It involves creating new features (columns), transforming existing ones, and selecting the most relevant attributes to improve the performance and accuracy of machine learning models.

Feature engineering helps the model understand the data’s underlying patterns, relationships, and nuances. It plays a crucial role in the success of a machine learning project, as it can turn a good model into a great one by optimizing the input features. Effective feature engineering also leads to faster training times, and more interpretable results.

Before delving into the realm of feature engineering, you need to recognize the pivotal role of data cleaning in ensuring the success of Feature Engineering. Addressing missing values, handling outliers, and resolving inconsistencies not only improves the integrity of the data but also establishes a solid groundwork for subsequent feature extraction and transformation.

In this tutorial, we'll explore the concept of feature engineering for structured data. We'll cover some techniques such as feature scaling, feature creation, feature selection, and binning. We'll use a hypothetical dataset to demonstrate these various feature engineering techniques.

Let's start by creating some dummy data.

How to Create Dummy Data

We'll use Python and the pandas library to create a dummy dataset for our feature engineering examples. Here's how you can generate dummy data:

import pandas as pd
import numpy as np
# Create a sample dataframe
data = {
'Age': [25, 30, 35, 40, 45],
'Income': [50000, 60000, 75000, 90000, 80000],
'Education': ['High School', 'Bachelor', 'Master', 'Ph.D.', 'Bachelor'],
'City': ['New York', 'San Francisco', 'Chicago', 'Los Angeles', 'Miami'],
'Gender': ['Male', 'Female', 'Male', 'Female', 'Male'],
'Productivity': [5, 4, 3, 2, 4]
}
df = pd.DataFrame(data)

Code Output

As seen above, the dummy dataset created consists of six features: 'Age,' 'Income,' 'Education,' 'City,' 'Gender,' and 'Productivity.'

Now, let's explore different feature engineering techniques.

One-Hot Encoding

One-hot encoding is a method used to convert categorical variables into a binary matrix. Our dataset has some columns filled with words such as gender, education, and cities.

But machines like numbers, not words. So we’ll use a technique called one-hot encoding. It’s like giving each category its own switch – either it’s on (1) or off (0). It creates a binary column for each category within a categorical feature.

This technique is essential because many machine learning algorithms cannot work directly with categorical data.

One-Hot Encoding Example

Let's apply one-hot encoding to the 'Education' , 'City' and 'Gender' columns in our dataset. We'll use the get_dummies function from pandas for this purpose:

## Perform one-hot encoding
df_encoded = pd.get_dummies(df, columns=['Education', 'City', 'Gender'])

After one-hot encoding, our dataset will now have binary columns for each category within 'Education' 'City' and 'Gender'. This transformation allows machine learning models to understand and utilize these categorical variables effectively.

Code Output

As you can see in the image above, the categorical columns are now being transformed into a binary matrix.

Feature Scaling

Feature scaling is crucial when working with numerical features that have different scales. Scaling ensures that all features contribute equally to the model, preventing any one feature from dominating the others.

In our data, ‘Age’ and ‘Income’ might have different scales. ‘Age’ might go from 0 to 100, while ‘Income’ could go from 20,000 to 200,000. Feature scaling makes them play nicely together by putting them on the same scale.

Feature Scaling Example

In our dataset, 'Age' and 'Income' have different scales. To scale these features, we'll use the StandardScaler from the sklearn.preprocessing module:

from sklearn.preprocessing import StandardScaler
# Initialize the StandardScaler
scaler = StandardScaler()
# Fit and transform the selected columns
df_encoded[['Age', 'Income']] = scaler.fit_transform(df_encoded[['Age', 'Income']])

Code Output

From the code output above, now we can see that the 'Age' and 'Income' have been scaled to have a mean of 0 and a standard deviation of 1. This makes them compatible for modeling and helps algorithms that rely on distances or gradients perform better.

Feature Creation

Feature creation is one of the most pivotal techniques in Feature Engineering. It involves generating new features (columns) from existing ones to provide additional information to the model. It can help uncover complex relationships and patterns within the data.

Let’s say we mix ‘Age’ and ‘Income’ to create a new feature called ‘Income per Age’. This new feature might help our model understand how money and age are related.

Feature Creation Example

In our dataset, we can create a new feature, 'Income per Age,' to capture the relationship between income and age. This can be a useful feature for certain prediction tasks:

# Create a new feature 'Income per Age'
df_encoded['Income per Age'] = df_encoded['Income'] / df_encoded['Age']

Code Output

The 'Income per Age' feature provides a measure of income relative to age, which could be valuable in various modeling scenarios. With this an additional column has been created.

Feature Selection

Feature selection is the process of choosing the most relevant features for a given modeling task. Reducing the number of features can improve model performance, reduce overfitting, and speed up training. It makes your model’s job easier because it doesn’t have to deal with unnecessary things.

Feature Selection Example

Let's assume we want to select the most relevant features for predicting 'Productivity.' We can use feature selection techniques like Recursive Feature Elimination (RFE) with a linear regression model:

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

# Separate the target variable and the features
X = df_encoded.drop('Productivity', axis=1)
y = df_encoded['Productivity']

# Initialize the linear regression model
model = LinearRegression()

# Perform RFE with cross-validation
rfe = RFE(model, n_features_to_select=3)  # Select the top 3 features
fit = rfe.fit(X, y)  # Corrected: use X instead of x

# Print the selected features
selected_features = X.columns[fit.support_]
print('Selected Features:', selected_features)

From our data, we used RFE to select the top 3 features that are most relevant for predicting 'Productivity.' This reduces the dimensionality of the data and can improve model performance.

Code Output

This output received indicates that, according to the Recursive Feature Elimination (RFE) process, the columns: 'Income', 'City_Miami', and 'Gender_Male' are considered the most important or informative for predicting the target variable 'Productivity' using a linear regression model.

Let's break down the interpretation:

1. Income: The 'Income' feature is likely identified as an important predictor. It suggests that, according to the RFE process, changes in income have a significant impact on predicting 'Productivity' based on the given linear regression model.
2. City_Miami: The presence of 'City_Miami' as an important feature suggests that, in the context of our data, whether an individual is from Miami or not contributes significantly to predicting 'Productivity.'
3. Gender_Male: Similarly, the 'Gender_Male' feature is considered important. This implies that, according to the model, the gender of an individual, specifically being male, is informative for predicting 'Productivity.'

It's important to note that the interpretation of "importance" here is specific to the linear regression model used in combination with the RFE feature selection technique. RFE ranks features based on their contribution to the model's performance in predicting the target variable.

Binning and Bucketing

Binning or bucketing involves grouping continuous numerical data into discrete intervals. It can help capture non-linear relationships and make modeling more robust.

Let’s consider a practical example. Based on our dummy data, Instead of treating the "Age" column as a continuous variable, you decide to bin it into categories like "Young," "Mid-career," and "Senior" to better understand how age relates to productivity.

After your analysis, you might discover that employees in the "Mid-career" category tend to have the highest productivity levels. This doesn't mean that age directly causes productivity, but it suggests a potential relationship that could be influenced by factors such as experience, skill development, and familiarity with the job.

By binning age into categories, you've made the age-productivity relationship more interpretable, which can provide your model with more detailed information.

Binning Example

Let's say we want to create bins for the 'Age' feature. We can use the cut function from pandas to define bin boundaries:

# Create bins and labels for the single column
bins = [-float('inf'), -0.5, 0.5, float('inf')]  # Adjust the bin edges as needed
labels = ['Young', 'Mid_Career', 'Senior']

# Bin the Age column
df_encoded['Binned_Column'] = pd.cut(df_encoded['Age'], bins=bins, labels=labels, right=False)

# Print the updated DataFrame
df_encoded

Binning 'Age' into discrete intervals allows the model to consider the age groups as a categorical feature, capturing non-linear relationships.

Code Output

As you can see above, the 'Age' column has been transformed into a binned column, which further enhances the information given to the model.

Conclusion

Feature engineering techniques enables data scientists and machine learning practitioners to create more informative and relevant features for modeling.

In this tutorial, we explored various feature engineering techniques, including one-hot encoding, feature scaling, feature creation, feature selection, and binning.

Experiment with these techniques to enhance your data analysis and modeling capabilities. Remember that the choice of feature engineering techniques should be driven by the specific requirements of your project and the nature of your data.

"[Machine learning is a] Field of study that gives computers the ability to learn and make predictions without being explicitly programmed."

ML is a sub-field of Artificial Intelligence. It's based on the idea that computers can learn from historical experiences, make vital decisions, and predict future happenings without human intervention.

Machine Learning is behind product suggestions on e-commerce sites, your movie suggestions on Netflix, and so many more things. The computer is able to make these suggestions and predictions by learning from your previous data input and past experiences.

In recent years, Machine Learning has garnered a lot of attention around the world, it has become one of the most important ways that people use Artificial Intelligence.

How Does Machine Learning Work?

Machine Learning involves building algorithms. Data Scientists build these algorithms, and the type of algorithm they build depends on the type of data they're working on.

The Machine Learning process begins with gathering data (numbers, text, photos, comments, letters, and so on). These data, often called “training data,” are used in training the Machine Learning algorithm. Training essentially "teaches" the algorithm how to learn by using tons of data.

Following the end of the “training”, new input data is then fed into the algorithm and the algorithm uses the previously developed model to make predictions.

The algorithm is trained several times until it reaches a desired outcome. This enables the Machine Learning algorithms to continually learn on their own. This produces optimal answers and increasing accuracy and predictions over time.

Here's a helpful overview of Machine Learning basics for developers to get you started.

Types Of Machine Learning

There are three types of Machine Learning, which include:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Let's look at each one briefly.

What is Supervised Learning?

Supervised Learning is the most common type of Machine Learning. It involves training the algorithm with a “labelled” dataset. The labelled training data helps the Machine Learning algorithm make accurate predictions in the future.

A practical example of supervised learning is training a Machine Learning algorithm with pictures of an apple. After that training, the algorithm is able to identify and retain this information and is able to give accurate predictions of an apple in the future. That is, it will typically be able to correctly identify if an image is of an apple.

What is Unsupervised Learning?

In Unsupervised Learning, the training data is NOT labelled or named. The unlabeled data are used in training the Machine Learning algorithms and at the end of the training, the algorithm groups or categorizes the unlabeled data according to similarities, patterns, and differences.

This type of Machine Learning can help in grouping and organizing data in such a way that you can come in and make sense of the grouped data.

A practical example is training a Machine Learning algorithm with different pictures of various fruits. The algorithm finds similarities and patterns among these pictures and is able to group the fruits based on those similarities and patterns.

What is Reinforcement Learning?

Finally, we have Reinforcement Learning. In this case, the algorithm discovers data through a process of trial and error. Favorable outputs are reinforced and non favorable outcomes are discarded. Over time the algorithm learns to make minimal mistakes compared to when it started out.

Tools Used For Machine Learning

There are a plethora of tools you can use for Machines Learning. These tools can be classified into three categories, which include:

• Programming languages: Programming languages used for Machine Learning include Python, Java, R, and C++.
• Machine Learning platforms: These platforms help you carry out machine learning procedures from beginning to the end. They provide you with the tools to develop and deploy Machine Learning algorithms. Examples include Jupyter Notebook, IBM Watson studio, R studio, KNIME, Azure ML Studio and so many others.
• Machine Learning Libraries: Examples of Machine Learning libraries in Python include Scikit-learn, Pytorch and JSAT used in Java.

What Are the Applications of Machine Learning?

The application of Machine Learning in our day to day activities have made life easier and more convenient. They've created a lot of buzz around the world and paved the way for advancements in technology.

Applications of Machine Learning include:

Product Recommendation

Product recommendation is one of the coolest applications of Machine Learning. Websites are able to recommend products to you based on your searches and previous purchases.

Social Media Features

Social media platform such as Instagram, Facebook, and Twitter integrate Machine Learning algorithms to help deliver personalized experiences to you.

These apps takes note of your likes and comments and are able to make pages and friend suggestions suited for you based off your behavior on the app/platform.

Virtual Assistants

Virtual assistants such as Siri and Alexa are built with Machine Learning algorithms. They make use of speech recognition technology in assisting you in your day to day activities just by listening to your voice instructions.

These assistants record your voice instructions and send them over the server, and decode it using Machine Learning algorithms.

Image Recognition

Image Recognition is one of the most common applications of Machine Learning. It's used to identify persons, objects, and places.

It makes use of Machine Learning techniques to identify and store images in order to match them with images in a pre-existing database.

Medical Diagnosis

In recent years, there have been tremendous advancements in medical technology. For example, the development of 3D models that can accurately detect the position of lesions in the human brain can help with diagnosis and treatment planning.

Machine learning also helps in identifying genetic factors for various diseases, by looking for genetic patterns amongst people with similar diseases.

The Future of Machine Learning

Companies and organizations around the world are already making use of Machine Learning to make accurate business decisions and to foster growth.

In the coming years, Machine Learning is expected to break more strides in various fields such as automobile development, drug devepment, education and so many more. Let's discuss some of these possibilities.

Machine Learning and Automobile Development

Machine learning is already helping car manufacturers develop smarter vehicles. Currently a few automobile companies use Machine Learning algorithms to automatically detect failures in newly built vehicles.

In the coming years, most automobile companies are expected to use these algorithm to build safer and better cars.

Also, we'll probably see Machine Learning used to enhance self-driving cars in the coming years. These self-driving cars are able to identify, classify and interpret objects and different conditions on the road using Machine Learning algorithms.

Machine Learning and Drug Development

In recent years, pharmaceutical companies have started using Machine Learning to improve the drug manufacturing process. They've also used it to help produce more efficient drugs.

Currently, patients' omics data are being gathered to aid the development of Machine Learning algorithms which can be used in producing personalized drugs and vaccines. These personalized drugs are individual and population-specific. The production of these personalized drugs opens a new phase in drug development.

Machine Learning and Education

Educational institutions are using Machine Learning in many new ways, such as grading students' work and exams more accurately. ML can also help improve students’ performance.

Researchers are already working on Machine Learning algorithms which are able to predict whether students will fail or pass a course. These algorithms help schools and teachers focus on helping failing students to increase their chances for success.

Conclusion

I hope you now understand the concept of Machine Learning and its applications.

Thank you for reading.

For questions, you can send me a message on Twitter. @heyfunmi.

Machine Learning Resources

Looking to get started in Machine Learning?

Here is a 10 hour video on Machine Learning by freeCodeCamp. It takes you from zero to hero in Machine Learning. 🥳

And here's a list of the 10 best Machine Learning courses to check out this year.

Happy Learning!