/Diabetes-data-analysis-machine-learning

Primary LanguageJupyter Notebook

Diabetes-data-analysis-machine-learning

Here basic data analysis and different machine-learning algorithms are applied over diabetes dataset. The dataset consists of 768 rows (patients), with 9 features. Which includes:

  • Pregnancies
  • Glucose
  • BloodPressure
  • SkinThickness
  • Insulin
  • BMI
  • DiabetesPedigreeFunction
  • Age
  • Outcome (Outcome 0 means No diabetes, 1 means diabetes)

Python Libraries

  • numpy
  • pandas
  • matplotlib
  • seaborn
  • sklearn

Machine-Learning Algorithms

  • Logistic Regression
  • k-Nearest Neighbors
  • Decision Tree
  • Random Forest
  • Gradient Boost
  • Support Vector Machine
  • Gaussian Naive Bayes
  • Neural Networks

Summary

Here the diabetes dataset analysed and different types of machine learning models for classification and regressions are applied. For the better results parameters are tuned and applied methods for reducing overfiting.