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Search Results for: python – Page 2

AI Mysteries
Sourabh Mehta

A guide to Regularized Discriminant Analysis in python

The Regularized Discriminant Analysis is a combination of both Linear and Quadratic discriminant analysis which analyze the observation-based set of measurements to classify the objects into one of several groups or classes.

AI Mysteries
Sourabh Mehta

A hands-on guide to principal component regression in Python

Principal Components Regression (PCR) is a technique for analyzing multiple regression data that suffer from multicollinearity. Principal components regression reduces errors in regression estimates by adding a degree of bias

AI Mysteries
Yugesh Verma

A complete tutorial on Ordinal Regression in Python

In statistics and machine learning, ordinal regression is a variant of regression models that normally gets utilized when the data has an ordinal variable. Ordinal variable means a type of variable where the values inside the variable are categorical but in order.

Bhasker Gupta

Python Libraries Repository for Data Science

The most comprehensive Repository of Python Libraries for Data Science Recent Tutorials EDA, Visualization & Data Handling S No. Library Year Released Release Docs Tutorials

AI Origins & Evolution
Sri Krishna

Top 10 Python cheat sheets in 2022

Python for Data Science is a one-page Python cheat sheet to learn the fundamentals of this programming language.

AI Mysteries
Yugesh Verma

A hands-on guide to implementing ggplot in python using plotnine

The ggplot package of the R programming language makes the R richer on the side of data visualization. In python as well, various packages are also available for data visualization. If the features and capabilities of ggplot can be used in python, it will be a valuable advantage in many visualization specific tasks.

AI Mysteries
Yugesh Verma

A guide to Kats: python tool by Meta for effective time-series analysis

Kats stands for Kits to Analyze Time Series, which was developed by the researchers at Facebook, now Meta. One of the most important things about Kats is that it is very easy to use. Also, it is a very light weighted library of generic time series analysis in a very generalized nature.

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