Hi, welcome to another video in the series of machine learning. In this video we have learnt to create our first beginner's friendly machine learning model which can predict student performance based on previous performance.
Linear regression model can easily be called from sklearn library.
Ordinary least squares Linear Regression.
LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.
We have seen some more information about loss functions. And we have learnt how OLS finds out optimal values of coefficients.
deriving ols - [ Ссылка ]
interactive lin. reg - [ Ссылка ]
rss/ sum of squared err - [ Ссылка ]
chapters:
0:00 - what to expect
0:31 - loss used in OLS
1:49 - interactive linear regression
2:14 - how ols minimize loss function
4:01 - Simple Linear Regression
4:35 - Multiple Linear Regression
6:10 - Getting the Dataset
8:24 - Setting up Spyder
9:05 - loading dataset
9:50 - Choosing useful independent vars
12:18 - making X and y
13:03 - creating and fitting model
13:49 - splitting in test and train
17:21 - Visualising result
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
FOR CONTACTING ME FOLLOW ANY OF THE LINK
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
contact me : 📞
-----------------------------------------------
facebook : [ Ссылка ]
twitter 🐦 : [ Ссылка ]
github : [ Ссылка ]
discord : [ Ссылка ]
linkedin : [ Ссылка ]
youtube ❤️: [ Ссылка ]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Thanks for watching. your 1 views means a lot to me. Make sure to Like if you found it helpful.
Also don't forget to subscribe for more upcoming videos.
tags:
machine learning,linear regression,using ols,predicting student grade,student-mat.csv,student grade prediction using ml,python ml,linear regression ols,loss,sor,sum of residuals,what is use of loss,how ols works,ordinary least squares,model sore
