As you can see in the picture there is task given. Please slove the each task

please slove the task in the picture

Dverview Timelines and Expectations Trevoniage Valin of Take: 3SSY But. Week. 11 Project Dotails Tasks aition arbiden, anowe mopocating actroper. appropriate style. The dataset should be chosen from the following repository: UC Irvine Machine Learning Repository attps:/larchive.ics.uci.edu/ml/index.php The aim is to use the data set allocated to provide interesting insights, trends, and patterns amongst the data. Your intended audience is the CEO and middle management of the Company for whom you are employed, and who have tasked you with this analysis. Tasks Task 1 - Choosing a dataset. Choose any dataset from the repository that has at least five attributes, and for which the default task is classification. Transform this dataset into the ARFF format required by WEKA. Task 2 - Background information. Write a description of the dataset and project, and its importance for the organization. Provide an overview of what the dataset is about, including from where and how it has been gathered, and for what purpose. Discuss the main benefits of using data mining to explore datasets such as this. This discussion should be suitable for a general audience. Information must come from at least two appropriate sources be appropriately referenced. Task 3 - Data description. Describe how many instances does the dataset contain, how many attributes there are in the dataset, their names, and include which is the class attribute. Include in your description details of any missing values, and any other relevant characteristics. For at least 5 attributes, describe what is the range of possible values of the attributes, and visualise these in a graphical format. Task 4 - Data pre-processing. Pre-process the dataset attributes using WEKA's filters. Useful techniques will include remove certain attributes, exploring different ways of discretising continuous attributes and replacing missing values. Discretising is the conversion of numeric attributes into "nominal" ones by binning numeric values into intervals. Missing values in ARFF files are Task 4 - Data pre-processing. Pre-process the dataset attributes using WEKA's filters. Useful techniques will include remove certain attributes, exploring different ways of discretising continuous attributes and replacing missing values. Discretising is the conversion of numeric attributes into "nominal" ones by binning numeric values into intervals. Missing values in ARFF files are represented with the character "?". If you replaced missing values explain what strategy you used to select a replacement of the missing values. Use and describe at least three different preprocessing techniques. Task 5 - Data mining. Compare and contrast at least three different data mining algorithms on your data, for instance: k-nearest neighbour, Apriori association rules, decision tree induction. For each experiment you ran describe: the data you used for the experiments, that is, did you use the entire dataset of just a subset of it. You must include screenshots and results from the techniques you employ. Task 6 - Discussion of findings. Explain your results and include the usefulness of the approaches for the purpose of the analysis. Include any assumptions that you may have made about the analysis. In this discussion you should explain what each algorithm provides to the overall analysis task. Summarize your main findings. Task 7 - Report writing. Present your work in the form of an analytics report. Submission The assignment is to be submitted via the Assignment submission box in Moodle. This can be found in the Assessments section of the course Moodle shell. Your report file will be submitted as either a MS word file or a PDF. If you are using MacOS, please submit as a PDF. Your report will include the following in the order provided below