Hi, my name is Yasmine.
I'm a Fullstack Python and JavaScript Developer.

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About me

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I'm soon to be a graduate of Brooklyn College at CUNY with a Bachelors of Science in Computer Science. I have working experience with Python, JavaScript, Java and SQL programming. I'm especially experienced with Python data science and machine learning libraries like Pandas and Scikit Learn as well as web frameworks like Django, Django REST, React.js and Dash. I'm especially interested in data driven web development.

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College Debt Forecast

U.S. accumulated college debt reached $1.6 trillion in 2020. Students need to know what to expect and and machine learning can provide a forecast they can rely on. The College Debt Forecast app provides a sleek and user friendly interface, enabling users to get an accurate prediction of student debt with just a few clicks. I Built this app using python, Django REST Framework, and the Federal College Scorecard data API. I used Pandas and Numpy for data wrangling and analysis of a dataset containing student debt data for 10s of thousands of vocational schools and colleges. I leveraged Scikit Learn machine learning library to build a random forest regression model with 0.74 R-Squared score which was then integrated with the application backend. I built the application user interface using React.js, Material UI, and JavaScript and deployed the application using Heroku.

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Listen Party

Jam, dance, learn, or laugh with friends. Use your Spotify premium account to listen to your favorite music and podcasts together. The app enables users to listen in sync. Guests can vote to skip, with hosts determining the number of votes required for a skip to take place. I built Listen Party using python and Django REST Framework for the backend API. I built the frontend logic and user interface, which consumes the Django API, using React.js and the Material-UI library. I leveraged Spotify's Web API to facilitate user authentication, to get playback data, and to affect playback-state changes. I used Spotify's Web Playback SDK to enable content streaming through the browser. I deployed Listen Party using Python Anywhere.

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NYPD Complaints Navigator

NYPD Complaints Navigator is a data visualization "dashboard" application, that enables users to filter and analyze data regarding civilian complaints against members of the NYPD, including 12,000 complaints and over 30,000 allegations. My project partner and I used Pandas and Numpy for data wrangling and analysis, the Plotly Graphing Library to generate interactive visualizations, and Google Geolocation API to populate our dataframes with geographic data for map visualizations. We used the Dash Components to build the user interface including interactive input and visualization toggling features. Finally, we styled the app using the Dash Bootstrap Components library and deployed NYPD Complaints Navigator using Heroku.

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