https://github.com/gregoritsch3/ml_eda_classification_diabetes

An EDA and Machine Learning Classification exercise on the Diabetes dataset demonstrating the use of SQLAlchemy data import from an SQL database (PostgreSQL), Pre-processing Pipelines, ANOVA, 9 ScikitLearn ML models, Hyperparamter Tuning for the best performing one, and feature importance.
https://github.com/gregoritsch3/ml_eda_classification_diabetes

anova machine-learning matplotlib numpy pandas pipelines scikit-learn seaborn sql sqlalchemy statistics

Last synced: 21 days ago
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An EDA and Machine Learning Classification exercise on the Diabetes dataset demonstrating the use of SQLAlchemy data import from an SQL database (PostgreSQL), Pre-processing Pipelines, ANOVA, 9 ScikitLearn ML models, Hyperparamter Tuning for the best performing one, and feature importance.

• Host: GitHub
• URL: https://github.com/gregoritsch3/ml_eda_classification_diabetes
• Owner: Gregoritsch3
• Created: 2024-12-17T14:20:20.000Z (30 days ago)
• Default Branch: main
• Last Pushed: 2024-12-22T09:07:18.000Z (25 days ago)
• Last Synced: 2024-12-22T09:32:40.209Z (25 days ago)
• Topics: anova, machine-learning, matplotlib, numpy, pandas, pipelines, scikit-learn, seaborn, sql, sqlalchemy, statistics
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/mathchi/diabetes-data-set
• Size: 2.83 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ML_EDA_Classification_Diabetes

An EDA and Machine Learning Classification exercise on the Diabetes dataset demonstrating the use of SQLAlchemy data import from an SQL database (PostgreSQL), Pre-processing Pipelines, ANOVA, 9 ScikitLearn ML models,hyperparamter tuning for the best performing one, and feature importance. The .ipynb file contains comments that describe the problem-solving methodology.