Dimensionality-Reduction-on-Wine-Dataset

Followed steps to classify wines:

1. Preprocessed the dataset
2. Used knn for classification
3. Performed feature extraction
4. Again, completed the classification process and compared the results
5. Used linear dimensionality techniques such as PCA and LDA, and non-linear dimensionality techniques such as kernel PCA, Isomap, Locally Linear Embedding (LLE), Laplacian Eigenmap (sklearn.manifold.SpectralEmbedding) and t-SNE.
6. Analyzed and compared performance of each.