Problem $4$: Rule$-$based methods

The goal is to build a rule$-$based classifier to predict the class label based on these attributes. Here's the dataset:

$1.$ Initial Rule Generation: Generate an initial rule that classifies instances based on a single attribute with the highest information gain or Gini index.

$2.$ Rule Expansion: Expand the rule from question $1$ by incorporating another attribute to improve the classification accuracy.

$3.$ Handling Continuous Attributes: Propose a method to handle continuous attributes in rule$-$based classification, especially for attributes like 'Age' and 'Temperature.$\text{'}$

$4.$ Discrete Attribute Rule: Create a rule based on a discrete attribute with more than two categories, like 'Education.$\text{'}$

$5.$ Rule Evaluation: Evaluate the rules generated so far on the training dataset. Identify any instances that are misclassified.

$6.$ Pruning Rules: Discuss the concept of rule pruning and why it might be necessary in rule$-$based methods. Provide an example of when rule pruning could be beneficial.

$7.$ Handling Missing Values: Discuss how rule$-$based methods handle instances with missing attribute values and propose a strategy to address missing values in this dataset.