While making decisions we tend to assume lots of if-buts scenarios and then come up to a conclusion. Decision tree in machine learning also work in the similar concept. Visually, it is a flowchart like structure, it consist of : parent node , branch nodes and leaf nodes.

Referring to the above image **Figure 1.0**, **Age** is the parent node, **Eat Pizza **& **Exercises **are the branch nodes, **Fit **& **Unfit **are the leaf nodes. What it tells is that if your age is less than 30 and you eat pizza you are unfit. …

You can relate our decision making process with functionality of decision tree. When an important event is going to happen, we prepare ourselves for n-different scenarios in which event can take place, i.e we branch out potential solution(s) or response for each scenario. If scenario A happens then I will jump , else I will sit. Recursively branching this event with your potential response until we reach the potential outcome, i.e we are making decisions one step at a time until we reach the final outcome. When you visualize this process ,the structure it forms is of a tree.

Suppose…

In my last article : Logistic Regression: The Theoretical Way , I have talked to you about the important key concepts of logistic regression, one should know before starting to implement practically. I hope you have gone through the concepts. If not, prior reading this, I would request you to kindly go through my previous article.

This article will not just cover logistic regression , the main aim of this is to talk about key approach to address a business problem in brief. Building model fascinates us, but in reality an Analyst only spend 20% of his time. Rest 80%…

Before moving ahead , I believe you must have knowledge of **Linear Regression**. In case you don’t , kindly go through my prior articles : Linear Regression I & Linear Regression II.

So, let me brief you all, this is the first part of the logistic regression which would cover all the theories needed to know before we apply it practically. The second part of the article which will come next week, will have the practical approach. This practical approach will not only be about applying Logistic Regression , it will also tell you the approach to the particular business…

I hope the first part of this article has enlightened the basics of Linear regression. In this article, in continuance to the previous one, we will dive in depth of Linear Regression.

I will try to make this article as explanatory as I can , if anyone still has any ambiguity, you can comment here or reach me at **startmljourney@gmail.com.**

Before we get started with linear regression, I would like to introduce a small concept of types of variable. The essence of this topic is to get acquainted to when to use **Linear Regression.**

We can classify variables broadly in…

**Why I have decided to write the article?**

I work as a mentor in field of data science and machine learning. My day to day job is to solve the technical doubts of students over the online discussion forum. During my tenure, I have felt that students pay X amount of money for the course , which lacks in content, wrong information being provided . One can learn data science and machine learning for much less than what one pay for the course . …