You are required to extract data from relevant dataset(s), namely the transaction.txt, frauddescription.txt, and descriptions.txt.

Three datasets are provided. The first dataset(description.txt) contains the description of genuine transactions. The second dataset(fraudulent-transaction.txt) contains the description of fraudulent transactions. The last dataset(transaction.txt) contains the actual transactions (10,000). The transaction dataset contains the description of the transactions, the amount of the transactions and the locations of the transactions. The locations are not real location to avoid tracing any transaction to anyone. The locations are just Euclidean coordinates in x, and y points. The following are the attributes of the transaction dataset, also see Figure 1.

Figure 1: sample data from the transaction.txt

* The user id

* The transaction id

* The description of the transaction

* The amount of the transaction

* The x coordinate of each transaction

* The y coordinate of each transaction

* A Boolean label that represents whether the transaction is fraudulent or not.

Q1. Implement distance_module containing two functions.

A function that computes the distance between any two given transactions of a user.

And another function should be implemented for computing the distance of transactions of any two users.

Q2. Implement the main module(test_module) containing the function that implements the user interface through which users can query and interact with all distance function implemented. An informative menu-driven user interface should be implemented allowing friendly user interaction with your deliverable.