Project: Investigate a Dataset (No-show appointments) The goal of this project is to determine if patients actually show up for their appointments and to identify the variables that can help us forecast this outcome. Introduction This Data comprises of information about 100,000+ patients across 81 Neighbouring Hospitals. The exploratory process follows a systematic order. The Aim of this analysis is to express the relationship between the dependent variable(No-Show) and the independent variables, and to establish possible factors that determines why patients show up or not show up for their scheduled appointment. Also, I tried to unravel any possible trend/traits amongst a class of patients,to see if there's is a common observation amongst these group of patient. Conclusion The Exploratory Data Analysis followed a systematic process which involved; Importing Relevant Libraries(Numpy, Pandas,Matplotlib). It then followed with loading the data using the Pandas Dataframe to have an overview of how the data looks like. The data was then studied for null values,duplicates, typographical errors in order for the data to be cleaned. After cleaning and wrangling the data, I carried out some further analysis using the describe, info, value count etc to have more insight into the data. The data was visualized using the matplotlib.pyplot function. No statistical analysis like OLS or Linear Regression was carried out.
In conclusion, it is observed that there are 27 unique variables for Appointment Day. There were peak days in the datasets which could imply that days of appointment could be a factor if a patient will show up or not. Hence, we can conclude that patient having appointments on Tuesdays and Wednesday have a higher chance of showing up for their appointment.
However, due to factors which can't be established from this dataset, the Hospitals grouped the patients into 27 unique days which resulted in a surge. Hence, patients who do not get preferred dates might miss appointment dates if other events shows up. This in itself is a factor that determined if a patient will show up for his/her appointment.
The result of the chart showing the distribution of the gender as well as the value count indicates that there are more females than males that had appointment. Although further analysis can be carried out to estimate the Male - Female relationship that received scholarship. The result from the relationship between Patients not showing up for their appointment with the Gender, Scholarship, SMS received does not really affect the result as there were similarities. Additional research can be carried out to determine the population of doctors in the hospitals to determine the possibilities of patients to be attended to on a daily basis.