"Never accept the world as it appears to be, dare to see it for what it could be." - Dr. Harold Winston
I created a logistic regression and random forest model to predict customer churn, resulting in substantial cost savings and the identification of predictors of churn.
I revisited Oliver's Four Factors to evaluate its accuracy in today's NBA. I used feature engineering to create a more accurate and insightful model to predict wins.
I clustered customers (K-Means) into 6 segments based on age, income, and spending score, facilitating targeted marketing to decrease customer acquisition cost and to increase customer retention. I identified ideal segments to target and recommended actionable initiatives tailored to the segments.
I performed exploratory data analysis to understand playcalling tendencies in increasingly important situations. I used feature engineering to evaluate teams' play calling tendencies given the pressure of the situation in the game.
I created a sales dashboard for a retailer using Power BI.
Here are example queries I made on a club membership database (Microsoft SQL Server).