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# Tag Archives: Multiple Linear Regression

## 204.1.11 Interaction Terms

This is the final post in our Linear Regression Series. This post is about a trick called Interaction Terms, which may improve the accuracy of the model. Interaction Terms Interaction terms are when we use a derived variable from one or more per-existing variables, it can be multiple or division …

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## 204.1.10 Practice : Multiple Regression with Multicollinearity

In this practice post we will build a Multiple Regression model and try to improve it by clearing the problem of multicollinearity in the model. Practice : Multiple Regression Dataset: Webpage_Product_Sales/Webpage_Product_Sales.csv Build a model to predict sales using rest of the variables Drop the less impacting variables based on p-values. …

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## 204.1.9 Issue of Multicollinearity in Python

In previous post of this series we looked into the issues with Multiple Regression models. In this part we will understand what Multicollinearity is and how it’s bad for the model. We will also go through the measures of Multicollinearity and how to deal with it. Multicollinearity Multiple regression is …

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## 204.1.8 Practice : Multiple Regression Issues

In last posts of this session we did cover basics of Multiple variable Linear Regression. In this post we will Practice and try to solve issues associated with Multiple Regression. Practice : Multiple Regression- issues Import Final Exam Score data Build a model to predict final score using the rest …

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## 204.1.6 Multiple Regression in Python

For the last few post of the machine learning blog series 204 we were just going through single input variable regression. In this post we will see how to take care of multiple input variables. Multiple Regression Using multiple predictor variables instead of single variable We need to find a …

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