AI in Finance

Feature Engineering: to extract the right info out of your data

Building the best machine learning model directly correlates with having the best model type and architecture as well as training it on the right data set. With a combination of the two the performance of ML models will reach a certain level. Beyond that the ability to extract more inferential power out of the data becomes necessary to get an improved performance and to do so various techniques have to be implemented.

Feature engineering allows us to extract extra information out of the already available values or features to improve the model’s performance and understand the patterns within the data even better. It involves modifying the data such that

  1. it becomes usable to the ML model,
  2. it becomes error-free and standardized,
  3. it only has the relevant things that are required for the training.

Feature engineering has become an important part of the machine learning pipeline and without it the true performance of the model can never be achieved. Over time many techniques have been included into the umbrella term of feature engineering and we will try to explore some of the common ones in this article.

How are features engineered to extract more info from them?

Various techniques are used to improve the quality of the data using feature engineering starting from the basics such as data cleansing that includes pre-processing the data, removing any errors found and making it suitable for further analysis.

Cleansing the data

The raw form, that is in which the data is sourced, needs to be cleaned and improved in the most basic aspects. The data can have multiple missing parameters, repeating duplicate values and some non-sensical values too. To get rid of such issues, various methods are implemented such as imputation to fill in the missing values, and also outright deletion for duplicate and non-sensical values.

Data imputation involves inputting values that can be mean, median or mode of the entire feature set or they can also include inputting synthetic values using techniques such as SMOTE(read more about it here) or Linear regression, in place of the missing values.

Cleansing the data as such is the basic step that always needs to be done before starting any ML-related project. It helps to achieve better analysis and model training further down the line but it cannot be considered a performance improver.

Again, the data needs to be modified in such a way that the ML model can be trained. Machine Learning models such as Linear Regression, Random Forests, and even neural networks rely on the data being in a numeric format, as most of these models try to change their weights based on the input during training. If the data is not suitable for the model the whole training pipeline will never work.

Modifying the data

The data can have certain elements within it that are in a categorical format that the model can have a hard time understanding. Hence, such values need to be converted into numbers that the model can interpret and learn from. Encoding techniques such as Label encoding, One-hot encoding, etc., can make the categorical variables into numeric ones.

Dealing with continuous features, such as age or income, can also be made suitable to the model through techniques such as Binning, which involves dividing the entire continuous distribution into parts and naming/categorizing each part as a separate entity.

Standardization and normalization of the features can also help with bringing the feature range within a certain limit so that the values are more contained and smaller so that the model finds them easier to deal with during training.

The above two techniques mostly deal with making the data ready for analytics and model training to achieve the least possible expected accuracy from the model. But to improve on that accuracy is a challenge and one that can be handled if the features themselves are modified and combined to find deeper hidden patterns.

Playing around with existing features

The pre-existing features that come with the data might not have as much information as two or three combined features might possess. This is because some of the information in a feature might be having a lower impact on the model due to its comparatively lower values and if this feature is combined with another feature, those values might become significant. This combination of features is known as Feature extraction and it is a process of adding, subtracting, multiplying, or even dividing two or more features to amplify the information hidden within them.

This allows us to create entirely new features that the model can train on and get even deeper insights into the data. Feature extraction involves a lot of trial and error and in some cases, domain knowledge about the data can also help.

Other than that, many features can be verified through correlation matrices that show some variability with the target value and if they don’t, they can be discarded and removed completely to save space and energy while training.

But although all of these techniques, lead to a model that has a deeper knowledge about the data and can produce inferences that are accurate and meaningful, such feats always take up the majority of the time in a machine learning model development life cycle. But what if such processes were automated to give us directly the features which create the most impact on the decisions? Such automated feature engineering pipelines might eliminate the need for hiring and consulting an expert for that particular field all the time and can save around 60 to 80% of a data scientist’s time.

Auto Feature Engineering

There are numerous ways through which the entire feature engineering process can be automated and we can get the features that show the most promise with not much hassle of domain knowledge and finding each feature relation separately. Many techniques, such as FeatureTools, AutoFeat, TsFresh, and PyFeat are available that can automatize the entire feature engineering pipeline and give back data that has all the relevant features that can be directly fed into the ML model. These methods have their own benefits and some limitations that must be considered before opting out of them but they help us save a lot of time during the ML development cycle.


Feature engineering has always been the most tedious but also has been an important and an integral part of any ML pipeline and hence it is necessary to involve it in your workflow as well using methods that were mentioned in this article. Although, consuming the majority of the time, new methods for automated feature engineering can also be looked into and incorporated to generate models that are built for accuracy and robustness.

ANAI, built to automate and democratize AI

ANAI is an all-in-one ML-based solutions platform that manages the A to Z of AI, from data engineering to explainability. We offer a solution that focuses more on a no-code-based approach with a low code solution to be released in the future. ANAI’s AI engine easily outperforms and provides the best performance for any AI-based system. Our eXplainable AI based-solutions lets you easily generate explanations on your model’s outcome and helps you with generating systems that are responsible, fair and robust.

ANAI also allows for automated feature engineering using its proprietary tools to enable organizations with quick and effective feature modification and extraction to help them save a lot of time and cost on such processes. The automated feature engineering creates combinations of features to see which features gives the best results, saving computational resources while training using the data efficiently.

Connect with us at to get more details on ANAI and/or if you have some other queries and visit ANAI’s site at

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We offer a comprehensive ecosystem that blends Data Engineering and Automated Machine Learning that enables the ‘Democratizing of AI and ML’.

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ANAI - AI for AI

+1 646 699 8676

We offer a comprehensive ecosystem

that blends Data Engineering and Auto-

mated Machine Learning that enables the

‘Democratizing of AI and ML’.