Identifying different regressions Linear Polynomial from a graph and fit regression model accordingly

Question

I am working with different machine learning algorithms for my dataset. I am using python. I am a beginner in machine learning.

This is the visualization of my dataset. It is understandable there linear regression won't perform well for the whole set. 

I am willing to divide the graph into 3 regions. Like this image. I want to have 3 regions which are a constant part, polynomial part, and a linear part. 

I want to predict X first, then need to know in which part it lies and then it will fit the model according to the part. Like if X lies on the polynomial part the system will identify as polynomial part and it needs to fit as polynomial regression.

My question is how can I implement it in such a way? 

Answer 1

The constant part of your equation isn't really constant. For varying x, constant indicates the same y. However, given your data, it's the opposite: different y for the same x.
You can't use a constant function to fit data like this (just fitting an intercept).