Guide to Feature Engineering - Types | Importance | Tools

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Feature engineering equips an advanced computer with the best tools to solve a problem. Suppose, you're trying to teach a computer to recognize cats in pictures. Instead of just showing it thousands of random pixels, you'd carefully select and prepare the most important details - like the shape of ears, eyes, and whiskers. That's feature engineering! It's the art of turning raw data into information that a computer can easily understand and use to make accurate predictions.

What is Feature Engineering?

Feature engineering entails extracting and organizing crucial features from raw data to precisely suit the requirements of the machine learning model. In addition, it is the skill of selecting essential features and transforming them into refined and meaningful characteristics that precisely meet the needs of the model.
 

It involves carefully picking the most important information from the data, cleaning it up, and organizing it in a way that makes sense to the computer. Moreover, this crucial step ensures that the machine learning model has the best possible information to learn from and make accurate predictions.

Types of Feature Engineering

Feature engineering transforms raw ingredients into a gourmet meal for your machine learning model. It's the process of taking messy, real-world data and turning it into something a computer can easily understand and use to make smart decisions. Follow the below types. 
 

1. Feature Creation
    

• Domain-Specific: Use your knowledge of the problem to create features.
• Data-Driven: Find patterns in the data to invent new features.
• Synthetic: Combine existing features to create something new.
   

2. Feature Transformation
    

• Normalization and Scaling: Make sure all features are measured consistently.
• Encoding: Convert features with different names (like "red" or "green") into numbers the computer understands.
• Transformation: Sometimes, changing the shape of a feature can make it better for the set.
   

3. Feature Selection
    

• Filter Method: Quickly choose different combinations of features to see what works best.
• Wrapper Method: Experiment with different combinations of features and see which data makes the best set.
• Embedded Method: Let the machine learning algorithm decide which features are most essential.
   

4. Feature Scaling
    

• Normalization, Standardization, Robust Scaling: Ensure all ingredients contribute equally to the final model performance.
   

Remember: Feature engineering is an art and a science. It often involves trial and error to find the perfect combination of features for your machine learning model. By mastering these techniques, you can create models that are not only accurate but also efficient) and easy to understand.

Importance of Feature Engineering

Machine learning needs clean, high-quality data to work. Here's why it's so important:
 

• Dirty Data, Bad Results: Raw data is often messy, with missing bits, weird formats, and even mistakes. Feeding this mess to a machine learning model would result in a terrible dish (prediction). Feature engineering cleans and organizes the data, making it easier for the model to understand and learn from.
   
• 80% of the Work: A whopping 80% of a data scientist's time goes into data cleaning and preparation – that's feature engineering! It's the prominent factor behind every successful machine learning project.
   
• Unlocking Patterns: By selecting the most critical information from the data, feature engineering helps the model see patterns it wouldn't have seen before. Moreover, it uses the right tools to reveal hidden patterns in the data.
   
• Building a Better Model: Feature engineering makes the machine learning model's job easier. It can choose the right algorithm and work much more efficiently.

In short, feature engineering transforms raw data into delicious insights and powerful machine-learning models. 

Feature Engineering Example

Here's an example of feature engineering using a dataset about house prices.

Example Scenario

Dataset: Suppose we have a dataset with the following columns:
 

• House_ID: Unique identifier for each house
• Area: Total area of the house in square feet
• Bedrooms: Number of bedrooms
• Bathrooms: Number of bathrooms
• Age: Age of the house in years
• Price: Price of the house

How to Prepare This Data?

The following section has some steps that are necessary to prepare feature engineering. Follow the below steps. 

Step 1: Analyze Your Data

First things first, take a good look at your data. What kind of information do you have? Are there any values that need to be added? As a result, this helps you understand what needs fixing.

Step 2: Find Out Missing Data

Data can have missing bits. Here's how to deal with them:
 

• Deleting Columns: A whole column with too many missing values might not be very helpful. You can delete it completely.

For example, 

threshold=0.7

dataset = dataset[dataset.columns[dataset.isnull().mean() < threshold]]

print(dataset)
 

• Imputing Missing Values: For smaller gaps, you can fill them in using tricks like using the average value in that column.

For instance, 

x= dataset.iloc[:,1:-1].values

y= dataset.iloc[:,-1].values

print (x)
 

Step 3: Categorize Your Variables

Sometimes your data might have categories, like "red" or "blue" peppers. These need advanced treatment:
 

• Dropping Rows: You can remove rows with missing categories entirely.

Example - dataset.dropna(axis=0, subset=['Gender'], inplace=True)

dataset.head(10)
 

• Assigning a New Category: Create a new category, like "unknown," for missing values. 

Instance - dataset['Gender']= dataset['Gender'].fillna('U')

dataset.head(10)
 

• Imputing with Mode: Fill in the blanks with the most common category. 

Example - dataset['Gender']= dataset['Gender'].fillna(dataset['Gender'].mode()[0])

dataset.head(10)
 

Remember: There are no one-size-fits-all sets for data wrangling. The best approach depends on your specific data and what you're trying to achieve.

This is just a basic of data wrangling, but it's a crucial step for building a successful machine learning model.

How to Use Feature Engineering For Fraud Detection?

GBDT algorithm can analyze a ton of financial data to figure out if something fishy is going on. They do this to catch fraudsters. In addition, it looks at past cases of fraud and normal transactions to learn the patterns of hackers.
 

Most transactions are normal, and only a tiny fraction are fraudulent. However, this makes it hard to train the algorithm properly. To solve this, we can:
 

• Make the number of good and bad examples more equal, but this can sometimes mislead the detective.
• Instead of just counting how many times the detective is right, we can focus on how well it finds the hackers.
• Other advanced algorithms are better at dealing with these tricky situations.

By combining different approaches and focusing on what matters most – catching the hackers – we can build even better fraud-fighting systems.

Feature Engineering Tools

Remember our feature engineering analogy, where we transform raw data into accurate features for machine learning models? Well, there are advanced tools to help with this process!
 

• Featuretools: This Python library analyzes your data and automatically creates new, informative features based on patterns it finds.
• TPOT: TPOT doesn't just mix features, it also picks the best ones and even chooses the perfect machine learning algorithm for your data.
• DataRobot: It uses advanced machine learning techniques to not only create new features but also pick the best combination for your model.
• Alteryx: It allows you to drag and drop tools to create pipelines that clean, transform, and generate features from your data, all in a user-friendly interface.
• H2O.ai: It has everything from automatic feature scaling to manual custom scripting for advanced users.

These tools can be a huge time-saver, helping you create the perfect features for your machine learning models quickly and efficiently.

Concluding Words

Feature engineering is the culinary art of transforming raw data into a feast for machine learning models. By carefully selecting, cleaning, and transforming data, we create the perfect ingredients for building accurate and powerful models. It's the often overlooked, yet crucial step that separates good models from great ones. While it can be time-consuming, the rewards are immense, as a well-engineered dataset can dramatically improve model performance and unlock valuable insights hidden within the data.