Pima-DiabetesPrediction

This project explores Pima Dataset to classify Diabetes.

Using Pima-Data to understand a ML Workflow

This project can be used as a template to understand and apply Machine Learning models on real world problems.

The project uses Skilearn, numpy, pandas as supporting libraries.

The process of Machine Learning Consist of -

• Collecting the Data.
• Cleaning the Features, reducing the strongly corrilated columns.
• Understanding the behavior of the features (Curves and boundaries)
• Imputing
• Partitioning the Data into Train and Test
• Selecting the ML algorithm
• Training the Model.
• Evaluating the Performance.
• Refining the Model.
• Trying more complex algorithm.
• Cross Validation.
• Understanding Where to Stop.!!

All the above steps are applied and used in the example. Please click on the Pima-Predictions.ipynb to see the flow.