https://github.com/johnkou97/spaceship-titanic
https://github.com/johnkou97/spaceship-titanic
classification data-science decision-trees machine-learning python scikit-learn supervised-learning
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/johnkou97/spaceship-titanic
- Owner: johnkou97
- Created: 2023-07-04T15:25:56.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2023-07-13T21:26:09.000Z (about 3 years ago)
- Last Synced: 2025-07-11T00:20:56.688Z (about 1 year ago)
- Topics: classification, data-science, decision-trees, machine-learning, python, scikit-learn, supervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 72.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Spaceship-Titanic on Kaggle
## Introduction
This is my attempt to solve the Spaceship Titanic problem on Kaggle. The problem is to predict whether a passenger was transported to another dimension or not. The data is provided by Kaggle and can be found [here](https://www.kaggle.com/c/Spaceship-Titanic/data).
All the models that I have used are from the [scikit-learn](https://scikit-learn.org/stable/) library. For consistency, the same data preprocessing steps were used for all the models.
## Files
`DownloadData.py` - Downloads the data from Kaggle
`AdaBoost.py` - AdaBoost classifier
`Bagging.py` - Bagging classifier
`ExtraTrees.py` - ExtraTrees classifier
`GBM.py` - Gradient Boosting classifier
`KNN.py` - K-Nearest Neighbors classifier
`LogReg.py` - Logistic Regression classifier
`Naive_Bayes.py` - Naive Bayes classifier
`Neural.py` - Neural Network classifier
`Random_Forest.py` - Random Forest classifier
`Stacking.py` - Stacking classifier
`SVM.py` - Support Vector Machine classifier
`Tree.py` - Decision Tree classifier
`Voting.py` - Voting classifier
`Visualization.ipynb` - Jupyter notebook for visualization of the data
## Running the code
First, download the data from Kaggle either manually or by running `DownloadData.py`. Then, run any of the classifiers to get the predictions.
## Results
The best result was achieved by the AdaBoost followed by the Gradient Boosting classifier.
The top 5 models are as follows:
| Model | Accuracy |
| --- | --- |
| AdaBoost | 0.79541 |
| Gradient Boosting | 0.79237 |
| Bagging | 0.79191 |
| SVM | 0.79074 |
| Voting | 0.79074 |