Logistic Regression is a classification algorithm. It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables. To represent binary / categorical outcome, we use dummy variables. In this post, I will explain the math behind the Logistic Regression Model and how to implement it using Python.

In this article, we will learn about the mathematical intuition behind simple linear regression and implement it in Python through a simple and easy-to-understand example with storytelling.

GridSearchCV is a function in scikit-learn for hyperparameter tuning. It is used to find the best parameters for a given model. In this blog post, we will discuss how to use GridSearchCV in scikit-learn and what are its advantages over other methods of hyperparameter tuning.

The `timeit` module in Python is a built-in module that allows to measure time of code snnippets. It is very useful tool for comparing the performance of different approaches