Problem statement
A new pharmaceutical startup is recently acquired by one of the world's largest MNCs. For the acquisition process, the startup is required to tabulate all drugs that they have sold and account for each drug's effectiveness. A dedicated team has been assigned the task to analyze all the data. This data has been collected over the years and it contains data points such as the drug's name, reviews by customers, popularity and use cases of the drug, and so on. Members of this team are by the noise present in the data.
Your task is to make a sophisticated NLP-based Machine Learning model that has the mentioned features as the input. Also, use the input to predict the base score of a certain drug in a provided case.
Data
The dataset has the following columns:
| Variable Name | Description |
| patient_id | ID of patients |
| name_of_drug | Name of the drug prescribed |
| use_case_for_drug | Purpose of the drug |
| review_by_patient | Review by patient |
| drug_approved_by_UIC | Date of approval of the drug by UIC |
| number_of_times_prescribed | Number of times the drug is prescribed |
| effectiveness_rating | Effectiveness of drug |
| base_score | Generated score (Target Variable) |
Data description
The data folder consists of the following two .csv files:
- train.csv - (32165x 7)
- test.csv - (10760x6)
The sample_submission is described as follows:
patient_id,base_score
206461,9.05
95260,8.85
92703,5.26
138000,8.03Evaluation metric
score=100∗max(0,1−RMSE(actual_values,predicted_values))
Note: To avoid any discrepancies in the scoring, you must ensure all the patient_id column values in the submitted file must match the values in test.csv provided.
