This is a showcase on how PCA is used for dimensionality reduction. Putting aside non-essential dimensions, especially for the case of big datasets, helps programmers and data scientists to work with data in a more efficient way.
Employing PCA, this code tries to find important principal components of images provided in Database as a whole and by omitting insignificant features it attempts to reduce its size.
✔️ After installing required python libraries by entering
pip install -r requirements.txt
into a terminal, the program can easily start with
python PCA_Showcase.py.
✔️ Also you can play around with variables like the number of Eigen vectors to keep to get more insight into PCA.
