Data Science Simplified Part 11: Logistic Regression

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. In this post, we dwell a little deeper in how regression models can be used for classification tasks. Logistic Regression is a widely used regression […]

Data Science Simplified Part 8: Qualitative Variables in 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: price = -55089.98 + 87.34 engineSize + 60.93 horse power + 770.42 width The model predicts or estimates price (target) as a function of engine […]

Data Science Simplified Part 7: Log-Log 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.

Data Science Simplified Part 6: Model Selection Methods

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. Fernando indeed has a better model. Yet, he wanted to select the best set of variables for input. This article will elaborate on model selection […]

Data Science Simplified Part 2: Key Concepts of Statistical Learning

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 will define what is Statistical learning. Then, we will dive into key concepts in Statistical learning. Believe me; it is simple. As per Wikipedia, Statistical […]

Data Science Simplified Part 5: Multivariate Regression Models

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 the price of the car. The regression model created by Fernando predicts price based on the engine size. One dependent variable predicted using one independent […]