Data transformation for Machine Learning implementation
Machine Learning enables businesses to get the most out of their data, but to get accurate results it is key to prepare that data correctly. Data transformation is the essential process that converts information into an ideal format for Machine Learning models, ensuring effective analysis and informed decisions.
Want to know how to transform your data and apply this technology to your business? Read on!

What is data transformation in Machine Learning and why is it important?
In Machine Learning, data quality and data preparation are critical to the success of the model. Data transformation consists of modifying and structuring the information so that it is interpretable and useful to the algorithm. A model that receives inconsistent or poorly formatted data will have difficulty generalising and making accurate predictions.
For example, a company that collects sales data with prices in different currencies must normalise this variable before using it in a predictive revenue model. Data transformation ensures, in this case, that prices from different countries are standardised to a common currency and the model can work with consistent data.
Discover different data transformation techniques
There are several techniques for transforming data into a format suitable for Machine Learning. Below, we highlight some of the most common ones:
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Normalisation and standardisation: these techniques fit variables to a specific range or scale so that Machine Learning algorithms are not disproportionately influenced by large values. Normalisation fits values between 0 and 1, while standardisation centres the data around the mean, with a standard deviation of 1.
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Outlier removal: out liers can distort the results of the model, as they represent extreme cases that do not follow the pattern of the rest of the data. There are several methods to identify them, such as the use of boxplots or z-scores.
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Categorical variable encoding: Categorical variables (such as countries or product categories) must be converted to a numerical format so that Machine Learning models can process them. There are several encoding techniques, such as One-Hot Encoding or Label Encoding, that assign numerical values to each category.
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Encoding categorical variables: categorical variables (such as countries or product categories) must be converted to a numerical format in order to be processed by Machine Learning models. There are several encoding techniques, such as One-Hot Encoding or Label Encoding, which assign numerical values to each category.
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Imputation of missing values: incomplete data are common and should be handled before feeding the model. Imputation consists of filling missing data with estimated values, either using the mean, median or more advanced techniques such as the K-Nearest Neighbors algorithm.
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Logarithmic transformation: when there is data with a skewed distribution, the logarithmic transformation helps to adjust the variability and create a more uniform pattern, making it easier for the model to capture relationships between variables more accurately.
Data transformation tools
There are several tools and platforms that facilitate the transformation of data for Machine Learning, some of the most widely used are:
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Pandas: a Python library widely used in data science that allows you to manipulate and analyse data efficiently.
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NumPy: another Python library that provides support for large multidimensional arrays, helping to perform complex calculations and transformations.
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Apache Spark: a clustered processing platform that allows working with large amounts of data, with options for real-time transformation.
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KNIME: a data analysis platform that allows you to transform, visualise and model data graphically, ideal for non-technical users.
Practical example: how Amazon transforms data for recommendations
Amazon is a prime example of how data transformation improves the performance of Machine Learning models. With millions of users and products, Amazon collects large amounts of browsing, shopping and feedback data. Before the recommendation system can suggest personalised products, Amazon applies several transformations:
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Collection and pre-processing: Amazon captures data from interactions on its platform, including searches, products visited and purchases. This data is processed and cleaned to remove any duplicate entries or noise that may affect the accuracy of the model.
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Coding and clustering: the platform converts categorical data such as product types or categories into numerical values and performs clustering to find similarities between products. This process allows the Machine Learning model to match similar products based on user behaviour.
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Logarithmic transformation and normalization: to handle variables such as product prices in different markets, Amazon uses logarithmic transformations and normalization, adjusting the values to uniform scales.
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Training and tuning the model: with the data already transformed and standardized, Amazon's recommendation system uses Machine Learning models such as neural networks or collaborative filtering to predict the products that each user might be most interested in.
Data transformation is key for machine learning to work accurately and effectively. Dare to take the first step and discover how data transformation can boost your growth!