There was an exception. Asthma is the most serious condition that can accompany pneumonia. Doctors always send patients with asthma to intensive care, which results in low death rates. The algorithm assumed that asthmatic deaths during pneumonia were rare and that the machine would recommend sending patients with asthma home. At the same time, they are at the highest risk for complications.
Data is a key component of ML. This is the key aspect that allows algorithm training to be possible. It also explains why machine learning has grown in popularity over recent years. However, no matter how much terabytes and data science knowledge you have, your machine will be useless or potentially harmful if you don't understand data records.
All datasets are flawed. Data preparation is an essential step in types of machine learning. Data preparation is a series of steps that make your data more compatible with machine learning. Data preparation also includes the establishment of the appropriate data collection method. These procedures take up most of the time used for machine learning. Sometimes, it can take months to create the first algorithm.
How to Gather Data for Machine Learning, Even if you don't Have It
Years of data collection have led to a clear line between those who can play with ML and those who can't. Many organizations have been collecting records for decades, with great success. They now need trucks to transport it to the cloud because conventional broadband is not wide enough. Data is a common problem for newcomers to the industry. However, it's possible to make this a positive.
To start ML execution, you should first rely on open-source datasets. Machine learning is possible with a lot of data available. Some companies, such as Google, are willing to share it. We'll discuss public dataset opportunities later. These opportunities are great, but the real value is usually found in internally collected golden data. This can be derived from your company's business decisions and activities.
You can now collect data properly, which is not surprising. Companies that began data collection using paper ledgers but ended up with.xlsx or.csv files are likely to have a more difficult time with data preparation than companies with small but well-organized ML-friendly data sets. You can create a machine learning-friendly data collection system by knowing the tasks you want to solve.
What about big data? Big data is the hottest topic. It's a good idea to start with big data, but it's about something other than the petabytes. It all comes down to the ability and the skill to properly process the data. It is more difficult to use your data and get insights. You don't have to have tons of lumber to make a warehouse full of tables and chairs. Beginners should start small and simplify their data.
1. Articulate the Problem Early
By identifying the problem early, you can decide which data is more valuable by knowing what you are trying to predict. Conduct data exploration to determine the problem. We discussed the classification, clustering, and regression categories in our whitepaper about machine learning in business. These tasks can be described as follows:
Classification: An algorithm that answers binary yes-or-no questions (cats, dogs, good or poor, etc.) or creates a multiclass sort (grasses, trees or bushes, cats, dogs or birds, etc. An algorithm can also learn from the correct answers if labeled correctly. Our guide will show you how to address data labeling within an organization.
Clustering: An algorithm is needed to determine the rules for classification and the number of classes. You don't know the names of the groups or the principles in that they are divided. This is the main difference to classification tasks. This is often the case when you have to segment customers and tailor an approach for each segment based on their characteristics.
Regression: An algorithm should yield a numeric value. Regression algorithms are useful for helping you estimate the price of your product if you spend too long trying to figure it out.
Ranking: Other machine learning algorithms rank objects based on features. Scale is used to suggest movies on video streaming services or show customers products they might buy based on their previous searches and purchases.
This segmentation will likely solve your business problem. You may then start to adapt a data set accordingly. Avoid complicated issues at this stage.
2. Establish Data Collection Mechanisms
The most difficult part of any initiative is creating a data-driven culture within an organization. This point is briefly discussed in our story about machine learning strategy. The first step to using ML to perform predictive analytics tasks is to reduce data fragmentation.
Take, for example, travel tech. This is one of Cyber Infrastructure Inc.'s core areas of expertise. Data fragmentation is one of the most common analytics problems. Hotel business departments responsible for physical property can learn a lot about their guests. The hotel understands the credit card numbers of guests, what amenities they select, their home addresses, how often they use room service, and even the drinks and meals, they order during their stay. However, the website that hosts these rooms may treat them like strangers.
These data can be siloed within different departments or even at other tracking points within one department. Although marketers may have access to the CRM, customers need help to be able to use the web analytics process. If you have multiple engagement, acquisition, and retention channels, it may only be possible to consolidate some data streams into one centralized storage. However, in most cases, it is manageable.
A data engineer is a specialist in creating data infrastructures. Usually, data collection falls under the responsibility of a data engineer. In the initial stages of your project, you may need to hire a software engineer with database experience.
Data Warehouses and ETL
First, you can deposit data in warehouses. These storages can store structured records (or SQL), which means they are compatible with standard table formats. This category includes all sales records, payrolls, CRM data, and CRM data. Another important aspect of warehouse management is data transformation before it is loaded. This article will discuss data transformation techniques. It means you know what data you need and how you want it to look. Then you can do all the processing before you store it. This is Extract, Transform, and Load (ETL).
This approach has the problem that you can't know beforehand which data will be valuable and which won't. Warehouses are used to access data via business Intelligence Interfaces. This allows us to see the metrics that we need to track. There's another way.
Data Lakes and ELT
Data lakes can store structured and unstructured information, such as images, sounds, PDF files, etc. Even if the data is structured, it cannot be transformed before storing it. It would be possible to load the data as it is and then decide how to process it. This is known as Extract, Load, and then Transform when needed.
In our article, you can read more about the differences between ETL and ELT. Which one should you choose? Generally, both. Data lakes are better suited for machine learning. However, you are confident with at least some data. In that case, it is worth having it ready, so you can use it for analysis before you start any data science project. Keep in mind both are supported by modern cloud data warehouse providers.
Human Factor
The human factor is another important aspect. Data collection can be tedious and overwhelming for your employees. People who are required to record data manually will likely dismiss these tasks as a bureaucratic chore and let the job go. Salesforce, for example, offers a good toolset to track and analyze salespeople's activities. Still, manual data entry is not an option.
Robotic process automation systems can solve these problems. RPA algorithms can be used to automate repetitive and tedious tasks.
Read More: What Is Machine Learning? Different Fields Of Application For ML
3. Check your Data Quality
You should first ask yourself if you can trust your data. Poor data can stop even the most beneficial machine-learning algorithms from working. Although we have written in depth about data quality in another article, it is important to remember several key skills points.
How tangible is human error? You can estimate the frequency of human error if your data is labeled or collected by humans.
Did you encounter any technical issues when you transferred data? You might have duplicated the same records due to a server error, a storage failure, or a cyberattack. Consider how these events affected your data.
What are the omitted values in your data? There are many ways to deal with forgotten records. We discuss them below. However, it is important to estimate if they are critical.
Is your data sufficient to accomplish your task? Can you use the same data to predict demand and stock levels if you have been selling appliances in the US?
Is your data imbalanced? You might imagine that you are trying to reduce supply chain risk and filter out suppliers you don't trust. You will use several metadata attributes such as location, size, and rating to do this. Your labeled dataset will need more samples to learn about unreliable suppliers if it contains 1,500 entries considered reliable and 30 not.
4. To Make Data Consistent, Format It
Sometimes data formatting is referred to simply as the file format you use. It's easy to convert a dataset into the file format that best suits your aspects of the machine learning system.
Format consistency is the ability to maintain records in a consistent format. It's important to ensure that every attribute within an attribute is consistently written if you are aggregating data from multiple sources. These could be address formats, date formats, sums (4.03 and $4.03, or even just 4 dollars 3 cents), or sums of money (4.03 and $4.03, respectively); the input format must be consistent across all datasets.
There are also other aspects to data consistency. You should ensure data consistency if there is a numeric range within an attribute from 0.0 to 5.
5. Reduce Data
Because of big data, it's tempting to include as much data as possible. That's wrong-headed. You want all the data you can. It's better to keep the data as small as possible if you are preparing a dataset for specific tasks.
Common sense will help you determine your target attribute (the value you wish to predict). Without any forecasting, you can decide which values are important and which will add complexity and dimension to your data. This is known as attribute sampling.
You should know which customers will most likely purchase large quantities of merchandise from your online store. You can use the age, location, and gender of your customers to predict their likelihood of making large purchases in your online store. This works differently. To uncover more dependencies, think about what other values you might need. To predict conversion accurately, consider adding bounce rates.
Domain expertise is crucial. To return to the beginning, not all data scientists are aware that asthma can lead to pneumonia complications. This is true for large datasets as well. A data scientist who isn't a unicorn in healthcare fundamentals and a unicorn in data science may not understand the true significance of a dataset if you don't have one.
Another method is record sampling. You remove records (objects) with missing, erroneous, or less representative values to make predictions more accurate. This technique can be used later when you require a prototype model to determine if a field of machine-learning method produces the expected results.
You can also reduce data by merging it into larger records by dividing all attribute data into multiple groups and then drawing the number for each group. Instead of looking at the top-selling products over five years of online store existence for a day, you can aggregate them into weekly or monthly scores. This will reduce data size and compute time while avoiding prediction loss.
The Final Word
Machine learning is an integral part of this. It is used to transform data and create useful knowledge. The system improves as it processes data from various sources. Comprehensive data integration is a key takeaway to leveraging ML. This allows you to create predictive models that accurately reflect user behavior and changing trends.
While ML is still a basic technology for most companies, it is promising. It can be used to develop more flexible and powerful solutions. This emerging technology will continue to be improved through research and development. It will become more efficient in predicting the future and organizing it. These steps are simple and easy to prepare your dataset.
Suppose you want to automate data collection, create infrastructure, and scale complex machine-learning tasks. In that case, you will need data scientists and engineers. However, deep domain and problem knowledge will help you to structure your data in a meaningful way. It may be a good idea to revisit existing methods of sourcing and formatting records if you are just at the data collection stage.
