def calculateHyp(params, sample):
    """
        Calculates the predicted value (hypothesis)
        yHat = a+bx1+cx2+ ... nxn   # Simplified Formula for Linear Regression

        yHat -> Predicted Value,    b0 -> Bias,     b1 -> Coefficient of 1st parameter ,    bn -> n coefficent 
        x1 -> first input (feature -> ex: Age value),  xn -> n input/feature 

        params -> Coefficientes of each parameter
        sample -> instance contained in the features dataset
    """
    acc = 0
    acc = params * sample # pandas takes care of multiplying each parameter with the respective feature
    # Optimized version
    acc = acc.sum(axis=1) # To sum all in row and not columns, axis is set to 1
    # print(acc)
    # exit()
    # acc = acc * (-1) # This is done for logistic regression
    return acc