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Predictive Analytics Project

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Added on  2021-06-15

Predictive Analytics Project

   Added on 2021-06-15

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PREDICTIVEANALYTICSPROJECTStudent Name[Pick the date]
Predictive Analytics Project_1
Part 11.The objective of this predictive analysis is to develop a model whereby the house price inMinnesota can be predicted using various key variables such as size, number of bedrooms andage. Using the sample data obtained in this regards, various statistical techniques have beendeployed in order to develop the above mentioned predictive model.2. The dependent is shown below:Dependent variable: House prices 3. The independent variables are highlighted below:Independent variables: House size, Age, Number of bedrooms Independent VariableSummary of independentvariableCategorical orquantitative?Argument for / description of the associateswith the dependent variableHouse sizeQuantitative It is expected that house price should be directlyproportional to the house sizeAge QuantitativeIt is expected that house price should be inversely proportional to the house ageNumber of bedroomsQuantitativeIt is expected that house price should be directlyproportional to the number of bedrooms in the housePart 2 1.The scatter plot and regression equation for each of the case is highlighted below:Candidate Independent variablesIndependent variableRegression equationScreenshot of scatterplot1
Predictive Analytics Project_2
House Size Houseprice($'000)=3.7141+¿Age Houseprice($'000)=778.78(8.009Age)Number of bedroom Houseprice($'000)=690.95(7.5983Numberofbedrooms)2.The explanation with regards to the various independent variables is as highlighted below.House Area – The slope coefficient of simple regression model involving house piece and house areais positive and implies that with a unit increase in the area of the house, the house price wouldincrease by $50,911. The positive slope coefficient is on expected lines since a house with higher2
Predictive Analytics Project_3

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