We were discussing about the project report and thus far this is all that happened .

We’ve completed the visualization aspect of our research. These visualizations depict the connections between the predictor variables and the outcome variable using the test data. Here, obesity and inactivity serve as the predictor variables, while diabetes is the outcome variable. We’ve created plots to represent the relationships between obesity and diabetes, as well as inactivity and diabetes. Furthermore, I’ve determined the R^2 and mean squared error (MSE) values. An R^2 value of 0.395 suggests that approximately 39.5% of the variability in the diabetes data can be explained by the predictor variables. R^2 values can range between 0 and 1, with values closer to 1 implying a better model fit. Meanwhile, the MSE value stands at -0.400063, which measures the average squared difference between the estimated and observed values. A lower MSE typically indicates a better model, although its scale is dependent on the nature of the outcome variable.