KNN Algorithm Using R 765

The huge amount of data that we’re generating each day , has led to an increase of the need for advanced Machine Learning Algorithms.
It is quite essential to know Machine Learning basics. Here’s a fast introductory section on what’s Machine Learning and its types.

Machine learning could also be a subset of AI that provides machines the power to hunt out out automatically and improve from their gained experience without being explicitly programmed.

There are mainly three kinds of Machine Learning discussed briefly below:

Supervised Learning: it’s that a neighborhood of Machine Learning during which the data provided for teaching or training the machine is well labeled then it becomes easy to work with it.

Unsupervised Learning: it is the training of knowledge employing a machine that’s unlabelled and allowing the algorithm to act thereon information without guidance.

Reinforcement Learning: it’s that a neighborhood of Machine Learning where an agent is put in an environment and he learns to behave by performing certain actions and observing the numerous possible outcomes which it gets from those actions.

Now, moving to our main blog topic,

What is KNN Algorithm?
KNN which stands for K Nearest Neighbor could also be a Supervised Machine Learning algorithm that classifies a replacement datum into the target class, counting on the features of its neighboring data points.

Let’s decide to understand the KNN algorithm with an essay example. Let’s say we might sort of a machine to differentiate between the sentiment of tweets posted by various users. to undertake to to the present we must input a dataset of users’ sentiment(comments). And now, we’ve to teach our model to detect the emotions supported certain features. as an example , features like labeled tweet sentiment i.e., as positive or negative tweets accordingly. For positive tweet, it’s labeled as 1 and for negative, it,’s labeled as 0.

Features of KNN algorithm:

KNN could also be a supervised learning algorithm, supported feature similarity.

Unlike most algorithms, KNN could also be a non-parametric model which suggests it doesn’t make any assumptions about the data set. which makes the algorithm not only simpler but also effective because now it can handle realistic data.

KNN is taken under consideration to be a lazy algorithm, i.e., it suggests that it memorizes the training data set rather than learning a discriminative function from the training data.

KNN is typically used for solving both classification and regression problems.

Disadvantages of KNN algorithm:

After multiple implementations, it has been observed that KNN algorithm doesn’t work with good accuracy on taking large datasets because the worth of calculating the space between the new point and each existing points is large , and successively it degrades the performance of the algorithm.

Disadvantages of KNN algorithm:

It has also been noticed that performing on high dimensional data is quite difficult with this algorithm because the calculation of the space in each dimension isn’t correct.

It is quite needful to perform feature scaling i.e., standardization and normalization before actually implementing KNN algorithm to any dataset. Eliminating these steps may cause wrong predictions by KNN algorithm.

Sensitive to noisy data, missing values and outliers: KNN is sensitive to noise within the dataset. we’d wish to manually impute missing values and deduct outliers.

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we hope we got the detail idea about KNN algorithm through this blog. This blog is inspired by Excelr Solution. 

Difference Between Correlation and Covariance

Correlation vs Covariance

 Connection and Covariance are closely related terms but there are lot to differ when we need to choose one of these.  Connection and Covariance are two normally utilized factual ideas significantly used to gauge the direct connection between two factors in information. At the point when used to think about examples from various populaces, covariance is utilized to recognize how two factors fluctuate together while connection is utilized to decide how change in one variable is influencing the adjustment in another variable. Despite the fact that there are sure likenesses between these two scientific terms, these two are unique in relation to one another. Peruse further to comprehend the distinction among covariance and relationship.

Covariance:

It is a pointer of how much two factors change concerning each other i.e.., it gauges the heading of straight connection between these two factors .The estimations of covariance can lies in the scope of – ? to +?

Xi – values of X variable

Yj – values of Y variable

X?- mean of x variable

Y?- mean of y variable

N- Number of data points ( n-1 for sample covariance)

Correlation:

Correlation defines the strength and direction of linear relationship between two variables otherwise you can simply say that it is a normalized version of covariance. so when you divide the covariance with standard deviation of the variables, it scales down the range to -1 to +1 , comparatively correlation values are more interpretable.

Now let’s see see the difference between Correlation and Covariance:

one of major difference in these two is Covariance is influenced by the change in scale but in case of correlation values are not influenced by change in scale. rest we can with below table:- 

Reference:-

This blog is inspired by Excelr Solutions .   If you are interested to know the calculation of same terms listed above by using python with inbuilt function visit here correlation vs covariance

Concept of Simple Linear Regression

Simple linear regression is used to estimate the relationship between two variables, where the variables are continuous in nature.  so it used to define relation between single input variable and single output variable and also defines how this relation can be presented by straight line.

Below plot shows the graphical relation between two continuous variable.

This scatter shows three terms – 

  1. The direction
  2. The strength
  3. The linearity

 Here plot shows that variable x and y shares positive liner relationship.  So the most exact way to define this data is straight line.if relationship between two variables x and y stands strong then we can predict output variable y on the basic of input x variable nature. And this can be represented by straight line.Now we have correlation coefficient (r) to check collinearity between variable X and Y.
correlation coefficient (r)stands for numerical value of correlation between two variables. if value of r is higher then it means that the input variable x is good for y.
During this we have to count on some properties of ‘r’, listed below-

  1. Range of r: -1 to +1
  2. Perfect positive relationship: +1
  3. Perfect negative relationship: -1
  4. No Linear relationship: 0
  5. Strong correlation: r > 0.85 (depends on business scenario)

Here, Command used for calculation “r” in RStudio is:

> cor(X, Y)

where,

 X: independent variable 

 Y: dependent variable

 Now, there are two conditions depends on value of ‘r’ ,i.e result of above equation.

case 1- if r > 0.85 then choose simple linear regression and 

case 2- If r < 0.85 then use transformation of data to increase the value of “r” and then build a simple linear regression model on transformed data.

There are four steps to Implement Simple Linear Regression:

  1. Analyze data (analyze scatter plot for linearity)
  2. Get sample data for model building
  3. Then design a model that explains the data
  4. And use the same developed model on the whole population to make predictions.

The equation that represents how an independent variable X is related to a dependent variable Y.

For Example:

Let’s Consider we want to calculate the weight gain based upon calories taken. And for this we have below data. 

here we want to know weight gain when you consume 2500 calories. First, we need to draw a  graph of the data which will show that calories consumed is  independent variable X to predict dependent variable Y.

here “r”  can be calculated as follows:

As mentioned above case 1 here, r = 0.9910422 which is greater than 0.85, we can consider calories consumed as the best independent variable(X) and weight gain(Y) as the predict dependent variable.

Now, if we  try to draw a line  in a way that it should be close to every data point in the above plot diagram. It will be like this-

To calculate the weight gain for 2500 calories, simply extend the straight line further to the y-axis at a value of 2,500 on x-axis . This projected value of y-axis gives you the rough weight gain. This straight line is a regression line.

Similarly, if we substitute the x value in equation of regression model such as:

y value will be predicted.

Following is the command to build a linear regression model.

We obtain the following values

Substitute these values in the equation to get y as shown below.

So, weight gain predicted by our simple linear regression model is 4.49Kgs after consumption of 2500 calories.

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we hope we got the detail idea about Simple Liner Regression through this blog. This blog is inspired by Excelr Solution. 

WHAT IS THE PROCESS OF DATA ANALYSIS?

Definition:

Data Analysis is the process that involves the collection, transformation, and cleaning. It also deals with the modeling of data with the objective of discovering and identifying the required information. The results obtained henceforth are then communicated with suggestions for conclusions to support decision-making. At certain times, the visualization of data is used for the purpose of portraying the necessary data and information, which thereby eases the discovery of useful patterns within the data that has been obtained. Data Analysis and Data Modelling are exactly identical terms used in the field of big data.

About:

The term ‘analysis’ in the term ‘data analysis’ refers to the procedure of breaking something into its separate individual components for the purpose of individual scrutiny and examination. Data analysis is actually the process of obtaining the raw data and thereby converting it into useful and important information, which is used for decision making by the users. The data, which has been collected, is analyzed to answer questions, disprove theories, or test hypotheses. 

There are multiple and numerous approaches and facets in data analysis, which encompasses diverse methodologies under various names that are used in different types of scientific, business, and socially scientific domains. In the modern era, data analysis plays an extremely important and essential role in making any form of decision that is much more scientific than without its implementation. For this reason, it helps the respective business enterprises to conduct their proceedings and operate much more effectively than ever before.

Procedure:

  • Confirming the fact that the main total is the sum of the subtotals
  • Checking the relationship between the numbers
  • Checking the raw data files for abnormalities prior to the performance of the user’s analysis
  • Re-performing important calculations, such as the process of verifying the columns containing the respective data which are formula driven
  • Normalizing the numbers for the purpose of making the process of comparison easier. It includes analyzing the amount per person, anything related to GDP, or as the index value which is relative to the base year
  • Breaking the problems into separate individual components by analyzing the factors like the DuPoint analysis of the return on equity. 
  • Identifying the areas for the purpose of increasing the efficiency and the automation of processes.
  • Setting up and maintaining automated data processes.
  • Identifying, evaluating as well as implementing external tools and services for the purpose of supporting cleansing and data validation.
  • Creating graphs, dashboards, and visualizations.
  • Providing competitor and sector benchmarking
  • Analyzing and mining large sets of data, drawing valid influences, and presenting them successfully to the management with the use of a reporting tool.
  • Designing and carrying out surveys as well as analyzing the survey data
  • Manipulating, analyzing, and interpreting complex sets of data that are related to the business of the employer.
  • Preparing reports for the external and internal audience through the use of business analytics reporting tools.

Inference: Data analysis and data analytics play an extremely vital role in the invention, manufacture, and production of technologies and products of the next generation. It is due to this reason that data analytics has gained so much importance in the private sector and various Multinational Companies (MNCs).    

Resource box:

People can pursue a career in data analytics with any kind of degree or subject if they possess the relevant skills and requirements for filling the respective posts. Postgraduate degrees in the field of data science is getting more popular day by day.

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Data Analytics, Types and its Advantages

The use of software and specialized system to examine data in order to draw conclusions about it in various aspects is called Data Analytics. Data Analytics is widely used by commercial companies nowadays. It helps the organizations to run the administration more efficiently. It also helps them to make a more appropriate business-related decision. It also helps the researchers, scientists to verify or approve the hypothesis and theory. Data Analysts include various applications under it. Some of the applications under them are business intelligence (BI), online analytical process (OLAP) and advanced analytics.

Advantages of Data Analytics

There are several benefits of Data analysis. It helps us to increase operational efficiency, increasing the business revenues to a very high level, developing good marketing campaigns and also providing better service efforts to the customers. It can also be used for real-time analytics. The real-time analytics include the new as well as old information in the field of Data Analytics Courses

Types of Data Analytics Application

Data Analytics chiefly includes data analytics methodologies, including exploratory data analysis. The confirmatory data analysis also falls in the same category. It is widely used to find out whether the data is real or fake. It can also be distinguished as quantitative and qualitative data analysis. Quantitative data analysis involves the analysis of the mathematical data, whereas qualitative one involves understanding the nonnumerical data. Nonnumerical data includes text, pictures and also audio, video.

Advanced types of Data Analytics

Data mining, Predictive analysis, machine learning, and text mining are some examples of advanced data analysis. Data mining is identifying patterns and trends by sorting large data. Machine learning is another advanced method in which artificial intelligence technology is used by data scientists to go through the data sets. Text mining is also a data analytical process of analyzing the emails, documents, and other contents.

The inside process under Data Analytics

The data analysis is much more than analyzing data. Mainly, in the advanced analytics projects, much of the required work takes a plane in front of us. Preparing and collection of data, then preparing and integrating the data, later testing and revising the analytic process to ensure maximum accuracy of the result. The process starts with the collection of data only. Data Scientist identifies the information which they need for a definite analytic process. Then the IT sector and data engineers take over it. They prepare the desired contents. Once the desired data is made, the next task is to fix the quality of the data. The main work now is to fix the data quality. Now, data cleansing job comes into action. After this, additional data is also prepared to manipulate the previous one if needed. This is the main turning point. The data analysts build a model of it using programming languages such as Python, R language and SQL. Then it is again rechecked before further steps.

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Communication through Data Analytics

Data Analytics machines are often set to automatically do all business actions. The last step of the Data Analytics is Data Visualization. It is the process by which the desired results are communicated to the business heads. They are mainly incorporated into the Business Intelligence dashboard that displays all data on a single screen. It can also be updated in real time when needed. This is how the information becomes visible in Data Analytics.

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Data Analytics- The Key to an Unprecedented Future

In this technology-driven world, where we are informed about transformations on a daily basis, these transformations are all occurring in the field of ‘Data’. The incredible amount of information being created every second is as valuable as gold and it will undoubtedly continue to rule the future. One such domain that leverages the use of data is Data Analytics.

SO, WHAT IS DATA ANALYTICS?

Data Analytics is an approach that is used to predict results by monitoring and modeling data collected from careful analysis. It is the study where the outcomes result in the enhanced performance of business operations.

Due to rapid advancements in the business sector, organizations have been relying on technologies to enable the decide on strategic plans, which may lead their path to growth. Keeping in mind such demands, researchers have developed this field of Analytics- a comprehensive algorithm based on predictions and conclusions derived from the data. As data is a limitless entity- with tonnes of it being generated each day, data analytics remains a practice that will grow exponentially in the future ahead. That’s the reason why most of the companies recruit the candidates who’re having the knowledge of Data Analytics.

THE 4D PROCESS OF DATA ANALYTICS

Let’s take a look at the extensive process of this ingenious technology

DATA COLLECTION

Once we have defined the requirements, data is collected keeping in mind the outline specified in the first phase. There are several mechanisms which can be employed to gather data. The analysts can opt for research, interviews, documentation or any other specified format. The aim is clear: collect data from all information sources and fulfill the requirements as defined.

DATA PROCESSING

Data collected from different sources is in its raw form and must be converted into a suitable format to conduct the analysis. The data must be organized to generate it into a structured format. We can organize the data in the form of rows and columns for effective interpretation.

DATA CLEANING

After the aforementioned steps, the data is processed and structured. There might be some underlying errors or redundancies or inadequate information. For instance, while entering the collected information in a tabular format, human errors in the form of misspelled words can have an entirely different meaning from the original data. Thus, thorough identification, checking and inspection must be undertaken to evade any conflicting data.

After the 4D’s of the Analytical process defined above, the data is analyzed through various techniques such as exploratory data analysis, data visualization as well as descriptive statistics. In the final step, data modeling makes use of novel algorithmic models to generate quantitative results.

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THE FUTURE AS A DATA ANALYST

What would the scenario be if Data Analytics hadn’t come into the picture?

A simple answer- Failure! The rate at which organizations would face inescapable negative outcomes would be tenfold.

Harnessing value from data and generating insights is the key to the bright future of business. Individuals possessing such skills are in high demand and are seen as a valuable asset. If you are good with numbers, possess logical and cognitive thinking, can make inferences from figures and generate insights, you can undoubtedly have a great career as a Data Analyst.

Analytics is an emerging field showing bits of development at a tremendous rate. Its potential is yet to be truly discovered, but one thing is certain, it will assume an essential role in the trailblazing transformations of the future!

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Data Analytics Work Prospects

Data analytics is such a huge field with an ample number of fields within it, provides multiple job options that are highly paid. Maybe you are eager to learn a new skill set in data analytics, but before that, you are more likely to be inquisitive regarding earning capabilities of the respective positions. This will give an actual boost to your leaning knowing how your real and fresh skills will be remunerated.

A lot of employers have been hired for these positions in data analytics and it’s currently still going on. Many people dream of working in the data science field, especially data analytics, so let’s look and pay some heed at some of the job prospects of the data analytics industry. Below is a list of some job titles that are highly valued in the world.

IT Analyst: If any problem arises in the Information Technology department, these system analyst design use systems to make solutions and try to eradicate such problems. The technical level of expertise differs in position and that is what creates multiple options for specialization according to the personal interest and industry. Some analysts make use of already present third-party tools for testing software in a company while the others create new tools from their knowledge and experience in the data analytics business. To view more about the role of an IT Analyst in the field of Data Analytics Courses Click here.

Healthcare Analyst: The primary task of a healthcare data analyst is to improve the health of many people by assisting scientists and doctors search for solutions to our daily basis problems. A great amount of data is generated through medical testing, labs, clinics, and hospitals that need to be channeled in the right direction. Also, the restrictions are increased on the storage, processing, managing techniques of the data which increases the demand for more efficient data analytics.

Operation Analyst: These analysts are generally present inside organizations and sometimes work as consultants. They mainly concentrate on the internal business process like product development and distribution, reporting systems, and smooth functioning of business operations. Operation Analysts are present pretty much everywhere from postal service providers to grocery chains.

Quantitative Analyst: A quantitative analyst is one of the highly desired and demanded professional specifically in finance. Quantitative analyst utilizes data analytics to look for appropriate financial investment options and risk handling issues. They have the flexibility to create their own trading models to estimate stock prices, exchange rates, commodities, and many more. Even many analysts in the industry go for their own firms.

Project Manager: The project manager uses data analytic tools and keeps an eye on the team’s progress, efficiency, and change processes to elevate the overall productivity of the team. At the least, the product manager must know the working of data analytics and sometimes more. These are often present internally in business corporations and commonly in consulting departments.

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ExcelR – Data Science, Data Analytics Course Training in Bangalore

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Data Analytics: Why’s it so important?

Companies and business have to collect a large quantity of data to see how their products or services are doing in the market and how they can improve and create more impact on the market. They would also collect data on gaps in the market so they can create products. The process of analyzing, sorting through and going through all the data and forming a conclusion on how to progress their business is called Data Analytics.

Benefits of Data Analytics

Companies are often under constant pressure due to the changing needs of people and competition from other businesses, it’s the best way for a company to figure out what is missing from the market and what might be required to boost their business. This is why a lot of companies are constantly asking for feedback and ratings and also take details like email and phone numbers; it’s all to help boost their business.

There are teams such as the project management team which have the sole responsibility of finding out gaps in the market, finding out what is currently preferred and delivering the relevant information to the company so the company knows where to put in their efforts and create a business plan for the future. Data analytics is a key part of creating business plans and seeing trends and how certain companies might perform in the future.

It can also help businesses improve their customer experience. Companies lookout for feedback through user and customer data to help them improve their services. Companies with bad customer experience can ruin their brand image and loyalty from customers. Customers often use star ratings and feedbacks and use the data provided by those to see what they can add or improve in their customer experience.

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The Downside of Data Analytics

The biggest concern of Data analytics is the invasion of privacy of the customers. Companies need customer information and data such as purchase history, what the customers might be looking for, their searches, etc. This is a major breach of privacy as companies are buying private information for their own gain.

Another downside of Data Analytics is it is time-consuming and expensive. Data analytics requires going through large quantities of Data to find relevant information and form conclusions using the data provided. This can be highly time-consuming as companies might have lots of data to go through. Sometimes, the tools used such as the applications might be difficult to use and would take time and money to teach their employees on how to use the software and use it correctly.

Data Analytics Course is more than just collecting and going through data, it requires planning on how to collect the data and how to form conclusions using the correct tools and collecting and filtering the information to the right places and determining which data is relevant and which is not. Data scientists will go through and analyze the information and data engineers would help create data sets.

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Data Analytics Training And Skills That You Need In The Current Scenario

In this article, we will be talking about data analytic training and its needs in the current scenario; basically, what is data science and how can you choose it as your new career option?

What Is Data Analytics Training?

So, basically in this course, you will learn how to collect data from different resources and analyze it to make a decision or get to know about your project. You can also calculate the probability about how your project will perform in the market based on using the past data as a predictor.

You can also say that data science is nothing but the study of all the information about every basic thing, and then converting it into the form of data that can be analyzed and acted upon to improve the profit of your business.

History Of Data Science

Data Science has been in existence for a very long time. If you look back, then you will find that many companies, even our ancient kings in their taxation plans, also used to collect data, and develop plans and actions according to the collected data. While data collection is not a new thing, it has evolved and now has become a whole new profession.

Skills that are Necessary for Data Analytics Course

There are many types of skills that are necessary which will be taught to you in data analytics training. These skills include

MS EXCEL Earlier, Excel was widely used for data analytics, but now, given that the amount of data has been increased dramatically, it has become impossible for a person to analyze data solely in Excel. Now Artificial Intelligence (AI) and machine learning are used for that. MS Excel is used for smaller amounts of data, such as for someone’s personal business. Where there is no need for large amounts of data you can simply collect data from different resources then analyze it using Excel. It is not useful for big multi-national companies such as Amazon, Flipkart, etc., as they have to use machine learning and artificial intelligence.

AI AND MACHINE LEARNING To compute a vast amount of data several AI and machine learning tools have developed. It is impossible for an individual to deal with that huge amount of data without additional support.

As one example, if you have noticed that if you ordered anything from Amazon or any other site, or even if you are using any other app they ask for feedback on your experience. If you submit the feedback, then the program will automatically try to understand where is the fault in their application that causes you to cut stars. It will collect that data and then send it to the company. Once they have collected the data and analyzed it, you will get future updates and feedback.

Some Programming

You need to know about many kinds of data processing, such as:
Data engineering
Data visualization
Data pre-processing
Data transformation
Data cleaning

To work with these data processing applications, you need to know about programming languages
such as Python.

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Data Analytics: The Future Of Dealing With Data

Since the time when the Internet has taken over the world, the importance of data and the rate at which data is increasing is at higher levels. Data can be organized as structured, semi-structured and unstructured. Handling and analyzing different sorts of data is the major challenge these days. Day-by-day, we can see that the amount of data emerging is drastically increasing, and the need to manage such vast amounts is very important. Choosing the right type of storage becomes the first priority in the field of Data Analytics
When you have to analyze data, it involves: • Studying the data.
• Storage medium.
• Characteristics.
• Security measures, and many more items
.

Big data consists of large data sets which are not possible for the traditional database systems to handle. Data storage techniques now used for data include clustered networks and object-based storage. The ability to store large amounts of data is what is necessary for business executives to use big data. Unique techniques are needed to analyze big data.

We need to have knowledge about methodologies, adopt technologies and ensure to improve skills with technology. In the earlier years, executives relied entirely on structured data. Later on, unstructured data were also provided with methods to handle and analyze this kind of data easily. It poses opportunities and challenges for the business.

Let’s just consider a simple example of an Excel sheet that holds different values, such as customer details, order details, inventories, etc. As it contains different kinds of information in one sheet, it has to be structured in such a way that it is easy to access whatever information is required from it.

Analyzing data is like handling business and technology together, which is a great challenge. Proper analysis of data reduces risk. Analytics architecture refers to infrastructure, tools, and practices that lead to access and analysis of information to enable easier decision making for businesses.

Let’s take a look at the big data analytic steps

Data extract and feed: Collect the data that you want to store and feed it to the storage device.
Discovery and visualization: This involves knowing about what sort of data it is, sorting it if it is unsorted, and saving it. There can be two types of data – public data and confidential data. Public data is the data that can be shared across the wider public. Confidential data is only accessed by a few authorized users. To develop the security of information, authentication, authorization, and validation processing software must be stored. To manage so much of data requires visualization of how you can store your data and develop the respective framework for it.
Decision support: After you store and validate data, unnecessary data has to be removed and a decision has to be taken as to which operations have to be performed on which data and so on.
Data and applications: Once decisions are made you are ready to apply these data into the areas where you want it to be used.

For more information regarding WHY DATA COLLECTION IS USED IN DATA ANALYTICS?

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               49, 1st Cross, 27th Main,

               Behind Tata Motors, 1st Stage,

               BTM Layout, Bengaluru,

               Karnataka 560068

               Phone: 096321 56744

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