May 29, 2022
+Predicting Housing Prices
++ A recent project I had for class was to use + scikit-learn + to create a regression model that will predict the price of a house based on some + features of that house. +
+How?
+-
+
- + Pick out and analyze certain features from the dataset. Used here is the + Ames Iowa Housing Data + set. + +
- + Do some signal processing to provide a clearer input down the line, improving + accuracy + +
- Make predictions on sale price +
- + Compare the predicted prices to recorded actual sale prices and score the results + +
What's important?
++ Well, I don't know much about appraising houses. But I have heard the term "price per + square foot" so we'll start with that: +
+
+ There is a feature for 'Above Grade Living Area' meaning floor area that's not basement. + It looks linear, there were a couple outliers to take care of but this should be a good + signal. +
+Next I calculated the age of every house at time of sale and plotted it:
+
+ Exactly what I'd expect to see. Price drops as age goes up, a few outliers. We'll + include that in the model. +
+Next I chose the area of the lot:
+
+ Lot area positively affects sale price because land has value. Most of the houses here + have similarly sized lots. +
+Pre-Processing
++ Here is an example where using + StandardScaler() + just doesn't cut it. The values are all scaled in a way where they can be compared + to one-another, but outliers have a huge effect on the clarity of the signal as a + whole. +
+ +
+ 
+ + You should clearly see in the second figure that an old shed represented in the top left + corner will sell for far less than a brand new mansion represented in the bottom right + corner. This is the result of using the + QuantileTransformer() + for scaling. +
+The Model
++ A simple + LinearRegression() + should do just fine, with + QuantileTransformer() + scaling of course. +
+
+
+ Predictions were within about $35-$40k on average.
+ It's a little fuzzy in the higher end of prices, I believe due to the small sample size.
+ There are a few outliers that can probably be reduced with some deeper cleaning however
+ I was worried about going too far and creating a different story. An "ideal" model in
+ this case would look like a straight line.
+
Conclusion
++ This model was designed with a focus on quality and consistency. With some refinement, + the margin of error should be able to be reduced to a reasonable number and then + reliable, accurate predictions can be made for any application where there is a need to + assess the value of a property. +
++ I think a large limiting factor here is the size of the dataset compared to the quality + of the features provided. There are + more features + from this dataset that can be included but I think the largest gains will be had from + simply feeding in more data. As you stray from the "low hanging fruit" features, the + quality of your model overall starts to go down. +
+Here's an interesting case, Overall Condition of Property:
+
+ You would expect sale price to increase with quality, no? Yet it goes down.. Why?
+ I believe it's because a lot of sellers want to say that their house is of highest
+ quality, no matter the condition. It seems that most normal people (who aren't liars)
+ dont't care to rate their property and just say it's average. Both of these combined
+ actually create a negative trend for quality which definitely won't help predictions!
+
+ I would like to expand this in the future, maybe scraping websites like Zillow to gather
+ more data.
+ We'll see.
+