How to Prepare Data for Machine Learning? 10 proven methods
Make a better outcome of your data by preparing datasets for Machine Learning. Use proved techniques that will make your data better to work with. Usually dedicated data scientists are the ones who take care of your datasets and feed your ML algorithm. Many companies cannot afford trained data engineers and they dedicate this task to their internal IT teams. Here are some tips on how to prepare your dataset for machine learning.
Collecting all the data – here lies the question of whether or not you have enough data to feed your ML algorithm. Many companies have had copies of everything happening in their organization in the last 10 years. Digitalize your data or use open-source datasets (if not enough data is collected) to initiate ML execution.
Define the problem – articulate what obstacles you want to overcome and then start collecting all the essential data you need to achieve it. Think about the different categories of ML algorithms and segment data early on.
Data collection – data-driven companies are a rare thing, so you need to establish clear mechanisms on what data where to store. This step could potentially be the hardest step of them all since data collection is a daily routine task that every employee has to adapt and turn into a habit. All the channels of engagement need to flow into a consolidated dataset to avoid fragmentation. Engage your data specialist to engineer the data infrastructure.
Data storage – data needs to fit into standard formats so it can store in structures (or SQL) records. Knowing how your data will look and where to store it gives you the confidence that it will be properly processed by the ML algorithm. Generally – most of your corporate data follow standards that fit into this category. Some of the data needs to be processed before storing and this process is called ETL (extract, transform and load). A better fit for ML algorithms is the data lakes where you can store structured as well as unstructured data. You can even have non-transformed data in your lakes and decide at a later point what to do with it – which makes them more flexible.
Data quality – when data is being handled by humans – errors can occur. It is most important to collect your data automatically with the least amount of human interaction possible. But consider the technical problems you could face along the way. Server errors, duplicated data, or even cyberattacks can happen. Prepare yourself for any unforeseen obstacles you may have along the way by reading our article about Disaster recovery planning.
Data formatting – consistency of data is what makes it useful to ML algorithms. Find the best working data format your ML will use and convert all of your data sets into it. Take care of data formatting in every document/table so the input format would be the same across the entire dataset.
Data reduction – the opposite of having not enough data is having big data. If you are looking for a certain outcome you most probably do not need all the data you have. Here lies the problem of consolidating data and rendering out the one you need. Use critical thinking and decide on what data is critical for the outcome you target. Consider all the values you need to collect to uncover more dependencies. This type of data reduction is called attribute sampling. Creating different groups of data with aggregation is another way of dividing your data. This is a much broader method but it works for data size reduction and more tangible predictions. Another data reduction approach is record sampling where you have to remove objects with missing values to make more accurate predictions.
Data cleaning – values that are entirely missing are influencing the predictions of an ML algorithm. To reduce prediction accuracy, it makes sense to fill these gaps by assuming an approximate value rather than leaving many empty spots. Even if you are not sure of the exact value that needs to be written – it is better to estimate it and then the ML algorithm will do a better prediction job.
Mary transactional and attribute data – attributes are static data that do not directly correlate with specific events. Where transactional data is information collected during certain events happening e.g. purchases. This data is a snapshot of the entire dataset at a specific moment. It makes sense to join them both to enhance the predictive value of the ML algorithm and to potentially achieve an enhanced experience.
Data re-scaling – the process of improving the quality of data via the procedure also called data normalization. It is happening by reducing detentions and making sure that all values do not overweight each other.
Follow these 10 important steps to configure your data in a suitable format for your ML algorithm. If you need any help along the way (data collection, infrastructure, scaling) – consider hiring a data engineer. Happy automation time!