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Task 1.1} Conduct an exploratory data analysis {EDA} of the house-prroeacsy data set using the Rapidlvfiner Studio data mining tool. Provide the foowing for Task

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Task 1.1} Conduct an exploratory data analysis {EDA} of the house-prroeacsy data set using the Rapidlvfiner Studio data mining tool. Provide the foowing for Task 1.1: {i} a screen capture of your final EDA process and briefly describe your EDI-\"r process {ii} summarise key results of your exploratory data analysis in a table named Table 1.1 Results of Exploratory Data Analysis forHousePricescsv. {iii} Discuss the key results of your exploratory data analysis presented in Table 1.1 and provide a rationale for why you have selected your 5-5 top variables for predicting house prices and in particular their relationship with the house price dran on the results of your EDA analysis and relevant literature {About Elli] W'd]. Table 1.1 should include the key characteristics of each variable in the house-priceaosy data set such as maximum, minimum values, average, standard deviation, most frequent values [mode], missing values and invalid values etc. Hint: The Statistim. Tab and the IChart Tab in Rapth provide a lot of descriptive statistical information and the ability to create useful charts like Earcharts, Scatterplots etc for the EDA. analysis. You might also lilce to look at running some correlations and chi square tests on the house-pricescsv data set to indicate which variables you consider to be the top 5-5 key variables which contribute most to predicting house prices. Task 1.2} Build a Linear Regression model for predicting house price using a RapidI't-'liner data mining process and an appropriate set of data mining operators and a reduced set of variables from the house-pricesrsv data set determined by your exploratory data analysis in Provide the following for Task 1.2: {i} A screen capture of Final Linear Regression Model process and briey describe your Final Linear Regression Model process {ii} A table named Table 1.2 narned Results of Final Linear Regression Model for Task 1.2 for house-prices.csv data set. {iii} Discuss the results of the Final Linear Regression Model for house-prroeaosv data set drawing on the key outputs (coefcients, standardised coefficients, t-statistics values, pvalues and significance levels etc} for predicting house prices and relevant supporting literanire on the interpretation of a Linear Regression Model {About S 1inti'urds]. Include all appropriate Rapidlvliner outputs such as Rapidhner Processes, lGraphs and Tables that support the key aspects of your exploratory data analysis and linear regression model analysis of the house-pricesrsv data set in your Assignment 2 report. Note you need exqi-nrt the Rapiir'tt'liner Processes and Graphs from Rapidl't'liner using the FiJeI'Printhxpor't Image option and include in the Task 1 section where relevant or in Appendix: 1 of Assignment 2' report

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