Visualizing dataset with Matplotlib plots.
Before analysis, the metadata was cleaned by checking for any mouse ID with duplicate time points and remove any data associated with that mouse ID.
The cleaned data then used for the following:
- Generate a summary statistics table consisting of the mean, median, variance, standard deviation, and SEM of the tumor volume for each drug regimen
- Generate a bar plot to show the number of total mice for each treatment regimen throughout the course of the study
- Generate a pie plot to shows the distribution of female or male mice in the study
- Calculate the final tumor volume of each mouse across four of the most promising treatment regimens: Capomulin, Ramicane, Infubinol, and Ceftamin.
- Calculate the quartiles and IQR and quantitatively determine if there are any potential outliers across all four treatment regimens.
Matplotlib was then used to generate a box and whisker plot of the final tumor volume for all four treatment regimens and potential outliers were highlighted with different color
User input was included to get user to select a mouse that was treated with Capomulin. A line plot of tumor volume vs. time point then generated for that mouse.
A scatter plot with a correlation coefficient and linear regression model of mouse weight versus average tumor volume for the Capomulin treatment regimen and
Observations and Jupyter Notebook can be explored here