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.
With the help of python, we can build many projects of web scraping, data science, and more. When you are building a project, logging is also an essential part of that. There are many benefits of logging into the program. Here we will talk about what is logging in python and how to do it by the in-built library as well as by building it from scratch.
There are mainly two types of problems with machine learning. The first one is regression and the second is a classification problem. After building any model, its evaluation is a very important part of making the best model and optimizing it. Here you will know when to use Precision, Recall, or F1 score to evaluate the classification problem.
In classification-related supervised machine learning projects, sometimes we get imbalanced datasets. There are many methods available to deal with that. SMOTEEN is one of the methods that make an imbalanced dataset a balanced dataset. Here you will see Handle imbalanced datasets with SMOTEENN.
After creating the machine learning model, we can use the Flask framework to create API for web applications. After creating a web app, we can deploy it on a cloud platform like Heroku. 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… Continue readingDeploy Flask app using docker and GitHub actions on Heroku
You can’t make any machine learning or deep learning model without data. You must have a dataset of a particular domain to create a model from it. But many times it happens that you can’t find your desired dataset on a particular website. Here are ways to get the dataset of your choice.
In many scenarios, you want to group columns with respect to one column with their mode values. Especially in categorical columns. So here is how you can group columns with their mode.
Many feature selection techniques include RFE(Recursive feature elimination), VIF(Variance Inflation Factor), VarianceThreshold, and many more. Lasso is one of the feature selection techniques in Data Science, which is used for feature selection for linear regression problems only. Lasso has two use cases. First, it is used to get rid of the overfitting of the linear regression model. And second, as… Continue readingFeature Selection with Lasso Regression in machine learning
Recently I have been searching to convert my images to Webp. But I found that the most popular WebP converter websites were not fully free versions. They are giving let’s say 5 or 10 images to convert to WebP for free and then they will ask to buy their paid version to convert unlimited images. Therefore after some research, I built the flask app to convert my PNG or any other format images into WebP format. So here I provided that app here.
So I was recently doing one machine learning project and I came across a situation where I wanted to sort values of months in the pandas’ DataFrame(Like January, February, etc..). Sorting months in their order is necessary for EDA when you want to create plots on month vs any other feature. So here is a method to sort the month… Continue readingHow to sort pandas DataFrame by month names