CLASSIFICATION MODEL FOR BRICK/NON BRICK HOUSES IN US

Presented By : Ashish Ranjan

Vaibhav Jain

Introduction & Objective Variables Data Set Rattle Implementation Distribution of Variables – Histogram Decision Tree Overview Induction of Decision Tree Model Evaluation : Receiver Operating

Characteristic Conclusion

AGENDA

Mr. Peter in US, after completing his MBA from University of California started working with a realtor Mannubhai Patel, who has hired him as a business analyst.

Mannubhai has told him that they are in the competitive New York retail market and therefore he needs all the help from him to get ahead.

Peter brainstormed a bit and skills to make his Boss understand the classification of Brick and Non Brick Houses relation with Price in US Real Estate Sector. He has collected some data to analyze.

Source of Data – www.analyticstraining.in

CASE STUDY – INTRODUCTION & OBJECTIVE

House Prices.xls contains data on 128 recent sales of single-family houses in MidCity. The variables are:

Price: Price at which house was eventually sold SqFt: Floor area in square feet Bedrooms: Number of bedrooms Bathrooms: Number of bathrooms Offers: Number of offers made on the house prior to

the accepted offer Brick: Whether the construction is primarily brick or

not (yes or no) Neighborhood: One of the three neighborhoods in

MidCity (east, west or north)

VARIABLES

Zone/Brick No Yes  East 26 19 45

North 37 7 44West 23 16 39

  86 42 128

DATA SET

RATTLE IMPLEMENTATION

Target Variable: Brick

Min: .69 , Max: 2.1 , 1st Qu : 1.1, 3rd Qu : 1.5, Mean : 1.3, Median : 1.26 (All figures in Lakhs)

DISTRIBUTION OF VARIABLES

Continue..

Min: 1520, Max: 2590, 1st Qu : 1900, 3rd Qu : 2150,

Mean : 2018, Median : 2000

DECISION TREE

Gini Index Calculation[1-SUM(P^2)]ROOT Node 0.4278Internal price node 0.3078 0.12Diff b/w Root and Internal price nodeInternal neighbourhood node 0.3648 0.063

Diff b/w Root and Internal neighbourhood Node

Internal SQ FT NODE 0.4422

Information Gain Calculation[-SUM(PLOG 2 (P)] GAINROOT Node 0.893173458

Internal price node 0.701471460.1917019

98Diff b/w ROOT and Internal price nodeInternal neighbourhood node 0.795040279

0.098133179

Diff b/w Root and Internal neighbourhood Node

CONFUSION MATRIXPREDICTEDNO YES TOTAL

ACTUAL NO (TN)14 (FP)2 16YES (FN)3 (TP)7 10TOTAL 17 9 26

ACCURACY(TP+TN/P+N) 0.807692308ERROR RATE(FP+FN/P+N) 0.192307692

INDUCTION OF DECISION TREE

Model Evaluation : Receiver Operating Characteristic (ROC)

CONCLUSION

Brick houses are more costlier than wooden houses. Wooden houses are relatively light compared to brick and more

flexible. Brick houses work well in cold climates as it retains natural heat

whereas wooden houses are used in areas where erosion & silt accumulation can damage brick walls.

Wooden houses are biodegradable, affordable, healthy & easier to renovate than Brick.