Data Preparation for Machine Learning: 5 Best Practices for Better Insights | Pecan AI

Data Preparation for Machine Learning: 5 Best Practices for Better Insights

Resource description: Discover the different steps in the data preparation process and five best practices to ensure optimal performance for your models.

Here’s a hard truth about the data science process: most of your data isn’t ready for modeling. At least not out of the gate. Data preparation has long been the bane of data science and ML projects, estimated to eat away anywhere from 50-70% of a project’s time.

But it doesn’t have to be that way.

With AI-powered technology like Pecan’s low-code predictive analytics platform, you can turn raw data into clean, pristine records for powerful machine-learning use cases in just minutes. In this blog, we explore best practices for data preparation in machine learning so you can make better predictions and guide your business forward with confidence.

The importance of data preparation in the ML workflow

If your data is not clean, complete, or accurate, your modeling results will be skewed. Simply put, machine learning models need quality data.  Additionally, because ML models improve incrementally over time, poor initial results based on flawed data become the foundation for future results. All it takes is one bad batch of data to create a downward spiral of unreliable insights.

Other consequences of not prioritizing data prep in your ML workflow include:

  • A lack of high-level understanding of the data set
  • Inaccurate predictions, often due to outliers or typos
  • Overfitting, or performing well on training data but not on fresh data
  • Model bias, where certain less-important data is inadvertently prioritized higher
  • Data leakage, where the model gets access to data it shouldn’t (such as training data) and uses it to make predictions
  • Inconsistent scaling, where features with larger data ranges have more influence over the process and skew the results as more data is added
  • Skipping data sets, such as those with missing values or values that aren’t formatted properly

Six steps to preparing data for machine learning

At a minimum, your data will go through several steps to be ready for use in your machine-learning projects. Using a low-code Predictive GenAI platform like Pecan can streamline and simplify every step. Let’s explore each. 

  1. Data ingestion

This step involves importing and collecting data from every source in your data pipeline. This could be your enterprise applications — such as your ERPs or CRMs — a data warehouse, data lake, third-party data sets, various marketing and campaign tools, machinery sensors, local files, and so on. With the right technology, you can turn data ingestion into a dynamic process, connecting to real-time data sources that continually feed data into your workflow. 

Pecan supports real-time integration with and ingestion from virtually any data source, such as CRMs and data lakes. It allows for real-time transfers without the need for complex or home-grown configurations, helping you always have the right data at hand. 

  1. Data cleansing

Also called data cleaning or data wrangling, data cleansing involves the laborious task of checking and repairing data errors and inconsistencies. The raw data may be missing values or possess outliers, so you’ll need to scrub your data to make sure there aren’t any duplicate or missing entries, etc. If you have any outliers or anomalies that could skew results, this is the time to remove them. 

Since this is often the most laborious step in the data process, relying on a platform like Pecan can help. Pecan instantly removes duplicates, flags outliers, and ensures that only the most applicable and appropriate data gets included in your models. There’s no longer a need for manual scrutiny of every data set. 

Get started today and let your data drive results in weeks

  1. Data transformation 

Raw data from a single source comes into the data pipeline in its own format, which will differ from the data from other sources. For example, dates may be shown in several formats, including DD/MM/YYYY or DD/MM/YY. If this data isn’t transformed to all be in the same format, your machine-learning models won’t process them correctly. 

This step is also where you might encode categorical variables and create new features through feature engineering. Taking a data set that includes geographical and income data and creating a new feature showing how these two values interact could be an example of a new feature. Another example could be aggregating data so that multiple features appear as one. Earnings data for a single day could be aggregated into monthly or quarterly features. 

In this step, you may also want to turn text data into numerical data, such as “four” into “4” to standardize it for all future machine learning uses. This is your opportunity to take the large data sets you already have and make them accessible for the model to use and learn from over time. 

Pecan’s built-in automated data preparation helps you transform raw data into various formats to save substantial time, meaning you and your team can focus on uncovering insights. 

  1. Data splitting 

Data splitting separates your data for different uses, such as training, testing, and validation. You won’t want to use the same sets for each, which can lead to overfitting. By having your data in different buckets for each part of the process, you can be sure that the data you use to assess your model is unseen (never used before). You’ll get a better picture of just how well your model works and what changes you need to make to improve it. 

Pecan automatically partitions your data into training, validation, and test sets so you can keep data separate throughout the process. 

  1. Data augmentation

This step creates new data samples from existing ones by transforming them. For example, it may take an image and rotate it or increase the size of text data. This augmented data helps train the models, giving them more samples to prevent overfitting. 

  1. Data balancing

If there’s more of one class type than another, it may cause an imbalance. Data professionals can work around this by using fewer samples of the majority data and more of the minority data.  Data balancing is one way to avoid creating bias with your algorithms. 

Pecan balances your data so the same type is included in each set, providing visualization tools to easily see how the data is distributed.

5 best practices for data preparation

While advances in AI may someday bring us closer to when we may need far less data to get accurate data models, those applications are now limited to specific physics use cases. Most machine learning models built for business insights, marketing, or consumer buying behavior predictions still need copious amounts of data to train. And it must be pristine. These best practices can help you make the most of all your data. 

  1. State the problem early on

“What do we want to predict?” 

It’s a simple question, yet, business leaders may not know what they want their machine-learning projects to answer. However, knowing this early on is important to perfectly tune your data for your ML use case. 

When forming your business question, consider things like:

  • Business context, including challenges, goals, and pain points. Each industry or customer group may have very unique approaches to the data.
  • What do stakeholders hope to learn from the data? How will they use your model’s insights in their daily work? Soliciting their input can help you refine the business problem.
  • Do you need to go beyond rear-view data/descriptive analytics? If you’ve ever thought, “This report is great, but it’s too bad it doesn’t also show XYZ,” you may have the basis for a great machine learning opportunity. 
  • Consider the data output you want. Are you looking for yes or no (binary) answers or a ranking of your most profitable products? 
  • KPIs that can help you answer the question. If you already track lifetime value (LTV) or churn rate, consider how the machine learning algorithm can utilize these familiar terms and not create new metrics. 

You may find you have many questions you want answered, and that’s OK! Prioritize one problem at a time. After you have a few operational ML projects, you will find you can come back to the less urgent but still valuable business problems. 

  1. Establish proper data governance

The policies, processes, and tools you use to administer your data properly are your data governance. Governance frameworks are essential to keeping your data safe, secure, and compliant with the laws and industry standards for your business. 

You may already have a data governance framework in place, but the use of machine learning may require you to rethink your strategy. That’s because ML is dynamic and continually learning while also using a higher volume of data with more variety in its form and structure. Governance helps you keep data safe while also preserving the consistency and accuracy of your ML results. 

  1. Use a tool to help

Visit any Reddit thread where data professionals discuss their work, and you’ll see a trend. Much of the time spent in building ML models is preparing the data on the front end, and it’s a point you can’t escape without hiring several data scientists or engineers.

That is unless you use a technology like Pecan, which reduces much of the preparation work by letting you import data in all forms and formats. It automates the standardization, deduplication, and parsing to let you focus on the questions you need answered so you can build your model.

After the data has been prepared, you can rely on Pecan to continue checking the quality of your model results and recommend validation tasks to keep it on target and continually providing high-quality results. 

Get started today and let your data drive results in weeks

  1. Start with visualization

Since most business leaders aren’t data analysts, the raw, unstructured data on its own won’t mean much. But after it’s been through a successful machine learning model and transformed into meaningful insights, there are many options for how those insights get displayed and consumed.

We often think of data visualization as a final step in the analytics lifecycle, but it can actually be a beginning, a chance to see and uncover new patterns and relationships. Visualizations such as scatter plots, histograms, and charts can be very effective for this, revealing the relationships between different variables. This can also be the quickest way to catch a problem with algorithm performance, allowing you to make corrections while they can still affect results. 

  1. Prioritize good documentation

Documentation for everything, including data governance and your initial prediction questions, ensures you can always check your work and go back to a previous step if necessary. It’s essential for collaboration, too, so that teammates can easily understand what’s happening with your model and workflow.

Reasons to document through the entire lifecycle include:

  • Showing your work: Detail your preprocessing steps, such as the transformations you performed or the way you aggregated data features, so stakeholders understand the underlying logic. 
  • Ramp-up time: Newcomers to the projects can easily see how the data was prepared and more easily make their own contributions.
  • Efficiency: Documentation can create reproducibility if you want to make another similar model. 
  • Transparency: Documentation is key to knowing how your model works and keeping it performing well over time. 

The CLeAR Documentation Principles from the Shorenstein Center on Media, Politics, and Public Policy also notes the cultural implications of proper documentation. Since models are often built upon older models, having documentation can help engineers know if they are choosing the right base algorithm to build upon in the first place. Cultural differences, biases, and other differences between countries or cultures require full transparency before using another culture’s ML model; documentation can help them choose the right fit. 

Pecan supports and facilitates documentation with Interactive Notebooks that show how models were built as well as their results. Pecan also allows users to export data, model results, and visualizations, which can be included in documentation for comprehensive reporting. With explainable AI and transparent processes, it’s far easier to create documentation that others can follow along with and even build upon. 

Next steps for a high-quality machine-learning workflow

Clean, accurate, and quality data may be just the first step in creating a robust machine-learning model, but it’s also the most important. Unfortunately, it often requires a lot of effort to do well. 

With Pecan, you can ingest raw, transactional data from multiple sources and use our built-in features to clean and prepare your data, select the right features and business questions, and build a machine-learning model in minutes. We also provide transparency and documentation featureo, so you can always feel confident in your model’s predictions.

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