Developed in Swift, this IOS application uses a sentiment-predicting machine learning model to predict whether the users journal entry could contain signs on a mental health problem. Using natural language processing, after the user enters their journal entry, the application will classify the writing as one of five possible cases.
This project performs sentiment analysis on customer book reviews, using various machine learning models and text vectorization to predict whether the review was positive or negetive. In this binary classification task, methods like grid search, random search, and hyperparameter tuning were used to optimize the model's performance, ending up with an accuracy of 78% and an AUC score of 87%.
I created a web application that allows users to make a unique Spotify playlist based on a custom algorithm I designed, which recommends songs based on the user's data such as their favorite artists, genres, and songs. Simply authenticate with spotify, name your playlist, then add songs that are recommended by the algorithm. (Unfortunately, you can't test it out for yourself because the app is still in development mode per Sptofy's policies.)
If you're ever curious about my current music taste, this project uses the Spotify API to map my top artists and what song I'm currently listening to to a web application. It also helps me carefully curate my Spotify wrapped, which is something I'm very passionate about because I've had some embarassing top artists over the years.
This is a web scraper I used to get from a clothing reselling website (of which I won't name directly, but can be inferred). I wanted to do a class project on something I was interested in, so I decided to get the data myself. This uses Python and the Selenium library to parse through the HTML and gather things like item name, description, and price.