1. CLASSIFICATION MODEL FOR BRICK/NON BRICK
HOUSES IN US
Presented By : Ashish Ranjan
Vaibhav Jain

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

3. CASE STUDY – INTRODUCTION &
OBJECTIVE
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

4. VARIABLES
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)
Zone/Brick
East
North
West
No
26
37
23
86
Yes
19
7
16
42
45
44
39
128

5. DATA SET

6. RATTLE IMPLEMENTATION
Target Variable:
Brick

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

8. Continue..
Min: 1520, Max: 2590, 1st Qu : 1900, 3rd Qu : 2150,
Mean : 2018, Median : 2000

9. DECISION TREE

10. INDUCTION OF DECISION TREE
Gini Index Calculation[1-SUM(P^2)]
ROOT Node
Internal price node
Internal neighbourhood
node
Internal SQ FT NODE
0.4278
0.3078
0.3648
0.4422
0.12Diff b/w Root and Internal price node
Diff b/w Root and Internal
0.063neighbourhood Node
Information Gain Calculation[-SUM(PLOG 2 (P)]
GAIN
ROOT Node
0.893173458
0.1917019
Internal price node
0.70147146
98Diff b/w ROOT and Internal price node
Internal neighbourhood
0.0981331Diff b/w Root and Internal
node
0.795040279
79neighbourhood Node
ACTUAL
NO
YES
TOTAL
ACCURACY(TP+TN/P+N)
ERROR
RATE(FP+FN/P+N)
CONFUSION MATRIX
PREDICTED
NO
(TN)14
(FN)3
YES
(FP)2
(TP)7
17
0.807692308
0.192307692
TOTAL
16
10
9
26

11. Model Evaluation : Receiver
Operating Characteristic (ROC)

12. 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.