Added two new models

[hananahoney]

The IPython notebook file on the Github directory showcases the application of various machine
learning classifiers on a diabetes dataset, including Logistic Regression, SVM, KNN, Random Forest, naive
Bayes, gradient boosting, Decision Tree, and XGBoost. The author of the notebook has performed
extensive data preprocessing prior to applying the classifiers, including data cleaning, scaling, and
feature engineering. The classifiers used in the notebook have been thoroughly evaluated using
performance metrics such as accuracy, and the results have been visualized to aid in the interpretation
of the results. The application of Decision Tree and XGBoost classifiers, in particular, were contributed
by me and have been added to the existing set of classifiers. The notebook evaluates these classifiers in
the same manner as the previous ones, providing valuable insights into their strengths and weaknesses.
This collaborative effort has resulted in a comprehensive analysis of the diabetes dataset using a diverse
set of classifiers, aiding in the selection of the best one for the task at hand.