This analysis builds a Neural Network (NN) to classify bacteria cell DNA into two groups; Flagged, and Not Flagged for further study. The dataset consists of 14 columns and 1,217 observations. The target label is scaled 0 to 10, where 0 is not flagged for further study.

This analysis generates a K-means clustering of more than 9.2 million NYSE observations and six trading information columns.

This analysis constructs a C4.5 (aka J48) Decision Tree to display the survival (Y/N) of Titanic passengers, using the categorical features Class, Gender and Age. It also performs Apriori Association data mining to search for strong directional relationships between values. These findings are used to generate a wordcloud visual.

This analysis fits a stepwise linear regression model to predict Systolic blood pressure. It uses seven numeric and categorical features.

This analysis constructs a cross-validated lasso regression model to predict Systolic blood pressure, using various numeric and categorical factors.

This analysis uses a Support Vector Machine (SVM) to classify bacterium cell DNA into two groups;Flagged, and Not Flagged for further study. The dataset consists of 14 unidentified columns and 1,217 observations. The target label is scaled 0 to 10, where 0 is not flagged for further study.