The amount of time that employees are off the job can vary widely. To help an employer predict the amount of time employees
will miss work, I used Python and SQL to preprocess a dataset that categorized reasons for absenteeism. I created a Logistic
Regression Model, which predicted the length of time an employee would be absent from work based on the reason for their
absence.
Out of sheer curiosity, I have created this project to explore Covid-19 infection rate and death rate on a
global level. Using SQL, I developed complex queries for various Covid-19 databases while ensuring the integrity of the data.
Initially curious to explore global effects of Covid-19, I decided to use SQL to also examine
U.S. Covid-19 data and observe local infection rate and death rate in each states using SQL.
In this project, I focused on data cleansing in SQL and demonstrated my ability to write complex scripts in SQL to maintain data integrity
by resolving structural errors, duplicative data, and missing values to make the data more usable.
Storytelling is integral to the analyst's role, and my Tableau platform demonstrates my ability to communicate
key findings, insights, and predictions using appropriate data visualization techniques.