Statistics

Missing Value treatment is no doubt one of the most important parts of the whole process of building a model. Why?Because we can't afford to eliminate rows wherever there is a missing value in any of the columns. ...

Why do we predict?We predict in order to identify the trend of the future by using our sample data set. Whenever we create a model, we try to create a formula out of our sample data set. And ...

We all know the definition of multi-collinearity i.e. when 2 or more explanatory variable in multi regression model are highly linearly related then it's called multicollinearityExample - Age and Selling price of a CarEducation and Annual IncomeHeight and ...

I was onto our next book - Linear,Ridge, LAASO, and Elastic Net Algorithm explained in layman terms with code in R , when we thought of covering the simple concepts which are quite helpful while creating models.Cross Validation ...

Linear, LASSO, Elastic Net, and Ridge Regression are the four regression techniques which are helpful to predict or extrapolate the prediction using the historic data. Linear doesn't have any inclination towards the value of lambda.LASSO ...

So, I just started solving the latest Hackathon on Analytics Vidhya, Women in the loop . Be it a real-life Data Science problem or a Hackathon, one-hot encoding is one of the most important part of ...