To reduce features in a dataset, two methods are there in machine learning: Feature selection and Feature extraction. Recursive feature elimination or in short RFE is the feature selection method. Here is given a complete guide on Recursive Feature Elimination for feature selection in machine learning. What is RFE? In Feature extraction, two or more features are combined from existing… Continue readingRecursive Feature Elimination(RFE): Effective Feature selection method in machine learning
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. What is Matplotlib… Continue readingPlotly vs Matplotlib: Which is best for data visualization in python?
If you are doing Data science projects, then at the end you must have to make some web app or GUI so that you can demo your model or make that demo available to a large audience to use. With Gradio, you can create your machine learning web app very easily. Gradio is so easy that you can learn that… Continue readingGradio: Easy Guide for making ML web app for beginners
Classification machine learning algorithms use probability to predict the correct class, such as Logistic regression, SVC, decision tree classifier, Lasso regression, etc. Naive Bayes is the supervised machine learning algorithm used for classification problems. Let’s see how it works. Principle of Naive Bayes classifier The Naive Bayes classifier is one of the fastest and simplest algorithms in machine learning. The… Continue readingNaive Bayes Classifier in machine learning- All you need to know
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.
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
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