Data processing
Remove the data with missing values >50
Normalize the Data
Univariate distribution should be done
Compare two sets of quantities using one another (Do scatter plot type of thing to compare the data) QQ plots or any other plots similar to it.
Compare the levels of Hba1c in which race and age group it is high in range
compare the effect of chronic kidney diseases and diabetes
compare different age groups 45 -60 and 60 – 70, 70 -80 above HbA1c levels
See the rate of readmission
(Readmitted Days to inpatient readmission. Values: “<30” if the patient was readmitted in less than 30 days, “>30” if the patient was readmitted in more than 30 days, and “No” for no record of readmission)
Which race people readmitted more
Run Hierarchial clustering method using cosine distance and compared the results with K-Means for both Race and Readmitted attributes.
Silhouette score for hierarchial clustering based on race & readmittance
Implement Models: Logistic Regression and Decision Trees
Do principal component analysis if possible.
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