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.
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