claude Cooked

lw1/README.md

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+# T1 - Principal component analysis
+
+## Study the dataset
+
+1. Choose 2 datasets from Kaggle or any source in Internet with more than 6 dimensions.
+1. For each dataset, determine which feature is discrete or continuous? quantitative or qualitative? numerical or categorical? Explain.
+1. What are the issues lying within data? for example: missing data, incorrect data? Do you
+1. need to prepare data?
+1. Is there any label in the datasets? Explain.
+1. Calculate mean, variance, covariance, correlation of the selected datasets. How do you calculate these measures for categorical features (if any)?
+1. Find the most corelated couple of features of each dataset. Comment on the results.
+   Is there any missing data? How do you handle this issue?
+
+## PCA
+
+1. Apply PCA on the two selected datasets. Describe how you select the principal components.
+   Is there any difference while using principal components with highest and lowest values?
+1. Vary the number of used principal components, analyze and comment on the obtained results.
+1. Visualize data distribution in 2D.