Python Program for Drug Activity Prediction using Dimensionality Reduction and Classification
- The input sparse binary training data and testing data matrices were first expanded to full binary matrix of size 800 X 100000 and 350 X 100000 respectively.
- The dimensions of both the matrices were then reduced from 100000 to 100 using Principal Component Analysis (PCA).
- The test data was then classified using Bernoulli’s Naïve Bayes Classifier.