In the last video we discussed how to write down code for Linear Regression algorithm. So which out of the four functions discussed is this ?def fun(x_train, y_train) num = (x_train*y_train).mean() -x_train.mean() * y_train.mean() den = (x_train**2).mean() -x_train.mean()**2 m =num/den c = y_train.mean() -m * x_train.mean() return m,c

The function fun(x_train, y_train) is implementing the calculation of the slope (m) and the y-intercept (c) in a simple linear regression model.

Here's a step-by-step explanation:

1. num = (x_train*y_train).mean() -x_train.mean() * y_train.mean(): This line calculates the numerator of the slope formula, which is the mean of the product of x_train and y_train minus the product of the mean of x_train and the mean of y_train.

2. den = (x_train**2).mean() -x_train.mean()**2: This line calculates the denominator of the slope formula, which is the mean of the square of x_train minus the square of the mean of x_train.

3. m =num/den: This line calculates the slope (m) of the regression line, which is the ratio of the numerator and the denominator calculated in the previous steps.

4. c = y_train.mean() -m * x_train.mean(): This line calculates the y-intercept (c) of the regression line, which is the mean of y_train minus the product of the slope (m) and the mean of x_train.

5. return m,c: This line returns the slope (m) and the y-intercept (c) of the regression line.

So, this function is implementing the calculation of the parameters of a simple linear regression model.