Descriptive Statistics:

Variable	Mean	Median	Std. Dev.	Min	Max
Selling Price	626.28	617.5	185.01	282	1049
Bedrooms	3.38	3	0.74	2	5
Bathrooms	3.05	3	0.57	2	4
Days on Market	116.42	104	87.43	2	378
Age	137.81	123	100.09	2	373
Square Feet	2255.42	2239	597.99	1210	3500
Asking Price	607.73	598	183.43	262	1029

As part of a strategic plan, the management of a realtor company requested the analytics team to create a clean dataset that can be used for developing new analytics. The focus of the company is on the houses in Hollywood Beach neighborhood. The historical data of 100 houses that were in the market during the past six months is provided for this analysis. Data includes the following attributes:

	Description
Selling Price ($000)	selling price of the house
Bedrooms	number of bedrooms
Bathrooms	number of bathrooms
Days on Market	number of days that the house where available for sale in the market
Age	age of the house
Square Feet	area of the house
Location	location of the house
Asking Price ($000)	asking price of the house
Foreclosed	indicates whether the house was a foreclosure or not

used tool of interest (Rapidminer or R or both) to complete this assignment

Part 2

Your job is to design and develop a predictive model that predicts the selling price of the housesbased on a series of attributes. Your goal is to improve the performance of your predictive model.Identify the target variable and select the attributes that help you to predict the target variable. Build and apply a predictive model. Discuss the performance evaluation of the model. Interpret the results and explain your conclusion in details.