In the last blog post of this series, we discussed classifiers. The categories of classifiers and how they are evaluated were discussed. We have also discussed regression models in depth.… Read more Data Science Simplified Part 11: Logistic Regression →
Data Science Simplified Part 10: An Introduction to Classification Models
In the last few blog posts of this series discussed regression models at length. Fernando has built a multivariate regression model. The model takes the following shape: price = -55089.98… Read more Data Science Simplified Part 9: Interactions and Limitations of Regression Models →
The last few blog posts of this series discussed regression models. Fernando has selected the best model. He has built a multivariate regression model. The model takes the following shape:… Read more Data Science Simplified Part 8: Qualitative Variables in Regression Models →
In the last few blog posts of this series, we discussed simple linear regression model. We discussed multivariate regression model and methods for selecting the right model. In this article will address that question. This article will elaborate about Log-Log regression models.
In the last article of this series, we had discussed multivariate linear regression model. Fernando creates a model that estimates the price of the car based on five input parameters.… Read more Data Science Simplified Part 6: Model Selection Methods →
In the first article of this series, I had touched upon key concepts and processes of Data Science. In this article, I will dive in a bit deeper. First, I… Read more Data Science Simplified Part 2: Key Concepts of Statistical Learning →
In the last article of this series, we discussed the story of Fernando. A data scientist who wants to buy a car. He uses Simple Linear Regression model to estimate… Read more Data Science Simplified Part 5: Multivariate Regression Models →
