In this project first of all we did EDA and preprocessed our data.
Later, we have trained different models (KMeans, Agglomrative Clustering, DBSCAN, Gaussian Mixtures models, Spectral Clustering) in order to separate all laptops from our dataset into groups, knowing laptop's specifications.
In order to reduce the number of dimensions in our data we've used: SVD, UMAP, PCA, tSNE, MDS.
Later we explored our clusters to uderstand the logic of separation.
Description in detail can be found in report: Laptops_Clustering_report.pdf