Analyzing a dataset that shows the information of patients of a hospital and shows whether the patient arrived at their booked appointment or not. The goal of this project is to build a Machine learning model that can predict whether the patient with the given info will arrive or not.
In this Project I will analyze, wrangle and clean the dataset of patients of a hospital that shows data about the patient and whether the patient arrived at their scheduled appointment or not. After making the data ready for processing I will be testing various model candidates and will be choosing the best model that can predict the arrival. After getting the golden model I will export the model into a PKL file to be shown on a UI web page using Flask.
This data set is from Kaggle.com
● PatientId: Identification of a patient
● AppointmentID: Identification of each appointment
● Gender: Male or Female
● DataMarcacaoConsulta: The day of the actual appointment, when they have to visit the doctor
● DataAgendamento: The day someone called or registered the appointment
● Age: How old is the patient
● Neighbourhood: Where the appointment takes place
● Scholarship: True or False, indicates if the patient is in the Bolsa Familia program
● Hipertension: True or False
● Diabetes: True or False
● Alcoholism: True or False
● Handcap: handicap level of severeness (5 levels)
● SMS_received: 1 or more messages sent to the patient
● No-show "No" indicates if the patient showed up to their appointment and "Yes'' if they didn't show up