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missed-appointments-analysis

Introduction

Dataset Description: This dataset collects information from 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are included in each row.

  • PatientId: indicates the patient ID; duplication is possible due to cases where the same patient booked more than one appointment.
  • AppointmentID: indicates appoint ID, this field should be unique
  • Gender: indicates the patient's gender (M/F)
  • ScheduledDay: indicates the Date/Time the patient set up their appointment.
  • AppointmentDay: indicates the date/time the patient called to book their appointment.
  • Age: indicates the patient's age.
  • Neighborhood: indicates the location of the hospital.
  • Scholarship: indicates whether or not the patient is enrolled in Brasilian welfare program Bolsa Família.
  • Hipertension: indicates whether or not the patient is experiencing Hypertension.
  • Diabetes: indicates whether or not the patient is experiencing Diabetes.
  • Alcoholism: indicates whether or not the patient is experiencing Alcoholism.
  • Handcap: indicates whether or not the patient is with special needs.
  • SMS_received: indicates whether or not the patient has received a reminder text message.
  • Show-up: ‘No’ if the patient showed up to their appointment, and ‘Yes’ if they did not show up.

pairplot

pairplot

About

Analyzed 110K+ medical appointments to identify key no-show factors using Python (Pandas, Seaborn); engineered features, cleaned data, and visualized trends to generate actionable insights for healthcare decision-making.

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