# Cloud Faculty Institute Workshop — Data

来源：https://www.kaggle.com/competitions/cloud-faculty-institute-workshop/data

采集时间：2026-10-20T10:12:19Z

## Context

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

## Content

The datasets consists of several medical predictor variables and one target variable, `Outcome`. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.  Specifically, the training dataset has columns Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, and Outcome.  The testing dataset has these same columns but does not contain the column 'Outcome'.  This is the column that you predict yourself!

## Acknowledgements

Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988). [Using the ADAP learning algorithm to forecast the onset of diabetes mellitus][1]. *In Proceedings of the Symposium on Computer Applications and Medical Care* (pp. 261--265). IEEE Computer Society Press.

## Inspiration

Can you build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?

  [1]: http://rexa.info/paper/04587c10a7c92baa01948f71f2513d5928fe8e81
File descriptions
train.csv
- the training set
test.csv
- the test set
sample_submission.csv
- a sample submission file in the correct format