Python

Perform data analysis with unsupervised methods k$-$means and agglomerative clustering for the following dataset:

from sklearn.datasets import load$\_$wine

wine $=$ load$\_$wine$()$

df$\_$wine $=$ pd$.$DataFrame$($data$=$wine.data, columns$=$wine.feature$\_$names$)$

df$\_$wine$[\text{'}$target$\text{'}]=$ wine.target

Steps:

EDA

Visuals $$ dendrograms, elbow plots, scatter plots

Data Prep $$ PCA, feature selection

Model hyperparameter setup

Results and analysis $$ compare methods