• Introduction
• Data Wrangling
• Exploratory Data Analysis
• Conclusions

This projects attempts to analyse the Medical Appointment No Shows dataset.

The dataset provides information about 110,527 medical appointments and has 14 associated variables (characteristics). The No-show variable is the dependent variable. Age is a numerical variable while the others are categorical. The columns provided in the table as explained below:

1. PatientId: This is an identifier for each patient.
2. AppointmentID: This indicates the appointment identification for the patients.
3. Gender: The gender of the patients are also provided as one of either males (M) or females (F).
4. ScheduledDay: This refers to the day the patient is scheduled to their hospital.
5. AppointmentDay: Refers to the day the patient has an appointment to visit the doctor.
6. Age: This is the age of patients
7. Neighbourhood: Refers tp where the appointment takes place.
8. Scholarship: Refers to whether or not the patient is a beneficiary of Bolsa Família, government social welfare scheme. It is represented as either 1 (True) or 0 (False) on the dataset.
9. Hipertension: Column indicates whether or not a patient has Hypertension. It is represented as either 1 (True) or 0 (False) on the dataset.
10. Diabetes: Column indicates whether or not a patient has Diabetes. It is represented as either 1 (True) or 0 (False) on the dataset.
11. Alcoholism: This column indicates if a patient has an issue of alcoholism or not. It is represented as either 1 (True) or 0 (False) on the dataset.
12. Handcap: Shows if a patient is handicapped or not. It is represented as either 1 (True) or 0 (False) on the dataset.
13. SMS_received: Indicates whether or not the patient received one or more messages.
14. No-show: This column indicates whether or not patients turn up for their appointment in the hospital. It says ‘No’ if the patient showed up to their appointment, and Yes if they did not show up.

To read the complete report of the investigation of this No_Shows_Appointments dataset, please refer to the EduTechTainMent_Udacity_Data_Analyst_ND_Project_Investigate_a_Dataset_No_Show_Appointments