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What are the Commonly Used Machine Learning Algorithms?

Machine Learning is a sub-branch of Artificial Intelligence, used for the analysis of data. It learns from the data that is input and predicts the output from the data rather than being explicitly programmed. Machine Learning is among the fastest evolving trends in the IT industry. It has found tremendous use in sectors across industries, with its ability to solve complex problems which humans are not able to solve using traditional techniques. ML is now being used in IT, retail, insurance, government and the military. There is no end to what can be achieved with the right ML algorithm.  Machine Learning is comprised of different types of algorithms, each of which performs a unique task. Users deploy these algorithms based on the problem statement and complexity of the problem they deal with. Generally, ML algorithms are a combination of both “Mathematics and Logic” that help write new algorithms for the problem statements or to implement existing algorithms with slight modifications. If ML algorithms consume more amounts of data while solving a problem, it results in better performance. Thus, Machine Learning works on the strategy of: “The more the data that is fed as input, the better is the performance of the model at the end”  Machine Learning follows a systematic way of solving problems to get the desired results in the end. Initially, it collects data from different sources. Then it is subjected to an Exploratory Data Analysis step to get rid of unwanted data or noise hidden in the data, and to replace or remove Null values from the data to convert it into a structured format. The whole data is subjected to the ML algorithm as input to draw insights and get the desired results at the end of the given problem statement. The accuracy of the model decides the best algorithm for the data we take in at the end.Styles of ML Algorithms Most Machine Learning algorithms are categorized into three types. This categorization is based on the kind of problem the specific algorithm deals with. The categories are:   SUPERVISED   UNSUPERVISED  SEMI – SUPERVISEDIn supervised ML algorithms, the user knows both the Input and Output data before applying any algorithm on the data. In simple terms, this can be said to be an Input-Output pair of data. Here all the data is called training data, and the information consists of labels. The training process of the data is continued till the model achieves the desired accuracy. These sets of algorithms are used to predict the results from the input data.   E.g.: - Classification & Regression algorithms   In unsupervised ML algorithms, the data is not labelled, and the output is also not known. In this case, an ML model is prepared by performing some strategies like Data selection, cleaning, preprocessing, and transformation on the data to get a perfect structure to apply algorithms on the data.  E.g.: - Clustering, Dimensionality Reduction and Association Rule Learning  In semi-supervised ML algorithms, the data consists of both labelled and unlabeled examples. Here the model must learn the structures to organize the data and make predictions from the data at the end.    Overview of Machine Learning Algorithms  Apart from the Learning styles, algorithms are further grouped by similarity. Each group possesses different learning capabilities of algorithms to solve the given problem statements and produce good results at the end. A grouping of machine learning algorithms is basically done using the area of problems they deal with, in order to increase their accuracy and reliability.  ML algorithms are grouped based on whether they are numerical, categorical, boolean, grouping, classification, video, audio, images etc. The grouping brings out a clear picture of the particular type of data taken, that addresses the specific problems faced by the user. The grouping is done to ensure that the accuracy increases while using the algorithms, as compared to the previous ones. A developer can negotiate that his/her model is the best model, when he/she compares the accuracy of his model with that of the other algorithms. This comparison helps us in finding the algorithm that best suits the given problem statement.   In today’s scenario, Machine Learning algorithms have gained great significance in many industries and can be used to advantage in day-to-day life. Machine Learning is a boon for all types of industries, as it solves many difficult problems, and helps to mitigate risk and loss of human power. This technology is used in robotic process automation, satellites, space research centres, underwater areas, self-driving cars, the automotive industry, the health industry and many others.  ML can be used to solve problems of any scale—big or small. Its advantage lies in its intuitive power. The more you teach a machine learning model, the more it learns from the data and the more perfect it becomes, helping you get better output with higher accuracy. ML is now moving towards automation and this potent combination of machine learning and automation can change our lives like we have never imagined.  The application of “Re-engineering” has been evolving at present with Machine Learning. The older applications are complex and require a large amount of code to be written, whereas ML helps make applications with fewer code, written in a universal ML language that can be better understood by developers who can make the application richer by adding more to it. Regression Algorithms It tells the relationship between two variables, like an input variable that takes data into the model with some parameters and predicts the output variable. Regression algorithms are used to predict continuous values.  They are mainly used to predict the linear data in the output, from the input data we take from a user and apply it to the model. These are the simplest algorithms to implement in real-world projects as they deal with Numerical data in preparing the model. In ML, the types of regression algorithms are as follows: Linear Regression Logistic Regression Stepwise Regression Source linkInstance Based Algorithms These algorithms are based on data and training data because the model learns from activities to predict the new cases of data given by the user. They are used for decision-based problems with instances.   Here, the model learns from the training data. The new data is compared against the trained data with a similarity measure in the data to find the best match and make predictions according to the model performance in the data classification. Generally, these types of algorithms build up a database of data and are applied upon large databases. They are also called memory-based algorithms. In machine learning, the various kinds of Instance-Based Algorithms are as follows:   Image SourceRegularization Algorithms These algorithms are used to make slight changes to the model prepared by a developer. Regularization algorithms help to overcome the problem of Over-fitting of data during the training of the model. Over-fitting learns the unwanted areas from the data, its noise, etc., and at the end, it may lead to the wrong prediction in the new data given by the user.  To overcome such types of problems during the development of the model, the regularization concept is used. In machine learning, there are two types of Regularization Algorithms, and they are: L1 Regularization or Lasso Regularization L2 Regularization or Ridge Regularization Elastic Net Image sourceDecision Tree Algorithm These algorithms are best used for decision-based problem solving on the data's attributes and are represented using a tree-like structure consisting of leaves and nodes. It consists of a root node that describes the problem we are dealing with, and it is further split into leaves, with the leaf node indicating the end to the tree. The tree structure is extended with the number of nodes till a perfect prediction is made from the given data using the model.  Decision tree algorithms are best used in classification as well as regression problems. These can operate on both small amounts and large amounts of data. Decisions taken using this algorithm are often fast and accurate. In machine learning the different types of Decision Tree algorithms include Classification and Regression Tree (CART)Decision stump Chi-squared automatic interaction detection (CHAID) Image sourceBayesian Algorithms These types of algorithms are built based on the Bayes theorem. All the algorithms in this category have a probabilistic approach in solving the problems. They tell about the probability of prediction of the outcome at the end using the given data. We can draw probabilistic insights from the data.  These are the most used algorithms in stock predictions or weather predictions, done by taking in a lot of data and examining them to predict the best outcome. They can solve both the classification and regression types of problems. In machine learning, some of the different types of these algorithms are: Naïve Bayes Bayesian Belief Networks Bayesian Networks Gaussian Naïve Bayes Clustering Algorithms Image sourceThe word clustering indicates a grouping of something. Clustering algorithms contain similar types grouped or clustered together with common similarities between the data. The models in this algorithm are generally approached in a Centroid based and a Hierarchal way. They inherit structures from the older structures and find out the new ones. The data is mostly organized into groups, based on the similarity present in the structures. In ML, the different types of clustering algorithms are: K-means clustering Hierarchal clustering K-medians Clustering Image sourceAssociation Rule Learning Algorithms These algorithms explain the relationship between the data, with the help of some rules that are predefined by them. They contain a minimum threshold predefined by the user, and this process is carried out until we get the desired results. This process is held in a sequence of steps satisfying the threshold value given by the user. If the operation fails to satisfy the minimum threshold value, the given function's data is eliminated further. The rules defined by these types of algorithms help to discover commercially useful and important associations among large datasets. In ML, the different types of Association algorithms are:Apriori Algorithm Eclat Algorithm Image sourceArtificial Neural Networks Algorithms Artificial neural network algorithms are similar to the functioning of the neural networks in our brain. Generally, these algorithms fall under the category of Deep Learning, which is a core field in Machine Learning. The ANN also has a little significance in Machine Learning as it can deal with solving the problems related to Classification and Regression. In ML, the different types of algorithms in this category are:Perceptron Back-propagation Stochastic Gradient Descent Multilayer Perceptron etcImage sourceDeep Learning Algorithms They are the core part of Machine Learning algorithms and deal with large amounts of data to get better accuracy and results. These represent an updated version of Artificial Neural Networks, used to perform better computation on the given data. Deep Learning Algorithms consists of various algorithms that can solve many real-time problems, even with huge amounts of data present. These algorithms can be used in Speech Recognition, Video analysis & Recognition, Audio, and in Text formats. The different types of Deep Learning algorithms are: Convolution Neural Networks (CNN)Recurrent Neural Networks (RNN)Long Short-Term memory networks (LSTMs)  Dimensionality Reduction Algorithms These types of algorithms are quite similar to clustering algorithms in inheriting structures from the data. They are Unsupervised machine learning algorithms, and draw insights from data in an Unsupervised manner. They follow a special technique in describing data using lesser information, i.e., they can describe whole data using some pictorial representations, graphs, pie charts etc. Even though it is an Unsupervised ML algorithm, by presenting the data in a visual format we can simplify the tasks and can solve the given problems using a Supervised learning approach. These algorithms are mainly helpful in solving classification and Regression Problems. In ML the algorithms under this category are: Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) Quadratic Discriminant Analysis (QDA)Multidimensional Scaling (MS)  Image sourceEnsemble Algorithms These are unique algorithms from the machine learning perspective. These models comprise multiple weaker models that are independently trained. The predictions given by these weaker models are combined to form an overall prediction from the data. It requires a lot of effort to combine the weak models and form an effective model among the weaker models to generate great accuracy.  These are very popular and powerful algorithms in ML. In real-world scenarios, we use this type of algorithm a lot, as we cannot get good accuracy from the input data. These algorithms are used to boost the accuracy of the model for the better prediction of the given problem compared to other algorithms. In ML the different types of algorithms in this category are: BoostingBagging (Bootstrapped Aggregation) Adaboost Random Forest Gradient Boosting Mechanism Weighted Average (Blending) Image sourcePopularity of Machine Learning Algorithms Machine Learning Algorithms are going to revolutionize our way of life—by helping us solve not just critical problems but offering solutions to our everyday needs.  Almost all industries today use the techniques of ML to find solutions to their problems.  ML algorithms are constantly evolving, by learning and improving themselves. The algorithm’s ability to learn from the data on its own further helps in predicting better outcomes with improved accuracy.  ML deals with many problems related to Audio, Video, Text, Classification of species or items and so on. These algorithms are further extending their applications into the fields of Robotics, Health care, Automation and more. Many industries are getting automated with the use of latest technologies like Machine Learning, Artificial Intelligence, and Data science. The main use of ML algorithms is to predict the outcomes based on the given input data by creating a model that in turn produce better results. Some of the popular applications of ML are found in sales prediction, weather prediction, stocks exchange detection etc. How to study Machine Learning Algorithms Algorithms are at the heart of machine learning. There is no predefined structure or format indicating how you should study algorithms. You should, however, have a clear picture of what the algorithm is, how it works, and where and how it should be implemented.  It is important to know the Mathematics and logic behind the algorithm, as Mathematics is key to the right implementation of the algorithm to solve real-world problems. After learning math concepts, ML enthusiasts must implement the algorithm in a programming language to know how it works. At present PYTHON and R are the two programming languages that are most widely used to implement Machine Learning in the real world. You can refer to websites, blogs, or enrol in trainings, to get a clear picture of the world of ML and algorithms.  How to Run Machine Learning Algorithms To run Machine Learning algorithms, we need to have a platform to write the Script and run the script to see the outcome. We can run ML algorithms in Python IDLE, Jupyter Notebooks, Anaconda, Google Colab, Kaggle etc., but the most widely used tool by many people is Jupyter Notebooks, due to the ease of writing and running code. When writing and running ML algorithms, a series of steps are involved such as importing the required Libraries, loading the dataset from the system or any other sources, defining the model, testing the model, and predicting the outcome with better accuracy.   ConclusionThis blog provides an overview of different types of ML Algorithms, where they are used, how to study the algorithms and how they can be run, the steps involved and so on. We hope this article has helped you to get a basic idea of machine learning, if you want to pursue a career in this field.   

What are the Commonly Used Machine Learning Algorithms?

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What are the Commonly Used Machine Learning Algorithms?

Machine Learning is a sub-branch of Artificial Intelligence, used for the analysis of data. It learns from the data that is input and predicts the output from the data rather than being explicitly programmed. Machine Learning is among the fastest evolving trends in the IindustryIt has found tremendous use in sectors across industries, with its ability to solve complex problems which humans are not able to solve using traditional techniques. ML is now being used in IT, retail, insurance, government and the military. There is no end to what can be achieved with the right ML algorithm 

Machine Learning is comprised of different types of algorithms, each of which performs a unique task. Users deploy these algorithms based on the problem statement and complexity of the problem they deal with. Generally, ML algorithms are a combination of both “Mathematics and Logic” that help write new algorithms for the problem statements or to implement existing algorithms with slight modifications. If ML algorithms consume more amounts of data while solving a problem, it results in better performance. Thus, Machine Learning works on the strategy of: 

The more the data that is fed as inputthe better is the performance of the model at the end  

Machine Learning follows a systematic way of solving problems to get the desired results in the end. Initially, it collects data from different sources. Then it is subjected to an Exploratory Data Analysis step to get rid of unwanted data or noise hidden in the data, and to replace or remove Null values from the data to convert it into a structured format. The whole data is subjected to the ML algorithm as input to draw insights and get the desired results at the end of the given problem statement. The accuracy of the model decides the best algorithm for the data we take in at the end.

Styles of ML Algorithms 

Most Machine Learning algorithms are categorized into three types. This categorization is based on the kind of problem the specific algorithm deals with. The categories are:   

  1. SUPERVISED   
  2. UNSUPERVISED  
  3. SEMI – SUPERVISED

In supervised ML algorithms, the user knows both the Input and Output data before applying any algorithm on the data. In simple terms, this can be said to be an Input-Output pair of data. Here all the data is called training data, and the information consists of labels. The training process of the data is continued till the model achieves the desired accuracy. These sets of algorithms are used to predict the results from the input data.   

E.g.: - Classification & Regression algorithms   

In unsupervised ML algorithms, the data is not labelled, and the output is also not known. In this case, an ML model is prepared by performing some strategies like Data selection, cleaning, preprocessing, and transformation on the data to get a perfect structure to apply algorithms on the data.  

E.g.: - Clustering, Dimensionality Reduction and Association Rule Learning  

In semi-supervised ML algorithms, the data consists of both labelled and unlabeled examples. Here the model must learn the structures to organize the data and make predictions from the data at the end.   

 lustering, Dimensionality Reduction and Association Rule Learning

Overview of Machine Learning Algorithms 

 Apart from the Learning styles, algorithms are further grouped by similarity. Each group possesses different learning capabilities of algorithms to solve the given problem statements and produce good results at the end. A grouping of machine learning algorithms is basically done using the area of problems they deal with, in order to increase their accuracy and reliability.  

ML algorithms are grouped based on whether they are numerical, categorical, boolean, grouping, classification, video, audio, images etc. The grouping brings out a clear picture of the particular type of data taken, that addresses the specific problems faced by the user. The grouping is done to ensure that the accuracy increases while using the algorithms, as compared to the previous ones. A developer can negotiate that his/her model is the best model, when he/she compares the accuracy of his model with that of the other algorithms. This comparison helps us in finding the algorithm that best suits the given problem statement.   

In today’s scenario, Machine Learning algorithms have gained great significance in many industries and can be used to advantage in day-to-day life. Machine Learning is a boon for all types of industries, as it solves many difficult problems, and helps to mitigate risk and loss of human power. This technology is used in robotic process automation, satellites, space research centres, underwater areas, self-driving cars, the automotive industry, the health industry and many others 

ML can be used to solve problems of any scale—big or small. Its advantage lies in its intuitive power. The more you teach a machine learning model, the more it learns from the data and the more perfect it becomes, helping you get better output with higher accuracy. ML is now moving towards automation and this potent combination of machine learning and automation can change our lives like we have never imagined.  

The application of “Re-engineering” has been evolving at present with Machine Learning. The older applications are complex and require a large amount of code to be written, whereas ML helps make applications with fewer code, written in a universal ML language that can be better understood by developers who can make the application richer by adding more to it. 

Regression Algorithms 

It tells the relationship between two variables, like an input variable that takes data into the model with some parameters and predicts the output variable. Regression algorithms are used to predict continuous values.  

They are mainly used to predict the linear data in the output, from the input data we take from a user and apply it to the model. These are the simplest algorithms to implement in real-world projects as they deal with Numerical data in preparing the model. In ML, the types of regression algorithms are as follows: 

  1. Linear Regression 
  2. Logistic Regression 
  3. Stepwise Regression 

Regression Algorithms

Source link

Instance Based Algorithms 

These algorithms are based on data and training data because the model learns from activities to predict the new cases of data given by the user. They are used for decision-based problems with instances.   

Here, the model learns from the training data. The new data is compared against the trained data with a similarity measure in the data to find the best match and make predictions according to the model performance in the data classification. Generally, these types of algorithms build up a database of data and are applied upon large databases. They are also called memory-based algorithms. In machine learning, the various kinds of Instance-Based Algorithms are as follows:   Instance-Based Algorithms

Image Source

Regularization Algorithms 

These algorithms are used to make slight changes to the model prepared by a developer. Regularization algorithms help to overcome the problem of Over-fitting of data during the training of the model. Over-fitting learns the unwanted areas from the data, its noise, etc., and at the end, it may lead to the wrong prediction in the new data given by the user.  To overcome such types of problems during the development of the model, the regularization concept is used. In machine learning, there are two types of Regularization Algorithms, and they are: 

  1. L1 Regularization or Lasso Regularization 
  2. L2 Regularization or Ridge Regularization 
  3. Elastic Net 

Regularization Algorithms

Image source

Decision Tree Algorithm 

These algorithms are best used for decision-based problem solving on the data's attributes and are represented using a tree-like structure consisting of leaves and nodes. It consists of a root node that describes the problem we are dealing with, and it is further split into leaves, with the leaf node indicating the end to the tree. The tree structure is extended with the number of nodes till a perfect prediction is made from the given data using the model.  

Decision tree algorithms are best used in classification as well as regression problems. These can operate on both small amounts and large amounts of data. Decisions taken using this algorithm are often fast and accurate. In machine learning the different types of Decision Tree algorithms include 

  1. Classification and Regression Tree (CART)
  2. Decision stump 
  3. Chi-squared automatic interaction detection (CHAID) 

Decision Tree Algorithms

Image source

Bayesian Algorithms 

Decision Tree Algorithms

These types of algorithms are built based on the Bayes theorem. All the algorithms in this category have a probabilistic approach in solving the problems. They tell about the probability of prediction of the outcome at the end using the given data. We can draw probabilistic insights from the data.  

These are the most used algorithms in stock predictions or weather predictions, done by taking in a lot of data and examining them to predict the best outcome. They can solve both the classification and regression types of problems. In machine learning, some of the different types of these algorithms are: 

  1. Naïve Bayes 
  2. Bayesian Belief Networks 
  3. Bayesian Networks 
  4. Gaussian Naïve Bayes 

Clustering Algorithms 

Clustering Algorithms

Image source

The word clustering indicates a grouping of something. Clustering algorithms contain similar types grouped or clustered together with common similarities between the data. The models in this algorithm are generally approached in a Centroid based and a Hierarchal way. They inherit structures from the older structures and find out the new ones. The data is mostly organized into groups, based on the similarity present in the structures. In ML, the different types of clustering algorithms are: 

  1. K-means clustering 
  2. Hierarchal clustering 
  3. K-medians Clustering 

Clustering Algorithms

Image source

Association Rule Learning Algorithms 

These algorithms explain the relationship between the data, with the help of some rules that are predefined by them. They contain a minimum threshold predefined by the user, and this process is carried out until we get the desired results. This process is held in a sequence of steps satisfying the threshold value given by the user. If the operation fails to satisfy the minimum threshold value, the given function's data is eliminated further. The rules defined by these types of algorithms help to discover commercially useful and important associations among large datasets. In ML, the different types of Association algorithms are:

  1. Apriori Algorithm 
  2. Eclat Algorithm 

Association Rule Learning Algorithms

Image source

Artificial Neural Networks Algorithms 

Artificial neural network algorithms are similar to the functioning of the neural networks in our brain. Generally, these algorithms fall under the category of Deep Learning, which is a core field in Machine Learning. The ANN also has a little significance in Machine Learning as it can deal with solving the problems related to Classification and Regression. In ML, the different types of algorithms in this category are:

  1. Perceptron 
  2. Back-propagation 
  3. Stochastic Gradient Descent 
  4. Multilayer Perceptron etc

Artificial Neural Networks Algorithms

Image source

Deep Learning Algorithms 

They are the core part of Machine Learning algorithms and deal with large amounts of data to get better accuracy and results. These represent an updated version of Artificial Neural Networks, used to perform better computation on the given data. Deep Learning Algorithms consists of various algorithms that can solve many real-time problems, even with huge amounts of data present. These algorithms can be used in Speech Recognition, Video analysis & Recognition, Audio, and in Text formats. The different types of Deep Learning algorithms are: 

  1. Convolution Neural Networks (CNN)
  2. Recurrent Neural Networks (RNN)
  3. Long Short-Term memory networks (LSTMs)  

Dimensionality Reduction Algorithms 

These types of algorithms are quite similar to clustering algorithms in inheriting structures from the data. They are Unsupervised machine learning algorithms, and draw insights from data in an Unsupervised manner. They follow a special technique in describing data using lesser information, i.e., they can describe whole data using some pictorial representations, graphs, pie charts etc. 

Even though it is an Unsupervised ML algorithm, by presenting the data in a visual format we can simplify the tasks and can solve the given problems using Supervised learning approach. These algorithms are mainly helpful in solving classification and Regression Problems. In ML the algorithms under this category are: 

  1. Principal Component Analysis (PCA) 
  2. Linear Discriminant Analysis (LDA) 
  3. Quadratic Discriminant Analysis (QDA)
  4. Multidimensional Scaling (MS)  

Dimensionality Reduction Algorithms

Image source

Ensemble Algorithms 

These are unique algorithms from the machine learning perspective. These models comprise multiple weaker models that are independently trained. The predictions given by these weaker models are combined to form an overall prediction from the data. It requires a lot of effort to combine the weak models and form an effective model among the weaker models to generate great accuracy.  

These are very popular and powerful algorithms in ML. In real-world scenarios, we use this type of algorithm a lot, as we cannot get good accuracy from the input data. These algorithms are used to boost the accuracy of the model for the better prediction of the given problem compared to other algorithms. In ML the different types of algorithms in this category are: 

  1. Boosting
  2. Bagging (Bootstrapped Aggregation) 
  3. Adaboost 
  4. Random Forest 
  5. Gradient Boosting Mechanism 
  6. Weighted Average (Blending) 

Ensemble Algorithms

Image source

Popularity of Machine Learning Algorithms 

Machine Learning Algorithms are going to revolutionize our way of life—by helping us solve not just critical problems but offering solutions to our everyday needs.  Almost all industries today use the techniques of ML to find solutions to their problems.  ML algorithms are constantly evolvingby learning and improving themselves. The algorithm’s ability to learn from the data on its own further helps in predicting better outcomes with improved accuracy.  

ML deals with many problems related to Audio, Video, Text, Classification of species or items and so on. These algorithms are further extending their applications into the fields of Robotics, Health care, Automation and moreMany industries are getting automated with the use of latest technologies like Machine Learning, Artificial Intelligenceand Data science. The main use of ML algorithms is to predict the outcomes based on the given input data by creating a model that in turn produce better results. Some of the popular applications of ML are found in sales prediction, weather prediction, stocks exchange detection etc. 

How to study Machine Learning Algorithms 

Algorithms are at the heart of machine learning. There is no predefined structure or format indicating how you should study algorithms. You should, however, have a clear picture of what the algorithm is, how it works, and where and how it should be implemented.  

It is important to know the Mathematics and logic behind the algorithm, as Mathematics is key to the right implementation of the algorithm to solve real-world problems. After learning math concepts, ML enthusiasts must implement the algorithm in a programming language to know how it works. At present PYTHON and R are the two programming languages that are most widely used to implement Machine Learning in the real world. You can refer to websites, blogs, or enrol in trainings, to get a clear picture of the world of ML and algorithms.  

How to Run Machine Learning Algorithms 

To run Machine Learning algorithms, we need to have a platform to write the Script and run the script to see the outcome. We can run ML algorithms in Python IDLE, Jupyter Notebooks, Anaconda, Google Colab, Kaggle etc., but the most widely used tool by many people is Jupyter Notebooks, due to the ease of writing and running code. When writing and running ML algorithms, a series of steps are involved such as importing the required Libraries, loading the dataset from the system or any other sources, defining the model, testing the model, and predicting the outcome with better accuracy.   

Conclusion

This blog provides an overview of different types of ML Algorithms, where they are used, how to study the algorithms and how they can be run, the steps involved and so on. We hope this article has helped you to get a basic idea of machine learning, if you want to pursue a career in this field  

Harsha

Harsha Vardhan Garlapati

Blog Writer at KnowledgeHut

Harsha Vardhan Garlapati is a Data Science Enthusiast and loves working with data to draw meaningful insights from it and further convert those results and implement them in business growth. He is a final year undergraduate student and passionate about Data Science. He is a smart worker, passionate learner,  an Ice-Breaker and loves to participate in Hackathons to work on real time projects. He is a Toastmaster Member at S.R.K.R Toastmasters Club, a Public Speaker, a good Innovator and problem solver.

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Role of Unstructured Data in Data Science

Data has become the new game changer for businesses. Typically, data scientists categorize data into three broad divisions - structured, semi-structured, and unstructured data. In this article, you will get to know about unstructured data, sources of unstructured data, unstructured data vs. structured data, the use of structured and unstructured data in machine learning, and the difference between structured and unstructured data. Let us first understand what is unstructured data with examples. What is unstructured data? Unstructured data is a kind of data format where there is no organized form or type of data. Videos, texts, images, document files, audio materials, email contents and more are considered to be unstructured data. It is the most copious form of business data, and cannot be stored in a structured database or relational database. Some examples of unstructured data are the photos we post on social media platforms, the tagging we do, the multimedia files we upload, and the documents we share. Seagate predicts that the global data-sphere will expand to 163 zettabytes by 2025, where most of the data will be in the unstructured format. Characteristics of Unstructured DataUnstructured data cannot be organized in a predefined fashion, and is not a homogenous data model. This makes it difficult to manage. Apart from that, these are the other characteristics of unstructured data. You cannot store unstructured data in the form of rows and columns as we do in a database table. Unstructured data is heterogeneous in structure and does not have any specific data model. The creation of such data does not follow any semantics or habits. Due to the lack of any particular sequence or format, it is difficult to manage. Such data does not have an identifiable structure. Sources of Unstructured Data There are various sources of unstructured data. Some of them are: Content websites Social networking sites Online images Memos Reports and research papers Documents, spreadsheets, and presentations Audio mining, chatbots Surveys Feedback systems Advantages of Unstructured Data Unstructured data has become exceptionally easy to store because of MongoDB, Cassandra, or even using JSON. Modern NoSQL databases and software allows data engineers to collect and extract data from various sources. There are numerous benefits that enterprises and businesses can gain from unstructured data. These are: With the advent of unstructured data, we can store data that lacks a proper format or structure. There is no fixed schema or data structure for storing such data, which gives flexibility in storing data of different genres. Unstructured data is much more portable by nature. Unstructured data is scalable and flexible to store. Database systems like MongoDB, Cassandra, etc., can easily handle the heterogeneous properties of unstructured data. Different applications and platforms produce unstructured data that becomes useful in business intelligence, unstructured data analytics, and various other fields. Unstructured data analysis allows finding comprehensive data stories from data like email contents, website information, social media posts, mobile data, cache files and more. Unstructured data, along with data analytics, helps companies improve customer experience. Detection of the taste of consumers and their choices becomes easy because of unstructured data analysis. Disadvantages of Unstructured data Storing and managing unstructured data is difficult because there is no proper structure or schema. Data indexing is also a substantial challenge and hence becomes unclear due to its disorganized nature. Search results from an unstructured dataset are also not accurate because it does not have predefined attributes. Data security is also a challenge due to the heterogeneous form of data. Problems faced and solutions for storing unstructured data. Until recently, it was challenging to store, evaluate, and manage unstructured data. But with the advent of modern data analysis tools, algorithms, CAS (content addressable storage system), and big data technologies, storage and evaluation became easy. Let us first take a look at the various challenges used for storing unstructured data. Storing unstructured data requires a large amount of space. Indexing of unstructured data is a hectic task. Database operations such as deleting and updating become difficult because of the disorganized nature of the data. Storing and managing video, audio, image file, emails, social media data is also challenging. Unstructured data increases the storage cost. For solving such issues, there are some particular approaches. These are: CAS system helps in storing unstructured data efficiently. We can preserve unstructured data in XML format. Developers can store unstructured data in an RDBMS system supporting BLOB. We can convert unstructured data into flexible formats so that evaluating and storage becomes easy. Let us now understand the differences between unstructured data vs. structured data. Unstructured Data Vs. 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

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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...

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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