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SAS

To predict the sales of NYC property

a) Data-set Details:

  1. This dataset is a record of every building or building unit (apartment, etc.) sold in the New York City property market over a 12 - month period
  2. It contains the location, address, type, sale price, and sale date of building units sold.
  3. The areas are subdivided into smaller locations called boroughs viz. Manhattan, Bronx, Brooklyn, Queens, and Staten Island.
  4. Trading of every property in NYC built since 1900 has been documented in a rich dataset of around 70,000 rows.
  5. From this grandiose property data, we identified a pattern that would help us determine the “sale price” of every property with respect to zip code.
  6. This would help the owners to fetch the current value of their property. Similarly, our miniature will provide insights into the current trends in property sales in NYC for new buyers & real estate agencies.

b) SAS Logic Details:

  1. Data Cleaning
  2. Data Classification with respect to Borough (1= Manhattan, 2=Bronx, 3=Brooklyn, 4=Queens, 5=Staten Island)
  3. Univariate Analysis
  4. Regression Modelling

c) Problem Statement:

  1. Determine which zip codes within a borough prove to be significant for determining sale price of a property
  2. Determine whether total units (cumulative of residential and commercial units), gross square feet and land square feet are significant for determining the sale price of a property

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