fix this one it was bad

lw1/lw1.md

@@ -605,16 +605,7 @@ plt.show()
     
 
 
-Interpretation of PCA 2D Scatter
-
-For both datasets, the PCA 2D scatter plots (PC1 vs PC2) do not show clearly separate clusters corresponding to the labels:
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-- In the apple dataset, ‘good’ and ‘bad’ quality apples overlap heavily in the first two principal components. This suggests that the directions of greatest variance in the data are not strongly aligned with the quality labels.
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-- In the elevator dataset, the ‘peak hour’ vs ‘non-peak hour’ samples are also not clearly separable in the first two PCs. This indicates that the primary sources of variance in the elevator features do not correspond strongly with peak hour status.
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-In both cases, PCA captures high variance directions, but because it is unsupervised, it does not necessarily capture class distinctions — especially when those distinctions don’t drive the strongest variance in the data.
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+## Scree plot
 
 
 ```python