Application to utilize Machine Learning libraries in Python and linear regression supervised learning to predict profits of ice cream sales according to the temperature. I am using a data set to feed it and a test data set.
Y = mx + b
Scikit-Learn library in Python will help me configure a linear regression model to the dataset I provide (temp and ice cream sales).
It uses the method of least squares, which squares the distance in units between the data point, and the line build between the data points, (the accurate values). Reason for squares instead of simple difference between line and inaccurate data point: This will establish a heavier weight on the errors, which helps put more emphasis on bigger errors and minimize the threshold. It will always be positive value which means differences which are positive and negative will not cancel each other out.