A Complete Python Guide to ANOVA - Analytics India Magazine

Getting informative insights from the raw data in hand is vital in a successful machine learning project. The selection of the right machine learning algorithm and tuning of the model parameters to achieve better performance are possible only with proper data analytics in the pre-processing stage. Traditional statistical analysis is simple and powerful in extracting the essence out of the raw data.

Statistical analysis is performed reliably and quickly with statistical software packages. The famous multi-purpose language, Python, has a great collection of libraries and modules to do statistical analysis in a lucid way. In this article, we discuss a widely used statistical tool called ANOVA with hands-on Python codes.

ANOVA is one of the statistical tools that helps determine whether two or more data samples have significantly identical properties. Let’s assume a scenario- we have different samples collected independently from the same dataset for cross-validation. We wish to know whether the means of the collected samples are significantly the same. Another scenario- we have developed three different machine learning models. We have obtained a set of results, and we wish to know whether the models perform significantly in the same manner. Thus, there are many scenarios in practical applications where we may need to use ANOVA as part of data analytics.

ANOVA is the acronym for Analysis of Variance. It analyzes variations among different groups and within those groups of a dataset (technically termed as population). However, there are some assumptions that the data must hold to use ANOVA. They are as follows:

  1. The data follows normal distribution
  2. The variance of data is the same for all groups.
  3. Data among groups are independent of each other.

Math concept behind ANOVA and its usage can be explored with the following hands-on Python example.

#developers corner #analysis of variance #anova #data analytics #data preprocessing #post hoc #python #statistical significance #statistics #tukey

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A Complete Python Guide to ANOVA - Analytics India Magazine
Ray  Patel

Ray Patel

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Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

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Ajay Kapoor

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Shardul Bhatt

Shardul Bhatt

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Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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A Complete Python Guide to ANOVA - Analytics India Magazine

Getting informative insights from the raw data in hand is vital in a successful machine learning project. The selection of the right machine learning algorithm and tuning of the model parameters to achieve better performance are possible only with proper data analytics in the pre-processing stage. Traditional statistical analysis is simple and powerful in extracting the essence out of the raw data.

Statistical analysis is performed reliably and quickly with statistical software packages. The famous multi-purpose language, Python, has a great collection of libraries and modules to do statistical analysis in a lucid way. In this article, we discuss a widely used statistical tool called ANOVA with hands-on Python codes.

ANOVA is one of the statistical tools that helps determine whether two or more data samples have significantly identical properties. Let’s assume a scenario- we have different samples collected independently from the same dataset for cross-validation. We wish to know whether the means of the collected samples are significantly the same. Another scenario- we have developed three different machine learning models. We have obtained a set of results, and we wish to know whether the models perform significantly in the same manner. Thus, there are many scenarios in practical applications where we may need to use ANOVA as part of data analytics.

ANOVA is the acronym for Analysis of Variance. It analyzes variations among different groups and within those groups of a dataset (technically termed as population). However, there are some assumptions that the data must hold to use ANOVA. They are as follows:

  1. The data follows normal distribution
  2. The variance of data is the same for all groups.
  3. Data among groups are independent of each other.

Math concept behind ANOVA and its usage can be explored with the following hands-on Python example.

#developers corner #analysis of variance #anova #data analytics #data preprocessing #post hoc #python #statistical significance #statistics #tukey

A Complete Python Guide to ANOVA - Analytics India Magazine

Getting informative insights from the raw data in hand is vital in a successful machine learning project. The selection of the right machine learning algorithm and tuning of the model parameters to achieve better performance are possible only with proper data analytics in the pre-processing stage. Traditional statistical analysis is simple and powerful in extracting the essence out of the raw data.

Read more: https://analyticsindiamag.com/a-complete-python-guide-to-anova/?fbclid=IwAR0BFMQu8f9xGGFj_7PvdW1b7FfD-OdxAmc9FDttDfiowCRcQR26TB42REE

#tutorial #python #anova