Explanation You must apply Classification, Association Rules Mining, Clustering and Text Mining as your approaches. This means that you will need to choose a dataset that is amenable to each of these types of data mining -- i.e., to building a model that will determine, predict, or estimate one of the attributes in the dataset, based on the values of other attributes. he overall requirement is summarised below: Task 1: Apply classification on a selected dataset using R & SAS Enterprise Miner (e.g., Decision Tree, K-Nearest Neighbours, Logistic Regression). (25 Marks) Task 2: Apply Association Rules Mining on a selected dataset using R & SAS Enterprise Miner. (25 Marks) Task 3: Apply K-Means Clustering on a selected dataset using R & SAS Enterprise Miner. (20 Marks) Task 4: Apply Sentimental Analysis on selected 20 hotels in the hotel_reviews.csv dataset Using R & SAS EM (dataset can be downloaded from the Blackboard) (20 Marks) Extra feature to be implemented (10 Marks) Students can gain 10 marks, if they build a Shiny dashboard using R shinydashboard package for any of an above main task. (Guide: https://rstudio.github.io/shinydashboard/index.html)