Feature Engineering Techniques in Data Science using Python

Introduction

One of the most critical steps in the process of building a machine learning model or embarking on a data science project is Feature Engineering. Feature engineering involves the transformation and processing of data attributes such that they become more meaningful and effective when used by machine learning algorithms. The ultimate goal of feature engineering is to improve model accuracy and generalizability.

Feature Engineering Techniques In Data Science

Feature engineering techniques often require a strong understanding of the data and its domains. But with a respective understanding of Python libraries and suitable methodologies, we can simplify and automate much of the process. Therefore, in this article, we will explore some of frequently used feature engineering techniques in Python. This guide is designed for data science enthusiasts of all levels, from beginners to seasoned veterans.

By the end of this article, readers should feel more confident in implementing the various feature engineering techniques covered. We will delve into discussions in a manner that maintains the utmost accuracy and reliability, citing credible sources where necessary.

What is Feature Engineering?
Data Preprocessing
Handling Categorical Variables
Handling Numerical Variables
Feature Scaling
Feature Selection
Conclusion

What is Feature Engineering?

Feature engineering is the process of utilizing domain knowledge to create features (variables) that make the machine learning algorithms work. A feature is an attribute or property shared by all of the independent units on which analysis is to be done.

It’s often said that “Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering.” This statement underscores the importance of feature engineering in any data science project.

“Feature Engineering is an art.” -Dr. Andrew NG

Data Preprocessing

One of the first steps in feature engineering is preprocessing your data. Data in the real world is rarely clean and homogeneous. Therefore, preprocessing of the data is a crucial step in which we make sure that the data can be handled by the algorithm.

Python provides us with powerful libraries like pandas & NumPy to preprocess the data and get it ready for use.

import pandas as pd

data = pd.read_csv('your_file.csv')

# Fill NaN values with zero
data = data.fillna(0)

# Drop unnecessary columns
data = data.drop('column_name', axis = 1)

# Print first five rows of the data
print(data.head())

Handling Categorical Variables

Categorical features include any type of data that falls into a specific category or a label. Machine learning models typically expect numerical inputs, so preprocessing categorical variables into a numerical form is a critical step in feature engineering.

There are several techniques to handle categorical variables:

One-Hot Encoding: The process of converting categorical data variables so they can be provided to machine learning algorithms to improve predictions. Python’s sklearn library provides the OneHotEncoder module to encode categorical integer features as a one-hot

from sklearn.preprocessing import OneHotEncoder

# Assuming `car_type_column` is the categorical column we want to encode.
ohe = OneHotEncoder(sparse=false)
encoded_column = ohe.fit_transform(data['car_type_column'].values.reshape(-1, 1))

Label Encoding: This approach involves converting each value in a column to a number. You might consider this approach if there’s an ordered relationship between the categories, like “low”, “medium” and “high”.

from sklearn.preprocessing import LabelEncoder

label_encoder = LabelEncoder()
data['column_name'] = label_encoder.fit_transform(data['column_to_encode'])

Handling Numerical Variables

Even though numerical variables can be used directly into most ML models, scaling or normalization might be required for algorithms where the scale of the features is important (like SVM, KNN and PCA).

Numerical variables can have different scales. For instance, age typically ranges from 0 to 100 while income can range from 0 to very high values. There are two common ways to bring all features to the same scale: Normalization and Standardization.

Feature Scaling

Standardization is a scaling technique where the values are centered around the mean with a unit standard deviation. This means that the mean of the attribute becomes zero and the resultant distribution has a unit standard deviation.

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
data['numerical_column'] = scaler.fit_transform(data['numerical_column'].values.reshape(-1, 1))

Normalization (or Min-Max Scaling) rescales the features from 0 to 1. This scaler works better for cases where standard scaling may not work well.

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
data['numerical_column'] = scaler.fit_transform(data['numerical_column'].values.reshape(-1, 1))

Feature Selection

Feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.

Here are some techniques to do feature selection:

The Correlation matrix: This is a table showing correlation coefficients between variables.

import seaborn as sns

correlation_matrix = data.corr().round(2)
sns.heatmap(data=correlation_matrix, annot=True)

Recursive feature elimination (RFE): This is based on the idea to repeatedly construct a model and choose either the best or worst performing feature.

from sklearn.feature_selection import RFE
from sklearn.linear_model import LinearRegression

model = LinearRegression()
rfe = RFE(model, 7)
X_rfe = rfe.fit_transform(X,y)

Disclaimer – Remember, each feature engineering technique can affect dataset differently. Always cross validate and check how the model responds to your changes.

Conclusion

Feature engineering is a vital step in creating powerful machine learning models. Python, with its robust libraries, provides a wide variety of techniques in handling data and creating new features, ultimately enabling the development of highly accurate and efficient models. Despite this, deciding on which feature engineering strategies to employ remains more of an art than a science and comes with numerous choices based on the data and project at hand.

Feature Engineering Techniques In Data Science