Well, I hope you all have already grasped the Python essentials, Statistics and Maths from day 1 -- day 8(links shared below), Pandas part 1 and part 2 on Day 9, Day 10, Numpy as Day 11, Data Preprocessing Part 1 as Day 12, Data Preprocessing part 2 as Day 13th. In this post we will cover how we can implement Regression - part 1 as Day 14. It's a technique to estimate the relationship between two quantitative variables. It works on the assumption that the relationship between the independent and dependent variable is linear: the line of best fit through the data points is a straight line as shown in the diagram. Linear regression uses mean-square error (MSE) to calculate the error of the model.

Well, I hope you all have already grasped the Python essentials, Statistics and Maths from day 1 -- day 8(links shared below), Pandas part 1 and part 2 on Day 9, Day 10, Numpy as Day 11, Data Preprocessing Part 1 as Day 12, Data Preprocessing part 2 as Day 13th.

From Apache Beam (Dataflow) batch and streaming to wide and deep neural networks, I've started the journey of committing data science code to Github. Disclaimer, I'm currently focused on quantity and then stretching towards code that others can use. I'll be writing mostly in Python but I am an R lover so you'll see R occasionally as well.