How to select most important features? Feature Engineering

Question

I used the function for gower distance from this link: https://sourceforge.net/projects/gower-distance-4python/files/. My data (df) is such that each row is a trade, and each of the columns are features. Since it contains a lot of categorical data, I then converted the data using gower distance to measure "similarity"... I hope this is correct (as below..): D = gower_distances(df) distArray = ssd.squareform(D) hierarchal_cluster=scipy.cluster.hierarchy.linkage(distArray, method='ward', metric='euclidean', optimal_ordering=False) I then plot the hierarchical_cluster from above into a dendogram: plt.title('Hierarchical Clustering Dendrogram (truncated)') plt.xlabel('sample index or (cluster size)') plt.ylabel('distance') dendrogram( hierarchal_cluster, truncate_mode='lastp', # show only the last p merged clusters p=15, # show only the last p merged clusters leaf_rotation=90., leaf_font_size=12., show_contracted=True # to get a distribution impression in truncated branches ) I cannot show it, since I do not have enough privilege points, but on the dendogram I can see separate colors. What is the main discriminator separating them? How can I find this out? How can I use PCA to extract useful features? Do I pass my 'hierarchal_cluster' into a PCA function? Something like the below..? pca = PCA().fit(hierarchal_cluster.T) plt.plot(np.arange(1,len(pca.explained_variance_ratio_)+1,1),pca.explained_variance_ratio_.cumsum()) JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)

Answer 1

I hope you do know that PCA works only for continuous data? Since you mentioned, there are many categorical features. From what you have written, it occurs that you got mixed data. A common practice when dealing with mixed data is to separate the continuous and categorical features/variables. Then find the Euclidean distance between data points for continuous (or numerical) features and Hamming distance for the categorical features [1]. This will enable you to find similarity between continuous and categorical feature separately. Now, while you are at this, apply PCA on the continuous variables to extract important features. And apply Multiple Correspondence Analysis MCA on the categorical features. Thereafter, you can combine the obtained relevant features together, and apply any clustering algorithm. So essentially, I'm suggesting feature selection/feature extraction before clustering. [1] Huang, Z., 1998. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data mining and knowledge discovery, 2(3), pp.283-304.