Tree-based methods for regression and classification involve stratifying or segmenting the predictor space into a number of simple regions. In order to make a prediction for a given observation, we typically use the mean or the mode of the training observations in the region to which it belongs. In this exercise we compare the criterion for splitting, fit a tree, bagged tree, and random forest to the Carseats data, and fit a tree to the OJ data.

using tidyworse, plotly and ggplot

using ggplot

Chapter #7 from ISLR Book Exercise # 6 and 10

Chapter 6

Assignment #3 Chapter 04 (page 168): Q. Nos: 10, 11, 13

Data Cleaning , Normality Assumptions, Linear regression Model and Interpretation

Performed Linear Regression Model

Assignment #2 Chapter 03 (page 120): 2, 9, 10, 12