How do you use Python for feature engineering?

To identify the relevant features or attributes that will be used for a Machine Learning task from your dataset, you should explore the characteristics of your dataset. This is performed by a process called as Exploratory Data Analysis (EDA). After EDA, you perform feature selection step that extracts the most important features or labels from all the features in the data. For instance, given an ML task to predict the marks scored by students for an upcoming test. The dataset consists of various columns like number of hours studied, names, id, gender, number of classes attended and previous grade scored. Out of all these, 3 features are important: number of hours studied, classes attended and previous grade scored.

It is also called exploratory data analysis, abbreviated as EDA. It is a process where a data analyst reviews, evaluates, or browses a dataset without having any expectations of what will be discovered. EDA is mostly used to understand what data might show in addition to the standard procedure of testing hypotheses. It offers a greater comprehension of the components and variables of the data as well as how they relate to one another. It can also help determine whether the statistical techniques under consideration are appropriate for data analysis. According to reports, advanced data scientists and machine learning engineers spend 50% to 60% of their time working on EDA and pre-processing.

Once we have the data, it is very important to play with the data and understand its nature and characteristics. Exploring the dataset involves assessing the structure of the dataset, checking for summary statistics, visualizing the data to identify trends, patterns and outliers, checking for missing data and normalizing the data.

Exactly. This is the process of selecting the most important features from all the features in your dataset. The important features are those which help determine the target label by a Machine Learning model. It helps reduce the dimensions of your dataset, eliminate noisy features and redundancies. This step also helps to avoid overfitting problem.

Not every characteristic in a dataset is equally crucial for model prediction. Finding and maintaining only the most relevant features while tossing out less significant ones is what is meant by feature selection. Feature selection methods include: • Measure the association between each attribute and the target variable using univariate statistical tests. • Removing the least significant features iteratively using recursive feature removal. • Scores indicating how important a feature is from tree-based models like Random Forest or Gradient Boosting. Few of the feature selection techniques are: • Univariate feature selection. • Recursive Feature Elimination. • Feature Importance- Measure the relevance of features and choose the best ones.

This step helps in identifying patterns in your dataset and extract or create new features from a set of features. Few instances of Feature Extraction techniques include: Dimensionality reduction techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), Topic Modeling methods like Latent Dirichlet Allocation (LDA) and Non Negative Matrix Factorization (NMF), and so on. After this step, your dataset with the extracted features can be visualized as plots using techniques like t-SNE or UMap.

Feature Extraction/ Feature Creation is the process of creating new features from ones that already exist. By mixing, altering, or extracting data from the original features, new features can be created. For instance: • By dividing or multiplying already-existing characteristics, interaction features are created. • Powering up variables to create polynomial features. • Obtaining important textual data, such as word counts, mood ratings, or text length. • Date-related information, such as day of the week, month, or year, can be produced using timestamps.

Numeral input is necessary for many machine learning methods like logistic regression, SVM etc. Categorical variables can be dealt with by using methods like one-hot encoding or label encoding to transform them into a format that the model can use. Few of the Categorical Data Handling techniques are: • One-Hot Encoding: For machine learning models, categorical variables are transformed into binary (0/1) columns. • Label encoding: Giving categories numerical labels. • Target Encoding: Depending on how categorical variables relate to the target variable, they are encoded.

The size of the input characteristics affects the performance of several machine learning methods. If the scales of the characteristics are different, the algorithm could give excessive weight to the features with bigger scales, producing biased or less-than-ideal outcomes. The method of feature scaling involves changing numerical features to have a same scale, usually between 0 and 1. Min-Max scaling and z-score (standardization) are two popular scaling techniques. Few of the Numerical Data Scaling techniques are: • Min-Max Scaling (Normalization): Scaling numerical features to a specific range, often between 0 and 1. • Standardization (Z-score Scaling): Scaling features to have a mean of 0 and a standard deviation of 1.

True that. Leveraging tools in Python for feature engineering helps you achieve your goals for a Machine Learning project by transforming raw data into meaningful features that can be used for analysis and generating key insights. Feature engineering is an integral part of a ML project.

Libraries such as Scikit-learn for basic transformations and feature selection, Pandas for data manipulation, and Dask for parallel computing are instrumental, but for real-time data, you might lean towards libraries like Apache Storm or Faust. Apache Storm, for instance, allows for real-time computation and processing of unbounded streams of data, while Faust provides stream processing in Python, enabling real-time feature extraction and transformation. By leveraging these libraries, you can efficiently manage and utilize real-time data, ensuring your feature engineering is robust and your models remain relevant as new data streams in.