https://github.com/melling/ml-kaggle-titanic
Kaggle Titanic Competition Notebooks
https://github.com/melling/ml-kaggle-titanic
Last synced: 12 days ago
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Kaggle Titanic Competition Notebooks
- Host: GitHub
- URL: https://github.com/melling/ml-kaggle-titanic
- Owner: melling
- Created: 2022-06-28T14:53:02.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-10-31T01:17:38.000Z (over 3 years ago)
- Last Synced: 2025-02-23T01:35:41.836Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 8.15 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Learn to Solve a Binary Classification Problem with the Kaggle Titanic Competition
## Notebooks
- [Quickstart](titanic-quickstart.ipynb)
- Analyze
- EDA
- [Biased Outliers EDA](titanic-eda01-biased-outliers.ipynb)
- [Logistic Regression](logistic-regression-series.ipynb)
- [Decision Tree](titanic-decision-tree.ipynb)
- GridSearch
- Naive Bayes
- [Support Vector Machines (SVM)](titanic-svm.ipynb)
- [XGBoost](titanic-xgboost.ipynb)
- [Voting Classifier](titanic-votingclassifier.ipynb)
- [XGBoost + Optuna](titanic-xgboost-optuna.ipynb)
- [XGBoost+LightGBM+Catboost Blend](titanic-xgb-lgbm-cat-blend.ipynb)
- Stacking Classifier
## Topics
- Logistic Regression
- LDA/QDA
- Decision Trees
- Random Forests
- Support Vector Machines (SVM)
- Ensemble Learning
- Hyperparameter Optimization
- Feature Engineering
- Feature Importance
- Pseudo Labling
- https://www.kaggle.com/code/cdeotte/pseudo-labeling-qda-0-969