What is Feature Engineering? Feature engineering is the process of creating or selecting relevant features from raw data to improve the performance of machine learning models. Feature engineering is the process of transforming raw data into features that are suitable for machine learning models. In other words, it is the process of selecting, extracting, and transforming the most relevant features from the available data to build more accurate and efficient machine learning models. In the context of machine learning, features are individual measurable properties or characteristics of the data that are used as inputs for the learning algorithms. The goal of feature engineering is to transform the raw data into a suitable format that captures the underlying patterns and relationships in the data, thereby enabling the machine learning model to make accurate predictions or classifications

1. Feature Engineering

2. What is Feature Engineering?
Feature engineering is the process of creating or selecting relevant
features from raw data to improve the performance of machine
learning models.
Feature engineering is the process of transforming raw data into
features that are suitable for machine learning models. In other
words, it is the process of selecting, extracting, and transforming the
most relevant features from the available data to build more accurate
and efficient machine learning models.
In the context of machine learning, features are individual measurable
properties or characteristics of the data that are used as inputs for the
learning algorithms. The goal of feature engineering is to transform the
raw data into a suitable format that captures the underlying patterns
and relationships in the data, thereby enabling the machine learning
model to make accurate predictions or classifications.

3. Feature engineering is a critical and iterative
process in machine learning that involves
transforming raw data into a format suitable for
training predictive models. It encompasses a wide
range of techniques and approaches to extract
meaningful information from the available data
and create relevant features that can enhance the
model's performance.

4. Scope of Feature
Engineering

5. The scope of feature engineering can be summarized as follows:
• Data Cleaning: This involves handling missing values, dealing with outliers,
and addressing other data quality issues. Imputation techniques, outlier
detection, and data normalization are some common methods employed
during this phase.
• Feature Creation: This step involves creating new features from the existing
ones or combining multiple features to generate more informative
representations. It can include operations such as arithmetic
transformations, scaling, binning, polynomial expansion, or creating
interaction terms.
• Feature Selection: This process focuses on identifying the most relevant
features that contribute significantly to the predictive power of the model
while removing irrelevant or redundant ones. Techniques like correlation
analysis, statistical tests, or regularization methods are commonly used for
feature selection.
• Dimensionality Reduction: In cases where the dataset has a high number of
features, dimensionality reduction techniques are applied to reduce the
feature space while preserving the most important information. Methods
like Principal Component Analysis (PCA) or t-SNE (t-Distributed Stochastic
Neighbor Embedding) can be used to achieve this goal.

6. • Encoding Categorical Variables: Since many machine learning
algorithms require numerical inputs, categorical variables
need to be encoded into a numerical representation. This can
be done through techniques such as one-hot encoding, label
encoding, or target encoding.
• Time-Series and Temporal Features: For time-series data,
feature engineering may involve creating lagged variables,
aggregating data over specific time windows, or extracting
seasonality or trend information.
• Domain-specific Feature Engineering: Depending on the
problem domain, additional domain-specific feature
engineering techniques may be employed. For example, in
natural language processing (NLP), features like bag-of-words,
n-grams, or word embeddings can be utilized.
• Iterative Process: Feature engineering is an iterative process
that involves experimentation, evaluating the impact of
different feature sets on the model's performance, and refining
the feature engineering pipeline based on the results.

7. It's important to note that the scope of
feature engineering may vary depending
on the specific problem, dataset, and
available resources. The expertise of the
data scientist and their understanding of
the problem domain play a crucial role in
identifying and engineering relevant
features.

8. Data Understanding
Feature Generation
Encoding Categorical
Variables Feature Scaling
Feature Selection
Data Cleaning
Handling Text or
Image Data
Iterative
Refinement

9. Data preprocessing techniques commonly used in
feature engineering:
• Handling missing values: Filling missing values in numerical and categorical
features with mean and mode values, respectively.
• Encoding categorical features: Using label encoding to convert categorical
features into numerical representations.
• Scaling numerical features: Standardizing numerical features using standard
scaler to ensure consistent scales.
• Creating interaction features: Generating new features by performing
mathematical operations on existing features.
• Text preprocessing: Lowercasing text and removing extra whitespaces in a
text feature.
• Handling datetime features: Extracting information from datetime features,
such as month and day of the week.
• Feature scaling using min-max scaling: Scaling numerical features to a
specified range using min-max scaler.
• Handling imbalanced classes: Applying oversampling technique (SMOTE) to
address class imbalance in the target variable