vinaydeshmukh94 / cancer-diagnosis

Cancer-Diagnosis

Data Overview: Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/data We have two data files: one conatins the information about the genetic mutations and the other contains the clinical evidence (text) that human experts/pathologists use to classify the genetic mutations. Both these data files are have a common column called ID Data file's information:

training_variants (ID , Gene, Variations, Class) training_text (ID, Text)

ID : the id of the row used to link the mutation to the clinical evidence Gene : the gene where this genetic mutation is located Variation : the aminoacid change for this mutations Class : 1-9 the class this genetic mutation has been classified on

Problem statement : Classify the given genetic variations/mutations based on evidence from text-based clinical literature.

Featuraization: Two ways to featurize Categorical Data : 1) One hot encoding 2) Responce Encoding For text feature : Instread of Word2Vec we use Countvectorizer(BOW)

Moodels: Tried different models among them following works best.

1. Logistic Regression with onne hot encoding + balancing
2. Voting (Logistic regression, SVM, RF) (but intrepretability is lost in this)