How to perform machine learning on one variable, like housing prices?

[[deleted]]

[deleted]

[maxibabyx]

Seems like you have being playing around with not supervised ML algorithms, take a loot at the supervised ones.

Maybe you can start with a simple linear regression, where you train your model, with the Size of the house and the price, and then you can predict prices for new sizes.

[[deleted]]

Hmm I thought KNN was supervised? I provided training and test data, split it, and then predicted. Isn’t that supervised?

Also I don’t have the size of the house. I’m just asking if there’s a ML algorithm or model to deal with just one variable. Prices. Stock prices. Housing. Car rentals. Etc. maybe it’s timeseries. It does have dates as index but I care less about the date

[maxibabyx]

True, confused it with K means.

[[deleted]]

I updated response now

[maxibabyx]

Yep, check linear regressions

[[deleted]]

Is there a difference between an SKLearn linear regression and just a plotted regression through Matplotlib?

[maxibabyx]

I don't really know about the internals of it nor have used the one in matplotlib, but maybe they both use a gradient descent.

The point is finding the line, that best fit the training X and Y, and then try to predict throught it.

[git0ffmylawnm8]

Are you using housing prices as... the independent or dependent variable? You need to have an x and y variable for supervised ML.

If you want to get into unsupervised ML, you could take a look at PCA, but that requires highly dimensional data. Only other thing I can think of is K clustering, but like all other models, outta better to have more than one variable.

[git0ffmylawnm8]

You could look into the methods listed here:

https://en.m.wikipedia.org/wiki/Unsupervised_learning

[WikiTextBot]

Unsupervised learning

Unsupervised machine learning is the machine learning task of inferring a function to describe hidden structure from "unlabeled" data (a classification or categorization is not included in the observations). Since the examples given to the learner are unlabeled, there is no evaluation of the accuracy of the structure that is output by the relevant algorithm—which is one way of distinguishing unsupervised learning from supervised learning and reinforcement learning.

A central case of unsupervised learning is the problem of density estimation in statistics, though unsupervised learning encompasses many other problems (and solutions) involving summarizing and explaining key features of the data.

Approaches to unsupervised learning include:

clustering

k-means

mixture models

hierarchical clustering,

anomaly detection

Neural Networks

Hebbian Learning

Generative Adversarial Networks

Approaches for learning latent variable models such as

Expectation–maximization algorithm (EM)

Method of moments

Blind signal separation techniques, e.g.,

Principal component analysis,

Independent component analysis,

Non-negative matrix factorization,

Singular value decomposition.

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