In orange data program

Using the credit$\_$risk$\_$clean.csv dataset, perform the analytic experiments described below.credit$\_$risk$\_$clean.csvDownload credit$\_$risk$\_$clean.csvCreate an $80\%$ random sample using the provided Python script. The random$\_$seed should be the numeric part of your UID without any leading zeroes:create$\_$deterministic$\_$random$\_$sample.pyDownload create$\_$deterministic$\_$random$\_$sample.pyTake a screenshot of the Python code where you set the sample$\_$size and random$\_$seed.Connect the dataset to the random sample Python script. This creates the $80\%$ random sample.Create two models from the random sampled dataset $($using the 'Select Columns' widget$).$ Each model should have different independent variables $($at most one of them can be in both models$)$ and the same target variable, 'loan$\_$status'. Take screenshots of the two 'Select Columns' opened to show the model variables.Use the Python script provided to you to run the k$-$Means algorithm:k$\_$means$\_$clustering.pyDownload k$\_$means$\_$clustering.pyFor each model, run the k$-$Means algorithm, varying k $=2,3,4,$ and $5.$Record the Overall Silhouette Scores as follows: