ml2/lw1

T1 - Principal component analysis

Study the dataset

1. Choose 2 datasets from Kaggle or any source in Internet with more than 6 dimensions.
2. For each dataset, determine which feature is discrete or continuous? quantitative or qualitative? numerical or categorical? Explain.
3. What are the issues lying within data? for example: missing data, incorrect data? Do you
4. need to prepare data?
5. Is there any label in the datasets? Explain.
6. Calculate mean, variance, covariance, correlation of the selected datasets. How do you calculate these measures for categorical features (if any)?
7. 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?
2. Vary the number of used principal components, analyze and comment on the obtained results.
3. Visualize data distribution in 2D.