Statistical Decision Making in Data Science with Case Study Understand how Statistics is Applied to Data Science Problem like ANOVA, t-test, F-test in Python Rating: 4.8 out of 54.8 (34 ratings) 16,792 students Description Welcome to the course "Statistical Decision Making in Data Science with a Case Study in Python" You will learn the approaches towards regression with case study. First we start with understanding linear equation and the optimization function value sum of squared errors. With that we find the values of the coefficient and makes least square regression. Then we starts building our linear regression in python. For the model we build we necessary test like hypothesis testing.

Classification is the problem of categorising data in one of two or more possible classes. In the classical supervised learning framework, examples of each class have already been obtained and the task of the decision maker is to accurately categorise new observations, whose class is unknown. The accuracy is either measured in terms of the rate of misclassification, or in terms of the average cost, for problems where different types of errors carry different costs. In that setting, the problem has three phases: (a) the collection of training data, (b) the estimation of a decision rule based on the training data and (c) the application 1 of the decision rule to new data. Typically, the decision rule remains fixed after the second step.