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Jupyter notebook files for a machine learning project in the course COGS 118A Supervised Machine Learning Algorithms. In short the object of this project was to empirically test different machine learning algorithms using a variety of data sets from the UCI Machine Learning Repository.

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tomasspangelo/UCSD-Project-Spring2020

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UCSD_project_spring2020

Jupyter notebook files for a machine learning project in the course COGS 118A Supervised Machine Learning Algorithms. In short the object of this project was to empirically test different machine learning algorithms using a variety of data sets from the UCI Machine Learning Repository.

  • Resulting paper from project: COGS118A___Final_Project_HEADLINEFIXED.pdf
  • Folder Tomasspangelo.finalproject contains all jupyter notebook files and data. Each jupyter notebook corresponds to a data set, where the data set is prepared and preprocessed and then the three machine learning algorithms k-NN, random forests and suport vector machine are run on the dataset. Results are presented in each notebook at the bottom using heatmaps etc. from parameter tuning using cross validation.

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Jupyter notebook files for a machine learning project in the course COGS 118A Supervised Machine Learning Algorithms. In short the object of this project was to empirically test different machine learning algorithms using a variety of data sets from the UCI Machine Learning Repository.

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