Armanik, a multinational pharmaceutical company based in Texas, USA, is one of the largest pharmaceutical companies by both market capitalization and sales. Armanik manufactures drugs across multiple therapy areas – Cardiovascular, Diabetes, HIV and Immunology therapy. Their innovative SGLT 2 is the market leader in diabetes therapy. Recently, the company announced that they have successfully completed a Phase 3 trial for an Anti-TNF drug in Rheumatoid Arthritis therapeutic. The company expects to get approval for its new drug in the next 6 months. Given the competition in the market, Prakash Vishwanathan, CEO of Armanik, has reached out to ZS to help identify the patient population in the U.S who are likely to switch any product in the RA market. ZS has proposed a machine learning-based approach using medical transactional data to first identify the factors that are most closely associated with the switching RA patients that will help predict patients who are likely to switch in the near term.
Can you help ZS in achieving the below-mentioned objectives?
- Train_data.csv – Data for training (This is a transactional data for feature creation)
- Train_labels.csv – Outcome flag for train patients (1/0)
- Test_data.csv – Data for testing (Create final features for modelling)
- Fitness_values.csv – Fitness values for features created with train data. Must be used to match the fitness values with the Feature created using Train Data Only.
- Sample Submission.csv – Sample submission Format to submit the Predictions (Don’t Shuffle the Patient_ID, Keep the sequence entact).
- Train Data: 627 MB
- Test Data: 273 MB
Train Data Preview
Test Data Preview
Packages Allowed (Applicable only for Objective 1)
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