Assignmet 1 focused on predicting the larger stock markets movements using daily news headlines.
The Jupyter notebook in this repo is the notebook I submitted to be graded. This notebook was a guided application involving prepocessing a dataset, training and testing binary classifiers (Random Forest and XGBoost), use of confusion matrix, tuning of hyperparameters(GridSearchCV), and plotting and comparing ROC curves.
The end results was that I built two models, a Random Forest binary classifier and and XGBoost binary classifer, then improved the RandomForest model's performance by fine tuning the hyperparameters via a search across the hyperparameter values space (a gridsearch).