This work is a sequence classification, we predict whether a DNA sequence belongs to the SARS-CoV-2 (Covid-19).
- **Data description**
- Xtr.csv - the training sequences.
- Xte.csv - the test sequences.
- Ytr.csv - the sequence labels of the training sequences indicating Covid-19 DNA (1) or not (0).
- Xtr_vectors- the training vectors which contain the encoding vector of each DNA sequencing reads from training.
- Xte_vectors- the testing vectors which contain the encoding vector of each DNA sequencing reads from testing.
- Each row of Xtr.csv represents a sequence.
- Xte.csv contains 1000 test sequences, for which you need to predict the labels.
- Ytr.csv contains the labels corresponding to the training data, in the same format as a submission file.
- **Pre-processing** For features use the vectors dataset which are already encoded but still we need to convert the values 0 to -1. Therefore transforming {0,1} classification problem to {-1,1} classification problem. To do that we computed :
- **Models** We used many models to make classification like Logistic Ridge Regression (Newton-Ralphson ,SGD), Kernel Logistic Regression, Kernel Ridge Regression, Kernel SVM and HardMarginSVM. We got the best accuracy with using Kernel SVM using RBF kernel. Our model performs an accuracy of **100%** for training and **97%** for validation. We got this result by looking for the good hyper-parameters
- kernel = 'rbf'
- sigma =0.1407035175879397
- degree = 2
- C = 10.0
- tol = 1e-4