After today’s session, I gained insights into the concept of 5-fold Cross-validation. we applied this technique to a dataset comprising 354 data points. Initially, we divided the dataset into five approximately equal-sized segments. Each fold contained 71 data points, with the last fold having 70 data points. The procedure involved conducting five iterations. In each iteration, one fold was set aside as the test set, while the remaining four folds were utilized for training our polynomial regression model. This complex process allowed us to evaluate the model’s performance and calculate its average performance. We generate some example data with input features (x) and the target variable (y).We initialize a linear regression model. We create a 5-fold cross-validator using K-fold from scikit-learn. We store the MSE values for each fold in the MSE value list. we calculate the average MSE across all folds to assess the model’s overall performance.