All tools @ fj' Questions pdf Edit Convert ICT110 Introduction to Data Scienee Background A series of data sets are provided, and you are welcome to choose whichever you are interested in. SPORT: Suncorp Super Netball Data 20192023 by player and team by results. ACCIDENT: This data has been extracted from the Queensland Road Crash Database, WINE: This dataset is related to red variants of the Portuguese "Vinho Verde" wine, BUSINESS: Supermarket Sales for a retailer The data files are available to download from Task 3 in Canvas. Assignment Task You are a member of the team and need to perform data analysis on selected attributes. Key Questions you need to answer: Describe the data, Provide a comprehensive overview of the data and its attributes, things such as how many, what type, what it describes. Exploratory Data Analysis Describe the finding/s: What did you find, what did you predict. what did you thick is important. You have been requested to prepare a data analysis report about your work and explain your findings. The potential audiences include other rescarchers, business representatives, and government agencies. They may have limited ICT or mathematical knowledge. Therefore, the report should be technical but have clear explanations deseribing the findings. Note: not all columns are related to this purpose. To prepare the report, please include the following sections: 1. Introduction Introduce the problem. Include background material as appropriate: whe cares about this problem, what impact it has, where does the data come from, what are the dimensions and structure of the data. 2. Data Setup Deseribe how to load the data, and how the pre-processing is performed. The original dataset is not ready for analysis and it is different from the data forms that we = e or6 O Type here to search I A ey ICTH0 Introciuction to Data Science Task 3 are familiar with in previous practices. This means we need to do some pre-processing. either for the whole dataset, or for a subset of the dataset required for each sub task described later. Once you have some ideas of exploratory or advanced analysis, you need to adjust the form of dataset. This can be achieved either by manipulating records in R by transposition or subselting, or with other tools (.g. notepad or excel) before reading them into R. For simplicity, you can also rename the attribute names Please clearly explain the way you have cleaned the data in this section. If you use Excel please still explain the steps that you used for cleaning. 3. Exploratory Data Analysis [[AT LEAST FOUR OF THE FOLLOWING]| 3.1. One-variable analyses with graphs and tables One-variable analysis studies one variable (one column/atiribute) each time. It is up to you to decide which attribute/variable you use for this analysis but the attribute you select need to be related to the research objectives 3.2. Two-variable analyses with graphs and tables A two-variable analysis studies the relation between two variables. It is up to you to decide which attributestvariables you use for this analysis but the attributes you select need to be related to the research objectives. 4. Advanced Analysis [[AT LEAST TWO OF THE FOLLOWING (you can do the same type twice on different data)]| 4.1 Regression anelyses with graphs Briefly explain the coneept of linear regression (with references). It s up to you to decide which attributes/variables you use for this analysis but the attributes you select need to be related to the research objectives 4.2. Clustering with graphs Briefly explain the concept of clustering and k-means (with references). Perform 1 clustering analysis. It is up to you to decide which atribute(s) you use for this analysis but the attribute(s) you selct need to be related to the rescarch objectives. 5. Conclusion Sum up your findings and provide some insight into the findings. 6. Reflections In this part, discuss any difficulties you had performing the analysis and how you solved those difficulties. Reflect on how the analysis process went for you, what you learnt, and what you might do differently next time. Aim to write one paragraph. For all data analysis (Section 3 & 4), you need to provide both R script file and the explanation ta the code (in comments in code). Please submit a single R code file as part of your submission for compiling and running, Your R code MUST run. Fage i of6 2 7 619 PM J 14C Mostly clear 5 ) ENG TRy