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Alec Arroyo's Projects

covid19-machine-learning-research icon covid19-machine-learning-research

As part of my final project for Introduction to Data Science, I was tasked with a group to select any dataset available online and apply machine learning and regression models to it to develop some research notes.

food-calorie-categorization icon food-calorie-categorization

For my final project I used food macronutrient and micronutrient information to predict that calories that a certain food item will be. Preproecssing was done on the dataset and EDA was created. Machine learning models used were linear reegression, random forests, and K-Nearest-Neighbors on continuos variable. Then we split calories into groups using thresholds defined and ran a random forest and a neural network testing for accuracy.

football-sql-database icon football-sql-database

Final project for IST was asked with creating a database for any topic of my choice. I decided to go with creating a football database since I'm very interested in football statistics. All the data was researched and formatted by me.

music-genre-classification-using-text-mining-lyrics icon music-genre-classification-using-text-mining-lyrics

For my final project I read in a dataset of music artists and their song lyrics for 6 different genres of music, and trained and tested machine learning models to predict what genre of music a certain song was based off their lyrics. Challenges of the dataset and alaysis included preprocessing, vectorizing the data, splitting the data, using hldout and cross validation teechniques, and applying Naive Bayes and Support Vector Machines to do analysis. The categorical variable in the dataset was the "genre" field.

music-genre-detection-using-features-of-audio-files icon music-genre-detection-using-features-of-audio-files

For my final project I took in a Kaggle dataset on music data and trained and tested machine learning models to predict what genre of music a certain song file was based off their other attribute values. The categorical variable in the dataset was the "label" field.

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