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How to Effectively Test for Machine Learning Systems?

Machine Learning is a study of applying algorithms, behavioural data sets, and statistics to make your system learn by itself without any external help and procedure. As the Machine Learning model does not produce a concrete result, it generates approximate results or contingencies from your given dataset. The earlier software system was human-driven, where we wrote code and logic, and the machine validated the logic and checked for the desired behaviour of the system and program. Our desired testing was based on the written logic and expected behaviour. But when it comes to testing for machine learning systems, we provide a certain set of behaviours as a training example to produce the logic of the system, and ensure that the system understands the logic and develops the model according to the desired behaviour. How to write a model testModel testing is a technique where any software's runtime behaviour is recorded and tested under some dataset and prediction table that the model has already predicted. Some model-based testing scenarios are used to describe numerous aspects of the Machine Learning model. The way to test the modelTest the basic logic of the model. Manage the performance using the concept of manual testing. Work on the accuracy of the model. Check the performance on the real data, try to use unit testing. Pre-train TestingPre-train tests: As per the name, pre-train testing is the testing technique that allows you to catch the bugs before even running the model. It checks whether there is any label missing in your training and validation dataset; and it does not require any running parameter. The pre-train testing goal is to avoid wastage during training jobs. Problem statement of pre-train testing: Check leakage label in your training dataset and validation dataset. Check the single gradient to find the loss of data. Check the shape of the dataset to ensure the alignment of data. Post-train TestingPost Train Testing is used to check whether it performs all the validations correctly or not. The main purpose of post-train testing is to validate the logic behind the algorithm and find out the bugs, if any. The post-train testing deals with the job behaviour.They are basically of three types. Invariant tests Directional tests Minimum functional tests Invariant TestInvariant Testing is the testing technique where we check how the input data is changing without affecting the entire performance of the Machine Learning model. Here each input model is paired with the prediction and maintains consistency. Invariant testing provides a logical guarantee about the application; this is a very low testing technique. This type of testing is mainly observed in Domain-Driven Design (DDD). Invariant testing follows three basic steps: Identify invariants. Enforce invariants. Refactor necessary invariants. Directional TestDirectional testing is a type of hypothesis testing where a direction of testing is specified earlier to the testing. This testing technique is also known as a one-tailed test. Directional testing is way more powerful than the non-directional or invariant testing technique. Unlike invariant testing, perturbation can change the outcome of the model in the provided input. Minimum functional testFunctional testing is used to check whether the software or model is working according to the pre-requisite dataset or not. This uses the black box testing technique. Types of functional testing: Unit testing Smoke testing Sanity testing Usability testing Regression testing Integration testing The minimum functional testing model works in a similar manner to a traditional unit testing technique where the data is classified into different   components, and the testing is applied over those components. Ways to perform functional testing: Testing based on user requirements. Testing based on business requirements. Understanding the Model Development PipelineThe pipelining concept in machine learning is used to automate the workflows. Machine Learning pipelines are iterative processes, repeated one after the another to improve the algorithm's accuracy and model, and achieve the required successful solution. An evaluation of the Model development pipeline includes the following steps:Pre-Train Test. Post-Train Test. Train model. Evaluation of model. Review and approval of dataset. Benefits of Model Testing:Easy maintenance. Less cost. Early detection. Less time-consuming. More job satisfaction. Issues while performing Model-Based Testing in Machine LearningWhile working over any model, there are many shortcomings we have to deal with, which can be due to a design issue or implementation issues. Here are some drawbacks of the Model-Based Testing Technique: Deep understanding of problem statement is required. Different skill sets are required. More emphasis is placed on a learning curve. More human power is required. Adding testing in Machine LearningWhen it comes to machine learning, almost every library used in Machine Learning modeling is well tested. When you make a code call, it uses the model predict in your machine learning algorithm, and it assures you that all the layers in the method and function are calling other functions at an invariant level. This model prediction helps you to determine the function working together to deliver the required result set using the test dataset and input predictions.  Image SourceThere is always something to add to the Machine Learning libraries as they are not perfect. The initial test of the baseline is reasonable, and there is much more you can add to it as per the requirement. While working on the library, you can eventually find out the bug and limitation over the interface.  The complete testing procedure ends when all the functional and non-functional requirements of the product are fulfilled. The test case needs to be executed.  There are five test case parameters we have to deal with:  The initial state of product or preconditions.Data management Input dataset. Predicted output. Expected output. Different types of testing TechniquesThe main motive to perform the testing is to find the error and secure the system from future failure. The tester follows different testing techniques to assure the complete success of the system.  The main type of testingUnit testing: The developer performs this to check whether the individual component of the model is working in accordance with the user requirement or not. It calls each unit and then validates each unit, returning the required value. Regression testing: Regression testing ensures that even after adding the component or module, the overall model is not affected, and it works fine even after several modifications. Alpha testing: This is the testing performed just before the deployment of the product. Alpha testing is also known as validation testing and comes under acceptance testing. Beta testing: Beta testing or usability testing is released to a few members only for  testing purposes. This release is deployed several times to match the requirements of the user and validate them accordingly. Integration testing: In Integration testing, the result set is taken from the unit testing, and the combination makes the program structure of the produced output. It helps the functional module to work together efficiently to produce the required output. It makes sure that the necessary standards of the system and model are met. Integration Testing can be classified into two main testing mechanismsBlack Box Testing: Black Box Testing is used for validation testing techniques. White Box Testing: White Box Testing is used for verification testing techniques. Stress testing: Stress testing is a thorough testing technique where we follow deliberately intense mechanisms. It checks unfavourable conditions that might occur for the system and then checks how the modules react to those conditions. Testing is performed beyond the simple operation and integration testing capacity. It verifies the system's stability, maintains the reliability of the system, and validates the correctness of the system. What is predictive analysis, and what are its usesPredictive analysis is a branch of Advance analytics, where we predict the future events using past values and datasets. Predictive analysis in a simple way is the analysis of the future, and makes different predictions over the historical data. Many organizations turn to predictive analysis to make the correct use of data to produce valuable insight in faster, cheaper, and easier ways. How can predictive analysis be used? Predictive analytics can be used to reduce the risk, optimize operations, increase revenue, and develop valuable insights. Where is predictive analysis used? Retail sector. Banking and financial sector. Oil, gas & power utility sector. Health Insurance sector. Manufacturing sector. Public sector and government sector. Difference between Machine Learning and Predictive AnalysisTo understand the depth of the topic, here is the difference between Machine Learning and Predictive Analysis.  Machine LearningPredictive AnalysisMachine Learning is used to solve many complex problems using different ML models.Predictive analysis is used to predict the future outcomes, where it utilizes the past data.The Machine Learning model adapts and learns from the experience and datasets.The predictive analysis does not adapt the dataset.In Machine Learning, human intervention is not required.In Predictive Analysis, we are required to program the system with the help of human intervention.Machine Learning is said to be the data-driven approach because it depends on the dataset.Predictive analysis is not a data-driven approach.What does the tester need to know? A tester should be aware of the following considerations: The tester should have complete knowledge of various scenarios like the best case, average case, worst-case scenarios, how the system behaves, and how its learning graph varies. What is the expected output, and what is the acceptable output for each test case? The tester is not required to know how the model works; and just needs to validate the test cases, learning model, and required scenarios. The tester should be an expert in communicating test results in the form of statistical outputs. The tester should easily validate the algorithm and dataset and control the calculations according to the training data.Best practices of Testing for Machine Learning in Non-Deterministic applications Let us first understand what a Non-Deterministic Application is. A Non-Deterministic system is a system in which the final result cannot be predicted because there are multiple possible ways and outcomes for each input. To identify the correct result, we need to perform a certain set of operations. When dealing with the theoretical concept, the Non-Deterministic model is more useful than the deterministic one; therefore, in designing the system, sometimes we adopt a Non-deterministic approach and then move to a deterministic one. Best Practice for Testing Non-Deterministic Applications: While testing, the Non-deterministic model performs continuous Integration and testing. Use a model-based testing approach. Use an augmented approach as needes by the non-deterministic model. Use a test asset management system, and treat them as first-class products. When dealing with a large set of data, perform testing on each operation at least once. Test all the illegal sequences of inputs with their correct response set of data. Always perform unit testing with extreme aberrant points. The base goal of Machine Learning testing: QoS or Quality of Service, the main motive to provide the quality of the service to the user or the customer, can be said to be Quality Assurance. Remove all the defects and errors from the design implementation to avoid future consequences and issues. Find the bugs at the early stage of the project lifecycle. What is the importance of testing in a Machine Learning project? Small misconceptions bring a lot of issues in the development lifecycle, and defects at the initial stage of product development lifecycle can cause collateral damage to the project or complete crashing of the project. Testing helps to identify the requirements, issues, and errors at the initial stage of the product development lifecycle. Testing helps to discover the defects and bugs before deploying the project, software, or system.  The system becomes more reliable and scalable.  More thorough checking of software provides more high-performance and more chances of successful deployment.  It makes the system easy to use and gives more customer satisfaction. It improves the quality of the product and its efficiency.   There is increased success rate and an easier learning graph.ConclusionThis article is an attempt to cover the basic concepts for the tester in Machine Learning. It talks about testing mechanisms, and indicates how to determine the best fit for your requirement. You will learn about different types of model tests, model test deployment pipeline, and different testing techniques. You will get insights about the Machine learning test automation tools and requirements; and understand the most important aspect of machine Learning testing— data, dataset, and learning graphs. The tester is made aware of the Machine Learning project's basic requirement, deep understanding of the datasets, and how to organize the data so that it acts according to the user demand. If you work according to the procedure, the result will be accurate to some point. The model should be more responsive and informative to develop business insights. As part of the last phase of the project development lifecycle, testing is a very important and critical step to be followed. 

How to Effectively Test for Machine Learning Systems?

6K
  • by Abhresh S
  • 05th May, 2021
  • Last updated on 17th May, 2021
  • 11 mins read
How to Effectively Test for Machine Learning Systems?

Machine Learning is a study of applying algorithms, behavioural data sets, and statistics to make your system learn by itself without any external help and procedure. As the Machine Learning model does not produce a concrete result, it generates approximate results or contingencies from your given dataset. 

The earlier software system was human-driven, where we wrote code and logic, and the machine validated the logic and checked for the desired behaviour of the system and program. Our desired testing was based on the written logic and expected behaviour. But when it comes to testing for machine learning systems, we provide a certain set of behaviours as a training example to produce the logic of the system, and ensure that the system understands the logic and develops the model according to the desired behaviour. 

How to write a model test

Model testing is a technique where any software's runtime behaviour is recorded and tested under some dataset and prediction table that the model has already predicted. 

Some model-based testing scenarios are used to describe numerous aspects of the Machine Learning model. 

The way to test the model

  • Test the basic logic of the model. 
  • Manage the performance using the concept of manual testing. 
  • Work on the accuracy of the model. 
  • Check the performance on the real data, try to use unit testing. 

Pre-train Testing

Pre-train tests: As per the name, pre-train testing is the testing technique that allows you to catch the bugs before even running the model. It checks whether there is any label missing in your training and validation dataset; and it does not require any running parameter. 

The pre-train testing goal is to avoid wastage during training jobs. 

Problem statement of pre-train testing: 

  • Check leakage label in your training dataset and validation dataset. 
  • Check the single gradient to find the loss of data. 
  • Check the shape of the dataset to ensure the alignment of data. 

Post-train Testing

Post Train Testing is used to check whether it performs all the validations correctly or not. The main purpose of post-train testing is to validate the logic behind the algorithm and find out the bugs, if any. 

The post-train testing deals with the job behaviour.

They are basically of three types. 

  • Invariant tests 
  • Directional tests 
  • Minimum functional tests 

Invariant Test

Invariant Testing is the testing technique where we check how the input data is changing without affecting the entire performance of the Machine Learning model. Here each input model is paired with the prediction and maintains consistency. 

Invariant testing provides a logical guarantee about the application; this is a very low testing technique. This type of testing is mainly observed in Domain-Driven Design (DDD). Invariant testing follows three basic steps: 

  • Identify invariants. 
  • Enforce invariants. 
  • Refactor necessary invariants. 

Directional Test

Directional testing is a type of hypothesis testing where a direction of testing is specified earlier to the testing. This testing technique is also known as a one-tailed test. Directional testing is way more powerful than the non-directional or invariant testing technique. 

Unlike invariant testing, perturbation can change the outcome of the model in the provided input. 

Minimum functional test

Functional testing is used to check whether the software or model is working according to the pre-requisite dataset or not. This uses the black box testing technique. 

Types of functional testing: 

  • Unit testing 
  • Smoke testing 
  • Sanity testing 
  • Usability testing 
  • Regression testing 
  • Integration testing 

The minimum functional testing model works in a similar manner to a traditional unit testing technique where the data is classified into different   components, and the testing is applied over those components. 

Ways to perform functional testing: 

  • Testing based on user requirements. 
  • Testing based on business requirements. 

Understanding the Model Development Pipeline

The pipelining concept in machine learning is used to automate the workflows. Machine Learning pipelines are iterative processes, repeated one after the another to improve the algorithm's accuracy and model, and achieve the required successful solution. 

An evaluation of the Model development pipeline includes the following steps:

  • Pre-Train Test. 
  • Post-Train Test. 
  • Train model. 
  • Evaluation of model. 
  • Review and approval of dataset. 

Benefits of Model Testing:

  • Easy maintenance. 
  • Less cost. 
  • Early detection. 
  • Less time-consuming. 
  • More job satisfaction. 

Issues while performing Model-Based Testing in Machine Learning

While working over any model, there are many shortcomings we have to deal with, which can be due to a design issue or implementation issues. Here are some drawbacks of the Model-Based Testing Technique: 

  • Deep understanding of problem statement is required. 
  • Different skill sets are required. 
  • More emphasis is placed on a learning curve. 
  • More human power is required. 

Adding testing in Machine Learning

When it comes to machine learning, almost every library used in Machine Learning modeling is well tested. When you make a code call, it uses the model predict in your machine learning algorithm, and it assures you that all the layers in the method and function are calling other functions at an invariant level. This model prediction helps you to determine the function working together to deliver the required result set using the test dataset and input predictions.  Machine Learning

Image Source

There is always something to add to the Machine Learning libraries as they are not perfect. The initial test of the baseline is reasonable, and there is much more you can add to it as per the requirement. While working on the library, you can eventually find out the bug and limitation over the interface.  

The complete testing procedure ends when all the functional and non-functional requirements of the product are fulfilled. The test case needs to be executed.  

There are five test case parameters we have to deal with:  

  • The initial state of product or preconditions.
  • Data management 
  • Input dataset. 
  • Predicted output. 
  • Expected output. 

Different types of testing Techniques

The main motive to perform the testing is to find the error and secure the system from future failure. The tester follows different testing techniques to assure the complete success of the system.  

The main type of testing

  1. Unit testing: The developer performs this to check whether the individual component of the model is working in accordance with the user requirement or not. It calls each unit and then validates each unit, returning the required value. 
  2. Regression testing: Regression testing ensures that even after adding the component or module, the overall model is not affected, and it works fine even after several modifications. 
  3. Alpha testing: This is the testing performed just before the deployment of the product. Alpha testing is also known as validation testing and comes under acceptance testing. 
  4. Beta testing: Beta testing or usability testing is released to a few members only for  testing purposes. This release is deployed several times to match the requirements of the user and validate them accordingly. 
  5. Integration testing: In Integration testing, the result set is taken from the unit testing, and the combination makes the program structure of the produced output. It helps the functional module to work together efficiently to produce the required output. It makes sure that the necessary standards of the system and model are met. 

Integration Testing can be classified into two main testing mechanisms

  • Black Box Testing: Black Box Testing is used for validation testing techniques. 
  • White Box Testing: White Box Testing is used for verification testing techniques. 
  1. Stress testing: Stress testing is a thorough testing technique where we follow deliberately intense mechanisms. It checks unfavourable conditions that might occur for the system and then checks how the modules react to those conditions. 

Testing is performed beyond the simple operation and integration testing capacity. It verifies the system's stability, maintains the reliability of the system, and validates the correctness of the system. 

What is predictive analysis, and what are its uses

Predictive analysis is a branch of Advance analytics, where we predict the future events using past values and datasets. 

Predictive analysis in a simple way is the analysis of the future, and makes different predictions over the historical data. Many organizations turn to predictive analysis to make the correct use of data to produce valuable insight in faster, cheaper, and easier ways. 

How can predictive analysis be used? 

Predictive analytics can be used to reduce the risk, optimize operations, increase revenue, and develop valuable insights. 

Where is predictive analysis used? 

  • Retail sector. 
  • Banking and financial sector. 
  • Oil, gas & power utility sector. 
  • Health Insurance sector. 
  • Manufacturing sector. 
  • Public sector and government sector. 

Difference between Machine Learning and Predictive Analysis

To understand the depth of the topic, here is the difference between Machine Learning and Predictive Analysis.  

Machine LearningPredictive Analysis
Machine Learning is used to solve many complex problems using different ML models.Predictive analysis is used to predict the future outcomes, where it utilizes the past data.
The Machine Learning model adapts and learns from the experience and datasets.The predictive analysis does not adapt the dataset.
In Machine Learning, human intervention is not required.In Predictive Analysis, we are required to program the system with the help of human intervention.
Machine Learning is said to be the data-driven approach because it depends on the dataset.Predictive analysis is not a data-driven approach.

What does the tester need to know? 

A tester should be aware of the following considerations: 

  • The tester should have complete knowledge of various scenarios like the best case, average case, worst-case scenarios, how the system behaves, and how its learning graph varies. 
  • What is the expected output, and what is the acceptable output for each test case? 
  • The tester is not required to know how the model works; and just needs to validate the test cases, learning model, and required scenarios. 
  • The tester should be an expert in communicating test results in the form of statistical outputs. 
  • The tester should easily validate the algorithm and dataset and control the calculations according to the training data.

Best practices of Testing for Machine Learning in Non-Deterministic applications 

Let us first understand what a Non-Deterministic Application is. 

A Non-Deterministic system is a system in which the final result cannot be predicted because there are multiple possible ways and outcomes for each input. To identify the correct result, we need to perform a certain set of operations. 

When dealing with the theoretical concept, the Non-Deterministic model is more useful than the deterministic one; therefore, in designing the system, sometimes we adopt a Non-deterministic approach and then move to a deterministic one. 

Best Practice for Testing Non-Deterministic Applications: 

  • While testing, the Non-deterministic model performs continuous Integration and testing. 
  • Use model-based testing approach. 
  • Use an augmented approach as needes by the non-deterministic model. 
  • Use test asset management system, and treat them as first-class products. 
  • When dealing with a large set of data, perform testing on each operation at least once. 
  • Test all the illegal sequences of inputs with their correct response set of data. 
  • Always perform unit testing with extreme aberrant points. 

The base goal of Machine Learning testing: 

  • QoS or Quality of Service, the main motive to provide the quality of the service to the user or the customer, can be said to be Quality Assurance. 
  • Remove all the defects and errors from the design implementation to avoid future consequences and issues. 
  • Find the bugs at the early stage of the project lifecycle. 

What is the importance of testing in a Machine Learning project? 

Small misconceptions bring a lot of issues in the development lifecycle, and defects at the initial stage of product development lifecycle can cause collateral damage to the project or complete crashing of the project. Testing helps to identify the requirements, issues, and errors at the initial stage of the product development lifecycle. 

  • Testing helps to discover the defects and bugs before deploying the project, software, or system.  
  • The system becomes more reliable and scalable.  
  • More thorough checking of software provides more high-performance and more chances of successful deployment.  
  • It makes the system easy to use and gives more customer satisfaction. 
  • It improves the quality of the product and its efficiency.   
  • There is increased success rate and an easier learning graph.

Conclusion

This article is an attempt to cover the basic concepts for the tester in Machine Learning. It talks about testing mechanismsand indicates how to determine the best fit for your requirement. You will learn about different types of model tests, model test deployment pipeline, and different testing techniques. You will get insights about the Machine learning test automation tools and requirementsand understand the most important aspect of machine Learning testing data, dataset, and learning graphs. 

The tester is made aware of the Machine Learning project's basic requirement, deep understanding of the datasetsand how to organize the data so that it acts according to the user demand. If you work according to the procedure, the result will be accurate to some point. 

The model should be more responsive and informative to develop business insightsAs part of the last phase of the project development lifecycle, testing is a very important and critical step to be followed. 

Abhresh

Abhresh S

Author

An Online Technical Trainer by profession! And Content writer by hobby! Interested in sharing quality knowledge to make the Industry grow better towards better success and better tomorrow! With a Guru Mantra of - "Keep Learning & Keep Practicing".

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Structured Data In this section, we will understand the difference between structured and unstructured data with examples. STRUCTUREDUNSTRUCTUREDStructured data resides in an organized format in a typical database.Unstructured data cannot reside in an organized format, and hence we cannot store it in a typical database.We can store structured data in SQL database tables having rows and columns.Storing and managing unstructured data requires specialized databases, along with a variety of business intelligence and analytics applications.It is tough to scale a database schema.It is highly scalable.Structured data gets generated in colleges, universities, banks, companies where people have to deal with names, date of birth, salary, marks and so on.We generate or find unstructured data in social media platforms, emails, analyzed data for business intelligence, call centers, chatbots and so on.Queries in structured data allow complex joining.Unstructured data allows only textual queries.The schema of a structured dataset is less flexible and dependent.An unstructured dataset is flexible but does not have any particular schema.It has various concurrency techniques.It has no concurrency techniques.We can use SQL, MySQL, SQLite, Oracle DB, Teradata to store structured data.We can use NoSQL (Not Only SQL) to store unstructured data.Types of Unstructured Data Do you have any idea just how much of unstructured data we produce and from what sources? Unstructured data includes all those forms of data that we cannot actively manage in an RDBMS system that is a transactional system. We can store structured data in the form of records. But this is not the case with unstructured data. Before the advent of object-based storage, most of the unstructured data was stored in file-based systems. Here are some of the types of unstructured data. Rich media content: Entertainment files, surveillance data, multimedia email attachments, geospatial data, audio files (call center and other recorded audio), weather reports (graphical), etc., comes under this genre. Document data: Invoices, text-file records, email contents, productivity applications, etc., are included under this genre. Internet of Things (IoT) data: Ticker data, sensor data, data from other IoT devices come under this genre. Apart from all these, data from business intelligence and analysis, machine learning datasets, and artificial intelligence data training datasets are also a separate genre of unstructured data. Examples of Unstructured Data There are various sources from where we can obtain unstructured data. The prominent use of this data is in unstructured data analytics. Let us now understand what are some examples of unstructured data and their sources – Healthcare industries generate a massive volume of human as well as machine-generated unstructured data. Human-generated unstructured data could be in the form of patient-doctor or patient-nurse conversations, which are usually recorded in audio or text formats. Unstructured data generated by machines includes emergency video camera footage, surgical robots, data accumulated from medical imaging devices like endoscopes, laparoscopes and more.  Social Media is an intrinsic entity of our daily life. Billions of people come together to join channels, share different thoughts, and exchange information with their loved ones. They create and share such data over social media platforms in the form of images, video clips, audio messages, tagging people (this helps companies to map relations between two or more people), entertainment data, educational data, geolocations, texts, etc. Other spectra of data generated from social media platforms are behavior patterns, perceptions, influencers, trends, news, and events. Business and corporate documents generate a multitude of unstructured data such as emails, presentations, reports containing texts, images, presentation reports, video contents, feedback and much more. These documents help to create knowledge repositories within an organization to make better implicit operations. Live chat, video conferencing, web meeting, chatbot-customer messages, surveillance data are other prominent examples of unstructured data that companies can cultivate to get more insights into the details of a person. Some prominent examples of unstructured data used in enterprises and organizations are: Reports and documents, like Word files or PDF files Multimedia files, such as audio, images, designed texts, themes, and videos System logs Medical images Flat files Scanned documents (which are images that hold numbers and text – for example, OCR) Biometric data Unstructured Data Analytics Tools  You might be wondering what tools can come into use to gather and analyze information that does not have a predefined structure or model. Various tools and programming languages use structured and unstructured data for machine learning and data analysis. These are: Tableau MonkeyLearn Apache Spark SAS Python MS. Excel RapidMiner KNIME QlikView Python programming R programming Many cloud services (like Amazon AWS, Microsoft Azure, IBM Cloud, Google Cloud) also offer unstructured data analysis solutions bundled with their services. How to analyze unstructured data? In the past, the process of storage and analysis of unstructured data was not well defined. Enterprises used to carry out this kind of analysis manually. But with the advent of modern tools and programming languages, most of the unstructured data analysis methods became highly advanced. AI-powered tools use algorithms designed precisely to help to break down unstructured data for analysis. Unstructured data analytics tools, along with Natural language processing (NLP) and machine learning algorithms, help advanced software tools analyze and extract analytical data from the unstructured datasets. Before using these tools for analyzing unstructured data, you must properly go through a few steps and keep these points in mind. Set a clear goal for analyzing the data: It is essential to clear your intention about what insights you want to extract from your unstructured data. Knowing this will help you distinguish what type of data you are planning to accumulate. Collect relevant data: Unstructured data is available everywhere, whether it's a social media platform, online feedback or reviews, or a survey form. Depending on the previous point, that is your goal - you have to be precise about what data you want to collect in real-time. Also, keep in mind whether your collected details are relevant or not. Clean your data: Data cleaning or data cleansing is a significant process to detect corrupt or irrelevant data from the dataset, followed by modifying or deleting the coarse and sloppy data. This phase is also known as the data-preprocessing phase, where you have to reduce the noise, carry out data slicing for meaningful representation, and remove unnecessary data. Use Technology and tools: Once you perform the data cleaning, it is time to utilize unstructured data analysis tools to prepare and cultivate the insights from your data. Technologies used for unstructured data storage (NoSQL) can help in managing your flow of data. Other tools and programming libraries like Tableau, Matplotlib, Pandas, and Google Data Studio allows us to extract and visualize unstructured data. Data can be visualized and presented in the form of compelling graphs, plots, and charts. How to Extract information from Unstructured Data? With the growth in digitization during the information era, repetitious transactions in data cause data flooding. The exponential accretion in the speed of digital data creation has brought a whole new domain of understanding user interaction with the online world. According to Gartner, 80% of the data created by an organization or its application is unstructured. While extracting exact information through appropriate analysis of organized data is not yet possible, even obtaining a decent sense of this unstructured data is quite tough. Until now, there are no perfect tools to analyze unstructured data. But algorithms and tools designed using machine learning, Natural language processing, Deep learning, and Graph Analysis (a mathematical method for estimating graph structures) help us to get the upper hand in extracting information from unstructured data. Other neural network models like modern linguistic models follow unsupervised learning techniques to gain a good 'knowledge' about the unstructured dataset before going into a specific supervised learning step. AI-based algorithms and technologies are capable enough to extract keywords, locations, phone numbers, analyze image meaning (through digital image processing). We can then understand what to evaluate and identify information that is essential to your business. ConclusionUnstructured data is found abundantly from sources like documents, records, emails, social media posts, feedbacks, call-records, log-in session data, video, audio, and images. Manually analyzing unstructured data is very time-consuming and can be very boring at the same time. With the growth of data science and machine learning algorithms and models, it has become easy to gather and analyze insights from unstructured information.  According to some research, data analytics tools like MonkeyLearn Studio, Tableau, RapidMiner help analyze unstructured data 1200x faster than the manual approach. Analyzing such data will help you learn more about your customers as well as competitors. Text analysis software, along with machine learning models, will help you dig deep into such datasets and make you gain an in-depth understanding of the overall scenario with fine-grained analyses.
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Role of Unstructured Data in Data Science

Data has become the new game changer for busines... Read More

What Is Statistical Analysis and Its Business Applications?

Statistics is a science concerned with collection, analysis, interpretation, and presentation of data. In Statistics, we generally want to study a population. You may consider a population as a collection of things, persons, or objects under experiment or study. It is usually not possible to gain access to all of the information from the entire population due to logistical reasons. So, when we want to study a population, we generally select a sample. In sampling, we select a portion (or subset) of the larger population and then study the portion (or the sample) to learn about the population. Data is the result of sampling from a population.Major ClassificationThere are two basic branches of Statistics – Descriptive and Inferential statistics. Let us understand the two branches in brief. Descriptive statistics Descriptive statistics involves organizing and summarizing the data for better and easier understanding. Unlike Inferential statistics, Descriptive statistics seeks to describe the data, however, it does not attempt to draw inferences from the sample to the whole population. We simply describe the data in a sample. It is not developed on the basis of probability unlike Inferential statistics. Descriptive statistics is further broken into two categories – Measure of Central Tendency and Measures of Variability. Inferential statisticsInferential statistics is the method of estimating the population parameter based on the sample information. It applies dimensions from sample groups in an experiment to contrast the conduct group and make overviews on the large population sample. Please note that the inferential statistics are effective and valuable only when examining each member of the group is difficult. Let us understand Descriptive and Inferential statistics with the help of an example. Task – Suppose, you need to calculate the score of the players who scored a century in a cricket tournament.  Solution: Using Descriptive statistics you can get the desired results.   Task – Now, you need the overall score of the players who scored a century in the cricket tournament.  Solution: Applying the knowledge of Inferential statistics will help you in getting your desired results.  Top Five Considerations for Statistical Data AnalysisData can be messy. Even a small blunder may cost you a fortune. Therefore, special care when working with statistical data is of utmost importance. Here are a few key takeaways you must consider to minimize errors and improve accuracy. Define the purpose and determine the location where the publication will take place.  Understand the assets to undertake the investigation. Understand the individual capability of appropriately managing and understanding the analysis.  Determine whether there is a need to repeat the process.  Know the expectation of the individuals evaluating reviewing, committee, and supervision. Statistics and ParametersDetermining the sample size requires understanding statistics and parameters. The two being very closely related are often confused and sometimes hard to distinguish.  StatisticsA statistic is merely a portion of a target sample. It refers to the measure of the values calculated from the population.  A parameter is a fixed and unknown numerical value used for describing the entire population. The most commonly used parameters are: Mean Median Mode Mean :  The mean is the average or the most common value in a data sample or a population. It is also referred to as the expected value. Formula: Sum of the total number of observations/the number of observations. Experimental data set: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20  Calculating mean:   (2 + 4 + 6 + 8 + 10 + 12 + 14 + 16 + 18 + 20)/10  = 110/10   = 11 Median:  In statistics, the median is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution. It’s the mid-value obtained by arranging the data in increasing order or descending order. Formula:  Let n be the data set (increasing order) When data set is odd: Median = n+1/2th term Case-I: (n is odd)  Experimental data set = 1, 2, 3, 4, 5  Median (n = 5) = [(5 +1)/2]th term      = 6/2 term       = 3rd term   Therefore, the median is 3 When data set is even: Median = [n/2th + (n/2 + 1)th] /2 Case-II: (n is even)  Experimental data set = 1, 2, 3, 4, 5, 6   Median (n = 6) = [n/2th + (n/2 + 1)th]/2     = ( 6/2th + (6/2 +1)th]/2     = (3rd + 4th)/2      = (3 + 4)/2      = 7/2      = 3.5  Therefore, the median is 3.5 Mode: The mode is the value that appears most often in a set of data or a population. Experimental data set= 1, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4,4,5, 6  Mode = 3 (Since 3 is the most repeated element in the sequence.) Terms Used to Describe DataWhen working with data, you will need to search, inspect, and characterize them. To understand the data in a tech-savvy and straightforward way, we use a few statistical terms to denote them individually or in groups.  The most frequently used terms used to describe data include data point, quantitative variables, indicator, statistic, time-series data, variable, data aggregation, time series, dataset, and database. Let us define each one of them in brief: Data points: These are the numerical files formed and organized for interpretations. Quantitative variables: These variables present the information in digit form.  Indicator: An indicator explains the action of a community's social-economic surroundings.  Time-series data: The time-series defines the sequential data.  Data aggregation: A group of data points and data set. Database: A group of arranged information for examination and recovery.  Time-series: A set of measures of a variable documented over a specified time. Step-by-Step Statistical Analysis ProcessThe statistical analysis process involves five steps followed one after another. Step 1: Design the study and find the population of the study. Step 2: Collect data as samples. Step 3: Describe the data in the sample. Step 4: Make inferences with the help of samples and calculations Step 5: Take action Data distributionData distribution is an entry that displays entire imaginable readings of data. It shows how frequently a value occurs. Distributed data is always in ascending order, charts, and graphs enabling visibility of measurements and frequencies. The distribution function displaying the density of values of reading is known as the probability density function. Percentiles in data distributionA percentile is the reading in a distribution with a specified percentage of clarifications under it.  Let us understand percentiles with the help of an example.  Suppose you have scored 90th percentile on a math test. A basic interpretation is that merely 4-5% of the scores were higher than your scores. Right? The median is 50th percentile because the assumed 50% of the values are higher than the median. Dispersion Dispersion explains the magnitude of distribution readings anticipated for a specific variable and multiple unique statistics like range, variance, and standard deviation. For instance, high values of a data set are widely scattered while small values of data are firmly clustered. Histogram The histogram is a pictorial display that arranges a group of data facts into user detailed ranges. A histogram summarizes a data series into a simple interpreted graphic by obtaining many data facts and combining them into reasonable ranges. It contains a variety of results into columns on the x-axis. The y axis displays percentages of data for each column and is applied to picture data distributions. Bell Curve distribution Bell curve distribution is a pictorial representation of a probability distribution whose fundamental standard deviation obtained from the mean makes the bell, shaped curving. The peak point on the curve symbolizes the maximum likely occasion in a pattern of data. The other possible outcomes are symmetrically dispersed around the mean, making a descending sloping curve on both sides of the peak. The curve breadth is therefore known as the standard deviation. Hypothesis testingHypothesis testing is a process where experts experiment with a theory of a population parameter. It aims to evaluate the credibility of a hypothesis using sample data. The five steps involved in hypothesis testing are:  Identify the no outcome hypothesis.  (A worthless or a no-output hypothesis has no outcome, connection, or dissimilarities amongst many factors.) Identify the alternative hypothesis.  Establish the importance level of the hypothesis.  Estimate the experiment statistic and equivalent P-value. P-value explains the possibility of getting a sample statistic.  Sketch a conclusion to interpret into a report about the alternate hypothesis. Types of variablesA variable is any digit, amount, or feature that is countable or measurable. Simply put, it is a variable characteristic that varies. The six types of variables include the following: Dependent variableA dependent variable has values that vary according to the value of another variable known as the independent variable.  Independent variableAn independent variable on the other side is controllable by experts. Its reports are recorded and equated.  Intervening variableAn intervening variable explicates fundamental relations between variables. Moderator variableA moderator variable upsets the power of the connection between dependent and independent variables.  Control variableA control variable is anything restricted to a research study. The values are constant throughout the experiment. Extraneous variableExtraneous variable refers to the entire variables that are dependent but can upset experimental outcomes. Chi-square testChi-square test records the contrast of a model to actual experimental data. Data is unsystematic, underdone, equally limited, obtained from independent variables, and a sufficient sample. It relates the size of any inconsistencies among the expected outcomes and the actual outcomes, provided with the sample size and the number of variables in the connection. Types of FrequenciesFrequency refers to the number of repetitions of reading in an experiment in a given time. Three types of frequency distribution include the following: Grouped, ungrouped Cumulative, relative Relative cumulative frequency distribution. Features of FrequenciesThe calculation of central tendency and position (median, mean, and mode). The measure of dispersion (range, variance, and standard deviation). Degree of symmetry (skewness). Peakedness (kurtosis). Correlation MatrixThe correlation matrix is a table that shows the correlation coefficients of unique variables. It is a powerful tool that summarises datasets points and picture sequences in the provided data. A correlation matrix includes rows and columns that display variables. Additionally, the correlation matrix exploits in aggregation with other varieties of statistical analysis. Inferential StatisticsInferential statistics use random data samples for demonstration and to create inferences. They are measured when analysis of each individual of a whole group is not likely to happen. Applications of Inferential StatisticsInferential statistics in educational research is not likely to sample the entire population that has summaries. For instance, the aim of an investigation study may be to obtain whether a new method of learning mathematics develops mathematical accomplishment for all students in a class. Marketing organizations: Marketing organizations use inferential statistics to dispute a survey and request inquiries. It is because carrying out surveys for all the individuals about merchandise is not likely. Finance departments: Financial departments apply inferential statistics for expected financial plan and resources expenses, especially when there are several indefinite aspects. However, economists cannot estimate all that use possibility. Economic planning: In economic planning, there are potent methods like index figures, time series investigation, and estimation. Inferential statistics measures national income and its components. It gathers info about revenue, investment, saving, and spending to establish links among them. Key TakeawaysStatistical analysis is the gathering and explanation of data to expose sequences and tendencies.   Two divisions of statistical analysis are statistical and non-statistical analyses.  Descriptive and Inferential statistics are the two main categories of statistical analysis. Descriptive statistics describe data, whereas Inferential statistics equate dissimilarities between the sample groups.  Statistics aims to teach individuals how to use restricted samples to generate intellectual and precise results for a large group.   Mean, median, and mode are the statistical analysis parameters used to measure central tendency.   Conclusion Statistical analysis is the procedure of gathering and examining data to recognize sequences and trends. It uses random samples of data obtained from a population to demonstrate and create inferences on a group. Inferential statistics applies economic planning with potent methods like index figures, time series investigation, and estimation.  Statistical analysis finds its applications in all the major sectors – marketing, finance, economic, operations, and data mining. Statistical analysis aids marketing organizations in disputing a survey and requesting inquiries concerning their merchandise. 
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What Is Statistical Analysis and Its Business Appl...

Statistics is a science concerned with collection,... Read More

Measures of Dispersion: All You Need to Know

What is Dispersion in StatisticsDispersion in statistics is a way of describing how spread out a set of data is. Dispersion is the state of data getting dispersed, stretched, or spread out in different categories. It involves finding the size of distribution values that are expected from the set of data for the specific variable. The statistical meaning of dispersion is “numeric data that is likely to vary at any instance of average value assumption”.Dispersion of data in Statistics helps one to easily understand the dataset by classifying them into their own specific dispersion criteria like variance, standard deviation, and ranging.Dispersion is a set of measures that helps one to determine the quality of data in an objectively quantifiable manner.The measure of dispersion contains almost the same unit as the quantity being measured. There are many Measures of Dispersion found which help us to get more insights into the data: Range Variance Standard Deviation Skewness IQR  Image SourceTypes of Measure of DispersionThe Measure of Dispersion is divided into two main categories and offer ways of measuring the diverse nature of data. It is mainly used in biological statistics. We can easily classify them by checking whether they contain units or not. So as per the above, we can divide the data into two categories which are: Absolute Measure of Dispersion Relative Measure of DispersionAbsolute Measure of DispersionAbsolute Measure of Dispersion is one with units; it has the same unit as the initial dataset. Absolute Measure of Dispersion is expressed in terms of the average of the dispersion quantities like Standard or Mean deviation. The Absolute Measure of Dispersion can be expressed  in units such as Rupees, Centimetre, Marks, kilograms, and other quantities that are measured depending on the situation. Types of Absolute Measure of Dispersion: Range: Range is the measure of the difference between the largest and smallest value of the data variability. The range is the simplest form of Measure of Dispersion. Example: 1,2,3,4,5,6,7 Range = Highest value – Lowest value   = ( 7 – 1 ) = 6 Mean (μ): Mean is calculated as the average of the numbers. To calculate the Mean, add all the outcomes and then divide it with the total number of terms. Example: 1,2,3,4,5,6,7,8 Mean = (sum of all the terms / total number of terms)                = (1 + 2 + 3 + 4 + 5 + 6 + 7 + 8) / 8                = 36 / 8                = 4.5 Variance (σ2): In simple terms, the variance can be calculated by obtaining the sum of the squared distance of each term in the distribution from the Mean, and then dividing this by the total number of the terms in the distribution.  It basically shows how far a number, for example, a student’s mark in an exam, is from the Mean of the entire class. Formula: (σ2) = ∑ ( X − μ)2 / N Standard Deviation: Standard Deviation can be represented as the square root of Variance. To find the standard deviation of any data, you need to find the variance first. Formula: Standard Deviation = √σ Quartile: Quartiles divide the list of numbers or data into quarters. Quartile Deviation: Quartile Deviation is the measure of the difference between the upper and lower quartile. This measure of deviation is also known as interquartile range. Formula: Interquartile Range: Q3 – Q1. Mean deviation: Mean Deviation is also known as an average deviation; it can be computed using the Mean or Median of the data. Mean deviation is represented as the arithmetic deviation of a different item that follows the central tendency. Formula: As mentioned, the Mean Deviation can be calculated using Mean and Median. Mean Deviation using Mean: ∑ | X – M | / N Mean Deviation using Median: ∑ | X – X1 | / N Relative Measure of DispersionRelative Measures of dispersion are the values without units. A relative measure of dispersion is used to compare the distribution of two or more datasets.  The definition of the Relative Measure of Dispersion is the same as the Absolute Measure of Dispersion; the only difference is the measuring quantity.  Types of Relative Measure of Dispersion: Relative Measure of Dispersion is the calculation of the co-efficient of Dispersion, where 2 series are compared, which differ widely in their average.  The main use of the co-efficient of Dispersion is when 2 series with different measurement units are compared.  1. Co-efficient of Range: it is calculated as the ratio of the difference between the largest and smallest terms of the distribution, to the sum of the largest and smallest terms of the distribution.  Formula: L – S / L + S  where L = largest value S= smallest value 2. Co-efficient of Variation: The coefficient of variation is used to compare the 2 data with respect to homogeneity or consistency.  Formula: C.V = (σ / X) 100 X = standard deviation  σ = mean 3. Co-efficient of Standard Deviation: The co-efficient of Standard Deviation is the ratio of standard deviation with the mean of the distribution of terms.  Formula: σ = ( √( X – X1)) / (N - 1) Deviation = ( X – X1)  σ = standard deviation  N= total number  4. Co-efficient of Quartile Deviation: The co-efficient of Quartile Deviation is the ratio of the difference between the upper quartile and the lower quartile to the sum of the upper quartile and lower quartile.  Formula: ( Q3 – Q3) / ( Q3 + Q1) Q3 = Upper Quartile  Q1 = Lower Quartile 5. Co-efficient of Mean Deviation: The co-efficient of Mean Deviation can be computed using the mean or median of the data. Mean Deviation using Mean: ∑ | X – M | / N Mean Deviation using Mean: ∑ | X – X1 | / N Why dispersion is important in a statisticThe knowledge of dispersion is vital in the understanding of statistics. It helps to understand concepts like the diversification of the data, how the data is spread, how it is maintained, and maintaining the data over the central value or central tendency. Moreover, dispersion in statistics provides us with a way to get better insights into data distribution. For example,  3 distinct samples can have the same Mean, Median, or Range but completely different levels of variability. How to Calculate DispersionDispersion can be easily calculated using various dispersion measures, which are already mentioned in the types of Measure of Dispersion described above. Before measuring the data, it is important to understand the diversion of the terms and variation. One can use the following method to calculate the dispersion: Mean Standard deviation Variance Quartile deviation For example, let us consider two datasets: Data A:97,98,99,100,101,102,103  Data B: 70,80,90,100,110,120,130 On calculating the mean and median of the two datasets, both have the same value, which is 100. However, the rest of the dispersion measures are totally different as measured by the above methods.  The range of B is 10 times higher, for instance. How to represent Dispersion in Statistics Dispersion in Statistics can be represented in the form of graphs and pie-charts. Some of the different ways used include: Dot Plots Box Plots Stems Leaf Plots Example: What is the variance of the values 3,8,6,10,12,9,11,10,12,7?  Variation of the values can be calculated using the following formula: (σ2) = ∑ ( X − μ)2 / N (σ2) = 7.36 What is an example of dispersion? One of the examples of dispersion outside the world of statistics is the rainbow- where white light is split into 7 different colours separated via wavelengths.  Some statistical ways of measuring it are- Standard deviation Range Mean absolute difference Median absolute deviation Interquartile change Average deviation Conclusion: Dispersion in statistics refers to the measure of variability of data or terms. Such variability may give random measurement errors where some of the instrumental measurements are found to be imprecise. It is a statistical way of describing how the terms are spread out in different data sets. The more sets of values, the more scattered data is found, and it is always directly proportional. This range of values can vary from 5 - 10 values to 1000 - 10,000 values. This spread of data is described by the range of descriptive range of statistics. The dispersion in statistics can be represented using a Dot Plot, Box Plot, and other different ways. 
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Measures of Dispersion: All You Need to Know

What is Dispersion in StatisticsDispersion in stat... Read More