In this first post we will play a bit with Linear Regression in order to get confidence with some key concepts about machine learning.
Deep Learning Convolutional Neural Network, Recurring Neural Network, Support Vector Machine, Logistic Regression are great techniques for complex prediction, even the non-linear ones.
However Linear Regression is a great way to start when you have to perform prediction about data generally linearly correlated data.
Let’s consider the Australian athletes data set: a nice dataset collected in a study of how data on various characteristics of the blood varied with sport body size and sex of the athlete. These data were the basis for the analyses reported in Telford and Cunningham (1991).
Anybody interested in knowing more about that study can reference to the Telford, R.D. and Cunningham, R.B. 1991. Sex, sport and body-size dependency of hematology in highly trained athletes. Medicine and Science in Sports and Exercise 23: 788-794: https://europepmc.org/article/med/1921671
We are going to use Pyhton with Jupyter Notebook for such a model.
Let’s import some useful libraries to start:
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