can someone please help me with the following java question please

ALGORITHM DESCRIPTION

TREE

TABLE

using the above write a Java program that implements the basic algorithm for inducing a decision tree.

Note the following requirements:

You are not required to fully implement the algorithm, but to implement the partition that occurs at the root node, i.e. the generation of the first level after the root, use the description and the tree as a reference of the expected output.

Consider the splitting attribute to be discrete-valued and use information gain as the attribute selection measure.

Your implementation should use the training tuples given in the Table When run, the program should output the information given in the tree (the output does not need to look exactly in the way shown there, but must convey the same meaning).

please help

The algorithm is called with three parameters: D, attributelist, and Attribute selection method. We refer to D as a data partition. Initially, it is the complete set of training tuples and their associated class labels. The list of attributes describing the tuples. Attribute.selection.method specifies a heuris- tic procedure for selecting the attribute that "best" discriminates the given tuples according to class. This procedure employs an attribute selection measure such as information gain or the Gini index. Whether the tree is strictly binary is generally driven by the attribute selection measure. Some attribute selection measures, such as the Gini index, enforce the resulting tree to be binary. Others, like information gain, do not, therein allowing multiway splits (i.e., two or more branches to be grown from a node) parameter attribute list is a The tree starts as a single node, N, representing the training tuples in D (step 1).3 The algorithm is called with three parameters: D, attributelist, and Attribute selection method. We refer to D as a data partition. Initially, it is the complete set of training tuples and their associated class labels. The list of attributes describing the tuples. Attribute.selection.method specifies a heuris- tic procedure for selecting the attribute that "best" discriminates the given tuples according to class. This procedure employs an attribute selection measure such as information gain or the Gini index. Whether the tree is strictly binary is generally driven by the attribute selection measure. Some attribute selection measures, such as the Gini index, enforce the resulting tree to be binary. Others, like information gain, do not, therein allowing multiway splits (i.e., two or more branches to be grown from a node) parameter attribute list is a The tree starts as a single node, N, representing the training tuples in D (step 1).3