Recent innovations in machine learning have made headlines from Bloomberg to Time Magazine. Once a niche field practiced by researchers and developers, machine learning is now core to many business teams.
Sales and marketing teams use AI to score leads, predict customer lifetime value, and prevent churn. Finance teams use it to detect fraud and forecast prices, while HR teams use it to identify talent and stop attrition.
However, before businesses can capitalize on the power of machine learning, they need to ensure their data is prepared correctly. Without quality data, machine learning models are little more than guesswork. In this article, we’ll look at 5 ways to prepare your data for machine learning.
Data preparation, also referred to as data preprocessing, is an important step in the data science process.
The first step of the data preparation process is real-world data collection. This training data will be used in the machine learning process, but raw data often has many issues, including missing data or outliers. To ensure that new data meets data quality standards, data scientists would start with data exploration, feature selection, and feature engineering.
Data scientists use a variety of tools and techniques to complete the data preparation process. Popular open-source tools like Python, Pandas, and SQL are often used to clean, transform, aggregate, subset, and visualize data. Additionally, cloud-based services like Microsoft Azure or AWS can automate many of the manual tasks associated with data preprocessing.
After the data is cleaned and formatted in a CSV file (or any other type of structured data), it can be used for data analysis, machine learning, and even deep learning. Data preparation is a critical component of any successful machine learning workflow, as it helps to ensure that the right data is being used for the learning process.
Good data preparation can improve model performance, reduce training time, and enable real-time predictions—making it an essential skill for all data scientists. Now, with no-code tools like Akkio and tutorials like this one, anyone can learn the basics of data preparation and begin to unlock its potential.
If we “are what we eat,” then machine learning models “are what we feed them.” Poor quality data leads to poor decisions, and the most sophisticated algorithms are powerless against it.
Zillow's failed attempt to predict real estate prices is a perfect example. The coronavirus pandemic threw the housing market into disarray and Zillow's algorithms were unable to adapt quickly enough, leading to inaccurate predictions.
In another infamous example, Amazon's recruiting algorithm was found to be biased against female candidates. This happened because the algorithm was trained on data from previous hiring decisions, which may have been skewed toward male applicants.
In an even worse scenario, Google's image classification algorithms classified a black couple as gorillas. While this was an embarrassing incident, it also highlights the power of data preparation—had Google taken steps to remove erroneous and biased data from its datasets, this would have been avoided.
These examples are just scratching the surface. There are countless other ways in which bad data can lead to poor decisions, and it's essential that companies take steps to prevent them.
Data preparation also helps train models faster, as it reduces the training time by removing unnecessary information. It also helps improve accuracy by reducing errors caused by incomplete or incorrect information.
Finally, data preparation can help reduce overfitting by reducing the complexity of the model which in turn improves generalization capabilities.
There are 5 key steps that should be taken when preparing data for machine learning:
Let's take a closer look at each step.
The first step is to collect relevant data from diverse sources that are representative of your customer base. It's essential that the data was collected using reliable methods, as biases can lead to inaccurate predictions and poor decisions.
For instance, a predictive model for hiring might be trained on data from a single department, which could lead to the model being biased towards certain genders or ethnicities. To prevent this, it's important to check off your data sources and make sure that the sample is representative of your entire target audience.
You may want to entirely remove data that's likely not indicative of the underlying behavior you're trying to capture, such as gender or race. In many cases, using this kind of data can lead to inaccurate predictions and poor decisions.
Once you've collected your data, it's time to select the best machine learning techniques for your business needs. Different algorithms and technologies are better suited for different tasks—for example, a deep neural network might be more suitable for recognizing images than a support vector machine (SVM).
At Akkio, we use a variety of advanced machine learning algorithms and technologies. Neural Architecture Search (NAS) is one way we automatically search for the best neural network architecture that can solve a given task. This is useful in situations where you don't have the expertise to hand-pick architectures or when you want to boost performance quickly.
For instance, when NAS is applied to a relatively simple task like housing price estimation, it might determine that a simple two-layer neural network is best. On the other hand, for more complex tasks like fraud detection, it might determine that a more sophisticated 20-layer deep learning network is required.
That said, “best” is subjective when it comes to selecting a model. While maximizing accuracy is important, other metrics like the speed of training, ease of interpretation, and memory requirements may also be taken into account.
A decision tree, for instance, is highly explainable and provides a clear decision boundary, making it a popular choice for business decisions. In contrast, a deep neural network might have better accuracy and performance, but it can be difficult to interpret. Ultimately, the best model for your task depends on your data and business goals.
Once you've selected the appropriate ML techniques, it's time to clean your data. Data cleaning involves multiple steps, such as:
Adding missing values is useful when dealing with incomplete datasets, as it helps the model understand what to do with missing information. For instance, if you have a dataset of customer reviews but some customers haven't provided their ages, you could substitute this value with the average age in the dataset.
Instead of a simple average, you could also use more advanced techniques such as K-nearest neighbors or linear regression to fill in missing values. Or, you could augment the dataset by scraping additional data from other sources and merging the two datasets.
Data standardization or normalization is also important. This involves adjusting each variable in the dataset so that they all have roughly the same range or fall within certain bounds. This is particularly important when working with variables with vastly different ranges (e.g. prices vs the number of page views per user), as it helps ensure that all features are given equal weight when training the model.
Tokenizing words is also important for machine learning models. This is the process of breaking down words into individual components or “tokens” so that a computer can understand them. For instance, the word “I'm” would be tokenized into two tokens: “I” and “am”, and then given numerical representations. Without this step, computers won't be able to decipher words and make accurate predictions.
Removing outliers is also essential when dealing with datasets that contain errors or anomalies. This involves identifying and removing extreme values that could otherwise distort the model's predictions. For instance, if you're training a model to predict house prices but one property is worth ten times more than the others, it may be wise to remove this outlier from the dataset.
Finally, it's important to remove duplicates from your dataset. This can help reduce complexity within your model and ensure that you're using clean data when making predictions.
Once you've cleaned your data, it's time to structure it. This involves labeling data attributes according to your ML model and its ultimate function.
For instance, if your model is analyzing customer feedback to find trends, you could label the data as “positive” or “negative” based on the words or emotions expressed in reviews. You could also combine or separate datasets based on their correlation.
Data and data dimensionality reduction are also important for machine learning models. Dimensionality reduction involves reducing the number of variables within your dataset so that it contains only the most important information needed for making predictions.
This helps reduce complexity within the model and improves accuracy by removing any unnecessary data which might otherwise cause inaccuracies in predictions. It also helps reduce overfitting, as it reduces the complexity of the model which in turn improves generalization capabilities.
Finally, consider taking a smaller representative sample of your data to explore and experiment with solutions and algorithms before you use an entire dataset. This can help save time and resources, as it reduces the amount of data you have to process and train on.
Sampling isn't always the best approach, as you can lose important information from your data. For instance, if you're only dealing with a couple of thousand rows of customer data, it's likely better to use the entire dataset instead of a sample.
However, if you have millions of rows and hundreds of features, then sampling can help you quickly explore different options and identify which algorithms and models might work best for your data.
Data transformation was once a painstaking process, one that required a great deal of time, resources, and expertise. But with the emergence of advanced AI solutions, such as Akkio, businesses can now prepare and analyze their data for ML with ease.
Akkio is a no-code AI platform that provides businesses with the tools and features to quickly and easily transform their data, allowing them to get the most out of their machine learning initiatives.
Through Akkio's integrations with tools like Hubspot, Salesforce, Google BigQuery, Google Sheets, and more, businesses can ingest their data from virtually any source. Moreover, Akkio automatically handles missing values and different data formats, making data preparation a breeze.
When the user is ready to build their ML models, Akkio automatically splits the data into training and testing sets. Akkio also allows users to refresh their data on a regular basis, ensuring that their models are up-to-date with the latest information.
In addition, businesses can take advantage of Akkio's data augmentation features to enhance their ML output. By merging external datasets, users can uncover hidden patterns and get more accurate predictions.
With Akkio, businesses have access to the powerful features they need to transform their data and make their ML projects a success. From data ingestion and handling of missing values to data splitting and augmentation, Akkio provides an easy-to-use platform for preparing your data for ML.
Machine learning can be a powerful tool for businesses, but only if the data that is used to train models is of high quality. Data preparation is essential for making sure your models are accurate and reliable.
By following the five steps outlined in this article—collect relevant data and adjust for bias, use the best ML techniques, clean your data, structure your data, and consider sampling—you can make sure that your machine learning projects are successful.
Moreover, with Akkio's comprehensive no-code AI platform, businesses can quickly and easily transform their data, making it easier than ever before to create accurate and reliable models.
Ready to prepare your data for machine learning? Sign up for a free trial today.