Repository of notebooks used to complete assignment 1. The code covers analysis that was desired for assignment 1 but its not very cohesive, but all code required for a particular model is found within the notebook with the models name.
Assuming python 3.9 and conda are installed create the env using.
conda env create -f ML.yml
Open jupyter
jupyter notebook
execute code as needed
https://www.overleaf.com/project/5ebe55ec08e09b0001676b6a
mkdir data
download both of these files into data
https://www.kaggle.com/uciml/adult-census-income
https://www.kaggle.com/uciml/indian-liver-patient-records
The parent directory path inside the notebooks will need to be changed to reflect absolute path to the data
directory
https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
https://www.kaggle.com/alexandrago/income-prediction-xgbclassifier-auc-0-926
https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb9
https://scikit-learn.org/stable/modules/model_evaluation.html