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Series
This series covers what a beginner might need from scratch to master ML, AI, Deep Learning, Natural Language Processing
In this blog, we are using the data set called 'Data.csv' CountryAgeSalaryPurchased France4472000No Spain2748000Yes Germany3054000No Spain3861000No Germany40Yes France3558000Yes Spain52000No France4879000Yes Germany5083000No France376...
From our previous blog on Data Pre Processing , we came to know how to pre process the data. Let's work on this data set. YearsExperienceSalary 1.139343 1.346205 1.537731 243525 2.239891 2.956642 360150 3.254445 3.264445 3.757189 3.9...
Here, y is the dependent variable and X1, X2, ......Xn is the independent variable Assumptions of linear regressions Here you can see apart from the first one, all other models are not working well. These are the assumptions we need to keep in mind....
This is the mathematical equation for polynomial regression. Now, let's see where we use it if we use linear regression here, it basically does not event fit. but if we use polynomial regression, it basically fits all of the data. Let's learn it wi...
So far in Linear regression we have seen linear lines . We could calculate the minimum error by summation of (y-y(hat))^2 You can remind it from this image So, our target is always to minimize the error. In SVR, we came up with a concept that, we a...
Here what we can do is, we can take our independent variables in axis and keep the dependent variable on the axis towards us Then we can split our data based on their averages. How to do that? Let's first split on the basis of 20 So, the decision t...