The following project was designed to explore financial modeling using machine learning techniques. The database employed was used to predict whether a borrower would fully payoff the loan taken out. Each loan had the borrowers credit score, years of employment, credit history, annual income, purpose for loan, months delinquent of payment and so on. The predictions were made by taking one columns(attributes) of the data, known as the target and using the rest (the features)rest of the columns to find patterns of which would reveal information. Loan Status, which had the value Fully Paid Off or Charged Off seemed like the obvious choice for the target value. The next step of the processes was to organize the features into models where we could see the whether the loan was paid off or charged off. Then test if our models of features were correct with new data that was not previously used(New target values with their associated feature values). The most common way of modeling is to use a linear regression where a plot of a straight line best connects or is in the area of the most data points. However when dealing with data that is binary or non-continuous the accuracy of linear regression becomes inconsequential for making meaningful predictions. A logarithmic regression model would fit the criteria predicting the target value yet was only marked with an improvement yet its accuracy is still in the sixties. A very basic yet powerful tool in machine learning is the decision tree, which allows a model to break up data into variety of different groups down to the individual level. One of the most successful models constructed had eight-five percent accuracy was utilizing a basic decision tree. There are numerous ways of making such decision tree classifiers as there are levels of sophistication of such models. The most popular decision tree classifier used for regression analysis is currently Random Forest which gained its notoriety from being able to specialize each tree for improved accuracy. However the Random Forest Model was only able to obtain an accuracy in the seventy-sixth percentile. The Gradient Boosting Classifier essentially attempts to make a bad prediction and learn from its mistakes. The more mistakes the model makes and the slower it learns the better the levels of accuracy. XGBoost is known as being the best of the best in gradient boosting in data science. However with the dataset on hand while the XGBoost model scored two percent higher on the precision or the percentage of true positives to the sum of true positives and true negatives, the recall of the XGBoost was more than twenty-five percent lower than the basic gradient model (Recall is the true positives to the summation of true positives and false negatives). The GridSearch CV is also known as the exhaustive model because every possible combination is taken, however when the model created scored 100 percent on its test group it lends itself to skepticism. Going further down the rabbit hole of machine learning our accuracies began to diminish quickly. The K-Nearest Neighbor which groups similar data points together had an accuracy of seventy-six percent. The last model provided a sampling of several machine learning techniques, only to reconfirm that the simple Decision Tree Model was had better Principle Component Analysis and Support Vector Machines. To wrap this random walk through some of the possibilities opened by machine learning models some times occurrence are much easier to predict than we might initially imagine hence complicated models such as XGBoost or RandomForest are unnecessary and even computationally burdensome.