Codecademy CS102 Portfolio Project: Recommendation Program

Xiu — Sat, 23 Oct 2021 09:47:19 +0000

Introduction

Codecademy wants us to create a "basic recommendation program" using Python for the Portfolio Project in Codecademy's CS102: Data Structures and Algorithms. The program must contain the following features:

Implementing an autocomplete that, based on a user’s input, returns a list of possible categories based on the beginning of a word. You should use the data created in the last step to create the autocomplete. It is up to you how to properly store and retrieve the data.

Retrieving and displaying all of the data related to the category selected by the user. It is up to you how to properly store and retrieve the data.

I decided to make a program that recommends workout classes to a user depending on the type of workouts the user wants to do and what time they are free.

The code can be found on Github.

Running the Program

First, run studios.py. This will randomly generate random studios and classes which will be inserted into a binary search tree (BST). The resulting tree is pickled, ready for the next step.

Next, run script.py. The program will prompt for both the activities the user is looking for, and the timing for said classes. The program's final payload is a list of classes from the database (if any) that satisfy the user's above conditions.

The Database

Each time studios.py is run, random classes will be generated for 22 fictional studios. The studios already have a set of possible classes and the associated tags for said classes. Each class's timing, price, and instructor, however, is randomly generated each time.

I used Fake Address Generator to generate the addresses for the studios. Fossbytes was also used to generate 500 random names for the instructors.

The Data Structure

A self-balancing Binary Search Tree is used to store the unique tags from the generated classes. Each tag is a node on the tree containing as payload a list of all the classes that have this tag.

This BST is self-implemented for the purpose of practice. As many of the methods in this tree differ from the BST taught by Codecademy, I am incredibly grateful to online resources such as Runestone Academy and Algorithm Tutor that help me implement them.

Searching the Data Structure

Initially, a breadth-first search was employed to look for keys that contain the search term. This resulted in a linear complexity of O(n), where n is the number of tags in the data structure.

The program uses an "autocomplete" search function that should run faster as it takes advantage of the BST property. The search function has the following parts:

Look for the smallest term that is greater than or equal to the search term.
Traverse the tree for the next largest key until the search term no longer matches the relevant part of the search term (ie node.key[:len(search_term)]).
Return the list of nodes that contain these keys.

This search function should have a complexity of O(log(n) + t), where n is the number of nodes in the tree and t is the number of nodes that contain the search term.

Codecademy CS101 Final Project: Blackjack

Xiu — Fri, 09 Jul 2021 15:58:05 +0000

Introduction

I have been taking the Codecademy CS101 course in the past month or so. Prior to this, I had no background in Python.

Codecademy requires us to make a basic terminal program using Python in order to complete its CS101 course. The objectives of the project are as follows:

* Build a terminal program using Python
* Add at least one interactive feature using input()
* Use Git version control
* Use the command line and file navigation
* Write a technical blog post on the project

Blackjack was the first suggestion listed by Codecademy in the project write-up. I was leaning towards an original idea, but my mind started wondering what kind of logic I would write for a Blackjack program. I ended up writing the code in about a day.

The code can be found here at Github.

Design

I made a Card class that takes in the following parameters for its constructor: self, id, name, suit, value, visibility = True. Using a for loop, I generated a deck of 13 cards for each of the four suits (Diamond, Club, Spade, and Hearts).

The deck of cards is randomly shuffled using random.sample().

When the game starts up, the user is prompted to play by typing "Hit me" into the terminal. The user's input prompts
a deal_card function that pops the first card of the deck into the user's hands.

The user has two options from then on: to continue receiving cards ("Hit me") or to stand ("stand").

Adding the Dealer

After writing the basic logic of the game for a single user, I added a Dealer, which is played by the computer. The Dealer will be dealt a single card after the player's initial choice to be hit. The next card the Dealer receives is the Hole--it is hidden from the Player. Only at the end of the game does the Dealer reveal the Hole.

The logic behind the Dealer's decision to deal to itself or to stand is very simple--no card counting involved! The Dealer simply calculates if its hand is equal to or more than 18--if so, the Dealer stands.

The Rules of the Game

I personally have never played "official" Blackjack played in casinos. For the purposes of this game, I have stuck to the following very simple rules:

The user/Dealer is dealt a card, and can choose to continue playing and having a card dealt to them, or stand, which holds on to the current hand.
The value of the card follows the number on the card (ie 8 of hearts has a value of 9=8, and 5 of spades have a value of 5). Face cards (Jack, Queen, King) all have a value of 10. Ace has a value of either 1 or 11.
If the value of the user's or Dealer's hand sums up to 21, the user or Dealer wins.
If both players stand, the player with the bigger hand value wins.

Future improvements

There are many different outcomes that currently are somewhat clumsily rendered in a dozen or so if statements. This should be refactored.

It would be great to have a more intelligent AI for the Dealer. This is however out-of-scope in my study syllabus for now and the near-future.

DEV Community: Xiu

Codecademy CS102 Portfolio Project: Recommendation Program

Introduction

Running the Program

The Database

The Data Structure

Searching the Data Structure

Codecademy CS101 Final Project: Blackjack

Introduction

Design

Adding the Dealer

The Rules of the Game

Future improvements