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 ![workflow](workflow_sdb_gui.png "Workflow")
 
 ## Methods
-In order to make depth prediction, there are four methods available. The methods are [K-Nearest Neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor), [Multiple Linear Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression "MLR Regression"), [Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor "RF Regressor") and [Support Vector Machines](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR "SVM Regressor"). All of which are using [Scikit Learn](https://scikit-learn.org) module.
+In order to make depth prediction, there are four methods available. The methods are [K-Nearest Neighbors](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor "KNN Regressor"), [Multiple Linear Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression "MLR Regression"), [Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor "RF Regressor") and [Support Vector Machines](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR "SVM Regressor"). All of which are using [Scikit Learn](https://scikit-learn.org) module.
 ### K-Nearest Neighbors
 This method implements learning based on k nearest neighbors of each query point. The adjustable hyperparameters for this method are number of neighbors, weights, algorithm, and leaf size. The default values are 3, distance, auto, and 300.