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Getting Started With Machine Learning With Python: Step by Step Guide

Takeaways from the article This article helps you understand the cases wherein Machine learning can be used, and where it is relevant (and where it is not). It discusses the basic steps involved in a machine learning problem, along with code in Python. It discusses how the data involved in a Machine Learning problem can be visualized using certain Python packages.Machine Learning has remained a hot topic since many years. Many know how to make sense of it, and where it can actually be used. It is not a universal solution to all the challenging problems out there (that are difficult to be solved) in the universe. It can only be used when certain conditions are satisfied. Only then does a problem qualify to be solved using a Machine Learning algorithm. In general, Python is the most preferred language to work with algorithms that involve Machine Learning.  Introduction to Machine Learning Machine Learning, also known as ML in short, is a sub-topic that falls under Artificial Intelligence (AI), to achieve specific goals. ML is the art of understanding or designing an algorithm that can be used to process large or small amounts of data. This algorithm will not explicitly define or set the rules for the machine to learn from the data. The machine learns from the data on its own. There are no ‘if’ or ‘else’ statements to guide the machine.    This is very much similar to how humans learn from their experiences in day-to-day life, how a child learns to ride a bike, how a child learns to read letters, then words, then sentences, and conversations.  Getting started with Machine learning in Python Python has been used to implement machine learning algorithms, since it is open-source, extremely popular and has gained immense support from the community as well. In addition to this, there are loads of packages in Python, and they support usage of machine learning algorithms for a variety of version of Python application.  These algorithms can be implemented in python by calling simple functions and these functions are placed inside classes. In turn, these classes are encapsulated in a module as a package.  The ‘scikit-learn’ package for Python is one of the most popular and has most of the machine learning algorithms pre-implemented, and housed inside packages. To implement an algorithm, the package can be imported (or a specific class from the package can be imported) and it can be bound with the variable or the class object using a dot operator and accessed. In general, to begin implementing any machine learning algorithm, the following steps can serve as a blue-print: Define your problem, and confirm that it can be solved using machine learning (so that it is not a trivial “set of rules” related problem) Prepare the data: In this step, the data needed for this model is collected from various resources. Another way is to generate data using the innumerable functions that are present in Python. In either case, the data has to be cleaned, structured, analysed, and the outliers have to be identified. Also, the data has to be pre-processed so that it is easy for the algorithm to build a model based on the data. Certain irrelevant columns maybe removed, and missing data should be handled.  The data needs to be trained and hyperparameters need to be tuned so as to get better prediction accuracy.  Note: It is understood that the users have Python 3.5 or a higher stable version installed on their workstations before beginning to execute the code in the upcoming sections. Other packages can be installed as and when required.  Where Machine Learning can be used?The simplest place is when there is no prediction or complex data insight needed, it need not be used.  Machine Learning algorithm are built by humans to help understand data better, make predictions etc. When we try to solve a problem, there are certain principles that we hold as a foundation (when dealing with physics- gravity, newton’s law) but algorithms don’t. They are stochastic (random) in nature.  Not all problems that have a large amount of data is suited to work with Machine Learning algorithms. It is important to understand the deterministic nature of problems, and try to avoid solving such problems using Machine Learning.  Machine Learning in PythonLet us jump into a simple problem of linear regression using Machine learning, Linear regression is a simple algorithm that predicts the value of a variable, based on certain other values. There are many variations to Linear Regression that includes Multi-variate regression, etc.   Before jumping into the algorithm, let us understand what linear regression means. ‘Linear’ basically means a straight line, and ‘regression’ which is a part of machine learning, talks about how tasks can be solved without explicitly being programmed.   There are various machine learning algorithms, and Linear Regression is just the beginning to it. This includes supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.Why should Machine Learning be used? Certain task needs intricate detailing, and patterns might not be fully unveiled if manual or simple methods are used to extract patterns. Machine learning, on the other hand, will be able to extract all important, hidden patterns, and work well even when the amount of data increases exponentially. It also becomes easy to improve pattern recognition. It will also be possible to deliver results in a time manner, get deeper and better insights into the data in hand.   The results computed using a Machine Learning algorithm would be more accurate in comparison to traditional methods, and the models build can serve as a foundation for other data as well. There are different classifications in machine learning, depending on various types. The 4 basic classifications are:Supervised learning algorithms Semi-supervised learning algorithms Unsupervised learning algorithms  Reinforcement learning algorithmsMachine learning algorithms can also be classified based on how they learn- on the fly or incrementally, into 2 types:Online learning Batch learningMachine learning algorithms can also be classified based on how they detect patterns- whether they detect patterns in data or compare new data values with previously seen data values:Model-based learning  Instance-based learning Supervised LearningMost popular Easy to understand Easier to implement Gives decent results Expensive, since human intervention is requiredSupervised learning involves human supervision. In real-time, supervision is present in the form of labelled features, feedback loop to the data (insights on whether the machine predicted correctly, and if not, what the correct prediction has to be) and so on.  Once the algorithm is trained on such data, it can predict good outputs with a high accuracy for never-before-seen inputs. Applications of supervised learning:Spam classification: Classifying emails as spam or important.  Face recognition: Detecting faces, mapping them to a specific face in a database of faces. Supervised algorithms can further be classified into two types:Classification algorithms: They classify the given data into one of the given classes or group of data. This basically deals with data grouping/data mapping into specific classes.   Regression algorithms: This deals with fitting the data to a given model, predicting continuous or discrete values.   Semi-supervised LearningIn between the supervised and unsupervised learning algorithms.   Created to bridge the gap between dealing with fully structured and fully unstructured data.   Comes between supervised and unsupervised algorithms.   Input is a combination of unlabelled (more) and labelled (less) data.Applications of semi-supervised learning algorithms:Speech analysis, sentiment analysis Content classificationUnsupervised LearningNo data labelling No human intervention May not be very accurate Can’t be applied to a broad variety of situations Algorithm has to figure out how and what to learn from the data Similar to real-world unstructured data Can’t be applied to a broad variety of situationsApplications of unsupervised learning:Clustering Anomaly detectionUnsupervised data can be classified into two categories:Clustering algorithms Association algorithmsReinforcement LearningIt is a ‘punish and reward’ mechanism. Learns from surrounding and experience. An agent decides the next relevant step to arrive at the desired result.   If algorithm learns correctly, then it is rewarded indicating that it is on the right path. If the algorithm made a mistake, it is punished to indicate the mistake and to learn from it.Supervised learning algorithm is different from reinforcement, since the former has a comparable value, whereas the latter has to decide the next action and take it and bear the result and learn from it.Applications of reinforcement learning:Robotics in automation   Machine learning and data processingOther types of learning algorithmsOnline learning Batch learning: It has two different categories: Model-based learning, and instance-based learningOnline LearningAlso known as incremental/out of the core learning. Assumption is that the learning environment changes constantly.Machine learning models that are trained consistently and constantly on new data to predict output. On the other hand, during this period, the model is getting trained on new data in real time. Whenever the model sees a new example, it quickly has to learn from it and adapt to it. This way, even the newly learnt example will be a part of the trained model, and will be a part of giving the prediction/output.Batch LearningThis is also known as data learning in a group.  Data is grouped/classified into different batches.  There batches are used to extract different patterns since every batch would be considerably different from the other one. These patterns are learned by the model in time.  Model-based learningThe specifications associated with a problem in a domain is converted into a model-format. When this model sees new data, it detects patterns from it, and these patterns are used to make predictions on the newly seen data.   Instance-based learningIt is the simplest form of clustering and regression algorithms.They either result in grouping the algorithm into different classes (due to classification) or give continuous or discrete values as output (due to linear or logistic regression).Classification and regression is based on how similar or different the queries are, with respect to the values in the data.Linear RegressionIn this algorithm, we will understand the problems with two different variables in hand- one is an independent variable, and the other one- a dependant variable. We will take a basic problem of finding prices of a house when its area is given. Assume that we have the below dataset:Price of house (independent value)Area of the house (dependant value)356500 sq m5781000 sq m8901500 sq m13002000 sq m18002500 sq m?3000 sq mWhen the above data is given, and the price of house is asked to be found (see last row), given the area of the house, simple linear regression (that gives a decent amount of accuracy) can be used. Below is how the data will look when plotted on a graph. It yields an almost straight line, which means the dependant value depends on the independent value, i.e the area of the house matters when the price of the house is being fixed.The basic steps involved in a machine learning problem-  Identify the problem: see if it qualifies to be solved using a Machine Learning algorithm.  Gather the data: The data required can either be collected from a single source or various source, or it could be generated randomly (if it is for a specific purpose) using certain formulas and methods.  Data cleaning: The data gathered may not be clean or structured, make sure it is cleaned, and in a structured or at least semi-structured format.  Package installation: Install the packages that are required to work with the data.  Data loading: Load the data into the Python environment using any IDE (Usually, Spyder is preferred). This is done so that the machine learning algorithm can access the data and perform the operations.  Data cleaning: Data can be cleaned after it has been placed in the Python environment using certain packages and methods, or it can be cleaned before (manually or by applying some logic).  Summarize the data: Understand the terms we are looking at, perform some operations on them, get the type of value, mean, median, variance, and standard deviation, which are insights into the data. This can be done easily by importing packages that have these functions. Data training: In this step, the input dataset is trained by passing it as parameter to the respective algorithm. This is done so that it can predict the output for the not-ever-seen data also known as testing dataset.  Linear Regression application: Apply the Linear Regression algorithm to this data. Data visualization: The data that has interacted with the linear regression algorithm is visualized using many Python packages. Prediction: The predictions are made with the help of the data trained, and are then displayed on the console. Code for Linear Regression using Python Code to implement linear regression using Python  import numpy as np  import matplotlib.pyplot as plt  from sklearn.metrics import mean_squared_error, r2_score  from sklearn.linear_model import LinearRegression    #Random data set generated  np.random.seed(0)  x_dep = np.random.rand(100, 1)  y_indep = 5.89 + (2.45)* x_dep + np.random.rand(100, 1)    #The model is initialized using LinearRegression that is present in the scikit-learn package  model_of_regression = LinearRegression()    #The data is fit on the model, with the help of training  model_of_regression.fit(x_dep, y_indep)    #The output is predicted   predicted_y_val = model_of_regression.predict(x_dep)    #The model built is evaluated using mean squared error parameter  rmse = mean_squared_error(y_indep, predicted_y_val)    r2 = r2_score(y_indep, predicted_y_val)    print("The value of slope is: ", model_of_regression.coef_)  print("The intercept value is: ", model_of_regression.intercept_)  print("The Root Mean Squared Error value (RMSE) is: ", rmse)    #The data is visualized usign the matplotlib library  plt.scatter(x_dep, y_indep, s=8)  plt.xlabel('X-axis')  plt.ylabel('Y-axis')    #The values are predicted and plotted on a graph and displayed on the screen  plt.plot(x_dep, predicted_y_val, color='r')  plt.show() Output:Code review-Explanation of every step  The required packages are imported using the ‘import’ keyword.  Make sure that ‘scikit-learn’ package is installed before working on this code.  Instead of using precooked data, we are generating data here, using the ‘random’ function.  A seed is defined, and a formula is created that assumes random values for variables and generates random data.  The ‘LinearRegression’ function, present in the ‘scikit-learn’ package is initiated so as to create a model, and one of the functions inside the LinearRegression package-namely ‘fit’ is called by passing the dependant and the independent values.  The ‘predict’ function from the LinearRegression is used to predict the value that is not known for a given independent value. After the model is built with the data, it is important to see how it has fared.  Hence, an attribute named RMSE (Root Mean Squared Error) is used to see the difference between the value that had to actually be predicted and the value that was predicted.  Next, the data is visualized on the screen using a package named ‘matplotlib’.  ConclusionIn all, Machine Learning is a game changer when it comes to identifying its use cases, and applying the right kind of algorithm in the right place, with the right amount of data, and right computational resources and power. Linear Regression is just a simple algorithm of where Machine Learning begins to show its aspects. Usually, the Python language is used to implement Machine Learning algorithms, but other new languages could also be used.  

Getting Started With Machine Learning With Python: Step by Step Guide

1K
  • by Amit Diwan
  • 05th Sep, 2020
  • Last updated on 16th Mar, 2021
  • 14 mins read
Getting Started With Machine Learning With Python: Step by Step Guide

Takeaways from the article 

  • This article helps you understand the cases wherein Machine learning can be used, and where it is relevant (and where it is not). 
  • It discusses the basic steps involved in a machine learning problem, along with code in Python. 
  • It discusses how the data involved in a Machine Learning problem can be visualized using certain Python packages.

Machine Learning has remained a hot topic since many years. Many know how to make sense of it, and where it can actually be used. It is not a universal solution to all the challenging problems out there (that are difficult to be solved) in the universe. It can only be used when certain conditions are satisfied. Only then does a problem qualify to be solved using a Machine Learning algorithm. In general, Python is the most preferred language to work with algorithms that involve Machine Learning 

Introduction to Machine Learning 

Machine Learningalso known as ML in short, is a sub-topic that falls under Artificial Intelligence (AI), to achieve specific goals. ML is the art of understanding or designing an algorithm that can be used to process large or small amounts of data. This algorithm will not explicitly define or set the rules for the machine to learn from the data. The machine learns from the data on its own. There are no ‘if’ or ‘else’ statements to guide the machine.    

This is very much similar to how humans learn from their experiences in day-to-day life, how a child learns to ride a bike, how a child learns to read letters, then words, then sentences, and conversations.  

Getting started with Machine learning in Python 

Python has been used to implement machine learning algorithms, since it is open-source, extremely popular and has gained immense support from the community as well. In addition to this, there are loads of packages in Python, and they support usage of machine learning algorithms for a variety of version of Python application.  

These algorithms can be implemented in python by calling simple functions and these functions are placed inside classes. In turn, these classes are encapsulated in a module as a package.  

The ‘scikit-learn’ package for Python is one of the most popular and has most of the machine learning algorithms pre-implemented, and housed inside packages. To implement an algorithm, the package can be imported (or a specific class from the package can be imported) and it can be bound with the variable or the class object using a dot operator and accessed. In general, to begin implementing any machine learning algorithm, the following steps can serve as a blue-print: 

Define your problem, and confirm that it can be solved using machine learning (so that it is not a trivial “set of rules” related problem) 

Prepare the data: In this step, the data needed for this model is collected from various resources. Another way is to generate data using the innumerable functions that are present in Python. In either case, the data has to be cleaned, structured, analysed, and the outliers have to be identified. Also, the data has to be pre-processed so that it is easy for the algorithm to build a model based on the data. Certain irrelevant columns maybe removed, and missing data should be handled.  

The data needs to be trained and hyperparameters need to be tuned so as to get better prediction accuracy.  

Note: It is understood that the users have Python 3.5 or a higher stable version installed on their workstations before beginning to execute the code in the upcoming sections. Other packages can be installed as and when required.  

Where Machine Learning can be used?

  • The simplest place is when there is no prediction or complex data insight needed, it need not be used.  
  • Machine Learning algorithm are built by humans to help understand data better, make predictions etc. When we try to solve a problem, there are certain principles that we hold as a foundation (when dealing with physics- gravity, newton’s law) but algorithms don’t. They are stochastic (random) in nature.  
  • Not all problems that have a large amount of data is suited to work with Machine Learning algorithms. It is important to understand the deterministic nature of problems, and try to avoid solving such problems using Machine Learning.  

Machine Learning in Python

Let us jump into a simple problem of linear regression using Machine learning, Linear regression is a simple algorithm that predicts the value of a variable, based on certain other values. There are many variations to Linear Regression that includes Multi-variate regression, etc.  

Before jumping into the algorithm, let us understand what linear regression means. ‘Linear’ basically means a straight line, and ‘regression’ which is a part of machine learning, talks about how tasks can be solved without explicitly being programmed.  

There are various machine learning algorithms, and Linear Regression is just the beginning to it. This includes supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.

Why should Machine Learning be used? 

Certain task needs intricate detailing, and patterns might not be fully unveiled if manual or simple methods are used to extract patterns. Machine learning, on the other hand, will be able to extract all important, hidden patterns, and work well even when the amount of data increases exponentially. It also becomes easy to improve pattern recognition. It will also be possible to deliver results in a time manner, get deeper and better insights into the data in hand.  

The results computed using a Machine Learning algorithm would be more accurate in comparison to traditional methods, and the models build can serve as a foundation for other data as well. There are different classifications in machine learning, depending on various types. The 4 basic classifications are:

  • Supervised learning algorithms
  • Semi-supervised learning algorithms
  • Unsupervised learning algorithms 
  • Reinforcement learning algorithms

Machine learning algorithms can also be classified based on how they learn- on the fly or incrementally, into 2 types:

  • Online learning
  • Batch learning

Machine learning algorithms can also be classified based on how they detect patterns- whether they detect patterns in data or compare new data values with previously seen data values:

  • Model-based learning 
  • Instance-based learning 

Supervised Learning

  • Most popular
  • Easy to understand
  • Easier to implement
  • Gives decent results
  • Expensive, since human intervention is required

Supervised learning involves human supervision. In real-time, supervision is present in the form of labelled features, feedback loop to the data (insights on whether the machine predicted correctly, and if not, what the correct prediction has to be) and so on. 

Once the algorithm is trained on such data, it can predict good outputs with a high accuracy for never-before-seen inputs. 

Applications of supervised learning:

  • Spam classification: Classifying emails as spam or important. 
  • Face recognition: Detecting faces, mapping them to a specific face in a database of faces. 

Supervised algorithms can further be classified into two types:

  1. Classification algorithms: They classify the given data into one of the given classes or group of data. This basically deals with data grouping/data mapping into specific classes.  
  2. Regression algorithms: This deals with fitting the data to a given model, predicting continuous or discrete values.   

Semi-supervised Learning

  • In between the supervised and unsupervised learning algorithms.  
  • Created to bridge the gap between dealing with fully structured and fully unstructured data.  
  • Comes between supervised and unsupervised algorithms.  
  • Input is a combination of unlabelled (more) and labelled (less) data.

Applications of semi-supervised learning algorithms:

  • Speech analysis, sentiment analysis
  • Content classification

Unsupervised Learning

  • No data labelling
  • No human intervention
  • May not be very accurate
  • Can’t be applied to a broad variety of situations
  • Algorithm has to figure out how and what to learn from the data
  • Similar to real-world unstructured data
  • Can’t be applied to a broad variety of situations

Applications of unsupervised learning:

  • Clustering
  • Anomaly detection

Unsupervised data can be classified into two categories:

  • Clustering algorithms
  • Association algorithms

Reinforcement Learning

  • It is a ‘punish and reward’ mechanism.
  • Learns from surrounding and experience.
  • An agent decides the next relevant step to arrive at the desired result.  
  • If algorithm learns correctly, then it is rewarded indicating that it is on the right path.
  • If the algorithm made a mistake, it is punished to indicate the mistake and to learn from it.

Supervised learning algorithm is different from reinforcement, since the former has a comparable value, whereas the latter has to decide the next action and take it and bear the result and learn from it.

Applications of reinforcement learning:

  • Robotics in automation   
  • Machine learning and data processing

Other types of learning algorithms

  • Online learning
  • Batch learning: It has two different categories: Model-based learning, and instance-based learning

Online Learning

  • Also known as incremental/out of the core learning.
  • Assumption is that the learning environment changes constantly.

Machine learning models that are trained consistently and constantly on new data to predict output. On the other hand, during this period, the model is getting trained on new data in real time. Whenever the model sees a new example, it quickly has to learn from it and adapt to it. This way, even the newly learnt example will be a part of the trained model, and will be a part of giving the prediction/output.

Batch Learning

This is also known as data learning in a group 

Data is grouped/classified into different batches.  

There batches are used to extract different patterns since every batch would be considerably different from the other one. These patterns are learned by the model in time 

Model-based learning

The specifications associated with a problem in a domain is converted into a model-format. When this model sees new data, it detects patterns from it, and these patterns are used to make predictions on the newly seen data.   

Instance-based learning

It is the simplest form of clustering and regression algorithms.

They either result in grouping the algorithm into different classes (due to classification) or give continuous or discrete values as output (due to linear or logistic regression).

Classification and regression is based on how similar or different the queries are, with respect to the values in the data.

Linear Regression

In this algorithm, we will understand the problems with two different variables in hand- one is an independent variable, and the other one- a dependant variable. We will take a basic problem of finding prices of a house when its area is given. Assume that we have the below dataset:

Price of house (independent value)Area of the house (dependant value)
356500 sq m
5781000 sq m
8901500 sq m
13002000 sq m
18002500 sq m
?3000 sq m

When the above data is given, and the price of house is asked to be found (see last row), given the area of the house, simple linear regression (that gives a decent amount of accuracy) can be used. Below is how the data will look when plotted on a graph. It yields an almost straight line, which means the dependant value depends on the independent value, i.e the area of the house matters when the price of the house is being fixed.

Getting started with Machine Learning with Python: Step by step guide

The basic steps involved in a machine learning problem-  

  • Identify the problem: see if it qualifies to be solved using a Machine Learning algorithm.  
  • Gather the data: The data required can either be collected from a single source or various source, or it could be generated randomly (if it is for a specific purpose) using certain formulas and methods.  
  • Data cleaning: The data gathered may not be clean or structured, make sure it is cleaned, and in a structured or at least semi-structured format.  
  • Package installation: Install the packages that are required to work with the data.  
  • Data loading: Load the data into the Python environment using any IDE (Usually, Spyder is preferred)This is done so that the machine learning algorithm can access the data and perform the operations.  
  • Data cleaning: Data can be cleaned after it has been placed in the Python environment using certain packages and methods, or it can be cleaned before (manually or by applying some logic).  
  • Summarize the data: Understand the terms we are looking at, perform some operations on them, get the type of value, mean, median, variance, and standard deviation, which are insights into the data. This can be done easily by importing packages that have these functions. 
  • Data training: In this step, the input dataset is trained by passing it as parameter to the respective algorithm. This is done so that it can predict the output for the not-ever-seen data also known as testing dataset 
  • Linear Regression application: Apply the Linear Regression algorithm to this data. 
  • Data visualization: The data that has interacted with the linear regression algorithm is visualized using many Python packages. 
  • Prediction: The predictions are made with the help of the data trained, and are then displayed on the console. Code for Linear Regression using Python 

Code to implement linear regression using Python  

import numpy as np 
import matplotlib.pyplot as plt 
from sklearn.metrics import mean_squared_error, r2_score 
from sklearn.linear_model import LinearRegression 
 
#Random data set generated 
np.random.seed(0) 
x_dep = np.random.rand(100, 1) 
y_indep = 5.89 + (2.45)* x_dep + np.random.rand(100, 1) 
 
#The model is initialized using LinearRegression that is present in the scikit-learn package 
model_of_regression = LinearRegression() 
 
#The data is fit on the model, with the help of training 
model_of_regression.fit(x_depy_indep) 
 
#The output is predicted  
predicted_y_val = model_of_regression.predict(x_dep) 
 
#The model built is evaluated using mean squared error parameter 
rmse = mean_squared_error(y_indeppredicted_y_val) 
 
r2 = r2_score(y_indeppredicted_y_val) 
 
print("The value of slope is: ", model_of_regression.coef_) 
print("The intercept value is: ", model_of_regression.intercept_) 
print("The Root Mean Squared Error value (RMSE) is: ", rmse) 
 
#The data is visualized usign the matplotlib library 
plt.scatter(x_depy_indep, s=8) 
plt.xlabel('X-axis') 
plt.ylabel('Y-axis') 
 
#The values are predicted and plotted on a graph and displayed on the screen 
plt.plot(x_deppredicted_y_valcolor='r') 
plt.show() 

Output:

Getting started with Machine Learning with Python: Step by step guide

Code review-Explanation of every step  

  • The required packages are imported using the ‘import’ keyword.  
  • Make sure that ‘scikit-learn’ package is installed before working on this code.  
  • Instead of using precooked data, we are generating data here, using the ‘random’ function.  
  • A seed is defined, and a formula is created that assumes random values for variables and generates random data.  
  • The ‘LinearRegression’ function, present in the scikit-learn package is initiated so as to create a model, and one of the functions inside the LinearRegression package-namely ‘fit’ is called by passing the dependant and the independent values.  
  • The ‘predict’ function from the LinearRegression is used to predict the value that is not known for a given independent value. 
  • After the model is built with the data, it is important to see how it has fared.  
  • Hence, an attribute named RMSE (Root Mean Squared Error) is used to see the difference between the value that had to actually be predicted and the value that was predicted.  
  • Next, the data is visualized on the screen using a package named ‘matplotlib’.  

Conclusion

In all, Machine Learning is a game changer when it comes to identifying its use cases, and applying the right kind of algorithm in the right place, with the right amount of data, and right computational resources and power. Linear Regression is just a simple algorithm of where Machine Learning begins to show its aspects. Usually, the Python language is used to implement Machine Learning algorithms, but other new languages could also be used.  

Amit

Amit Diwan

Author

Amit Diwan is an E-Learning Entrepreneur, who has taught more than a million professionals with Text & Video Courses on the following technologies: Data Science, AI, ML, C#, Java, Python, Android, WordPress, Drupal, Magento, Bootstrap 4, etc.

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This range of values will have a lower bound and an upper bound, which we call the minimum and the maximum possible values.  Various factors on which plotting of a value depends are standard deviation, mean (or average), skewness, and kurtosis. All of these play a significant role in Data science as well. We can use probability distribution in physics, engineering, finance, data analysis, machine learning, etc. Significance of Probability distributions in Data Science In a way, most of the data science and machine learning operations are dependent on several assumptions about the probability of your data. Probability distribution allows a skilled data analyst to recognize and comprehend patterns from large data sets; that is, otherwise, entirely random variables and values. Thus, it makes probability distribution a toolkit based on which we can summarize a large data set. The density function and distribution techniques can also help in plotting data, thus supporting data analysts to visualize data and extract meaning. General Properties of Probability Distributions Probability distribution determines the likelihood of any outcome. The mathematical expression takes a specific value of x and shows the possibility of a random variable with p(x). Some general properties of the probability distribution are – The total of all probabilities for any possible value becomes equal to 1. In a probability distribution, the possibility of finding any specific value or a range of values must lie between 0 and 1. Probability distributions tell us the dispersal of the values from the random variable. Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution  Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm  import matplotlib.pyplot as mpl  import numpy as np  def normalDist() -> None:      fig, ax = mpl.subplots(1, 1)      mean, var, skew, kurt = norm.stats(moments = 'mvsk')      x = np.linspace(norm.ppf(0.01),  norm.ppf(0.99), 100)      ax.plot(x, norm.pdf(x),          'r-', lw = 5, alpha = 0.6, label = 'norm pdf')      ax.plot(x, norm.cdf(x),          'b-', lw = 5, alpha = 0.6, label = 'norm cdf')      vals = norm.ppf([0.001, 0.5, 0.999])      np.allclose([0.001, 0.5, 0.999], norm.cdf(vals))      r = norm.rvs(size = 1000)      ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2)      ax.legend(loc = 'best', frameon = False)      mpl.show()  normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial.   Probability Mass Function of a Bernoulli’s Distribution is:  where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli  import seaborn as sb    def bernoulliDist():      data_bern = bernoulli.rvs(size=1200, p = 0.7)      ax = sb.distplot(          data_bern,           kde = True,           color = 'g',           hist_kws = {'alpha' : 1},          kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'})      ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution')  bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def uniformDist():      sb.distplot(random.uniform(size = 1200), hist = True)      mpl.show()  uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl  def lognormalDist():      muu, sig = 3, 1      s = np.random.lognormal(muu, sig, 1000)      cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y')      x = np.linspace(min(bins), max(bins), 10000)      calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2))             / (x * sig * np.sqrt(2 * np.pi)))      mpl.plot(x, calc, linewidth = 2.5, color = 'g')      mpl.axis('tight')      mpl.show()  lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np  from matplotlib import pyplot as plt  from scipy.stats import pareto  def paretoDist():      xm = 1.5        alp = [2, 4, 6]       x = np.linspace(0, 4, 800)      output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp])      plt.plot(x, output.T)      plt.show()  paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def expDist():      sb.distplot(random.exponential(size = 1200), hist = True)      mpl.show()   expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb    def binomialDist():      sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal')      sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial')      plt.show()    binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl  def probability_to_occur_at(attempt, probability):      return (1-p)**(attempt - 1) * probability  p = 0.3  attempt = 4  attempts_to_show = range(21)[1:]  print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p))  mpl.xlabel('Number of Trials')  mpl.ylabel('Probability of the Event')  barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show)  barlist[attempt].set_color('g')  mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson  import seaborn as sb  import numpy as np  import matplotlib.pyplot as mpl  def poissonDist():       mpl.figure(figsize = (10, 10))      data_binom = poisson.rvs(mu = 3, size = 5000)      ax = sb.distplot(data_binom, kde=True, color = 'g',                       bins=np.arange(data_binom.min(), data_binom.max() + 1),                       kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'})      ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency')      mpl.show()      poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np  import matplotlib.pyplot as mpl  np.random.seed(99)   n = 12                      pvalue = [0.3, 0.46, 0.22]     s = []  p = []     for size in np.logspace(2, 3):      outcomes = np.random.multinomial(n, pvalue, size=int(size))        prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes)      p.append(prob)      s.append(int(size))  fig1 = mpl.figure()  mpl.plot(s, p, 'o-')  mpl.plot(s, [0.0248]*len(s), '--r')  mpl.grid()  mpl.xlim(xmin = 0)  mpl.xlabel('Number of Events')  mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments.  Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl   import numpy as np   from scipy.stats import nbinom    x = np.linspace(0, 6, 70)   gr, kr = 0.3, 0.7        g = nbinom.ppf(x, gr, kr)   s = nbinom.pmf(x, gr, kr)   mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion  Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course https://www.knowledgehut.com/data-science-courses. 
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Types of Probability Distributions Every Data Scie...

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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

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