What Drives Home Value

See final presentation here: What drives home prices?

Description

You are a junior data scientist on the Zillow data science team and recieve the following email in your inbox:

We want to be able to predict the values of single unit properties that the tax district assesses using the property data from those whose last transaction was during the "hot months" (in terms of real estate demand) of May and June in 2017. We also need some additional information outside of the model.

Zach lost the email that told us where these properties were located. Ugh, Zach :-/. Because property taxes are assessed at the county level, we would like to know what states and counties these are located in.

We'd also like to know the distribution of tax rates for each county.

The data should have the tax amounts and tax value of the home, so it shouldn't be too hard to calculate. Please include in your report to us the distribution of tax rates for each county so that we can see how much they vary within the properties in the county and the rates the bulk of the properties sit around.

Note that this is separate from the model you will build, because if you use tax amount in your model, you would be using a future data point to predict a future data point, and that is cheating! In other words, for prediction purposes, we won't know tax amount until we know tax value.

-- The Zillow Data Science Team

You wonder how you can recieve an email from the entire data science team at once, but figure it's best to get started on the project.

Specification

Audience

Your customer is the zillow data science team. state your goals as if you were delivering this to zillow. They have asked for something from you and you are basically communicating in a more concise way, and very clearly, the goals as you understand them and as you have taken and acted upon through your research.

Deliverables

What should the zillow team expect to receive from you? Again, as you were communicating to them, not to your instructors.

1. A report (in the form of a presentation, both verbal and through a slides)

The report/presentation slides should summarize your findings about the drivers of the single unit property values. This will come from the analysis you do during the exploration phase of the pipeline. In the report, you should have visualizations that support your main points.

The presentation should be no longer than 5 minutes, and consist of 3-5 slides.

2. A github repository containing your work.

This repository should consist of at least 1 jupyter notebook that walks through the pipeline, but you may wish to split your work among 2 notebooks, one for exploration, and one for modeling.

Make sure your notebooks answer all the questions posed in the email from the Zillow data science team.

The repository should also contain the .py files necessary to reproduce your work, and your work must be reproducible by someone with their own env.py file.

Data Dictionary

Column	Description
id	Unique id for each house
bathroomcnt	Number of Bathrooms; Includes halfbaths as 0.5
bedroomcnt	Number of Bedrooms
calculatedbathnbr	Unknown; but appears to be just a repeat of bathroomcnt
calculatedfinishedsquarefeet	Renamed to squarefeet; Total square feet of home; doesn't include property square feet
fullbathcnt	Number of full bathrooms
latitude	The latitude of the property
longitude	The longitude of the property
yearbuilt	The year the house was built
taxvaluedollarcnt	The tax accessed value of the property in USD.
taxrate	Calculated column of (taxamount(from SQL database)/ taxvaluedollarcnt) * 100
beds_per_bath	Calculated column of bedroomcnt / bathroomcnt; Includes half baths as 0.5
baths_per_bed	Calculated column of bathroomcnt / bedroomcnt
sqft_per_bed_and_bath	Calculated column of squarefeet / (bedroomcnt + bathroomcnt)

All above columns were chosen based on the specifications from the team and their lack of null values. While multiple coloumns held information about the beds, baths, and sqft; these columns had less then 10% null values for the team specifications

How to Reproduce

First clone this repo

acquire.py

• Must include env.py file in directory.

  • Contact Codeup to request access to the MySQL Server that the data is stored on.
  • env.py should include the following variables
    • user - should be your username
    • password - your password
    • host - the host address for the MySQL Server

• As long as you have the env file then get_data() will do the rest on it's own.

prep.py

• acquire_and_clean_data() will run the acquire function so you only need to run this one to get the data and have it cleaned at further stages of the pipeline.

model.py

• This file has three functions
  • get_model() returns a linear regression model that has been fit on the training data
  • make_predictions() takes in the linear model object, a series containing the target values, and a dataframe containing the features and returns a dataframe with the actual values, baseline predictions, and model predictions
  • evaluate_model() takes in the predictions dataframe from make_predictions() and returns the RMSE for each along with whether the model performed better.