Naive Data

Linear regression is one of the widely used statistical techniques for analysis. It provides a relationship between the target and the predictors. The below graph shows the linear relationship between the “dependent” variable Y, whose values we wish to predict and the “independent” variable X from which we wish to predict it. There are two types of linear regression: 1. Simple Linear Regression : It denotes the relationship between one variable and the target variable. The above graph represents the Simple Linear Regression. 2. Multiple Linear Regression :     It denotes the relationship between 2 or more independent variables and the corresponding dependent variable. The independent variables can be continuous or categorical. The below graph shows the multiple linear regression. Properties of Linear Regression Below are the main properties of linear regression: There is a linear relationship between X a...

  Photo by Fabrice Villard on Unsplash This the first post in Byte Series, where we are going to have discuss on overall concept but in brief to act a refresher. Decision Trees: As the name goes, a decision tree uses a tree-like model to make predictions. It resembles an upside-down tree. Decision trees are easy to interpret; you can always go back and identify the various factors leading to a decision. A decision tree requires you to perform tests on attributes in order to split the data into multiple partitions. Types: Single and Multiway class:                If a test splits the data into more than two partitions, this is called a multiway decision tree. Classification and Regression:              In regression problems, a decision tree splits the data into multiple subsets. The difference                          ...