Simple linear regression is a sort of regression analysis in which there are only one independent variable and the independent(x) and dependent(y) variables have a linear relationship. In the graph above, the red line is referred to as the best fit straight line.
The most significant benefit of linear regression models is their linearity: It simplifies the estimating process and, more crucially, these linear equations have an easy-to-understand modular interpretation (i.e. the weights).
A straight line is used in linear regression models, while a curved line is used in logistic and nonlinear regression models. You can use regression to predict how a dependent variable will change as the independent variable(s) change. The link between two quantitative variables is estimated using simple linear regression.
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Rishu Shrivastav
5
explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
5
very nicely explained
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Vrushali Kandesar
5
Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Mohd Mushraf
5
Amazing Teaching
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Juboraj Juboraj
5
Easy to understand & explain details.
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Joydeb
5
Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
5
Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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Neel Khairnar
5
Kushal is very good explainer he is covering all topics nicely 👍
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