This is a repository for implementing, describing, and testing a Single Layer Perceptron for predicting diabetes. The results are in form of a short conference paper. I achieved an accuracy of 80.58%. In the project i used 5 fold cross validation with shuffling for determining the ideal learning rate. I used the PIMA Indians dataset .The training was evaluated using evaluation metrics such as Accuracy, Execution and AUC on various learning rates. For a detailed analysis of the results, please refer to paper & code. Below are some of the results from the paper.

We use a very basic implementation of SLP(Single Layer Perceptron) in classifying if the patient is diabetic or not.

The project is implemented using a jupyter notebook, so its fairly straightforward to download it directly to retest. Notebook

The code utilizes the python packages as such

• matplotlib,
• seaborn,
• pandas,
• numpy,
• scikit-learn,
• Tensorflow.

Distributed under the MIT License. See LICENSE for more information.

Project Link:Project
Kaggle Notebook:
Collab Notebook: