Machine Learning Property Pricing

Estado: Closed
Premio: $50
Propuestas recibidas: 6
Ganador: JGuymont

Winner

Resumen del concurso

I am looking for a good prediction algorithm for property pricing.

The data can be accessed either from the ZIP file located here - https://s3.amazonaws.com/freelancepropertypricingproject/project.zip
or the S3 bucket located here - https://s3.console.aws.amazon.com/s3/buckets/freelancepropertypricingproject

which contains around 80,000 data points from the city's accessor's office as well as actual sales from 2002-2014. Data can is organized in json format.

Your code will have to be shared if you are the winner of the project for further customization and integration into the business.

The project winner will be judged by the best-commented and organized code (20%), score of predictions (y) vs testing data (40% predicting sales, and 40% predicting assessments).

Please let me know if you have any questions and enjoy!

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JGuymont Canada

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raghuginnu India

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nymengr Bangladesh

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tarunacharya India

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JGuymont Canada

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JGuymont Canada

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Tablero de aclaración pública

raviudal
- 5 años atrás
Hi i want to know that if you want two different classifers or you want to predict assessor(TOTALVALUE) or sales price(Gross_Sale_Price) or assessor depends on sales and you want to predict assessor?
- 5 años atrás
1. Organizador del concurso
  - 5 años atrás
  thank you for asking, I am asking for two different models. one to predict total_value and the other to predict gross sale price. I believe both will be dependent on the assessor features as well as similar and recent sales.
  - 5 años atrás
swetaroy06
- 5 años atrás
Can you provide some data dictionary? Since the data is in json format and cannot be imported/seen directly, it is very inconvenient to carry out an analysis.
- 5 años atrás
JGuymont
- 5 años atrás
OK, I saw in the previous comments that we should use the feature APN to map assessment data to sales data, but I don't see the APN feature in the sales data. Could it be named differently?
- 5 años atrás
1. Organizador del concurso
  - 5 años atrás
  yes it is named PIN_TEXT. I hope you will submit an entry, you sound right on track to provide exactly what I'm looking for.
  - 5 años atrás
JGuymont
- 5 años atrás
I have a model that predicts TOTALVALUE with some of the assessment features (BELOWGROUNDAREA, ABOVEGROUNDAREA, TOTAL_UNITS, ZIPCODE, PROPERTY_TYPE, ...). Now if I understand correctly, you would like to predict the sale price using all the features including the predicted TOTALVALUE.

My question is how can we map the features in the assessor.json with features in sales.json?
- 5 años atrás
JGuymont
- 5 años atrás
Are we allowed to use the BUILDINGVALUE as a feature to predict TOTALVALUE in the Assessment Data?
- 5 años atrás
1. Organizador del concurso
  - 5 años atrás
  Ha, thanks for asking that. No, that will not provide what I'm looking for.
  - 5 años atrás
nymengr
- 5 años atrás
do u have any csv file of the data instead of lots of jason files?
- 5 años atrás
1. Organizador del concurso
  - 5 años atrás
  I don't at this time, I will post on here if I can make one in time before the contest ends
  - 5 años atrás
tarunacharya
- 5 años atrás
Could you mention the attributes which you exactly want to be predicted? In both sales and assessors.
- 5 años atrás
1. Organizador del concurso
  - 5 años atrás
  Sure, in the Assessment Data I would like to predict the 'TOTALVALUE' attribute. The sales data is related to the Assessment Data by the 'APN' attribute, and I would like to predict the 'Gross_Sale_Price' in the sales data. Does that help clarify?
  - 5 años atrás
Organizador del concurso
- 5 años atrás
I've updated the link so that you can download the data through a zip file
- 5 años atrás
1. edgarguzmnblanco
  - 5 años atrás
  Great! I I could access to the data.
  - 5 años atrás
edgarguzmnblanco
- 5 años atrás
I can't access the data without an AWS account.
- 5 años atrás
1. Organizador del concurso
  - 5 años atrás
  what would be your preferred way to receive the data?
  - 5 años atrás