Conduct an exploratory data analysis (EDA) of the housing.csv data set using the RapidMiner Studio data mining tool. Provide the following:
(i) a screen capture of your final EDA process, briefly describe your EDA process
(ii) summarise key results of your exploratory data analysis in Table 2.1 Results of Exploratory Data Analysis for housing.csv.
(iii) Discuss the key results of exploratory data analysis presented in Table 2.1 andprovide a rationale for selecting top 5 variables for predicting house values and in particular their relationship with house values drawing on the results of EDA analysis and relevant literature (About 250 words).
Should include the key characteristics of each variable in the housing.csv data set such as maximum, minimum values, average, standard deviation, most frequent values (mode), missing values and invalid values etc.
The Statistics Tab and the Chart Tab in RapidMiner Studio provide a lot ofdescriptive statistical information and the ability to create useful charts like Barcharts, Scatterplots etc for the EDA analysis. You might also like to look at running some correlations and/or chi square tests as appropriate for the housing.csv data set to determine which variables contribute most to predicting house values.
Build a Linear Regression model for predicting house value using a RapidMiner data mining process and an appropriate set of data mining operators and a reduced set of variables from the housing.csv data set as determined by your exploratory data analysis in
(i) A screen capture of Final Linear Regression Model process and briefly describe your Final Linear Regression Model process
(ii) A table named Table 2.2 named Results of Final Linear Regression Model for Task 2.2 for housing.csv data set.
(iii) Discuss the results of the Final Linear Regression Model for housing.csv data set drawing on the key outputs (coefficients, standardised coefficients, t-statistics values, p-values and significance levels etc) for predicting house values and relevant supporting literature on the interpretation of a Linear Regression Model
Create a Geo map Graph view that displays house values and other relevant data using housing.csv. Comment on the (1) process of preparing a Geomap Graph view using Tableau Desktop and (2) key trends and patterns that are apparent in the Geomap Graph created for visual presentation of the housing.csv data set. Note this housing values data set is drawn from State of California USA so you need to modify the Map Menu Edit Locations options in Tableau so you can plot latitude and long
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