Machine Learning (ML) relies heavily on the data fed to give the desired outcome and predictions. It’s a critical aspect of the algorithm training. However, it won’t matter what amount of information you feed the model and the level of expertise you use in the project. If the project can’t decipher the records accurately, it’s useless or even dangerous in some cases. That’s why the initial and most important step in creating ML models is dataset preparation.
Data preparation includes collection, structuring—using tools or a data labeling platform––and cleaning. Data labeling is particularly a critical aspect of preparing data for ML. It’s referred to as the process of identifying raw data and creating meaningful or informative tags to enable the model to recognize and train better.
When you prepare your ML data correctly, it becomes easier to automate the other steps, allowing you to focus more on the goal of your model and spend less time in the engineering stage. This article discusses the data preparation stages for a successful ML model and outcome.
Step 1: Identify your project goal
Understanding what outcome you want or what you want to predict with your model helps you decide on the most valuable data for your project. When identifying your goal, undertake data exploration so you can segment and separate your datasets accordingly into categories, such as classes, regression, clusters, and ranks.
Determine if you want the algorithm to answer binary yes or no questions or multiclass classifications, yield numeric values, or find rules and number the classes. How you segment your data matters in how you end up organizing your data for your model. The end goal of your project will determine how to segment your data before you start collecting. The idea is to set the stage properly to avoid complications.
Step 2: Data gathering and discovery
Once you’ve formulated the problem you want to solve, you need to collect data from all sources, internally and from third parties. However, when gathering data, you must consider why the data was collected and what it represented at that time. This means knowing the context in which you can use the data to give the desired outcome.
There’s always a strong temptation to include all your data, but this isn’t a good idea. In data collection for ML, more isn’t always better. The quality and relevance of your data to the training process are more important––which is also one of the best practices in ML model building. This is important in mitigating and reducing data bias that may lead to erroneous representation and observations.
For example, if you want to train your ML model to predict customer behavior, examine the data and ensure that the dataset is from a diverse audience geographically and other perspectives. If you fail to conduct data discovery on your collection when preparing data for ML, you may end up with technically correct yet lacking data. Or, you may end up with data meant to solve a different problem. Such data will produce incomplete or inaccurate results.
Step 3: Data exploration and profiling
Exploration means reviewing each variable’s data type and the relationships between these variables. This should help you discover and understand how each dataset is relative to the desired outcome.
This step can help you discover issues with the dataset that you need to address. This may include identifying areas you may need data transformation or standardization. Exploration can also help you identify opportunities to improve the dataset for better ML model performance. You want to see patterns that don’t seem to match and catch issues that can flaw your model and findings.
Step 4: Formatting data for consistency
You must ensure to convert the data you’ve collected into a format you’re working with. This includes converting your database from a relational to a flat or text file. While converting data and creating annotations may not be a huge issue, you should ensure that it’s in the most suitable format for your ML system.
In addition, when you collect data from different sources or datasets that are manually input into the model, make sure that variables are consistent across the board. This includes things like currency and date formats. Not formatting your data consistently across the dataset may lead to wrong observations.
Step 5: Data cleansing and validation
After you’ve collected your data, you also need to assess its condition to determine its viability for use in training your ML model. Look for missing, incorrect, inconsistent, exceptions, outlines, flawed information, and outliers. Data cleansing is a crucial step, keeping in mind that your ML model learns from the data you input.
If you feed it with erroneous and inconsistent data, its quality and integrity are compromised, and you can expect problems with the outcome. Thus, if you’re using a dataset with information that can’t help build the model towards the desired outcome, it’s best to remove it.
Step 6: Transform the data
Finally, you need to transform your process data, also called feature engineering. The things that influence this step are the specific algorithm you’re using in your model and knowledge of the problem domain you’re looking to solve. There are three common data transformations: scaling, decomposition, and aggregation.
- Scaling: Your data may contain mixed scale attributes for different quantities, such as currency, volume, and weight. Many ML models work well with data attributes with the same scale for the smallest and largest features, such as between 0-1.
- Decomposition: This is when you have features representing a complex concept that can work better when you split them into constituent parts. A good example is a date with day and time components that you can consider splitting further. If only the hour is relevant to your model, you can look at the feature decompositions you can carry out.
- Aggregation: Your dataset may contain features that you can aggregate into one and be more meaningful in training your ML model. For example, instead of having separate entries for each time a customer buys something from your store, you can aggregate that into a number count.
Transforming your data may take time, but it can significantly benefit your ML model by improving algorithm performance.
Dataset preparation is crucial in training your ML model, and it helps you build better and more accurate projects. Understanding the problem you want to solve with your ML model and developing a goal is the first step to adequately preparing your dataset. Hopefully, the steps shared above will help you prepare your data for ML and develop better models and projects.