Hungry for Revenue!?

In this data science project, I have conducted a machine learing regression model in python on consumer purchase data that this alias company called ‘Apprentice Chelf’ has. I have posted the code in the tab above labeled code and here. My reason for the data analysis is that because food deliver today is ever apparent in our life. Many professionals young and old need the time to spare to exhale after a long day at work. Cooking can be a joy to some, but a chore to others. This allows many companies such as HelloFresh & Blue Apron to thrive and create successfull businesses.

Here are the top 2 insights for your convenience.

1. Customers on our app/website have at minimum 7 clicks per visit when seen in the distplots. This should be lowered as the more clicks generated the more likely the customer is to have a lower revenue, which can be found on the correlation matrix and in the scatter plots as the correlation matrix has it at -0.55 and the scatter plot has a linear decrease.

2. That our app/website is driven by the overall time they are on either or. When looking at the correlation matrix two of the most highly correlated categories are avg prep video time at 0.64 and total pictures viewed 0.47. This is a problem because we currently sell ourselves as company for quick and fast professionals, yet two of our most valuable correlations to revenue center around increased user time.