Python package for plug and play dimensionality reduction techniques and data distribution and visualization in a reduced space. Using this package, you can reduce and plot according a target variable your data set with a 3D o 2D chart and a matrix plot.
The available techniques are:
- t-distributed Stochastic Neighbor Embedding (t-SNE);
- Linear Discriminant Analysis (LDA);
- Uniform Manifold Approximation and Projection (UMAP);
- Principal Component Analysis (PCA);
- Factor Analysis (FA);
- Truncated Singular Value Decompisition (SVD);
- Kernel Principal Component Analysis;
- Multidimensional Scaling (MDS);
- Isometric Mapping (Isomap).
At the moment the packege is not available using pip install <PACKAGE-NAME>.
For the installation from the source code click here.
t-SNE description goes here.
t-SNE use cases goes here.
Put some examples.
LDA description goes here.
LDA use cases goes here.
Put some examples.
UMAP description goes here.
UMAP use cases goes here.
Put some examples.
PCA description goes here.
PCA use cases goes here.
Put some examples.
Factor Analysis description goes here.
Factor Analysis use cases goes here.
Put some examples.
Truncated SVD description goes here.
Truncated SVD use cases goes here.
Put some examples.
Kernel PCA description goes here.
Kernel PCA use cases goes here.
Put some examples.
Multidimensional Scaling description goes here.
Multidimensional Scaling use cases goes here.
Put some examples.
Isomap description goes here.
Isomap use cases goes here.
Put some examples.
For the installation from the source code type this command into your terminal window:
pip install git+<repository-link>
or
python -m pip install git+<repository-link>
or
python3 -m pip install git+<repository-link>