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4 Steps for Effective Machine Learning Data Direction - Finnegan Pierson - Blog

4 Steps for Effective Machine Learning Data Direction

Machine learning is an exciting and advanced area of data science. It uses models powered by data that are refined over time, mimicking the way that humans learn, in order to deliver progressively more accurate data analyses. The data is guided through a process from collection to analysis by a director. This is a very important task, as including the wrong data in the models could lead to incorrect results. Here are four steps that you'll need to follow when directing machine learning data.

Step One: Gather Your Data

The process of getting data into the models is called the machine learning pipeline, which is often abbreviated as the ML pipeline. The first step in the pipeline is to gather together the data that you plan to analyze. This means identifying your sources, pulling the data from those sources, and then saving it to a common location. Data can come from one source or multiple ones. For example, if you're trying to determine customers' reactions to a new product announcement, you'll want to pull data from various online sources that all reference that product. Keep this raw data saved in a secure location to aid in the next steps.

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Step Two: Prepare Your Data

In this step, you'll perform data cleaning. This involves filtering out any data that you don't want to be considered by the model. Start by listing out the parameters for the data that you do want to include. For example, you may only want to look at data that originates from a certain country or state, or that doesn't contain certain words or phrases, or that comes from reputable sites or verified purchasers. Once you've determined your parameters, then you just need to filter out all of the data points that don't meet those. Your newly-cleaned data is then ready for the next step in the pipeline.

Step Three: Standardize Your Data

In this third step, you'll standardize your data so that your model knows how to read it. This is different than cleaning, as you're not eliminating any more data at this point. Instead, you are standardizing your data through tasks such as grouping related words together based on their dictionary form. This is known as lemmatization. You'll also perform tokenization, or marking where individual words begin and end in text strings so computers can parse them correctly. The good news is that a lot of this work can be automated through the use of software, saving you and your time a lot of time and effort.

Test Model With Data

The last step is to take all your cleaned and prepared data and start plugging it into your model. You'll need three data sets for this: your training, validation, and testing sets. The training set is used first. This set teaches the model how to behave. Based on the results, you may need to adjust your model's parameters before moving on. The second data set is the validation set. This set is used to match the predictions made by the model with the actual data to ensure that the model is producing valid results. Finally, the testing set is, as the name implies, used to test out how well your model is analyzing real data. Once all three data sets have been plugged in and the necessary adjustments made, the final analysis can begin.

Machine learning in data science is an extremely powerful tool. The process of directing data through the pipeline from collection to full analysis is pivotal to ensuring that your results are valid and reliable. Identify your data sources carefully. Screen out the data that doesn't fit your identified parameters. Lemmatize, tokenize, and standardize your remaining data. Teach your model how to perform via the use of training, validation, and testing sets. And once all of that work is done, then you can sit back and enjoy the results of having successfully directed your data through the machine learning pipeline.

Publish Date: September 17, 2021 7:20 PM

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