How can I use the principal component analysis (PCA) in the machine learning pipeline?

 In the context of data science, you can address this particular scenario of reduction of the dimensionality in a dataset with a large number of features by using the Principal Component analysis by using the following steps:-

Import libraries

You can start by importing the necessary libraries such as NumPy, pandas, and sci-kit learn:-

Import numpy as np

Import pandas as pd

From sklearn.decomposition import PCA

From sklearn.preprocessing import StandardScaler

Prepare the data

You can load your dataset and preprocess but as needed. You can try to ensure that the data is standardized since PCA is sensitive to the scale of the features.

# Load dataset

Data = pd.read_csv(‘dataset.csv’)

# Separate features and target variable

X = data.drop(columns=[‘target’])

Y = data[‘target’]

# Standardize the features

Scaler = StandardScaler()

X_scaled = scaler.fit_transform(X)

Apply PCA

You can fit the PCA to the standardized feature and specify the number of principal components to retain.

# Initialize PCA with the desired number of components

Pca = PCA(n_components=0.95)  # Retain 95% of variance

# pca = PCA(n_components=10)   # Or specify number of components

# Fit PCA to the standardized data

X_pca = pca.fit_transform(X_scaled)

Evaluate variance retained

Optionally, you can evaluate the variance retained by the selected number of principal components.

Print(“Variance retained:”, np.sum(pca.explained_variance_ratio_))

Train machine learning model

You can use the transformed data with the reduced dimensionality for train your machine learning model:-

From sklearn.model_selection import train_test_split

From sklearn.svm import SVC

# Split the data into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)

# Initialize and train a classifier

Classifier = SVC()

Classifier.fit(X_train, y_train)

# Evaluate the model

Accuracy = classifier.score(X_test, y_test)

Print(“Accuracy:”, accuracy)