P5 Manual For Zillow

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CS 514
Applied Artificial Intelligence
Project 5
Codename - Purple

Zillow Prize: Zillow’s Home Value Prediction
(Zestimate)
(Can you improve the algorithm that changed the world of
real estate?)
https://www.kaggle.com/c/zillow-prize-1

1|Page

INDEX
Topic

Page number

Abstract
Usage Manual
Requirements
Results
Plots

3
4
4
5
7

Note: Suitable links have been provided for additional information wherever necessary in RULES AND
DESCRIPTION.

2|Page

ABSTRACT
Zillow’s Zestimate home valuation has shaken up the U.S. real estate industry since first released 11 years
ago.
A home is often the largest and most expensive purchase a person makes in his or her lifetime. Ensuring
homeowners have a trusted way to monitor this asset is incredibly important. The Zestimate was created
to give consumers as much information as possible about homes and the housing market, marking the
first time consumers had access to this type of home value information at no cost.
“Zestimates” are estimated home values based on 7.5 million statistical and machine learning models that
analyze hundreds of data points on each property. And, by continually improving the median margin of
error (from 14% at the onset to 5% today), Zillow has since become established as one of the largest, most
trusted marketplaces for real estate information in the U.S. and a leading example of impactful machine
learning.
Zillow Prize, a competition with a one million dollar grand prize, is challenging the data science community
to help push the accuracy of the Zestimate even further. Winning algorithms stand to impact the home
values of 110M homes across the U.S.
In this million-dollar competition, participants will develop an algorithm that makes predictions about the
future sale prices of homes. The contest is structured into two rounds, the qualifying round which opens
May 24, 2017 and the private round for the 100 top qualifying teams that opens on Feb 1st, 2018. In the
qualifying round, you’ll be building a model to improve the Zestimate residual error. In the final round,
you’ll build a home valuation algorithm from the ground up, using external data sources to help engineer
new features that give your model an edge over the competition.
Because real estate transaction data is public information, there will be a three-month sales tracking
period after each competition round closes where your predictions will be evaluated against the actual
sale prices of the homes. The final leaderboard won’t be revealed until the close of the sales tracking
period.

3|Page

USAGE MANUAL
INSTRUCTIONS:
Download the code from https://www.kaggle.com/cpvirani/draft-random/notebook or from the zip
folder attached. Unzip it and run the notebook code.

Requirements:
To run the source code, you must have the below software installed in your machine.

Software
Python 3.5
sklearn
matplotlib
numpy
Pandas
Xgboost
Lightbgm
gc
random
datetime
seaborn

Download link
https://www.python.org/downloads/
http://scikit-learn.org/stable/install.html
http://matplotlib.org/downloads.html
http://www.scipy.org/scipylib/download.html

4|Page

Results
XGBoost
Predicting with XGBoost ...
First XGBoost predictions:
0
0 -0.029928
1 -0.021941
2

0.025714

0.072211

0.010145

Setting up data for XGBoost ...
num_boost_rounds=150
Training XGBoost again ...
Predicting with XGBoost again ...
Second XGBoost predictions:
0
0 -0.084468
1 -0.033246
2

0.017929

0.067383

0.034122

Combined XGBoost predictions:
0
0 -0.040384
1 -0.024108
2

0.024222

0.071285

0.014741

63157

5|Page

LightBGM
Start LightGBM prediction ...
Unadjusted LightGBM predictions:
0
0

0.029938

0.032608

0.010775

0.009892

0.009784

Combined
Combining XGBoost, LightGBM, and baseline predicitons ...
Combined XGB/LGB/baseline predictions:
0
0 -0.016695
1 -0.004245
2

0.021221

0.053898

0.014187

Predicting with OLS and combining with XGB/LGB/baseline predicitons: ...
predict... 0
predict... 1
predict... 2
predict... 3
predict... 4
predict... 5
Combined XGB/LGB/baseline/OLS predictions:
ParcelId

201610

201611

201612

201710

201711

201712

10754147 -0.0181 -0.0181 -0.0181 -0.0181 -0.0181 -0.0181

10759547 -0.0072 -0.0072 -0.0073 -0.0072 -0.0072 -0.0073

10843547

0.0749

10859147

0.0526

10879947

0.0156

0.0155

0.0156

0.0155

6|Page

Plot # 1: Total Number of NaN’s in each column
parcelid airconditioningtypeid architecturalstyletypeid basementsqft \
0 10754147
NaN
NaN
NaN
1 10759547
NaN
NaN
NaN
2 10843547
NaN
NaN
NaN
3 10859147
NaN
NaN
NaN
4 10879947
NaN
NaN
NaN
0
1
2
3
4

bathroomcnt
0.0
0.0
0.0
0.0
0.0

bedroomcnt buildingclasstypeid buildingqualitytypeid
0.0
NaN
NaN
0.0
NaN
NaN
0.0
NaN
NaN
0.0
3
7
0.0
4
NaN

0
1
2
3
4

calculatedbathnbr decktypeid
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN

0
1
2
3
4

fireplaceflag
NaN
NaN
NaN
NaN
NaN

0
1
2
3
4

assessmentyear
2015.0
2015.0
2015.0
2015.0
2015.0

0
1
2
3
4

taxdelinquencyyear
NaN
NaN
NaN
NaN
NaN

...
...
...
...
...
...

structuretaxvaluedollarcnt
NaN
NaN
650756.0
571346.0
193796.0
landtaxvaluedollarcnt
9.0
27516.0
762631.0
585488.0
239695.0

numberofstories
NaN
NaN
NaN
1.0
NaN
taxvaluedollarcnt
9.0
27516.0
1413387.0
1156834.0
433491.0

taxamount taxdelinquencyflag
NaN
NaN
NaN
NaN
20800.37
NaN
14557.57
NaN
5725.17
NaN

censustractandblock
NaN
NaN
NaN
NaN
NaN
[5 rows x 58 columns]

7|Page

8|Page

Plot # 2: logerror
Checking logerror
parcelid airconditioningtypeid architecturalstyletypeid
0

17073783

basementsqft

NaN

17088994

NaN

17100444

NaN

17102429

NaN

17109604

NaN

bathroomcnt

bedroomcnt buildingclasstypeid buildingqualitytypeid

2.5

3.0

NaN

1.0

2.0

NaN

2.0

3.0

NaN

1.5

2.0

NaN

2.5

4.0

NaN

calculatedbathnbr decktypeid

...

landtaxvaluedollarcnt

2.5

NaN

...

76724.0

1.0

NaN

...

95870.0

2.0

NaN

...

14234.0

1.5

NaN

...

17305.0

2.5

NaN

...

277000.0

taxamount

taxdelinquencyflag

taxdelinquencyyear

censustractandblock

2015.06

NaN

61110022003007

2581.30

NaN

61110015031002

591.64

NaN

61110007011007

682.78

NaN

61110008002013

5886.92

NaN

61110014021007

logerror

transactiondate month

day_of_week

week_number

0.0953

2016-01-27

Wednesday

0.0198

2016-03-30

Wednesday

0.0060

2016-05-27

Friday

-0.0566

2016-06-07

Tuesday

0.0573

2016-08-08

Monday

[5 rows x 63 columns]

Boxplot
distplot

9|Page

10 | P a g e

Plot # 3: scrutinizing transaction date

11 | P a g e

Plot # 4: logerror vs variable
-

barplot
regplot
There are similar graphs for various variables

12 | P a g e

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Producer                        : Microsoft® Word 2016
Creator                         : Virani, Charvi Pulkit P
Creator Tool                    : Microsoft® Word 2016
Create Date                     : 2018:05:02 09:49:58-05:00
Modify Date                     : 2018:05:02 09:49:58-05:00
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Author                          : Virani, Charvi Pulkit P

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