A quick tutorial on installing and connecting PySpark and MySQL, and running a simple ML model.

This post is meant as a short tutorial on how to set up PySpark to access a MySQL database and run a quick machine learning algorithm with it. Both PySpark and MySQL are locally installed onto a computer running Kubuntu 20.04 in this example, so this can be done without any external resources.

Requirements

Installing MySQL onto a Linux machine is fairly quick thanks to the apt package manager with sudo apt install mysql-server. Once it’s installed, you can run sudo mysql in a terminal to access MySQL from the command line:

Screenshot of the MySQL prompt in a console window.

For PySpark, just running pip install pyspark will install…


Determining whether an LSTM trained on one stock can generalize to other stocks, depending on correlation.

When trying to look at examples of LSTMs in Keras, I’ve found a lot that focus on using them to predict stock prices in the future. Most are pretty bare-bones though, consisting of little more than a basic LSTM network and a quick plot of the prediction. Though I think the utility of these models is a little questionable, it brought a question into my head: how accurate are the predictions made by a model trained on one stock if it’s predicting on another stock?

The full code can be found here.

Problem Description

Stocks are correlated with each other to varying…


An attempt to explain the thought process before and during the making of a project.

I see a lot of examples of data science projects and their technical underpinnings. I also see plenty of posts giving broad but ultimately vague advice on how to do such a project. But few, if any, seem to actually walk through a thought process for making a project. …

Gregory Janesch

Early-career data scientist/statistician, recently finished a Master’s in Statistics.

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