The diabetes dataset is a binary classification problem where it needs to be analysed whether a patient is suffering from the disease or not on the basis of many available features in the dataset. Different methods and procedures of cleaning the data, feature extraction, feature engineering and algorithms to predict the onset of diabetes are used based for diagnostic measure on Pima Indians Diabetes Dataset.
Before clicking anything please read this!
We implemented 4 methods to predict whether a person has or will develop diabetes or not given some medical data about that person.
These are 4 models implemented by two people currently. Decision Trees and Random Forest by Emre Hakan Erdemir; Logistic Regression and Multilayer Perceptron by Ömer Faruk Aydın.
You can see the implementation and interpretation of the first two in the notebook called 'Emre Hakan Erdemir'.
You can see the implementation and interpretation of the latter two in the notebook called 'Omer Faruk Aydin'.
Before running the project, please install the requirements by executing the following command:
pip install -r requirements.txtClick on the notebook person's notebook you wish to see and select run all. The files that don't end with the extension .ipynb are the codes necessary to run the notebooks. Do not try to start them!