Exploratory Data Analysis, decision tree, logistic regression model using Rapid Miner and Tableau

(SEE ATTACHEMENTS FOR SPECIFICATIONS) Task 1.1 Conduct an exploratory data analysis and data preparation of [login to view URL] Task 1.2 Build a Decision Tree model for predicting whether it is likely to rain tomorrow based on today’s weather conditions and any other relevant variables using RapidMiner and a set of data mining operators and a reduced [login to view URL] data set in part determined by your exploratory data analysis in Task 1.1. Task 1.3 Build a Logistic Regression model for predicting whether it is likely to rain tomorrow based on today’s weather conditions and any other relevant variables using RapidMiner and an appropriate set of data mining operators and a reduced [login to view URL] data set determined in part by your exploratory data analysis in Task 1.1. Task 1.4 You will need to validate your Final Decision Tree Model and Final Logistic Regression Model using the Cross-Validation Operator, Apply Model Operator and Performance Operator in your data mining processes. Task 2.1 Define the concept of deep learning in context of artificial intelligence (AI) and identify and discuss an application of deep learning deploying a commonly used deep learning algorithm (advanced neural networks) successfully adopted in an industry and/or government sector drawing the relevant and current literature (15 marks 1000 words) Task 2.2 Based on your review of deep learning in context of AI identify and discuss the key ethical concerns raised by using a deep learning algorithm to support decision making in a specific context with appropriate in-text referencing support. (15 marks 1000 words). Task 3.1 Create a Tableau View of rainfall over month for each location and a specific state Provide a screen capture and describe Tableau view created and comment on the rainfall over one month across different locations and does this differ much for different states Task 3.2 Create a Tableau View of total rainfall by year for each location and a specific state. Provide a screen capture of and describe the Tableau view you have created and comment on variation of total rain over year across different locations for a specific state Task 3.3 Create a Tableau View of total evaporation over month by location for a specific state. Provide a screen capture and describe Tableau view created and comment on the levels of evaporation over month for different locations and states Task 3.4 Create a Tableau GeoMap View of all Australian weather stations that provides latitude, longitude and total rainfall for a selected year. Provide a screen capture and describe the Tableau Geomap view you have created and comment on two or more selected locations for a state. Task 3.5 Provide screen snapshot of AWL Dashboard and an accompanying rationale (drawing on relevant literature for good dashboard design) for the graphic design and functionality that is provided by AWL Dashboard for four specified Tableau views for sub Tasks 3.1, 3.2, 3.3 and 3.4