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Name: Scarlett Fan
Type: User
Name: Scarlett Fan
Type: User
NCAA Dataset
Classification model on the credit risk data
• Partition trading time series data into 30 minutes intervals by picking the mean transaction price and volumes in each interval and compute the log-return (aka ’U sequence’) and write it into a corresponding csv file: JNJ_1004_1015_2010_HFT_30min_.csv • Visualize the high frequency data with PCA by using 2 or 3 PCs: you need to calculate the variance explained ratios for your visualization. • Identify outliers in your PCA analysis • Visualize it by using KPCA and compare its results with those of PCA (you need to at least try two kernels)
Orphan designations matching
Suppose k PCs are selected in your PCA for the SP data. It means ith stock will have coordinates pi1, pi2 · · · pik in the subspace spanned by PCs. – 1. find the top 20 stocks with largest PC1 values – 2. find the top 20 stocks with largest PC2 values – 3. Find the top 20 stocks with largest PCk values – 4. RankeachstockbytheirPCArankingscoreρ= p2i1 +p2i2 +···+p2ik, list the top-ranked 20 stocks, and describe their characteristics. – 5. Discuss the relationships between the variables according to your PCA analysis
Logistic regression model developed to predict whether a pet gets adopted or not
fashion review analysis and wine review analysis
fashion review analysis and wine review analysis
use python twitter API to collect the number of followers for the 232 rich universities in the file endowments_2018.csv and updated the file as endow- ments_2018_2.csv to include the the number of followers in twitter. • Verify the following hypotheses via your analytics: – The richer a university, the more followers in its twitter – Private universities have more followers than its public peers • Visualize the data via PCA and t-SNE (or SOM) and analyze your findings • Label entries as – ’universities with a large number of followers – universities with a medium number of followers – universities with a small number of followers – then partition data as training and test by using extra-Trees, MLP and CNN to do classification by showing all classification measures including d-index.
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