1.⁠ ⁠Graph polynomials in python with matolotlib.pyplot(DONE)
learnings :-
1. How to plot polynomials and see results with matplotlib .
2. Flaw of having different scale of different things looks same in two seperate plot but different in 1 plot .

2.⁠ ⁠Import in any dataset and do linear regression on it with pandas to make it a dataframe and sklearn
Reference : - https://realpython.com/linear-regression-in-python

First thing is to know about regression:= what is it. 
regression searches for relation ship between different components such as how a employess salary
depends on the his exp, education , place etc
in regression we take a assumptions about the variable to be independent and look for a variable that de
-pend on that independent variables.
The dependent features are called the dependent variables, outputs, or responses. The independent features are 
called the independent variables, inputs, regressors, or predictors.

3.⁠ ⁠Do clustering on the iris dataset with seaborn for graphs
Reference :- https://realpython.com/k-means-clustering-python/
https://www.geeksforgeeks.org/analyzing-decision-tree-and-k-means-clustering-using-iris-dataset/
Learning :- seaborn is just a library build on matplotlib and numpy and its just for ploting .
Need to understand K mean clustering .
elbow method or the within-cluster sum of squares (WCSS) value .
Next time silhouette coefficient

Done LR on kaggle dataset :- https://www.kaggle.com/datasets/andonians/random-linear-regression
Next goal is to try something more complex with Multiple Linear Regression.(next week maybe)
for now bye .

Done Multi linear regression on kaggle dataset with income prediction on the basis of age and experience.