How to make your ML models better: 10 techniques to improve your data preparation

Data can make your business powerful and help put some distance between you and your competitors. Good quality data is the basis of a machine learning (ML) pipeline. 

The thing is that the machine learns from the historical statistical associations. Thus, it will be as good as the historical data it is trained on. That’s why feeding ML-based algorithms with good data is critical for creating positive business outcomes. 

In this post, we’re going to discuss how to prepare data for an ML algorithm.

What is data preparation?

A dataset is the first and one of the most critical components in the Machine Learning (ML) training process. In some cases, the data acquisition process can be more complex than the actual ML model building process. Collecting data for narrow business problems is not an easy task.

Data preparation, or data processing, is the complex process of turning raw data into data sets for data scientists and analysts. The more precise your dataset is, the better and more accurate outcomes you are likely to achieve in the long run. The process of getting data ready can be summarized in 3 steps:

1. Gather data
2. Clean data 
3. Transform data 

The process of data preparation isn’t linear, it can be iterated with many loops to achieve the best results. Not all the data can be turned into high-quality datasets due to: 

• Incomplete records
• Data anomalies 
• Improperly structured data 
• Limited or sparse features  

A vast majority of ML algorithms require data to be prepared and formatted in a very specific way, so there is a lot to be done before a data set is capable of yielding powerful insights. Let’s find out how to make your data better. 

10 techniques to get the most out of your data

When developing ML-powered solutions with Unicsoft, you can rely completely on our data scientists. However, knowing some techniques in advance can help you understand what your team is discussing. 

Collecting needed amounts of data. The main question here lies in whether to collect all the available data or focus on a certain period of time. Before feeding the model with collected data, an ML engineer doesn’t know what data is valuable and what isn’t. But the more data they have, the better they’ll be able to train the ML algorithms. 

Selecting type of data storage. It’s important to understand the type of data structure (structured, unstructured, etc.) to identify the best storage solution. Ml and AI workloads have very specific storage requirements. The choice of storage platform depends on your project’s requirements such as scalability, accessibility, latency, and throughput. 

Finding missing or incomplete records. Data can go missing due to incomplete data entry, lost files, tech malfunctions, and so on. Many ML-powered models can’t operate with missing values. Finding missing data values can improve your data and help minimize biased results, so it’s important to understand what causes blank cells. Missing data can come from non-response, attrition, or poorly designed research protocols. 

Detecting outliers or anomalies. Outlier and anomaly detection is an important step in the training of high-performance models. Real-world datasets often contain outlier data points or anomalies caused by data corruption or human/tech errors. The presence of anomalies in data fed to ML algorithms may impact the performance of a model. Simple statistical techniques can deal with anomalies and detect many of them in a few clicks. 

Formatting the data. In many situations, an ML engineer will work with multiple sources of data, and each of them has its own data format. Keeping the same format across all datasets is essential for training ML models. 

Removing duplicates. Duplicated data rows affect the performance of an ML-powered model since you are passing the same data twice before every round of backpropagation. Moreover, storing large amounts of data requires more money to support its storage demands. 

Data aggregation/reduction. Not all ML algorithms require large amounts of data, especially when it comes to datasets with particular tasks. Reducing the data load is a smart move to improve the efficiency of the training process since you can pay more attention to critical values and add more dimensions and complexity while removing unnecessary data. 

Data enrichment or augmentation. This process runs counter to that of data reduction. Data enrichment enhances existing data by filling in blank cells and supplementing missing or incomplete data. It allows for capturing more specific and local relationships. Enrichment can be done via processing existing data rows or finding additional attributes in the external data.

Data rescaling. It’s a process of data normalization to improve the quality of a dataset by standardizing dimensions. ML models are trained more efficiently when the data attributes have the same scale. 

Discretization. Some ML models can perform more effectively when numerical values are turned into categorical ones. You can divide multiple ranges of values into numbers of groups and simplify the work for an ML-powered model as well as make predictions more insightful. 

Finding a reliable ML development vendor for your project 

Data preparation is an essential step in ML training. In most cases, raw data cannot be fed to models since it contains different flaws like missing values, inconsistent values, irrelevant feature variables, etc. These flaws must be ironed out for the best performance and results before ML engineers can train an adequate model. The better data you have for ML training, the more powerful the ML model you will create in the long run. 

With over 15 years of ML expertise, we know how to get the most out of data. Unicsoft delivers powerful and well-trained ML models that aim to exceed your business needs and requirements. We know how to apply machine learning to data security, data management, sales forecasting, and real-time analytics to power up your business. Drop us a line, and we’ll help you achieve a new level of data-driven decision making.