This repository contains my solution for the "Spaceship Titanic" Kaggle competition. The competition requires participants to predict whether passengers were transported or not on a spaceship based on various features such as age, cabin, destination, etc.
The "Spaceship Titanic" competition is a binary classification problem, where the goal is to predict whether passengers were transported on a spaceship or not. This README provides an overview of the project and its objectives.
The dataset for this competition can be found on Kaggle: Spaceship Titanic Dataset. The dataset contains information about passengers, including their age, cabin, destination, and other features.
For this competition, the Random Forest model was chosen as the classification algorithm. The model is trained using the training dataset, and the accuracy achieved during training is 80.43%.
The performance of the trained model is evaluated using various metrics, including out-of-bag (OOB) accuracy during training and cross-validation. Additionally, the model is evaluated on a validation dataset to assess its generalization capabilities.
The trained model is used to make predictions on the test dataset, and the results are saved to a CSV file for submission to the Kaggle competition.
For more detailed code and execution, you can find my Kaggle notebook here: RandomForest Accuracy 80.43%.
This project is licensed under the MIT License - see the LICENSE file for details.