{"id":6477,"date":"2021-01-21T18:27:57","date_gmt":"2021-01-21T12:57:57","guid":{"rendered":"https:\/\/copyassignment.com\/?p=6477"},"modified":"2022-08-05T09:28:17","modified_gmt":"2022-08-05T03:58:17","slug":"linear-regression-machine-learning","status":"publish","type":"post","link":"https:\/\/copyassignment.com\/linear-regression-machine-learning\/","title":{"rendered":"Linear Regression: Your 1st Step in Machine Learning"},"content":{"rendered":"\n

Hi guys! So until now, we’ve learned about how we can use libraries to play with data. We did data analysis on a real dataset and we also learned how to visualize data. But what was the purpose behind it? Why do so many things? What are we trying to achieve? I’ll tell you all about it along with a seemingly simple ML algorithm called Linear Regression<\/strong>.<\/p>\n\n\n\n

Before going on it’s recommended you know how to use commonly used libraries like NumPy, pandas, etc. You can learn about them here<\/a><\/strong>.<\/p>\n\n\n\n\n\n

Purpose of Machine Learning<\/h2>\n\n\n\n

So let’s say John is a developer who is given a task to find the yield of crops given the amount of rain and temperature throughout the year. John tried finding the relation between yield and other factors and hard code an expression to find the yield. John is exceptional at math and found an expression takes predicts this with decent accuracy. The Boss was happy! <\/p>\n\n\n\n

Next data he is assigned another project where now he has to find if a person has diabetes or not based on given data. Well as intelligent as John is he got really tired after yesterday. He still took the same approach and submitted his project.<\/p>\n\n\n\n\n\n

Did you notice how John had to find the best-fit expression for the data every time he is given data? If only there was a way to predict the value for any dataset without recoding everything and there is! And that’s what Machine Learning can do.<\/p>\n\n\n\n

Machine learning<\/strong> allows an algorithm to become more accurate at predicting outcomes without being explicitly programmed to do so. Predicting is one of the things that ML can do but actually, you can do much more cool stuff with it too and once you go deep into it you’ll learn all about it.<\/p>\n\n\n\n

So until now, we’ve done a lot of things with data. We’ve handled missing values, handled string data and we’ll learn to do much more cool stuff in the future. But why? Why does it? To put it simply when you train a model you need the data to be all numerical, there shouldn’t be missing values or strings. Um Ok! But why analyze the data? <\/p>\n\n\n\n

The purpose of analyses is to find important features and the useless ones that were destined to be removed in order to improve the quality of data, by removing any unnecessary feature that might hinder the training algorithm.<\/p>\n\n\n\n