This is the code for a linear regression model used to predict revenue amount based on product usage.
I built a simple linear regression model using the Scikit-learn library. The inputs and outputs were both numeric values.
- numpy
- pandas
- scikit-learn
Install dependencies using pip.
The dataset was taken from the Udemy Python for DS and ML bootcamp. The particular subset I used (/input/customers.csv) contains 500 observations (customers) and 7 attributes.
| Column | Definition |
|---|---|
| Customer's email ID | |
| Address | Customer's address |
| Avatar | Colour of customer's profile |
| Avg. Session Length | Average in-person consulting time spent by customer |
| Time on App | Time spent by customer on mobile app |
| Time on Website | Time spent by customer on website |
| Length of Membership | For how many years has the customer been with the company? |
| Yearly Amount Spent | Average amount spent by customer on company app/website |
Run the notebook on a localhost server using jupyter notebook.