repository for dash summerschool

Data preprocessing is an integral step in the application of artificial intelligence. The ability of a machine-learning model to learn from the data depends on the quality of the data, the characteristics of the data and the amount of information it captures. Sometimes the way data is stored in files or databases is not in the right format for a particular task. Therefore, it is extremely important that we explore and preprocess our data, before feeding it into our model.

In machine-learning applications a significant amount of time is spent on data preparation: loading, exploring, cleaning, transformation and rearranging. Such tasks are often reported to take 80% or more on the analyst's time [1]. Fortunately can use program languages such as Python and libraries like pandas and seaborn to help us to explore, clean, improve the quality and manipulate the data in the right format.

This repository contains several demonstration notebooks and notebooks to practise your skills.

• Data_collection_and_ preparation.ipynb # demonstration notebook
• 01_heart_failure.ipynb # simple level assignment
• 02_brain_data_casus.ipynb # intermediate level assingment
• 03_glucose_level_casus.ipynb # intermediate level assingment
• 04_covid_casus.ipynb # challenging assingment

Data is to be found in the data directory or referenced in the notebooks

Fenna Feenstra [email protected]