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ROC-AUC-for-Multi-Class-Classification-Release Highlights for Scikit-learn 0.22

ROC-AUC-for-Multi-Class-Classification-Release Highlights for Scikit-learn 0.22

Today we are going to learn about the new releases from Scikit-learn version 0.22, a machine learning library in Python. We, through this video tutorial, aim to learn about the much talked about new release wherein ROC-AUC curve supports Multi Class Classification. Prior to this version, Scikit-learn did not have a function to plot the ROC curve.

To access our previous tutorial on the plotting of the ROC curve, click here.

The ROC-AUC score function can also be used in multi-class classification. Two averaging strategies are currently supported: the one-vs-one (OvO) algorithm computes the average of the pairwise ROC AUC scores and the one-vs-rest (OvR) algorithm computes the average of the ROC AUC scores for each class against all other classes.

In both cases, the multiclass ROC AUC scores are computed from probability estimates that a sample belongs to a particular class according to the model. The OvO and OvR algorithms support weighting uniformly (average=’macro’) and weighting by prevalence (average=’weighted’).

To begin with, we import multi classification, SVC and roc_auc_score. Then we specify the number of classes we want in the multi-classification function. Then we apply the SVC function and finally the roc_auc_score one. This function will give us the probable prediction for all the classes and we will then choose the one that has the highest probability. When we run it we get a ROC_AUC score of 0.99.

The code sheet is provided in a Github repository here.

 

For more on this do watch the video attached herewith. This tutorial was brought to you by DexLab Analytics. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.

Watch the video here.


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ROC-Curve-New-Plotting-API-Release Highlights for Scikit-learn 0.22

ROC-Curve-New-Plotting-API-Release Highlights for Scikit-learn 0.22

Today we are going to learn about the new releases from Scikit-learn version 0.22, a machine learning library in Python. We, through this video tutorial, aim to learn about the much talked about new release called Plotting API. Prior to this version, Scikit-learn did not have a function to plot the ROC curve.

A new plotting API is available for creating visualizations. The new API allows for quickly adjusting the visuals of a plot without involving any recomputation. It is also possible to add different plots to the same figure. In this tutorial we are going to study the plotting of the ROC curve.

The code sheet is provided in a Github repository here.

 

We will attempt to plot the ROC curve on two different algorithms and compare which one is a better function. First we choose to make a classification data. Then we go on to plot the ROC curve using SVC classifier and then further plot the curve using a random forest classifier.

Fig. 1

Fig. 1

For more on this do watch the video attached herewith. This tutorial was brought to you by DexLab Analytics. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.


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Stacking Regressor – Latest Releases of Scikit-Learn 0.22

Stacking Regressor - Latest Releases of Scikit-Learn 0.22

Today we are going to learn about the new releases from Scikit-learn version 0.22, a machine learning library in Python. First we learn how to install it on our systems. Then, we come to the much talked about new release called stacking regression.

Now, how does stacking regression work? Well, you have been using machine learning algorithms like Decision Tree or Random Forest. Have you heard of Voter Classifier? It is an algorithm in Scikit-learn. Ensemble algorithm is a combination of two or more algorithms to make it stronger.

When working on a set of data, we must apply all these algorithms to get predicted values. Then we vote out classified predicted values in Voter Classifier. Stacking Classifier is different. What we are doing in it is stacking together the predicted values to make a new input.

Initially, we make prediction by using various algorithms separately. Their results or output are then concatenated together. Then we use this output as a new input and apply the algorithms to it to get target variable. This method is known as stacking regression.

We try this out on a data set that can be taken from a github repository the link to which is given below.

 

Then we use two algorithms as estimators. Then we use stacking regression to build a model. For more on this do watch the video attached herewith. This tutorial was brought to you by DexLab Analytics. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.


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The Impact of latitude on The Spread of COVID-19 (Part-I)

The Impact of latitude on The Spread of COVID-19 (Part-I)

The COVID-19 pandemic has hit us hard as a people and forced us to bow down to the vagaries of nature. As of April 29, 2020, the number of persons infected stands at 31,39,523 while the number of persons dead stands at 2,18,024 globally.

This essay is on the phenomenon of detecting geographical variations in the mortality rate of the COVID-19 epidemic. This essay explores a specific range of latitudes along which a rapid spread of the infection has been detected with the help of data sets on Kaggle. The findings are Dexlab Analytics’ own. Dexlab Analytics is a premiere institute that trains professionals in python for data analysis.

For the code sheet and data used in this study, click below.


 

The instructor has imported all Python libraries and the visualisation of data hosted on Kaggle has been done through a heat map. The data is listed on the basis of country codes and their latitudes and there is a separate data set based on the figures from the USA alone.

Fig. 1.

The instructor has compared data from amongst the countries in one scenario and among states in the USA in another scenario. Data has been prepared and structured under these two heads.

Fig. 2.

The instructor has prepared the data according to the mortality rate of each country and it is updated to the very day of working on the data, i.e. the latest updated figures are presented in the study. When the instructor runs the program, a heat map is produced.

For more on this, do go through the half-an-hour long program video attached herewith. The rest of the essay will be featured in subsequent parts of this series of articles.

 

 


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Skills Data Scientists Must Master in 2020

Skills Data Scientists Must Master in 2020

Big data is all around us, be it generated by our news feed or the photos we upload on social media. Data is the new oil and therefore, today, more than ever before, there is a need to study, organize and extract knowledgeable and actionable insights from it. For this, the role of data scientists has become even more crucial to our world. In this article we discuss the various skills, both technical and non-technical a data scientist needs to master to acquire a standing in a competitive market.

Technical Skills

Python and R

Knowledge of these two is imperative for a data scientist to operate. Though organisations might want knowledge of only one of the two programming languages, it is beneficial to know both. Python is becoming more popular with most organisations. Machine Learning using Python is taking the computing world by storm.

GitHub

Git and GitHub are tools for developers and data scientists which greatly help in managing various versions of the software. “They track all changes that are made to a code base and in addition, they add ease in collaboration when multiple developers make changes to the same project at the same time.”

Preparing for Production

Historically, the data scientist was supposed to work in the domain of machine learning. But now data science projects are being more often developed for production systems. “At the same time, advanced types of models now require more and more compute and storage resources, especially when working with deep learning.”

Cloud

Cloud software rules the roost when it comes to data science and machine learning. Keeping your data on cloud vendors like AWS, Microsoft Azure or Google Cloud makes it easily accessible from remote areas and helps quickly set up a machine learning environment. This is not a mandatory skill to have but it is beneficial to be up to date with this very crucial aspect of computing.

Deep Learning

Deep learning, a branch of machine learning, tailored for specific problem domains like image recognition and NLP, is an added advantage and a big plus point to your resume. Even if the data scientist has a broad knowledge of deep learning, “experimenting with an appropriate data set will allow him to understand the steps required if the need arises in the future”. Deep learning training institutes are coming up across the globe, and more so in India.

Math and Statistics

Knowledge of various machine learning techniques, with an emphasis on mathematics and algebra, is integral to being a data scientist. A fundamental grounding in the mathematical foundation for machine learning is critical to a career in data science, especially to avoid “guessing at hyperparameter values when tuning algorithms”. Knowledge of Calculus linear algebra, statistics and probability theory is also imperative.

SQL

Structured Query Language (SQL) is the most widely used database language and a knowledge of the same helps data scientist in acquiring data, especially in cases when a data science project comes in from an enterprise relational database. “In addition, using R packages like sqldf is a great way to query data in a data frame using SQL,” says a report.

AutoML

Data Scientists should have grounding in AutoML tools to give them leverage when it comes to expanding the capabilities of a resource, which could be in short supply. This could deliver positive results for a small team working with limited resources.

Data Visualization

Data visualization is the first step to data storytelling. It helps showcase the brilliance of a data scientist by graphically depicting his or her findings from data sets. This skill is crucial to the success of a data science project. It explains the findings of a project to stakeholders in a visually attractive and non-technical manner.

Non-Technical Skills

Ability to solve business problems

It is of vital importance for a data scientist to have the ability to study business problems in an organization and translate those to actionable data-driven solutions. Knowledge of technical areas like programming and coding is not enough. A data scientist must have a solid foundation in knowledge of organizational problems and workings.

Effective business communication

A data scientist needs to have persuasive and effective communication skills so he or she can face probing stakeholders and meet challenges when it comes to communicating the results of data findings. Soft skills must be developed and inter personal skills must be honed to make you a creatively competent data scientist, something that will set you apart from your peers.

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Agility

Data scientist need to be able to work with Agile methodology in that they should be able to work based on the Scrum method. It improves teamwork and helps all members of the team remain in the loop as does the client. Collaboration with team members towards the sustainable growth of an organization is of utmost importance.

Experimentation

The importance of experimentation cannot be stressed enough in the field of data science. A data scientist must have a penchant for seeking out new data sets and practise robustly with previously unknown data sets. Consider this your pet project and practise on what you are passionate about like sports.


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8 Skills a Python Programmer Should Master

8 Skills a Python Programmer Should Master

Python has become the lingua franca of the computing world. It has come to become the most sought after programming language for deep learning, machine learning and artificial intelligence. It is a favourite with programmers because it is easy to understand and learn and it achieves a lot more in terms of productivity as compared to other languages.

Python is a dynamic, high-level, general-purpose programming language that is useful for developing desktop, web and mobile applications that can also be used for complex scientific and numeric applications, data science, AI etc. Python focuses a lot on code readability.

From web and game development to machine learning, from AI to scientific computing and academic research, Data science and analysis, python is regarded as the real deal. Python is useful in domains like finance, social media, biotech etc. Developing large software applications in Python is also simpler due to its large amount of available libraries.

The Python developer usually deals with backend components, apps connection with third-party web services and giving support to frontend developers in web applications. Of course, one might create applications with use of different languages but pretty often Python is the language chosen for it – and there are several reasons for that.

In this article, we will walk through a structured approach to top 8 skills required to become a Python Developer. These skills are:

  • Core Python
  • Good grasp of Web Frameworks
  • Front-End Technologies
  • Data Science
  • Machine Learning and AI
  • Python Libraries
  • Multi-Process Architecture
  • Communication Skills

Core Python

This is the foundation of any Python developer. If one wants to achieve success in this career, he/she needs to understand the core python concepts. These include the following:

  • Iterators
  • Data Structures
  • Generators
  • OOPs concepts
  • Exception Handling
  • File handling concepts
  • Variables and data types

However, learning the core language (as mentioned above) is only the first step in mastering this language and becoming a successful Python developer.

Good grasp of Web Frameworks

By automating the implementation of redundant tasks, frameworks cut development time and enable developers to focus greatly on application logic rather than routine elements.

Because it is one of the leading programming languages, there is no scarcity of frameworks for Python. Different frameworks have their own set of advantages and issues. Hence, the selection needs to be made on the basis of project requirements and developer preference. There are primarily three types of Python frameworks, namely full-stack, micro-framework, and asynchronous.

A good Python web developer has incredible honing over either of the two web frameworks Django or Flask or both. Django is a high-level Python Web Framework that encourages a good, clean and pragmatic design and Flask is also widely used Python micro web framework.

Front-End Technologies (JavaScript, CSS3, HTML5)

Sometimes, Python developers must work with the frontend team to match together the server-side and the client-side. This means Python developers need a basic understanding of how the frontend works, what’s possible and what’s not, and how the application will appear.

While there is likely a UX team, SCRUM master, and project or product manager to coordinate the workflow, it’s still good to have a basic understanding of front-end tasks.

Data Science

Data science offers a world of new opportunities. Being a Python developer, there are several prerequisites you need to know starting with things you learn in high school mathematics, such as statistics, probability, etc. Some of the other parts of data science you need to understand, and use include SQL knowledge; the use of Python packages, data wrangling and data cleanup, analysis of data, and visualization of data.

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning (as well as Deep Learning) are constantly growing. Python is the perfect programming language which is used in all the frameworks of Machine Learning and Deep Learning. This will be a huge plus for someone if he/she knows about this domain. If someone is into data science, then definitely digging in the Machine Learning topic would be a great idea.

Python Libraries

Python libraries certainly deserve a place in every Python Developer’s toolbox. Python has a massive collection of libraries, both native and third-party libraries. With so many Python libraries out there, though, it’s no surprise that some don’t get all the attention they deserve. Plus, programmers who work exclusively in one domain don’t always know about the goodies available to them for other kinds of work.

Python libraries are extensively used in simplifying everything from file system access, database programming, and working with cloud services to building lightweight web apps, creating GUIs, and working with images, ebooks, and Word files—and much more.

Multiprocessing Architecture

Multiprocessing refers to the ability of a system to support more than one processor at the same time. Applications in a multiprocessing system are broken to smaller routines that run independently. The operating system allocates these threads to the processors improving performance of the system. As a Python-Developer one should definitely know about the MVC (Model View Controller) and MVT (Model View Template) Architecture. Once you understand the Multi-Processing Architecture you can solve issues related to the core framework etc.

Communication Skills

In best software development firms the teams are made out of amazing programmers which work together to achieve the final goal – no matter if it means to finish the project, to create a new app or maybe to help a startup. However, working in a team means that a developer has to communicate well – not only to get the stuff done but also to keep the documentation clear so others can easily read and follow the thinking path to fully understand the idea.

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Conclusion

In this write-up, we have elaborated on the top skills one needs to have to be a successful Python Developer. One must have a working knowledge of Core Python and a good grasp of Web Frameworks, Front-End Technologies, Data Science, Machine Learning and AI, Python Libraries, Multi-Process Architecture and Communication skills. Though there are a few more skills not listed in this blog, one can achieve success in developing large software applications by mastering all the above skills only.

As delineated in the article, Python is the new rage in the computing world. And it is no surprise then that more and more professionals are opting to take up courses teaching Machine learning using Python and python for data analysis.

 

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Netflix develops in own data science management tool and open sources it

Netflix develops in own data science management tool and open sources it

Netflix in December last year introduced its own python framework called Metaflow. It was developed to apply to data science with a vision to make scalability a seamless proposition. Metaflow’s biggest strength is that it makes running the pipeline (constructed as a series of steps in a graph) easily movable from a stationary machine to cloud platforms (currently only the Amazon Web Services (AWS)).

What does Metaflow really do? Well, it primarily “provides a layer of abstraction” on computing resources. What it translates to is the fact that a programmer can concentrate on writing/working code while Metaflow will handle the aspect which ensures the code runs on machines.

Metaflow manages and oversees Python data science projects addressing the entire data science workflow (from prototype to model deployment), works with various machine learning libraries and amalgamates with AWS.

Machine learning and data science projects require systems to follow and track the trajectory and development of the code, data, and models. Doing this task manually is prone to mistakes and errors. Moreover, source code management tools like Git are not at all well-suited to doing these tasks.

Metaflow provides Python Application Programming Interfaces (APIs) to the entire stack of technologies in a data science workflow, from access to the data, versioning, model training, scheduling, and model deployment, says a report.

Netflix built Metaflow to provide its own data scientists and developers with “a unified API to the infrastructure stack that is required to execute data science projects, from prototype to production,” and to “focus on the widest variety of ML use cases, many of which are small or medium-sized, which many companies face on a day to day basis”, Metaflow’s introductory documentation says.

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Metaflow is not biased. It does not favor any one machine learning framework or data science library over another. The video-streaming giant deploys machine learning across all aspects of its business, from screenplay analysis, to optimizing production schedules and pricing. It is bent on using Python to the best limits the programming language can stretch. For the best Data Science Courses in Gurgaon or Python training institute in Delhi, you can check out the Dexlab Analytics courses online.

 

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Studies Show Indian Employers Prefer Experienced Workers Over Freshers

Studies Show Indian Employers Prefer Experienced Workers Over Freshers

Employability and the scramble for top Jobs in India 

Looking to hire new talent or searching for a job? Well, some insights several studies and surveys provide about the job scenario in India might interest you.

The Millenial

Indian millennials, aged between 18 and 35 years, according to studies ( wheebox.com/assets/pdf/ISR_Report_2020.pdf ) makes nearly half the Indian workforce and looks likely to remain so for the next decade. This generation of workers are not only working hands but likely consumers as well, strong in their opinions, with access to the internet and social media across urban and rural areas. What they are most ardently looking for are jobs that respect their talent, pay them adequately and improve their employability in the market. 

Employability in India

Employability has remained stagnant for several years now with around 46 per cent candidates job-ready. Of those employed, trends revealed

  • MBA’s in India are now projecting a rate of 54 per cent employability, acquiring the highest paying jobs
  • Employers prefer candidates with work experience, especially 1-5 years. Freshers are least preferred at 15 per cent.
  • The AI industry is showing promise wherein some reports pegged the number of job openings in AI and Machine learning sector at almost 1million in India last year. 
  • Employability for pass-outs of B.Pharma, B.com, BA and Polytechnics showed an increase of around 15% since 2019.
  • Prospective workers from Maharashtra, Tamil Nadu and Uttar Pradesh were found to be most employable
  • While women are as employable as men, women’s participation in the workforce remains at a low 25 per cent vis a vis that of men.

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What employers seek

  • Domain knowledge
  • Adaptability to the work environment
  • Learning ability and agility
  • Positive attitude

What employees seek

  • Majority of Students, around 88 per cent of those surveyed, sought internship opportunities though the supply did not meet demand in most cases
  • Maharashtra, Tamil Nadu and Andhra Pradesh were preferred and most sought after in terms of work opportunity
  • Over 55% students expect the annual salary to be above Rs. 2.6 lacs, a figure which has remained constant for the past few years

Ways to improve employability

Most students or potential candidates, surveys show, seek proper guidance and training and internship opportunities as varied as customer market analysis courses to customer marketing analysis training and courses teaching retail analytics using Python. While most universities lack the wherewithal to skill their outgoing students, students prefer to sign up for short courses online to equip themselves with the requisite knowledge specific to their industry. All this done with a view to increase their employability in a market deeply customer driven.

 

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A Handbook of the Basic Data Types in Python 3: Strings

A Handbook of the Basic Data Types in Python 3: Strings

In general, a data type defines the format, sets the upper & lower bounds of the data so that a program could use it appropriately. Data types are the classification or categorization of data items which describes the character of a variable. The most used data types are numeric, non-numeric and Boolean (true/false).

Python has the following standard Data Types:

  • Booleans
  • Numbers
  • String
  • List
  • Tuple
  • Set
  • Dictionary

Mutable and Immutable Objects

Data objects of the above types are stored in a computer’s memory for processing. Some of these values can be modified during processing, but the contents of the others can’t be altered once they are created in the memory.

Number values, strings, and tuple are immutable, which means their contents can’t be altered after creation.

On the other hand, the collection of items in a List or Dictionary object can be modified. It is possible to add, delete, insert, and rearrange items in a list or dictionary. Hence, they are mutable objects.

Booleans

A Boolean is such a data type that almost every programming language has, and so does Python. Boolean in Python can have two values – True or False. These values can be used for assigning and comparison.

Numbers

Numbers are one of the most prominent Python data types. In Numbers, there are mainly 3 types which include Integer, Float, and Complex.

String

A sequence of one or more characters enclosed within either single quotes ‘or double quotes” is considered as String in Python. Any letter, a number or a symbol could be a part of the string. Multi-line strings can be represented using triple quotes,”’ or “””.

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List

Python list is an array-like construct which stores a heterogeneous collection of items of varied data typed objects in an ordered sequence. It is very flexible and does not have a fixed size. The Index in a list begins with a zero in Python.

Tuple

A tuple is a sequence of Python objects separated by commas. Tuples are immutable, which means tuples once created cannot be modified. Tuples are defined using parentheses ().

Set

A set is an unordered collection of items. Set is defined by values separated by a comma inside braces { }. Amongst all the Python data types, the set is one which supports mathematical operations like union, intersection, symmetric difference etc. Since the set derives its implementation from the “Set” in mathematics, so it can’t have multiple occurrences of the same element.

Dictionary

A dictionary in Python is an unordered collection of key-value pairs. It’s a built-in mapping type in Python where keys map to values. These key-value pairs provide an intuitive way to store data. To retrieve the value we must know the key. In Python, dictionaries are defined within braces {}.

This article is about one specific data type, which is a string. The String is a sequence of characters enclosed in single (”) or double quotation (“”) marks.

Here are examples of creating strings in Python.

Counting Number of Characters Using LEN () Function

The LEN () built-in function counts the number of characters in the string.

Creating Empty Strings

Although variables S3 and S4 do not contain any characters they are still valid strings. S3 and S4 both represent empty strings here.

We can verify this fact by using the type () function.

String Concatenation

String concatenation means joining one or more strings together. To concatenate strings in Python we use + operator.

String Repetition Operator (*)

Just like in numbers, * operator can also be used with strings. When used with strings * operator repeats the string n number of times. Its general format is: 1 string * n,

where n is a number of type int.

Membership Operators – in and not in

The in or not in operators are used to check the existence of a string inside another string. For example:

Indexing in a String

In Python, characters in a string are stored in a sequence. We can access individual characters inside a string by using an index.

An index refers to the position of a character inside a string. In Python, strings are 0 indexed. This means that the first character is at index 0; the second character is at index 1 and so on. The index position of the last character is one less than the length of the string.

To access the individual characters inside a string we type the name of the variable, followed by the index number of the character inside the square brackets [].

Instead of manually counting the index position of the last character in the string, we can use the LEN () function to calculate the string and then subtract 1 from it to get the index position of the last character.

We can also use negative indexes. A negative index allows us to access characters from the end of the string. Negative index starts from -1, so the index position of the last character is -1, for the second last character it is -2 and so on.

Slicing Strings

String slicing allows us to get a slice of characters from the string. To get a slice of string we use the slicing operator. Its syntax is:

str_name[start_index:end_index]

str_name[start_index:end_index] returns a slice of string starting from index start_index to the end_index. The character at the end_index will not be included in the slice. If end_index is greater than the length of the string then the slice operator returns a slice of string starting from start_index to the end of the string. The start_index and end_index are optional. If start_index is not specified then slicing begins at the beginning of the string and if end_index is not specified then it goes on to the end of the string. For example:

Apart from these functionalities, there are so many built-in methods for strings which make the string as the useful data type of Python. Some of the common built-in methods are as follows: –

capitalize ()

Capitalizes the first letter of the string

join (seq)

Merges (concatenates) the string representations of elements in sequence seq into a string, with separator string.

lower ()

Converts all the letters in a string that are in uppercase to lowercase.

max (str)

Returns the max alphabetical character from the string str.

min (str)

Returns the min alphabetical character from the string str.

replace (old, new [, max])

Replaces all the occurrences of old in a string with new or at most max occurrences if max gave.

 split (str=””, num=string.count(str))

Splits string according to delimiter str (space if not provided) and returns list of substrings; split into at most num substrings if given.

upper()

Converts lowercase letters in a string to uppercase.

Conclusion

So in this article, firstly, we have seen a brief introduction of all the data types of python. Later in this article, we focused on the strings. We have seen several Python operations on strings as well as the most common useful built-in methods of strings.

Python is the language of the present age, wherein almost every field there is a need for Python. For example, Python for data analysisMachine Learning Using Python has been easy and comprehensible than they were ever before. Thus, if you are also interested in Python and looking for promising courses Computer Vision Course PythonRetail Analytics using PythonNeural Network Machine Learning Python, then get in touch with Dexlab Analytics now and step into the world of opportunities!

 

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