The graphical representation of predicting pay is a procedure that tries to construct a computerised system that can keep track of all daily salary growth graphs in any area and predict income after a set length of time.
One of the most often used predictive modelling approaches is linear regression. The formula is Y = a + bX + e, where an is the intercept, b is the slope of the line, and e is the error term. Based on a given predictor variable, this equation can be used to predict the value of a target variable (s).
In machine learning, regression refers to mathematical techniques that allow data scientists to forecast a continuous outcome (y) based on the values of one or more predictor variables (x). Because of its ease of application in predicting and forecasting, linear regression is perhaps the most popular type of regression analysis.
Machine learning model predictions allow organisations to generate very accurate guesses about the likely outcomes of a query based on historical data, which might be about anything from customer attrition to possible fraud.
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Suresh Kumar
5
Hi Sir,
I want a clearity up on these
1. To learn Data Science "Machine learning" is part of it but we have to learn additionally python libraries (panda, numpy, matplotlib) or else in ML enough.
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Ayush Bharti
4
how can i download the finaldata.csv?
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Jagannath Mahato
5
Hello Kushal Sir!
Your way of teaching is very good. I thank you from my heart ❤️ that you are providing such good content for free.
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Muhammad Qasim
5
Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world.
Extremely helpful and I recommend to my peer as well for this course.
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Shafi Akhtar
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None
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Aniket Kumar prasad
5
Very helpful and easy to understand all the concepts, best teacher for learning ML.
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Rishu Shrivastav
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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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