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  • Q1:1. Use the insurance fraud dataset. Consider the data quality issues (e.g., missing data) and preprocess the data. Split the data into a 10% train and 90% test set using random_state = 1. Create a decision tree with a max depth of 3 using a gini measure. Print the accuracy on the test set and the tree. Is this a good approach? Why or why not? 2. Create a decision tree on the same data with max depth of 3 and an entropy measure. Does the accuracy change? Does the tree change? Discuss which measure you think is better. 3. Now split the data into 70% train and 30% test using random_state = 1. Redo 2 and 3. Have the trees and accuracy changed? Are the trees more or less similar now? Discuss which split you think is better and why. 4. Evaluate how the accuracy changes with the depth of the tree with the 70-30 data. Look at the accuracy for a max depth of 1, 2, 3, ... 10, 15, 20. Plot the curve of changing. Do you see underfitting? Do you see overfitting? 5. What variable provides the most information gain in the insurance fraud data (for the 70-30 split)? 6. Decision trees are a "white box" method. What do you observe about the insurance fraud data using decision trees?See Answer
  • Q2:Q2. (10 points) Consider the following setting. You are provided with n training examples: (T₁, 9₁, h₁), (2, 92, h₂),, (In, Yn, hn), where z, is the input example, y, is the class label (+1 or -1), and h₁> 0 is the importance weight of the example. The teacher gave you some additional information by specifying the importance of each training example. How will you modify the perceptron algorithm to be able to leverage this extra information? Please justify your answer.See Answer
  • Q3:You are required to write a 1 page proposal for your project as a pdf. Your proposal must include the following pieces of information: 1. Data Mining Task: What is your data mining task? This task could be a series of exploratory questions that you want to investigate or analyze. What is your motivation behind choosing this task for your project? 2. Dataset: What is the source of your data? Provide a link to your data source if you acquired it online. 3. Methodology: How will you solve the data mining task? You should have some idea of the algorithms or software tools you plan to investigate. Please feel free to use existing data mining and machine learning tool kits (e.g., Weka, Scikit-Learn) as needed for your project. 4. Final product: What will be the outcome of this project? How will you measure the success of your course project? Will this project help you explore or learn something new?See Answer

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