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Build your own Diabetes Predictor in 5 Minutes!

Updated: Dec 5, 2020

Use packages: Sweetviz for EDA and TPOT for finding the best model in Python



Machine learning has provided significant breakthroughs in diverse areas in recent years. Areas like healthcare, finance, retail, transportation, etc have been using machine learning in various ways and they are getting benefitted when it comes to either targeting the right customer, predicting fraudulent transactions, optimizing inventories, forecasting future sales, etc.


What is AutoML

Automated machine learning is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model.

There are various popular AutoML frameworks and few of them could be illustrated below-

  1. ML Box

  2. Auto Sklearn

  3. TPOT

  4. H2O Automl

  5. Auto Keras

  6. Google Cloud auto ML

  7. TransmogrifAI

All these frameworks enable the data scientist to prepare a roadmap for the data science project and help them in developing a quick and basic model that would also provide a brief overview of the regressors/classifiers impacting the target variable.

In similar ways to that of AutoML, we are also seeing the arrival of a barrage of auto visualization packages in python like Pandas profiling, Sweetviz, etc which generate graphs and charts though not up to the standards of Tableau and Powerbi but good enough for the data scientists to do initial insight generation once they receive data.

Through this post, we would pick up a classification problem and try to build a quick baseline model using SweetViz and TPOT packages in python.

The data set could be found on Kaggle. The datasets consist of several medical predictor (independent) variables and one target (dependent) variable, Outcome( Diabetic/Non-diabetic). Independent variables include the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

The whole EDA report is generated with 2 lines of code.

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