There are many libraries in python available for making charts from the data. By which you can create charts and derive underlying information from the data. Plotly and Matplotlib are two of the libraries for it. Let's see the comparison between Plotly vs Matplotlib and see which is best for performing the best data visualization in python. Matplotlib is the library for plotting static visualizations in Python.

A machine learning pipeline consists of multiple data extraction, preprocessing, and model-building steps. It helps to automate processes that are required in model building. Pipeline helps to include all steps of preprocessing, feature selection, feature extraction, model selection, and model building In one entity. Here we will see how to make a pipeline in machine learning. In Applications where you need multiple machine learning models then, in that case, this pipeline will help you a lot.

To spit data into a training set and test set, you had indeed used the train_test_split library from scikit learn. There are some parameters in train_test_split like random_state, stratify, shuffle, test_size, etc. Here we will talk about one parameter called stratify in train_test_split in a simple way. Basically, we use stratify to create an unbiased dataset when you have a biased dataset. Suppose we have data and if that data is biased then we can have to use stratify to overcome train_test_split's biased random sampling problem.

After creating the machine learning model, we can use the Flask framework to create API for web applications. Here I will teach you how to deploy the flask app to Heroku using docker and GitHub actions. With Docker and Github actions, you can create CI/CD pipeline for your machine learning project. Github action is used to create CI/CD pipeline. CI/CD means continuous integration and continuous deployment.