A well-executed data preparation process is the key to building a robust, accurate, and effective machine learning model. However, this is quite difficult and complex to achieve due to some problems related to data for machine learning, e.g., varying data sources involved, especially when dealing with unstructured or semi-structured data. This article will take you through the process of data preparation for machine learning processes, from what it is to a step-by-step guide to completing a successful project. Let’s dive right in.
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You can make the process easier by paying attention to the kind of data you gather, how it suits its intended purpose, and how to best transform it into an appropriate format that fits a specific type of algorithm. Ultimately, good data preparation leads to more accurate and efficient algorithms that make it easier to solve new analytic problems and adapt to new model accuracy drifts. It also saves you time and effort down the line.
What is data preparation?
Data preparation is the process of collecting, combining, structuring, and organizing raw data so that it can be used in analytics, business intelligence, and machine learning applications. Simply put, data preparation involves any actions performed on an input dataset before it can be used in machine learning applications. The various components of data preparation for machine learning include preprocessing, cleansing, profiling, validation, and transformation. In most cases, this data is gathered from different internal and external sources.
Although data preparation is a tedious and lengthy process, it is vital to put data in context before it can be turned into insights. The process also allows you to eliminate biases that would otherwise result from poor data quality.
Purpose of data preparation in machine learning
Machine learning projects are fueled by data. They particularly use historical data to find patterns, and then using those patterns, they make predictions on new data. But before they can effectively make predictions, the algorithms require data to be formatted in a specific way.
This means that the various datasets involved require significant preparation before they can yield useful insights. In most cases, some datasets have missing values that are difficult and sometimes nearly impossible for an algorithm to process. Likewise, if data is missing, an algorithm can’t use it. And if it is invalid, the algorithm produces inaccurate or sometimes misleading insights.
In some cases, you might find that the data sets you are working with are relatively clean. They, however, have to be pivoted or aggregated to fit the intended algorithm. Some datasets also lack useful business context like well-defined ID values. In this case, they need to be enriched. A well-executed data preparation process ultimately produces clean and well-curated data, leading to more practical and accurate machine learning models. That said, various issues complicate the data preparation process. Read on for more insight.
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Factors that complicate the data preparation process
OUTLIERS OR ANOMALIES
These are the unexpected values that often surface when distributing your values in the data preparation process. In most cases, outliers result from data with poor validation controls , especially data from unknown sources.
MISSING OR INCOMPLETE RECORDS
Getting every data point for every record in a data set is difficult and time-consuming. In some cases, you might miss a few points. Missing data often appears as empty values, cells, or particular characters, such as a question mark.
IMPROPERLY FORMATTED OR UNSTRUCTURED DATA
At times, you may need to extract data in a different format or location. Various complications may arise from the process, particularly in terms of format compatibility and data structure. To avert this situation, you should consult a domain expert.
LIMITED OR SPARSE FEATURE
When enriching or building out the features in your data, you often need to combine datasets from different sources. If there are no exact columns to match these datasets, joining the files can prove difficult. In this case, you need to perform fuzzy matching, which can be achieved by combining multiple columns to achieve the match. Combining two databases with similar attributes could be easy, but combining datasets with different attributes is a bit tricky.
NON-STANDARDIZED CATEGORICAL VARIABLES AND INCONSISTENT VALUES
Sometimes when combining data from multiple sources, you may end up with variations in the way data is presented. For example, you may find company names or states represented in different forms. Say, for example, you have a state name like Texas presented as ‘Texas’ in one dataset and ‘TX’ in another. In that case, you need to find all variables and standardize them so that they match.
THE NEED FOR SPECIALIZED TECHNIQUES
At times, even if all the relevant data is available and standardized, the data preparation process may require additional techniques such as feature engineering. Feature engineering enables you to generate additional content that ultimately results in more accurate and relevant machine learning models.
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The data preparation process
The fundamental drive for all machine learning algorithms is data. As such, data collection is the first and most important step in the process. That said, the data sources you choose entirely depend on your unique needs.
For example, marketers might use customer data to predict sales churn, finance teams might use financial transaction data to detect financial fraud, and sales teams might use sales data to optimize the sales funnel.
There are various special-purpose tools that you can use to collect this data. Alternatively, your teams can build models off of simple CSV files or google sheets. That said, the right data should include your target variable, whether it’s conversion, churn, or attrition.
For machine learning models with use-case functionality like deep learning, a well-executed data gathering process is crucial to the success of their functionality. This is due to the fact that these models are very data-intensive.
After gathering data, you’ll need to cleanse it. The process is also known as data preprocessing and is a crucial step in the data preparation process. This is because even if you do a lot of data processing in the background, it is still vital for any machine learning project to be powered by high-quality, clean data.
Data cleansing typically does away with unnecessary noise in the data and makes sure that it has consistent formatting. This can be achieved through various techniques such as dimensional reduction, feature engineering, or normalization.
Say, for example, you are trying to use location as a variable for predicting consumer spending, but the location of ‘Texas’ is formatted in different places like ‘Texas’ and ‘TX.’ The machine learning model, in this case, will have poor quality, as it won’t treat the values as part of the same category. As such, data cleansing makes sure that these entries are listed as the same value.
Data enrichment  typically involves adding new information to existing data sets by importing information from external sources or applying additional transformations that were not present in the original dataset.
Say, for example, you want to predict sentiment from customer reviews, and you’ve used two different sources to collect feedback. In that case, you can enrich your training data by merging these sources. This will help your machine learning model learn more accurate patterns, which, in turn, helps it make better predictions based on your data. The resulting effect is a much more valuable dataset for your machine learning model since it will contain richer and far more contextual information than was previously available in the initial dataset.
FEATURE ENGINEERING AND SELECTION
Feature engineering and selection is the final stage in data preparation before developing a machine learning model. This often involves adding or creating new variables to improve the model’s output. You must also address feature selection, which involves choosing relevant features to analyze and rooting out the irrelevant ones. Skipping this process might lead to problems like overfitting and extended model training, which limit the model’s ability to analyze new data accurately.
What data do you need to prepare?
The more relevant data you have to work with, the better your machine learning model will be at performing its intended purpose. That’s why knowing what kind of data you need for your machine learning project is so important. You also need to decide how and where you’ll store the data.
Each machine learning model requires a specific type of data and, in some cases, a different approach. There are two ways to determine what kind of data you need for your machine learning model; classification and forecasting.
When classifying data, you need to determine what category each data point falls into. Say, for example, you want to know whether a new lead will convert to a new customer. In that case, you’ll need to collect information about the lead, from their name, job title, and company name. You can then use this information to make a somewhat accurate prediction of whether they will convert to a new customer.
To achieve this, you’ll need to build an algorithm that can analyze the data you have collected and enable you to make an informed decision on the lead’s likelihood to convert to a paying customer. The most common approach in this situation is called logistic regression.
When dealing with clustering models like K-means clustering, you’ll need to group data based on similarities between the data without necessarily knowing about it. For example, you could group together all leads with a high likelihood of conversion by using a K-means algorithm where K is the number of clusters you want to create, with each cluster containing a similar data set.
When dealing with regression models like linear regression, you are primarily trying to predict a successive variable outcome based on one or more successive predictor variables. Forecasting comes in handy for retail, wholesale, and e-commerce business applications since it enables you to predict various outcomes like the number of customers you can acquire over time or if a customer will make a purchase today.
At its core, forecasting differs from classification in that it predicts continuous outcomes rather than classification problems.
How to check the quality of collected data
In order to build an accurate and robust machine learning model, your data should be valid, accurate, complete, uniform, and consistent. That said, due to the inevitability of errors in data, it is vital that you ensure data quality. Here are five characteristics to look out for to ensure data quality.
Accuracy in data preparation provides a clear reflection of the data’s reliability. Say, for example, you are dealing with data pertaining to customer satisfaction. In that case, it would be best to rely primarily on customer reviews since employee feedback on customer satisfaction would give you fairly inaccurate data.
Validity in data preparation shows that it measures what it is supposed to measure. For example, if you want valid data on customer satisfaction, you’ll have to work with a dataset containing customer satisfaction data such as customer reviews and questionnaires that rate their satisfaction.
Completeness is basically the degree to which relevant data is collected. Still on customer satisfaction, if you want a competent predictive machine learning model, you’ll need a multitude of customer satisfaction scores to train your data.
All systems in your machine learning model need to refer to the same value in the same format. For example, the word ‘Texas’ and its abbreviation ‘TX’ may both refer to the state of Texas, but an algorithm would process them as two different categories. For better uniformity, the data needs to be formatted in the same way.
Consistency can simply be described as the similarity between different data sources. If, for example, you are using customer support data from intercom and email, you need to ensure that the data appears in the same measurements in both cases.
Data preparation for machine learning is vital in building a successful model. Although each predictive model is different, there are common similarities in the steps performed on each project. And due to the complexity of the steps involved, you should consult a data scientist or engineer to simplify your data preparation process.
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