https://github.com/leabrodyheine/ml-kaggle-cirrhosis-data

This project showcases skills in machine learning, data preprocessing, and model evaluation using Python libraries such as scikit-learn, XGBoost, and Optuna. It involves implementing various machine learning models, handling imbalanced data, and employing imputation techniques to enhance model performance for predicting cirrhosis outcomes.
https://github.com/leabrodyheine/ml-kaggle-cirrhosis-data

data-analysis data-pre imbalanced-data imputation machine-learning optuna pipeline scikit-learn xgboost

Last synced: 4 days ago
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This project showcases skills in machine learning, data preprocessing, and model evaluation using Python libraries such as scikit-learn, XGBoost, and Optuna. It involves implementing various machine learning models, handling imbalanced data, and employing imputation techniques to enhance model performance for predicting cirrhosis outcomes.

• Host: GitHub
• URL: https://github.com/leabrodyheine/ml-kaggle-cirrhosis-data
• Owner: leabrodyheine
• Created: 2024-03-18T14:19:34.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-04-05T13:41:26.000Z (10 months ago)
• Last Synced: 2024-11-12T21:41:34.486Z (2 months ago)
• Topics: data-analysis, data-pre, imbalanced-data, imputation, machine-learning, optuna, pipeline, scikit-learn, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 12.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:

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