Predicting House Prices with Linear Regression | Machine Learning from Scratch (Part II) | by Venelin Valkov | Towards Data Science

TL;DR Use a test-driven approach to build a Linear Regression model using Python from scratch. You will use your trained model to predict house sale prices and extend it to a multivariate Linear Regression.
Smart Discounts with Logistic RegressionPredicting House Prices with Linear RegressionBuilding a Decision Tree from Scratch in PythonColor palette extraction with K-means clusteringMovie review sentiment analysis with Naive BayesMusic artist Recommender System using Stochastic Gradient DescentFashion product image classification using Neural NetworksBuild a taxi driving agent in a post-apocalyptic world using Reinforcement Learning
Smart Discounts with Logistic Regression
Predicting House Prices with Linear Regression
Building a Decision Tree from Scratch in Python
Color palette extraction with K-means clustering
Movie review sentiment analysis with Naive Bayes
Music artist Recommender System using Stochastic Gradient Descent
Fashion product image classification using Neural Networks
Build a taxi driving agent in a post-apocalyptic world using Reinforcement Learning
I know that you’ve always dreamed of dominating the housing market. Until now, that was impossible. But with this limited offer you can... got a bit sidetracked there.
Let’s start building our model with Python, but this time we will use it on a more realistic dataset.
Complete source code notebook (Google Colaboratory):
colab.research.google.com
Our data comes from a Kaggle competition named “House Prices: Advanced Regression Techniques”. It contains 1460 training data points and 80 features that might help us predict the selling price of a house.
Let’s load the Kaggle dataset into a Pandas data frame:
We’re going to predict the SalePrice column ($ USD), let’s start with it:
count      1460.000000mean     180921.195890 std       79442.502883 min       34900.000000 25%      129975.000000 50%      163000.000000 75%      214000.000000 max      755000.000000 Name: SalePrice, dtype: float64
Most of the density lies between 100k and 250k, but there appears to be a lot of outliers on the pricier side.
Next, let’s have a look at the greater living area (square feet) against the sale price:
You might’ve expected that larger living area should mean a higher price. This chart shows you’re generally correct. But what are those 2–3 “cheap” houses offering huge living area?
One column you might not think about exploring is the “TotalBsmtSF” — Total square feet of the basement area, but let’s do it anyway:
Intriguing, isn’t it? The basement area seems like it might have a lot of predictive power for our model.
Ok, last one. Let’s look at “OverallQual” — overall material and finish quality. Of course, this one seems like a much more subjective feature, so it might provide a bit different perspective on the sale price.
Everything seems fine for this one, except that when you look to the right things start getting much more nuanced. Will that “confuse” our model?
Let’s have a more general view on the top 8 correlated features with the sale price:
Surprised? All the features we discussed so far appear to be present. Its almost like we knew them from the start...
We still haven’t discussed ways to “handle” missing data, so we’ll handle them like a boss — just not use those features:
|              | Row count | Percentage ||--------------|-----------|------------|| PoolQC       | 1453      | 0.995205   || MiscFeature  | 1406      | 0.963014   || Alley        | 1369      | 0.937671   || Fence        | 1179      | 0.807534   || FireplaceQu  | 690       | 0.472603   || LotFrontage  | 259       | 0.177397   || GarageCond   | 81        | 0.055479   || GarageType   | 81        | 0.055479   || GarageYrBlt  | 81        | 0.055479   || GarageFinish | 81        | 0.055479   || GarageQual   | 81        | 0.055479   || BsmtExposure | 38        | 0.026027   || BsmtFinType2 | 38        | 0.026027   || BsmtFinType1 | 37        | 0.025342   || BsmtCond     | 37        | 0.025342   |
Yes, we’re not going to use any of those.
Now that we have some feel of the data we’re playing with we can start our plan of attack — how to predict the sale price for a given house?
Linear regression models assume that the relationship between a dependent continuous variable Y and one or more explanatory (independent) variables X is linear (that is, a straight line). It’s used to predict values within a continuous range (e.g. sales, price) rather than trying to classify them into categories (e.g. cat, dog). Linear regression models can be divided into two main types:
Simple linear regression uses a traditional slope-intercept form, where a and b are the coefficients that we try to “learn” and produce the most accurate predictions. X represents our input data and Y is our prediction.
A more complex, multi-variable linear equation might look like this, where w represents the coefficients or weights, our model will try to learn.
The variables x_1, x_2, x_3 represent the attributes or distinct pieces of information, we have about each observation.
Given our Simple Linear Regression equation:
We can use the following cost function to find the coefficients/parameters for our model:
The MSE is defined as:
where
The MSE measures how much the average model predictions vary from the correct values. The number is higher when the model is performing “bad” on our training data.
The first derivative of MSE is given by:
We will apply a small modification to the MSE — multiply by 1/2 so when we take the derivative, the 2s cancel out:
The first derivative of OHMSE is given by:
Let’s implement it in Python (yes, we’re going TDD style!)
Now that we have the tests ready, we can implement the loss function:
run_tests()
time for the results:
..... -------------------------------------------------------------- Ran 5 tests in 0.007s  OK
We will do a little preprocessing to our data using the following formula (standardization):
where μ is the population mean and σ is the standard deviation.
But why? Why would we want to do that? The following chart might help you out:
What the doodle shows us that our old friend — the gradient descent algorithm, might converge (find good parameters) faster when our training data is scaled. Shall we?
We will only use the greater living area feature for our first model.
First, our tests:
Without further ado, the simple linear regression implementation:
You might find our Linear Regression implementation simpler compared to the one presented for the Logistic Regression. Note that the use of the Gradient Descent algorithm is pretty much the same. Interesting, isn’t it? A single algorithm can build two different types of models. Would we be able to use it for more?
run_tests()...... ----------------------------------------------------------------- Ran 6 tests in 1.094s  OK
Let’s use our freshly created model to begin our housing market domination:
So how did the training go?
Our cost, at the last iteration, has a value of:
1569921604.8332634
Can we do better?
Let’s use more of the available data to build a Multivariable Linear Regression model and see whether or not that will improve our OHMSE error. Let’s not forget that scaling too:
This space is intentionally left (kinda) blank
Now that the implementation of our new model is done, we can use it. Done!?
You see, young padawan, the magical world of software development has those mythical creates called abstractions. While it might be extraordinarily hard to get them right, they might reduce the complexity of the code you write by (pretty much) a ton.
You just used one such abstraction — called vectorization. Essentially, that allowed us to build a Multivariable Linear Regression model without the need to loop over all features in our dataset. Neat, right?
Our client interface stays the same, too:
And the results:
822817042.8437098
The loss at the last iteration is nearly 2 times smaller. Does this mean that our model is better now?
You can find complete source code and run the code in your browser here:
colab.research.google.com
Nice! You just implemented a Linear Regression model and not the simple/crappy kind.
One thing you might want to try is to predict house sale prices on the testing dataset from Kaggle. Is this model any good on it?
In the next part, you’re going to implement a Decision Tree model from scratch!
Smart Discounts with Logistic RegressionPredicting House Prices with Linear RegressionBuilding a Decision Tree from Scratch in PythonColor palette extraction with K-means clusteringMovie review sentiment analysis with Naive BayesMusic artist Recommender System using Stochastic Gradient DescentFashion product image classification using Neural NetworksBuild a taxi driving agent in a post-apocalyptic world using Reinforcement Learning
Smart Discounts with Logistic Regression
Predicting House Prices with Linear Regression
Building a Decision Tree from Scratch in Python
Color palette extraction with K-means clustering
Movie review sentiment analysis with Naive Bayes
Music artist Recommender System using Stochastic Gradient Descent
Fashion product image classification using Neural Networks
Build a taxi driving agent in a post-apocalyptic world using Reinforcement Learning
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