Here, we create a PCA object and specify the number of components we want to extract. Let’s see how to perform PCA on a dataset using scikit-learn: from composition import PCA # create a PCA object pca = PCA(n_components=2) # fit and transform the data X_pca = pca.fit_transform(X) # calculate the explained variance ratio print('Explained variance ratio:', pca.explained_variance_ratio_) The new features, called principal components, are orthogonal to each other and can be used to reconstruct the original dataset. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that reduces the number of features in the dataset by finding a new set of features that captures the most variance in the data. Here, we will discuss some popular methods for feature selection and extraction, along with Python code snippets.ġ. Let’s get started! Table of Contentsįeature selection and extraction is an essential part of machine learning that involves selecting the most relevant features from the dataset to improve the model’s accuracy and efficiency.

We will also cover scaling and normalization, creation of new features, handling imbalanced data, handling skewness and kurtosis, handling rare categories, handling time-series data, feature transformation, one-hot encoding, count and frequency encoding, binning, grouping, and text preprocessing.īy the end of this guide, you will have a comprehensive understanding of Feature Engineering techniques and how they can be used to enhance the performance of your machine learning models. Then, we will move on to encoding categorical variables, which is an essential step when working with non-numerical data. We will start with feature selection and extraction, which involves identifying the most important features in the data. In this guide, we will cover a range of techniques that are commonly used in Feature Engineering.

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