Here is another easy to follow code for Multiple Linear regression code on housing data !
Here is sample code for Simple Regression that you can easily follow !
Github link : https://github.com/jeswinaugustine/machine_learning_code/blob/master/Linear%20regression/Simple_Linear_Regression.ipynb
In the previous post, you saw some common interview questions asked on linear regression. The questions in that segment were mostly related to the essence of linear regression and focused on general concepts related to linear regression. This section extensively covers the common interview questions asked related to the concepts learnt in multiple linear regression.
Q1. What is Multicollinearity? How does it affect the linear regression? How can you deal with it?
Multicollinearity occurs when some of the independent variables are highly correlated (positively or negatively) with each other. This multicollinearity causes a problem as it is against the basic assumption of linear regression. The presence of multicollinearity does not affect the predictive capability of the model. So, if you just want predictions, the presence of multicollinearity does not affect your output. However, if you want to draw some insights from the model and apply them in, let’s say, some business model, it may cause problems.Read More…
It is a common practice to test data science aspirants on linear regression as it is the first algorithm that almost everyone studies in Data Science/Machine Learning. Aspirants are expected to possess an in-depth knowledge of these algorithms. We consulted hiring managers and data scientists from various organisations to know about the typical Linear Regression questions which they ask in an interview. Based on their extensive feedback a set of question and answers were prepared to help students in their conversations.
Q1. What is linear regression?
In simple terms, linear regression is a method of finding the best straight line fitting to the given data, i.e. finding the best linear relationship between the independent and dependent variables.Read More…