This dataset contains drug performance metrics for 37 common conditions, so it can be used to compare and analyze the effectiveness, cost, and satisfaction of different drugs.
To get started using this dataset, let’s take a look at the columns. The key fields to consider are Condition, Drug Name, Indication, Type/Form of Drug (such as pill), Reviews/Ratings (for efficacy), Ease-of-Use Ratings (rated by customers), Satisfaction Ratings (also rated by customers) and Average Price. Additionally you should consider the Information field which includes any additional information related to the drug.
Now that we have identified all the important figures we can begin analyzing our data set. A great first step is to make use of descriptive statistics such as mean or median values for each column in order to get an idea on how various drugs perform with regards to effectiveness or cost etcetera. We also might want group our data indexed based on condition – that way comparing drugs would be simpler based on what they treat! Additionally grouping by type and form can also help provide meaningful insights into our data set allowing us to gain greater understanding about various drug responses across different forms/types available for different conditions (e.g capsules vs pills versus injections). Comparing performance metrics amongst groups created this way will enable us draw correlations between cost effectiveness ratios and satisfaction rates towards certain types/forms of drugs compared with others treating similar diseases or health concerns!
Finally “Reviews” are a key component any analysis involving drug performance ratings – while they do not necessarily correspond directly towards efficacy; they play a vital measuring stick when it comes consumer perceptions regarding certain medications – both good and bad alike! Overall understanding how these reviews & ratings interact with all other factors provides us great insight into medication reception thereby aiding potential health news coverage as well making informed decisions related medical purchases & usage across consumer level demographics!