https://github.com/sayakpaul/pima_indian_diabetes

https://github.com/sayakpaul/pima_indian_diabetes

data-analysis data-science data-visualization machine-learning numpy pandas python scikit-learn

Last synced: 7 days ago
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• Host: GitHub
• URL: https://github.com/sayakpaul/pima_indian_diabetes
• Owner: sayakpaul
• Created: 2017-02-27T10:41:32.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-01-13T14:16:38.000Z (almost 7 years ago)
• Last Synced: 2024-12-15T12:07:52.264Z (20 days ago)
• Topics: data-analysis, data-science, data-visualization, machine-learning, numpy, pandas, python, scikit-learn
• Language: Python
• Size: 10.7 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Pima_Indian_Diabetes

The dataset details are available here: 

https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.names

What I did is pretty much straight forward. I took some well-known classifiers such as Logistic Regression, Support Vector Machines, 

kNN etc and compared and analyzed their performances on this dataset. Before that I standardized the dataset using our very own 

StandardScaler(). As with the classifiers I applied SGD (Stochastic Gradient Descent) with Logistic Regression and SVM for 

optimized training. I also applied PCA for Dimensionality Reduction. 

Among all of them Logistic Regression coupled with PCA seemed to be the best one.