[Verse 1] Data scattered like confetti on the floor Thousands of points, what are they for? Each investor's got their own unique tale But patterns hide beneath the veil Clustering comes to save the day Groups similar points in their own array K-means algorithm takes the lead Finds the centers that we need [Chorus] Group them up, break them down PCA spins data around Fewer dimensions, same information Eigenvalues drive the transformation Cluster tight, reduce the noise Principal components are our voice Keep the variance, lose the rest Unsupervised learning at its best [Verse 2] Hierarchical builds a family tree Dendrograms show what we can see Distance measures guide the way Euclidean, Manhattan hold the sway Principal Component Analysis starts Covariance matrix plays its part First component captures most Variance explained becomes our boast [Chorus] Group them up, break them down PCA spins data around Fewer dimensions, same information Eigenvalues drive the transformation Cluster tight, reduce the noise Principal components are our voice Keep the variance, lose the rest Unsupervised learning at its best [Bridge] Elbow method finds the perfect K Scree plots show which components stay Loadings tell us what each axis means Portfolio risk in data machines Explained variance ratio guides our choice Cumulative percentages give us voice [Verse 3] Financial data's multidimensional maze Stock returns in correlation's haze Cluster sectors by their behavior PCA becomes our data savior Ten variables become just three Ninety percent of info we still see Interpretable factors emerge so clear Market dynamics crystal appear [Outro] Unsupervised techniques reveal the truth Hidden structures, there's the proof Clustering groups, PCA transforms Complex data takes simpler forms
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