Let's see an example of linear regression learning about salary data in Python.
We need to have some historic data with some values for learning to happen. In our scenario, we have salary data in a .csv format (Salary_Data.csv). These are example records in the .csv:
| Years of experience | Salary |
| 1.1 | 39343 |
| 1.3 | 46205 |
| 1.5 | 37731 |
| 2 | 43525 |
| 2.2 | 39891 |
| 2.9 | 56642 |
| 3 | 60150 |
| 3.2 | 54445 |
| 3.3 | 64445 |
| 3.7 | 57189 |
| 3.9 | 63218 |
| 4 | 55794 |
| 4 | 56957 |
| 4.1 | 57081 |
| 4.5 | 61111 |
| 4.9 | 67938 |
At this point in time, we would use Python's sklearn library, which is scikit-learn used in ML.
For further information about scikit-learn, please refer to this URL at https://scikit-learn.org/stable/.
The code on how to ingest data ...
