
Kaggle for providing the dataset and hosting the competition.
This project is licensed under the MIT LICENSE.