Data Science is not a sector where theoretical knowledge can assist you in starting a career. The projects you work on and the practice you put in determine your chances of success.
Feature engineering is a crucial part of any data science project. There are many different methods and ideas behind feature engineering. However, the basic principle is that you write rules that describe how to identify features and groups of values in your dataset. These rules will transform your data into something meaningful again — it's not a black magic formula that just makes sense out of it, but an approachable and easy way to understand which thing looks essential for a specific topic.
Creating additional features allows you to grasp your data better and gain more essential insights. When done effectively, feature engineering is one of the most critical data science techniques, but it is also one of the most challenging.
Introduction to Feature engineering
Feature Engineering is the process of constructing predictive models that leverage the existing data in your dataset. It's a complex process which includes both cleaning and preparing the dataset. The techniques used to create new features vary depending on the data types and the domain in question.
Feature engineering aims to extract suitable characteristics from the raw data. It can create more informative and reliable predictions than when using only an original dataset. To learn more about feature engineering and other secrets of data science projects, visit the data science course in Pune, led by experts.
Why is Feature engineering used?
As you know, data preparation and data management determine the model's performance in Data Science.
Assume we develop a model without using data and get an accuracy of roughly 70%. Applying Feature engineering to the same model has the potential to enhance performance by 70% or more. Simply put, we can enhance the model's performance by applying feature engineering.
As a data scientist, feature engineering is one of the tasks you will have to do. It is a method of building features that can be used for classification and regression in machine learning. For 3 major reasons, all data scientists should grasp the process of developing new features:
- You can isolate and emphasize relevant information, allowing your algorithms to "focus" on what's vital.
- You may contribute your own domain knowledge.
- Most importantly, once you grasp the "vocabulary" of feature engineering, you can include domain expertise from others!
Common Feature engineering techniques used:
There are many interesting feature engineering techniques which are explained here:
- Imputation
One of the most typical challenges in machine learning is the unavailability of values in the datasets. Missing values can be caused by various reasons, including human mistakes, privacy concerns, and disruptions in the data flow, to name a few. Regardless of the cause, these missing values impact the performance of ML algorithms. A thorough explanation of these techniques would be explained in a machine learning course in Pune, by industry experts.
Machine learning systems occasionally discard rows with missing values, and other platforms refuse to accept datasets with missing data. As a result of the smaller data amount, the algorithm performs poorly. The imputation technique introduces values consistent with the current values into the dataset. Although there are several imputation strategies, one of the most frequent is to replace missing values with the column's median or the most significant value that occurred.
There are two types of imputation -
- Numerical Imputation
- Categorical Imputation
2.Grouping operation
A variable or instance is expressed in rows, while features are expressed in columns in machine learning algorithms. Many datasets often fit into the simple layout of rows and columns because each column contains several rows of an instance. In order to tackle such instances, data is organized so that each variable is represented by only one row. The goal of grouping operations is to create an aggregate that has the best connection with features.
3.One-hot encoding
A one-hot encoding is a form of encoding where each element of a finite set is represented by its index, with just one element having its index set to "1" and all other elements having indices in the range [0, n-1]. Unlike binary encoding techniques, in which each bit can represent two values (i.e., 0 and 1), this scheme assigns a unique value to each possible event.
4.Bag of words
Bag of Words (BoW) is a counting technique that determines the number of times a word appears in a document. This technique may be used to find similarities and differences in texts for purposes such as search and classification.
5.Log transformation
Skewness is a measure of asymmetry in a dataset that is defined as the amount to which a particular data distribution differs from a normal distribution. The skewness of data impacts the prediction models in ML algorithms. Log Transformations are used to lessen the skewness of data to address this. The less skewed the distributions, the better algorithms can recognize patterns.
6.Feature Hashing
By vectorizing features, feature hashing is a valuable method for scaling up machine learning algorithms. Characters are turned into integers using the feature hashing approach, extensively employed in document classification and sentiment analysis.
Hash values are generated by applying hash functions to features that serve as indexes for mapping data.
Feature engineering is the process of producing, transforming, extracting, and selecting features, also known as variables, that are most favorable to developing an accurate ML algorithm. These procedures include:
7.Feature creation
Creating features involves determining the most valuable variables in the prediction model. This is a selective process that needs human interaction and creativity. Existing features are combined using addition, subtraction, multiplication, and ratio to generate new derived features with higher predictive value.
8.Transformation
Transformation entails modifying predictor variables to optimize model performance. Examples:
- ensuring that the model is adaptable in terms of the types of data, it can accept
- ensuring that all variables are on the same scale, thereby making the model easy to interpret
- Increasing precision;
- Preventing computational errors by ensuring that all characteristics are within the model's permitted range
9.Feature extraction
Feature extraction is the automated generation of new variables from raw data. The goal of this stage is to automatically decrease the overall volume of data into a more manageable collection for modeling. Feature extraction approaches involve cluster analysis, edge detection algorithms, text analytics, and principal component analysis.
10.Feature selection
Feature selection algorithms examine, analyze, and rank numerous features to decide which are irrelevant and should be deleted, which are redundant and must be removed, and which are most valuable to the model and must be prioritized.
Steps in Feature Engineering
The technique of feature engineering may differ amongst data scientists. However, the stages for doing feature engineering for most machine learning algorithms include the following:
- Data preparation
This phase entails transforming raw data from many sources into a consistent format that can be utilized in a model. Data preparation can also include data augmentation, cleansing, delivery, fusion, ingestion, and loading.c
2.Exploratory data analysis (EDA)
Through data analysis and research, this process is used to discover and summarize the primary features of a data collection.
Data scientists utilize data visualizations to fully understand how to work with data sources, choose the best statistical methods for data analysis, and select the best characteristics for a model.
3.Benchmark
Benchmarking is the process of creating a baseline level for accuracy against which all variables are measured. This is done to lower the error rate and increase the model's predictability. Data scientists with specialized knowledge and business users experiment with, test, and optimize metrics for benchmarking.
Bottom Line!
By now, you probably have a good idea of what feature engineering is and why it's important, but let's summarize. Feature engineering refers to the process of augmenting raw features with additional input features that can help improve the effectiveness of a model. This so-called augmented dataset is then used as training and evaluation data to build better models, where the augmented features bring in more value than just looking at raw features alone.
Given the importance of features in data science and machine learning, you should spend time understanding and exploring not just the data but features that you can build from data.
This skill can be the difference between a business succeeding and failing, so every data scientist should be familiar with it as they develop their skills. With India’s best data science course in Pune with placement, you can master these concepts for your next data science projects.
