Last updated on April 4th, 2024 at 09:56 am
Raw input data are generally available in tabular formats, where rows highlight observations or instances and columns show attributes or features. Feature engineering is a tactical process which is used to transform raw data into valuable features that can be utilised for creating accurate predictive machine learning models. This uses Python programming and Power BI as key visualisation tools.
Feature engineering helps to prepare models with reasonable prediction even when a few missing raw data are missing. This is possible when the work is done using the most relevant features that eliminate undesirable or non-influential ones.
The Process of Feature Engineering
Feature engineering in machine learning broadly consists of four processes. They are as follows:
Feature creation
Feature creation is a process that uses the human brain’s creativity and is performed by addition, deletion or rationalisation of existing data variables. This activity is done by professionals who have chosen a career in data analytics.
Transformation
The process of adjusting the selected variable so that it may contribute effectively towards the accuracy and performance of the predictive model is known as transformation. The process ensures that all the features follow the same scale. It also helps to make the model flexible to accept a variety of data inputs.
Feature extraction
Feature extraction is an automated method of generating new meaningful variables out of the raw data provided. This makes the predictive model more reliable and accurate by reducing the input data volume. The process involves text analytics, cluster analysis, edge detection algorithms, and principal components analysis.
Feature selection
Feature selection is the process of selecting the most useful variables out of many for incorporating them into the predictive model. Irrelevant or noisy data are left out since they are useless to the model and negatively affect the model when infused into the system.
Tools of Feature Engineering
Many feature engineering tools help make good predictive models. A few of them are described below:
FeatureTools
FeatureTools helps to perform auto-feature engineering. It is particularly good at converting meaningful raw data to useful features in machine learning.
AutoFeat
Linear predictive models with automated feature engineering and selection process is a key strength area of the AutoFeat tool. AutoFeat helps us to select the unit of useful variables.
TsFresh
TsFresh is an open-source Python package tool that helps to correlate and automatically calculates a large number of time series data. It helps to extract details such as peak, average value, time reversal symmetry statistics etc. Knowing Python programming is of immense importance in today’s world.
OneBM
This tool works on the raw data, irrespective of whether they are relational or non-relational to the predictive model. It can generate both simple and complicated features.
ExploreKit
It is a structured framework to produce automated features. It can combine multiple data and may unearth common useful features thereby eliminating duplication. This makes the predictive model compact and error-free.
Feature Engineering Techniques in Machine Learning
Some of the regular feature engineering techniques used in preparing data for machine learning models are as follows:
Imputation
The most common problem is missing data, which arises out of the following typical cases of human errors, data flow interruptions, privacy issues etc. Numerical and categorical imputations are applied in these cases.
Handling outliers
This is a process of suitably dealing with specific data which is exceptional in terms of value and category. When several outliers are very few, the process of removal is applied. However, if the number of outliers is quite a few, then removal will cause us to lose enormous data and hence be avoidable. In these cases, the process of replacing values, capping or discretisation is applied.
Log transform
Logarithms are used to convert data of a skewed distribution into that of a normal distribution. This process is also used to handle confusing data. The efficiency of this tool may be best expressed visually with Power BI.
Scaling
It is the process of bringing all data under a common scale by scaling up or down, as required. The purpose is to make the features similar in terms of their range. The two standard procedures adapted here are normalisation and standardisation.
Binning
Excessive and irrelevant data and unwarranted numbers of parameters deter the performance of models. Binning is the process of segmenting several data and features and eliminating unwanted ones from the system.
Feature split
This is a process of segregating features into two or more parts to closely monitor the same with the help of the data available. This characteristic produces meaningful features with better algorithms and is better numerically representative.
One hot coding
It is a commonly used technique in machine learning. It is used to convert categorical data in a specific form which can be easily interpreted by machine learning algorithms and can be used in creating successful predictive models.
Benefits of Feature Engineering in Machine Learning Models
Using feature engineering in machine learning applications has some notable advantages, which are as follows:
Flexibility
Better features impart better model flexibility. Even if a wrong model is chosen by mistake, the flexibility of features will generate good predictions.
Simplicity
Flexible featured models are simple and quick to operate.
Better Results
With the same available data, the selection of better features gives way to better results in predictive models.
Conclusion
A career in data analytics is a booming option for modern youth. A data science course with placement assistance makes this opportunity lucrative. Having a machine learning certification is very necessary for a prospective candidate. Several reputed institutes in India offer machine learning certification courses.
The Postgraduate Program in Data Science and Analytics at Imarticus will give the prospective candidate a perfect start to their career. This is a data science course with placement and the duration of the program is 6 months. The classes are held on weekdays where the mode of teaching is both online as well as classroom training.
Visit the official website of Imarticus Learning for more course-related details.
