Offered By: IBMSkillsNetwork
Using PCA to reduce dimensionality
Principal Component Analysis (PCA) reduces the number of dimensions in large datasets. PCA is applicable to both supervised and unsupervised machine learning tasks. This means PCA can reduce dimensions without having to consider class labels or categories. This project provides a hands-on learning experience with PCA and exploratory data analysis (EDA), using a wine dataset for the training.
Continue readingGuided Project
Machine Learning
265 EnrolledAt a Glance
Principal Component Analysis (PCA) reduces the number of dimensions in large datasets. PCA is applicable to both supervised and unsupervised machine learning tasks. This means PCA can reduce dimensions without having to consider class labels or categories. This project provides a hands-on learning experience with PCA and exploratory data analysis (EDA), using a wine dataset for the training.
Principal component analysis (PCA) reduces the number of dimensions in large datasets to principal components that retain most of the original information. It does this by transforming potentially correlated variables into a smaller set of variables, called principal components.
A Look at the Project Ahead
- Explore the Dataset: Conduct an exploratory data analysis to understand the structure, variable types, and distributions within the wine dataset.
- Visualize Data: Use pair plots, histograms, and correlation heatmaps to explore the correlations and distributions of the dataset's features.
- Split the Dataset: Divide the dataset into training and test sets for subsequent modelling.
- Standardize Data: Implement feature scaling to standardize the data, ensuring a mean of zero and a standard deviation of one, which is crucial for PCA.
- Determine Optimal n_components for PCA: Use explained variance plots and scree plots to identify the ideal number of principal components.
- Apply PCA: Reduce the dimensionality of the training data using PCA, focusing on retaining the most variance.
- Visualize PCA Output: Create scatter plots to visualize the principal components and observe the separation between different wine types.
What you'll need
Certificate
No Certificate Offered
Estimated Effort
20 Min
Level
Beginner
Industries
Information Technology
Skills You Will Learn
Machine Learning, Python
Language
English
Course Code
GPXX0YXXEN