Hello! My name is Aarushi Pandey and I am a student attending University of Texas at Dallas. I am a sophmore and am in the class of 2023. My aim for the future is to become a developer. Nowadays, I am interested in learning Data Science.
My hobbies include reading, listening to music, and binge-watching dramas. I love hanging out with my friends as well as knitting. Currently, I'm attempting to learn Korean. In the future, I'd like to travel to different places around the world.
Code for an Ultimate Tic-Tac-Toe game which was a project in one of my CS classes. There are obviously easier ways to doing this but I wanted to enhance the code that we had worked on in the class with OOP concepts.
Created a game of Ultimate Tic-Tac-Toe of the user against AI using OOP concepts in Java.
Displayed game board, possible moves, and selected move for each player with Eclipse as IDE.
Ensured valid inputs from user too.
Link to the code in GitHub
Researched, brainstormed, as well as designed and created prototype to monitor beehive using DHT22 sensor.
Presented Project Plan and design, conducted meetings with project partner, and reflected on final result.
Role of Document Manager.
Link to the official website for the project
Currently open challenge in Kaggle: https://www.kaggle.com/c/house-prices-advanced-regression-techniques (link to the challenge)
The challenge is to predict house prices using advanced regression techniques (from the given data with around 80 features given in Kaggle).
The language I used for this challenge is R.
My best prediction was able to predict sale prices for the test data with error upto 14.404%.
I used feature engineering skills as well as popular models like svm (support-vector machines), glm (general linear model), decision tree(s), and random forests for these predictions.
Link to the code in GitHub
Currently open challenge in Kaggle: https://www.kaggle.com/c/titanic (link to the challenge)
The challenge is to determine if a person survived in the titanic or not (from the given data in Kaggle).
The language I used for this challenge is R.
My best prediction was able to predict survival rates for the test data with 78.229% success rate.
I used feature engineering skills as well as popular models like svm (support-vector machines), glm (general linear model), decision tree(s), and random forests for these predictions.
Link to the code in GitHub
Made a markdown previewer from scratch (with help from a youtube video) using the following user stories from freeCodeCamp:
User Story #1: I can see a textarea element with a corresponding id="editor".
User Story #2: I can see an element with a corresponding id="preview".
User Story #3: When I enter text into the #editor element, the #preview element is updated as I type to display the content of the textarea.
User Story #4: When I enter GitHub flavored markdown into the #editor element, the text is rendered as HTML in the #preview element as I type (HINT: You don't need to parse Markdown yourself - you can import the Marked library for this: https://cdnjs.com/libraries/marked).
User Story #5: When my markdown previewer first loads, the default text in the #editor field should contain valid markdown that represents at least one of each of the following elements: a header (H1 size), a sub header (H2 size), a link, inline code, a code block, a list item, a blockquote, an image, and bolded text.
User Story #6: When my markdown previewer first loads, the default markdown in the #editor field should be rendered as HTML in the #preview element.
Optional Bonus (you do not need to make this test pass): My markdown previewer interprets carriage returns and renders them as br (line break) elements.
Link to the code in GitHub
Made a random quote machine using the following user stories from freeCodeCamp:
User Story #1: I can see a wrapper element with a corresponding id="quote-box".
User Story #2: Within #quote-box, I can see an element with a corresponding id="text".
User Story #3: Within #quote-box, I can see an element with a corresponding id="author".
User Story #4: Within #quote-box, I can see a clickable element with a corresponding id="new-quote".
User Story #5: Within #quote-box, I can see a clickable a element with a corresponding id="tweet-quote".
User Story #6: On first load, my quote machine displays a random quote in the element with id="text".
User Story #7: On first load, my quote machine displays the random quote's author in the element with id="author".
User Story #8: When the #new-quote button is clicked, my quote machine should fetch a new quote and display it in the #text element.
User Story #9: My quote machine should fetch the new quote's author when the #new-quote button is clicked and display it in the #author element.
User Story #10: I can tweet the current quote by clicking on the #tweet-quotea element. This a element should include the "twitter.com/intent/tweet" path in its href attribute to tweet the current quote.
User Story #11: The #quote-box wrapper element should be horizontally centered. Please run tests with browser's zoom level at 100% and page maximized.
I took the quotes for the machine from a website which displayed 50 inspirational quotes. I tried to replicate the model submission (the example website given in the beginning) for this project.
Link to the code in GitHub
