El presente informe tiene como objetivo realizar un análisis detallado de las ventas y la rentabilidad de Adidas en el estado de Pensilvania. A través del uso de técnicas de regresión múltiple, se busca identificar los factores clave que afectan el margen operativo de los productos vendidos en este estado. Factores como el precio por unidad, las unidades vendidas y el producto se han considerado en el análisis para comprender su impacto en el desempeño financiero de las tiendas.

ggplot(merged_data, aes(x = reorder(Country, -AI_Capability_Index), y = AI_Capability_Index, fill = as.factor(Decision))) + geom_bar(stat = "identity") + coord_flip() + # Flip for horizontal bars labs(title = "Countries Ranked by AI Capability and Geopolitical Decision Influence", x = "Country", y = "AI Capability Index", fill = "Decision Impact") + theme_minimal()

> data_to_cluster <- merged_data[, c("AI_Capability_Index", "Talent", "Public_Sector_Innovation")] > > data_scaled <- scale(data_to_cluster) > pheatmap(data_scaled, + cluster_rows = TRUE, cluster_cols = TRUE, + main = "Clustered Heatmap of AI Capability and Geopolitical Factors", + display_numbers = TRUE)

study purpose

decision_tree <- rpart(Political.regime ~ Talent + Infrastructure + AI_Capability_Index + Public_Sector_Innovation, data = df, method = "class") # Plot the decision tree rpart.plot(decision_tree, type = 2, extra = 104, under = TRUE, faclen = 0, cex = 0.8)

Our team, Ctrl+Alt+Defeat, is excited to participate in this year’s Women in Data: Datathon 2024 with a project focused on generative AI and its role in supporting reproductive health decisions. As a team composed of individuals who have experienced menstruation, we understand the critical need for the 1.8 billion people worldwide who menstruate and approximately 1.2 billion people who are menopausal or postmenopausal to have access to reliable and unbiased information. Our research aims to uncover biases in generative AI and identify fairness gaps affecting inclusivity.