The Pima are a group of Native Americans living in Arizona. A genetic predisposition allowed this group to survive normally to a diet poor of carbohydrates for years. In the recent years, because of a sudden shift from traditional agricultural crops to processed foods, together with a decline in physical activity, made them develop the highest prevalence of type 2 diabetes and for this reason they have been subject of many studies.
The dataset includes data from 768 women with 8 characteristics, in particular:
- Number of times pregnant
- Plasma glucose concentration a 2 hours in an oral glucose tolerance test
- Diastolic blood pressure (mm Hg)
- Triceps skin fold thickness (mm)
- 2-Hour serum insulin (mu U/ml)
- Body mass index (weight in kg/(height in m)^2)
- Diabetes pedigree function
- Age (years)
The last column of the dataset indicates if the person is affected (1) by diabetes or not (0).
The original dataset is available at UCI Machine Learning Repository and can be downloaded from this address: http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes
The type of dataset and problem is a classic supervised binary classification. Given a number of elements all with certain characteristics (features), we want to build a machine learning model to identify people affected by type 2 diabetes.
To solve the problem we will have to analyse the data, do any required transformation and normalisation, apply a machine learning algorithm, train a model, check the performance of the trained model and iterate with other algorithms until we find the most performant for our type of dataset.
For the presentation part I've used RISE, which is being installed by requirements.txt but it has to be configured. You can find all the instructions on the original website: https://damianavila.github.io/RISE/index.html