In this tutorial, we are going to learn about how to split a Dataset into a Train set and a Test set using Python Programming Introduction While creating Machine Learning and Deep Learning Models we may come across scenarios where we may want to do both training and well as evaluation on the same dataset. In such cases, we may want to divide our dataset into different groups or sets and use each set for one task or specific process (e.g. training). In such situations, we may make use of training/test sets. Need for Train and Test sets It is ... Read More

In this Tutorial we will learn how to create simulated data from classification in Python. Introduction Simulated data can be defined as any data not representing the real phenomenon but which is generated synthetically using parameters and constraints. When and why do we need simulated data? Sometimes while prototyping a particular algorithm in Machine Learning or Deep Learning we generally face a scarcity of good real-world data which can be useful to us. Sometimes there is no such data available for a given task. In such scenarios, we may need synthetically generated data. This data can also be from Lab ... Read More

MediaPipe is a cross-platform open-source Machine Learning framework for creating complicated and multimodal applied machine learning pipelines. It can be used to create cutting-edge Machine Learning Models such as face identification, multi-hand tracking, object detection and tracking, and many more applications. MediaPipe simply functions as a middleman for managing model implementation for systems operating on any platform, allowing the developer to focus on experimenting with models rather than the system. This article will go over how to estimate full-body poses using MediaPipe holistic. The model will recognize all of our body's facial landmarks, hands, and positions. Installing and importing libraries ... Read More

The contemporary Olympic Games, sometimes known as the Olympics, are major international sporting events that feature summer and winter sports contests in which thousands of participants from all over the world compete in a range of disciplines. With over 200 nations competing, the Olympic Games are regarded as the world's premier sporting event. In this article, we will examine the Olympics using Python. Let’s begin. Importing necessary libraries !pip install pandas !pip install numpy import numpy as np import pandas as pd import seaborn as sns from matplotlib import pyplot as plt Importing and understanding the dataset We have ... Read More

In this article, we are going to learn about how to find the Z Critical Value in Python. What is Z Critical Value? In statistics, the region under the common normal model is referred to as the Z critical value. Every possible variable's probability is shown. A test statistic is what is produced when we do a hypothesis test. To determine if the outcome of the hypothesis test is statistically significant, the test statistic can be compared to a Z critical value. An outcome is regarded as statistically significant when its absolute value exceeds the Z critical value. The determination ... Read More

In this article, we are going to learn about how to find the F Critical Value in Python. What is F Critical Value? An F statistic is what you'll obtain after running an F test. Whether the results of the F test are statistically significant can be determined by comparing the F statistic to an F critical value. To put it simply, we compared our f value to the F-critical value as a standard. This post will look at a Python technique for finding the F critical value. Syntax To calculate the F critical value, use the Python function scipy.stats.f.ppf(), ... Read More

Data is a valuable asset that plays a crucial part in today's society, as everything is strongly dependent on data. Today, all technologies are data-driven, and massive volumes of data are generated on a regular basis. Data is unprocessed information that data scientists learn to exploit. A data scientist is a professional who analyses data sources, cleans and processes the data in order to understand why and how the data was created, in order to provide insights to support business choices, and hence profits for the company. To detect patterns and trends in data, data scientists employ a mix of ... Read More

A statistical test that compares two matched groups nonparametrically is the Wilcoxon test. Either the signed-rank test version or the rank sum test might be referenced. The tests are efficient in computing the difference between pairs of pairings and examining this difference to see if it is statistically different from the other. In this article, we'll examine the Wilcoxon signed-rank test and demonstrate how to run it in Python. What is Wilcoxon signed-rank test? The Wilcoxon signed-rank test is a non-parametric univariate test that can be used instead of the dependent t-test. The statistic value is typically referred to as ... Read More

It is never easy to select a backend programming language. After all, many languages have advantages and disadvantages that you must analyze to ensure that it is the best tool for the application you are trying to develop. Backend development languages such as Node.js and Python are popular. Both have very robust packaging ecosystems and communities, making it difficult to choose between the two. In this article, we will compare both Node.js and Python, as well as the instances in which one would be preferable to the other, so that you may select the best choice for your backend. NodeJS ... Read More

In this article, we will explain R and python and which is better for data analysis: R or Python. Python and R are both popular statistical programming languages. While R's functionality is designed with statisticians in mind (considering R's powerful data visualization features), Python is frequently complimented for its simple syntax. What is R? R is a statistical programming language that is largely used by statisticians, data miners, and data analysts. R was created especially for statistical analysis and visualization, therefore this is its greatest strength. Within R, there are hundreds of well-established packages and libraries for these tasks. RStudio, ... Read More