Each machine learning project is unique in its chosen dataset, tasks, algorithms, and objectives. Unless data scientists are practicing their skills and applying a standard dataset, they will have to go through a so-called applied machine learning process. This begins with defining a problem that involves the framing of the prediction task such as regression or classification as well as exploring the available data. The second step is data preparation or data preprocessing, which is the main focus of the article, and entails transforming raw data into datasets used in modeling. The process ends with evaluating and finalizing the model. In this article, we will explore data preparation for machine learning, and its main challenges and techniques.
Data preparation is the process of data transformation carried out by data science specialists that allows them to meaningfully use the datasets for machine learning algorithms and make predictions. Raw data cannot be utilized for these tasks for a plethora of reasons, including certain project requirements, statistical errors, complex nonlinear relationships, and more. The process is notorious for being complex, laborious, tedious, and lengthy. However, it is a crucial step in the applied machine learning process.
The most common tasks that data scientists or other AI engineers undertake in order to transform data include data cleaning whereby the analyst identifies errors in the data and eliminates them as well as feature selection to detect the most relevant variables for the project. The variables and requirements are contingent on the goals and specifics of the project. Data scientists adjust the scale and distribution of the variables to fit into the model and explore additional variables from the data available to them. Finally, they create projections based on the data and move to the next step of evaluating and finalizing the model.
Preparing data for machine learning is more art than a concrete step-by-step activity and closely depends on the data, project, requirements and more. In some cases, variables simply need to be converted from strings to numbers, whereas in other projects data scientists are tasked to scale data. In other words, the main goal is to figure out the most appropriate way to reveal the structure of the problem to the learning algorithms so they can make meaningful predictions and draw insights.
Since we don’t know the structure of the problem and are building the model to identify it, the process of finding the unknown is clearly difficult. The process becomes a journey of discovery, trial and error, and learning by doing, rather than a straightforward action plan. Data scientists work on the variables to find ways to improve the model and identify the best predictor representations to fine-tune the algorithms.
Overall, data preparation for machine learning might seem overwhelming and frustrating as it requires different methods for different variables. Considering a large amount of methods and techniques, data scientists spend years and years crafting their skills.
Without data preprocessing, you are bound to have inaccurate and misleading outputs that might lead to erroneous decisions. Even though machine learning data preparation can be complex, it is a crucial step in building meaningful algorithms that yield valuable results. Using invalid or incomplete datasets may jeopardize the entire project and deliver false outcomes.
As described above, the machine learning data preparation activities are complex and require high levels of expertise. There are a few roadblocks and challenges data scientists commonly encounter when launching this step, which we will investigate in this part. We will go over the main techniques, strategies, and best practices that will streamline and uncomplicate the processes and prepare data for machine learning models.
The very first step relies on the comprehensive problem definition that allows data scientists to select the corresponding datasets to build algorithms. While formulating the exact problem, use the following frameworks to optimize the machine learning data preparation process:
Using these frameworks will give you guidance on data exploration as well as prevent setting overly complicated problems that an algorithm cannot handle.
To properly prepare data for machine learning, you first need to have the right channels to collect and store information. The biggest challenge for businesses is data fragmentation, whereby data is collected from multiple sources and stored in different departments. As a result, data is underutilized and engineers have a harder time building predictive models.
For example, hotel workers usually hoard a lot of information pertaining to their customers, from addresses and credit card data to what drinks and food they order. However, when people make a reservation online, the website is mostly unaware of these details and cannot offer a truly custom experience. The same pattern may be seen in the education industry where institutions store large amounts of personal data. At the same time, whenever students register for a course, this data is mostly not utilized to provide a personalized experience. Pulling data from one source allows data scientists and business analysts to apply machine learning in the education sector and other industries and take advantage of insights and offer the best solutions.
There are two data depositories that are mostly used in corporations: data warehouses and data lakes. The former handles structured data that fits into standard formats. The latter is storage for unstructured data that preserves any type of records such as images, texts, videos, and more. Data lakes are more appropriate for machine learning purposes, whereas data warehouses are mostly used by data analysts.
Preparing data for machine learning requires making sure the quality of your data is not compromised and allows data scientists to build meaningful algorithms. In case your data has too many errors, inconsistencies, or duplications, the algorithms will likely fail to yield any trustworthy results.
First of all, check if the data was collected and labeled by humans, in which case the chances of human errors are high. Check a data subset to estimate how often errors occur. Secondly, check for technical roadblocks such as server errors, storage crashes, or cyberattacks, and identify the impact of these occurrences. Additionally, look for omitted values and duplicates as well as assess the fitness of this data for your task. For example, if your data pertains to customer data in the US and you are branching out to Europe, will this data be helpful? Finally, evaluate how balanced your data attributes are before launching a machine learning model. For instance, when assessing the risks of unreliable suppliers, make sure the number of reliable and unreliable suppliers doesn’t diverge.
While it is not a difficult task, it may be tedious and mundane, but it is highly vital. Alongside file format consistency which is not hard to deliver, make sure that records themselves remain consistent. Since the data is extracted from various sources, you might end up with the same variables entered in a different way. For example, date formats, addresses, sums of money, names, etc. Formats must be the same across the entire dataset, otherwise, the algorithm might ignore a certain record.
The possibilities of big data analytics allow us to handle vast amounts of data to make predictions or yield certain values. However, using too much data can blur the results and make them less accurate and on point. To prepare data for machine learning, figure out which datasets are essential for your strategy and which are simply adding more complexity without producing any value.
One of the methods of data reduction is called attribute sampling, which entails using common sense when eliminating predictors. For example, in an analysis to determine which customers are more likely to purchase a certain product, you can use their location, occupation, and/or age, but leave out data such as their credit card number. When we tackle more complex fields like healthcare or solar energy, domain expertise becomes significantly more important. Without expert knowledge about the causes of epilepsy and its complications, demographics, medications, associated conditions, etc., data scientists will not be able to select appropriate predictors.
Another method is referred to as record sampling and involves eliminating any records with incomplete, erroneous, or poor quality values. Finally, you can divide the entire dataset into multiple groups, thus separating records into broader categories. For instance, instead of accumulating daily sales data, you can aggregate it into weekly or monthly groups, which will reduce the number of records and computing time without losing valuable data.
When it comes to machine learning algorithms, having approximate values is a better plan than having none at all. This prompts a data cleaning process that requires data scientists to assume which value is incomplete. There are different approaches to data cleaning depending on your project and domain.
You can replace the missing numbers with dummy values such as not applicable or zero or with mean figures. Another approach entails using the most frequent value or item to fill in the blanks. For companies that already utilize specified platforms, cleaning and preparing data for machine learning can be optimized and automated to remove the burden from your staff.
As opposed to the data reduction method, this approach requires you to create additional value out of existing ones to derive more relevant outcomes. For example, if your sales go up on a certain day of the week, you could segregate this day in a separate dataset. As a result, you will have your weekly scores as well as a separate day of high sales like Friday or Saturday. This strategy can help you identify more specific relationships and correlations in your operations.
Uniting transaction and attribute data can help your business to yield better and more precise results. Transactional data captures specific events such as the concrete price of the product that a specific user added to their cart after following the link on your blog. Attribute data is less specific and refers to information like user demographics, equipment model, store location, etc.
Preparing data for machine learning by using both types of data in tandem can unlock more predictive power. Joining the information from our examples can help businesses identify dependencies between customer attributes and customer behavior. Moreover, you can later use customer attributes such as researcher (someone who visits more pages on average but rarely makes a purchase), instant buyer, reviews reader, etc., to improve your marketing campaigns, target users more directly, and forecast customer lifetime value.
Data rescaling might sound complicated, but it can be achieved relatively easily with the right approach. This strategy aims at normalizing data and improving the overall quality of datasets by balancing the values. For example, if your dataset includes several values that have one to two digits and one value that goes up to five digits, this will create an imbalanced dataset. A more concrete illustration is a car dealership dataset that contains information about car models, styles, and years of use, which is a one or two-digit number. If you add prices to the mix that come with four and even five digits, they will outweigh the others.
The solution is a so-called min-max normalization that involves transforming the values to ranges such as 0.0 to 1.0 to restore the balance. Another option is moving the decimal point in either direction to reach the appropriate value.
It may sound counterintuitive but sometimes data scientists transform numerical values into categorical ones. This can be helpful if you are working with a variety of numbers and would like to reduce the burden. Data discretization can be applied to age figures by dividing age values into several age groups. Utilizing categorical values, in this case, will make the algorithm simpler and deliver a more relevant prediction.
This is not a straightforward answer as the process itself is complex and requires a lot of domain expertise and technical acumen. Defining the problem in more detail can help you identify valuable insights. For example, this step involves collecting problem domain data, communicating with experts, variable selection, and data summary and visualization. During these tasks, you can gain more insight into the subject matter and problem, which will help you in choosing suitable methods of data preparation for machine learning.
There are also more advanced techniques that help data scientists select data preprocessing approaches. For example, building plots of data (a graph showing the relationship between two or more variables) can indicate whether data cleaning is required. Descriptive statistics can aid in determining the need for data scaling, whereas pairwise plots can help data analysts identify duplicate or irrelevant values and help with data reduction.
Additionally, your choice of algorithms may point you at a certain machine learning data preparation method. Based on selected algorithms, your variables might need to possess a specific probability distribution or exclude particular input variables that are not relevant. The same goes for your choice of a performance metric that might also add a new requirement.
At the first glance, having no data to process means that you will not be able to prepare data for machine learning and build any algorithms. Although lack of data is a disadvantage, you still can utilize other means to make predictions. For example, you can apply open source datasets to launch a machine learning model.
Public datasets are available to anyone interested and delivered by organizations that have shared their data. Clearly, these datasets cannot be used to identify dependencies and yield meaningful insights into your business, but they will help you explore the industry as a whole, including customer behavior. You can discover plenty of records associated with machine learning in healthcare, education, energy, and other industries that can greatly benefit from predictive analytics. Your data scientists can use these findings to gain more market understanding and test your machine learning algorithms on real data.
There is also a benefit of being a startup and having no data to process: now you have an opportunity to plan, collect, and store data the right way. Utilizing machine learning platforms and applying best practices will help you store your data in an organized, helpful, machine-learning-friendly manner. Especially if you know which problems you would like to solve with machine learning models, you can tailor your data and collection mechanism to accommodate these goals from day one.
Although collecting data is a crucial step of data analytics, it needs to be set up correctly. Simply accumulating large volumes of data will only complicate the analytical processes. Big data is not about zettabytes, it is about collecting, storing and processing it in the most meaningful and appropriate way. If you would like support in entering the big data era and utilizing your information in a purposeful manner, contact NIX United. Our team of data specialists can help you take advantage of the advanced technology without overcomplicating processes and overwhelming staff. Get in touch with us to learn more about data preparation for machine learning and unleash the power of your data.
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