Introduction to dimensionality reduction
Introduction Dimensionality reduction is a crucial step in data preprocessing, especially when dealing with high-dimensional datasets. It helps in simplifying models, reducing computation time, and mitigating the curse of dimensionality. This README provides an overview of dimensionality reduction techniques and how to implement them using Python.
Before proceeding, ensure you have the following installed:
- Python 3.6 or higher
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
DATA In this file I have used Wine data for my project. You can take this data if you want.
Techniques Principal Component Analysis (PCA) PCA is a statistical method used to reduce the number of variables in a dataset while retaining most of the variance. It transforms the data into a new coordinate system, where the greatest variances come to lie on the first coordinates (principal components).
Conclusion Dimensionality reduction is a powerful tool in data analysis and visualization. Using techniques like PCA, t-SNE, LDA, and UMAP, you can reduce the complexity of high-dimensional data while preserving its essential structure. This README provided a brief overview and example code to help you get started with these techniques in Python.