/Diabetes-Detection

Primary LanguageJupyter Notebook

Diabetes-Detection

Objective

To build a predictive model which classifies whether a person is diabetic or not based on the parameters

  1. Pregnancies
  2. Glucose
  3. Blood Pressure
  4. Skin Thickness
  5. Insulin
  6. BMI
  7. Diabetes pedigree function
  8. Age

Libraries Used:

  1. Numpy (for linear-algebra)
  2. Pandas (for data manipulation)
  3. Scikit-learn (for data modeling)

Steps Involve:

  • Importing Dataset
  • Analyse the Dataset
  • Splitting up of data
  • Applying ML algorithm
  • Evalutation of model

Applying ML Algorithm

Classification is the process of predicting the class of given data points. classification predictive modeling is the task of approximating a mapping function (f) from input variables (x) to discrete output variables (y).

Linear Classifiers:

  • logistic regression
  • Nearest neighbor
  • Random forest