This dataset contains patient information from 100k + medical appointments in Brazil. The data contains a number of possible datapoints that may indicate whether a patient will show up for an appointment or not. We will use the "CRISP-DM" approach to analyze this dataset.
The orignal dataset can be found here
- Python versions 3.0 and above
- unicodecsv
- datetime
- pandas
- numpy
- matplotlib
- seaborn
This project is an implementation of the "CRISP-DM" approach to create a Github post to present my skill in Analyzing a dataset as part of my Udacity's Data Scientist Nanodegree.
The main question that we wanted to answer and find was, what are the reasons that will result in the patient not showing. Is it because of the waiting time, scholarship, their locations, type of disease, or age.
There are two files, one is the dataset itself located inside the Dataset folder, and the notebook itself with two formats ".html" and ".ipynb". The notebook contains all the work.
The main findings of the code can be found here
Credit goes to Keras for providing the data.