Tortugas en el GoMx

Ejercicio entregable en r #1

The problem of predicting concrete compression strength, vital for ensuring the safety and durability of civil engineering structures, was first addressed by Prof. I-Cheng Yeh in 2007, building on his 1998 research. This project, using Yeh's dataset, aims to optimize concrete performance by exploring the effects of ingredient concentrations and testing age on compression strength. By applying advanced machine learning techniques, the goal is to create a predictive model that improves accuracy and provides insights into the factors influencing concrete strength, ultimately enhancing material design and construction practices.

In the earlier module, Breast_Cancer_Expression_Data, we examined the mRNA levels for 18,351 genes across 1,082 breast cancer patients. In another activity, Heatmaps, we worked with the smaller data set `mtcar` and saw how the function `heatmap()` can reorder objects in our data set to reveal patterns in the data: Objects and features in the same clusters are more similar to each other than to those in other clusters. Here, we apply these concepts and skills to the TCGA clinical and gene expression features.

tarea de ley de enfriamiento de newton

We will load and examine a data frame that contains clinical information from over 1,000 breast cancer patients from The Cancer Genome Atlas (TCGA).

Evaluación de modelos de regresión lineal y logística con tidymodels

Tablero con graficas de datos de puntajes de los exámenes icfes

Heatmaps are a way to colorize, visualize, and organize a data set with the goal of intuiting relationships among observations and features. We will use heatmaps in this course to find patterns in the gene expression data for the 1K breast cancer patients from The Cancer Genome Atlas. Here, we focus on what heatmaps are and how to create them by practicing with a small dataset.