For this task, you are required to download the Bank Marketing dataset (Moro et al., 2014) which is available in the UCI Machine Learning Repository. You will need to access the bank.zip file. Using the bank-full.csv and bank-names.txt files, you will need to Interrogate the dataset and prepare a Knowledge Discovery Report. The report must summarise your insights into the patterns of data for clients with term deposit subscription "yes" or "no" Knowledge obtained through all topics from Topic 1 up to Topic 6 can be useful for preparing this report The Knowledge Discovery Report must include: 1. An explanation and justification of your choice of suitable data mining algorithm/s and software tools that are used to extract knowledge from the nominated dataset. 2. At least 5 interesting rules you have identified using your chosen algorithm/s. Use the nine properties of interesting rules as a basis for nominating your interesting rules and include a justification of the interestingness of the rules discovered. 3. Your insight into the patterns of the dataset as a result of a comprehensive 360 degree analysis using the software tool/s of your choice. This must be supported by statistics and presented with appropriate tables, figures and graphs For this task, you are required to download the Bank Marketing dataset (Moro et al., 2014) which is available in the UCI Machine Learning Repository. You will need to access the bank.zip file. Using the bank-full.csv and bank-names.txt files, you will need to Interrogate the dataset and prepare a Knowledge Discovery Report. The report must summarise your insights into the patterns of data for clients with term deposit subscription "yes" or "no" Knowledge obtained through all topics from Topic 1 up to Topic 6 can be useful for preparing this report The Knowledge Discovery Report must include: 1. An explanation and justification of your choice of suitable data mining algorithm/s and software tools that are used to extract knowledge from the nominated dataset. 2. At least 5 interesting rules you have identified using your chosen algorithm/s. Use the nine properties of interesting rules as a basis for nominating your interesting rules and include a justification of the interestingness of the rules discovered. 3. Your insight into the patterns of the dataset as a result of a comprehensive 360 degree analysis using the software tool/s of your choice. This must be supported by statistics and presented with appropriate tables, figures and graphs