Data Science Lessons

 I worked on Regresission problems for three weeks. I read concepts and learned how to implement linear regression in python by studying Machine learning using a python book from Dinesh Kumar published by Wiley India Pvt Ltd. I built projects and multiple regression models to understand concepts, here in this blog I am sharing all concepts I learned:- Table of contents: Regression fundamentals and concepts The regression equation and coefficients (image) Python implementation (image) Metrics and errors (Image) Optimization and regulation (image) Other points to keep in mind 1) Regression fundamentals and concept > Regression is widely used in business applications such as sales prediction, house price prediction or cost of the patient in a hospital prediction, etc > regression models fit the best line to inputs(X) to predict the output (Y) 2) Regression equation and coefficients > Linear regression model follows very simple equation as shown below >

Python Programming language overview: Python is increasingly becoming a popular programming language as a general programming language as well as applications such as data science, machine learning, and artificial intelligence. Python has gained immense popularity among developers and has ranked 1st on preference by developers across the world. A major reason for its popularity could be the ease of reading and writing syntaxes, English like syntaxes make the programming highly interpretable and implementable. Python's philosophy has been the guiding force for its development and usage  One of the biggest advantages python offers is that it has a vast number of standard modules and libraries that can be used to write clean, efficient, and readable code to deliver and deploy in real-world scenarios faster Contains: 1) Python syntaxes 2) Data types and values 3) Conditionals statements 4) operators 5) Loops 6) Functions 7) Data structures 8) Classes and objects 9) String objects  10)