Solución propuesta completa y guiada del proyecto Business Data Lab. Incluye la revisión de la calidad de los datos, el análisis estadístico descriptivo, la comparación de segmentos, la identificación de anomalías y valores atípicos, la construcción de hallazgos empresariales y su traducción en conclusiones útiles para la toma de decisiones.

Basic three-level sankey diagram based of Brown's FY27 URC budget. Made with NetworkD3.

Do certain positions in baseball have a higher correlation to errors? Are there factors that determine if a player will play 80 or more games in a season? Does a player having more Gold Gloves and All-Star game appearances put them in special company in the Baseball Hall of Fame? Using a logistic regression model, this analysis explores which factors are most correlated with a fielder being error-prone. A principal component analysis (PCA) is then used to show that a player’s offensive output (hits, runs, RBIs) is closely tied to how many games they play in a season – i.e. whether they are an everyday “workhorse.” Finally, a K-Means clustering model groups Hall of Fame players by All-Star appearances and Gold Gloves won, revealing three distinct types of Hall of Famer. These models were built in R using the publicly available Lahman package (Friendly, Dalzell, Monkman, Murphy, Foot, & Zaki-Azat, 2020), which was cross-referenced against Baseball Reference for validation. The tables used were Fielding, Batting, People, HallOfFame, AwardsPlayers, and AllstarFull.