A complete analytical journey of linear regression. From EDA, model building, model diagnostics, residual plots, outlier treatment, co-linearity effects, transformation of variables, model re-building and validation for Boston housing price prediction problem.
It’s pretty obvious to summon the fact that you wouldn’t have clicked on this article if you have no understanding of the basics and intermediate level concepts of Python. You have? Then it is fair enough to go ahead with this article. You are confused with some basic stuffs, or perhaps forgot about it? I…
Links to all other posts in a structured way. Table of contents.
Tutorial on Time Series EDA. Contains time plot, seasonal plots and correlogram plots (ACF) for in-time problem with reusable R code.
Tutorial on Multivariate analysis which is the second part of EDA. Explained using in-time problem with reusable R code.
Explanation of class size paradox using Amrita University placement data. Contains reusable R code for web scraping.