Hello, I have another ML Python assignment that will not have time to fully complete. I have…

Hello, I have another ML Python assignment that will not have time to fully complete. I have attached the word doc with all of the questions that need to be answered, along with the assignment file that it needs to be completed in (assignment.ipynb) and the data can be downloaded -https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients#Regards,Ed Assignment: Decision Trees Learning outcomes Understand how to use decision trees on a Dataset to make a prediction Learning hyper-parameters tuning for decision trees by using RandomGrid Learning the effectiveness of
Hello, I have another ML Python assignment that will not have time to fully complete. I have attached the word doc with all of the questions that need to be answered, along with the assignment file that it needs to be completed in (assignment.ipynb) and the data can be downloaded -https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients#Regards,Ed Assignment: Decision Trees Learning outcomes Understand how to use decision trees on a Dataset to make a prediction Learning hyper-parameters tuning for decision trees by using RandomGrid Learning the effectiveness of ensemble algorithms (Random Forest, Adaboost, Extra trees classifier, Gradient Boosted Tree) In the first part of this assignment, you will use Classification Trees for predicting if a user has a default payment option active or not. You can find the necessary data for performing this assignmenthere This dataset is aimed at the case of customer default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification – credible or not credible clients. Because the real probability of default is unknown, this study presented the novel Sorting Smoothing Method to estimate the real probability of default. Required imports for this project are given below. Make sure you have all libraries required for this project installed. You may use conda or pip based on your set up. NOTE:Since data is in Excel format you need to installxlrdin order to read the excel file inside your pandas dataframe. You can runpip install xlrdto install Questions (15 points total) Question 1 (2 pts) Build a classifier by using decision tree and calculate the confusion matrix. Try different hyper-parameters (at least two) and discuss the result. Question 2 (4 pts) Try to build the decision tree which you built for the previous question, but this time by RandomGrid search over hyper-parameters. Compare the results. Question 3 (6 pts) Try to build the same classifier by using following ensemble models. For each of these models calculate accuracy and at least for two in the list below, plot the learning curves. Random Forest AdaBoost Extra Trees Classifier Gradient Boosted Trees Question 4 (3 pts) Discuss and compare the results for the all past three questions. How does changing hyperparms effect model performance? Why do you think certain models performed better/worse? How does this performance line up with known strengths/weakness of these models?