imane-ayouni / pima

Different classification algorithms to determine whether or not an individual from the Pima group will have type 2 diabetes

Pima-Indian-Diabetes

According to WHO about 422 million people worldwide have diabetes. Since diabetes affects a large population across the globe and the collection of these datasets is a continuous process and it comprises of various patient related attributes such as age, gender, symptoms, insulin levels, blood pressure, blood glucose levels, weight etc. We are working on Pima Indians Diabetes Dataset (PIDD), extracted from the University of California, Irvine (UCI) machine learning repository.

Data Discription

PIDD consists of several medical parameters and one dependent (outcome) parameter of binary values .This dataset is mainly for female gender and Description of dataset is as following 9 columns with 8 independent parameter and one outcome parameter with uniquely identified 768 observations having 268 positive for diabetes (1) and 500 negative for diabetes (0)

1. Pregnancies : Number of times pregnant
2. Glucose: Oral Glucose Tolerance Test result
3. BloodPressure: Diastolic Blood Pressure values in (mm Hg)
4. SkinThickness: Triceps skin fold thickness in (mm)
5. Insulin: 2-Hour serum insulin (mu U/ml)
6. BMI: Body mass index
7. DiabetesPedigreeFunction: Diabetes pedigree function
8. Age: Age in years
9. Outcome: Class 1 indicates person having diabetes and 0 indicates other.

Data source

https://www.kaggle.com/uciml/pima-indians-diabetes-database

Obejctives

Predict whether or not a person from the Pima tribe is likely to have Diabetes

Classification models used

• Kneighborsclassifier
• Logisticregression
• Decisiontreeclassifier
• Svc-svm xgbclassifier
• Randomforestclassifier