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13 Open 1 Closed
13 Open 1 Closed
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Issues list

Supervised Learning: Decision Tree (time)
#14 opened Oct 4, 2020 by du-hr
@du-hr
Supervised Learning: Decision Tree (region) (5-fold cross-validation)
#13 opened Oct 4, 2020 by du-hr
@du-hr
Supervised Learning: KNN (time)
#12 opened Oct 4, 2020 by du-hr
@matthewkourlas
Supervised Learning: KNN (region) (5-fold cross-validation)
#11 opened Oct 4, 2020 by du-hr
@du-hr @JacobCong001
Split the data (time): keep data for the last couple of timepoints (keep data after `2020-08-10') from all regions in the validation set and train on the rest of the data.
#10 opened Oct 4, 2020 by du-hr
@matthewkourlas
Split the data (region): keep all data from some regions in the validation set and train on the rest (keep 80% regions in training set and 20% in validation set, doing this multiple times to estimate cross-validation results).
#9 opened Oct 4, 2020 by du-hr
@du-hr @JacobCong001
K-means clustering: compare between raw data and PCA reduced dimensions.
#8 opened Oct 4, 2020 by du-hr
@matthewkourlas @du-hr
Use Principal Component Analysis (PCA) to reduce data dimensionality.
#7 opened Oct 4, 2020 by du-hr
@matthewkourlas @du-hr
Visualize: How the distribution of search frequency of each symptom aggregated across different regions changes over time.
#6 opened Oct 4, 2020 by du-hr
@matthewkourlas @du-hr
Clean data (You should remove regions and features that have too many missing or invalid data entries.)
#5 opened Oct 4, 2020 by du-hr
@JacobCong001
1
Merge datasets at the weekly resolution and thereafter merge them into one array (Numpy or Pandas).
#4 opened Oct 4, 2020 by du-hr
@JacobCong001
Load the datasets into Pandas dataframes or NumPy objects (i.e., arrays or matrices) in Python.
#3 opened Oct 4, 2020 by du-hr
@JacobCong001
Download datasets.
#1 opened Oct 4, 2020 by du-hr
@JacobCong001
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