Exploratory Data Analysis and Predictive Modeling with Synthetic Splicing

Contexto general del proyecto

Tasks: 1. Data Exploration: o Load the provided dataset, "splicing_data.csv," into R o Perform summary statistics of the dataset to get an overview. O Check for missing values and outliers in the dataset. O Generate a correlation matrix to assess the relationships between the splicing factors and the splicing event. 2. Data Visualization: o Create visualizations to explore the distribution of each splicing factor's expression and the splicing event. O Create graphs to visualize the relationships between each individual splicing factor and the splicing event. 3. Predictive Modeling: o Split the dataset into a training set (70%) and a testing set (30%). O Build a predictive model to estimate the splicing event values based on the expressions of the three splicing factors. You can use linear regression or any other appropriate modeling technique. O Evaluate the model's performance using appropriate metrics (e.g., R-squared, Mean Squared Error) on the testing set. O Interpret the model's coefficients to understand the relationships between the splicing factors and the splicing event. 4. Conclusions and Recommendations: o Summarize the key findings from your exploratory data analysis and modeling. O Provide insights into which splicing factors are most strongly associated with the splicing event. O Offer recommendations or suggestions for further analysis or experiments based on your results.

Categoría Programación y Tecnología
Subcategoría Otros
Tamaño del proyecto Pequeño
¿Es un proyecto o una posición? Un proyecto
Actualmente tengo No aplica
Disponibilidad requerida Según se necesite

Plazo de Entrega: No definido

Habilidades necesarias