About Me
I worked on the following projects using SQL, Pandas, Numpy, Matplotlib and Seaborn.
Using call center data, I used a combination of Common Table Expressions CTEs and windows function to determine which shift and dates were busiest in terms of the highest number of calls. I also used multiple CTEs and inner and outer join on sales and employee data to determine whether sales in the first few months of employment correlate with better performance for a sales rep at the one year mark.
I cleaned Google Playstore data. While working on this data, I identified null/missing values and outliers, did data imputation and removed duplicates.
I also analyzed Premier League match results by combining dataframes. To do this, I put my data wrangling skills to test, including merging and joining dataframes and performing analysis using groupby operations.
I also worked on the 2017 NBA season dataset where I merged and sorted the data, cleaned and did a thorough analysis of the results.
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