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Types of Classification in Machine Learning

Takeaways from this article In this post, we understand the concept of classification, regression, classification predictive modelling, and the different types of classification and regression.  We understand why and how classification is important. We also see a few classification algorithms and their implementations in Python.  We understand logistic regression, decision trees, random forests, support vector machines, k nearest neighbour and neural networks. We understand their inner workings and their prominence.Classification refers to the process of classifying the given data set into different classes or groups. The classification algorithm is placed under predictive modelling problem, wherein every class of the dataset is given a label, to indicate that it is different from other classes. Some examples include email classification as spam or not, recognition of a handwritten character as a specific character only, and not another character and so on.   Classification algorithms need data to be trained with many inputs and their respective output, with the help of which the model learns. It is important to understand that the training data must encompass all kinds of data (options) which could be encountered in the test data set or real world. ClassificationThe 4 different prominent types of classification include the following:Binary classification Multi-class classification Multi-label classification Imbalanced classification  Binary classificationAs the name suggests, it deals with the tasks in classification that only have two class labels. Some examples include: email classification as spam or not, whether the price of a stock will go up or go down (ignoring the fact that it could also remain as is), and so on. The value obtained after classifying the data would be either 0 or 1, yes or no, normal or abnormal.  The Bernoulli probability distribution is used as prediction to classify the data as 0 or 1. Bernoulli distribution is a discrete (discontinuous) distribution that gives a binary outcome -- a 0 or a 1. Algorithms that are used to perform binary classification include the following:Logistic regression Decision trees Support vector machine Naïve Bayes ‘k’nn (k nearest neighbors) Code to demonstrate a binary classification task:  from numpy import where  from collections import Counter  from sklearn.datasets import make_blobs  from matplotlib import pyplot  X, y = make_blobs(n_samples=560, centers=2, random_state=1)  print("Data has been generated ")  print("The number of rows and columns are ")  print(X.shape, y.shape)  my_counter = Counter(y)  print(my_counter)  for i in range(10):  print(X[i], y[i])  for my_label, _ in my_counter.items():  row_ix = where(y == my_label)[0]  pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label))  pyplot.legend()  pyplot.show()Output: Data has been generated   The number of rows and columns are   (560, 2) (560,)  Counter({1: 280, 0: 280})  [-9.64384208 -4.14030356] 1  [-0.8821407  4.2877187] 0  … Code explanation The required packages are imported using the ‘import’ function.  The dataset is generated using the ‘make_blobs’ function and by specifying the number of rows and columns that need to be generated.  In addition, the number of classes into which the data points need to be labelled into is also defined. Here, it is 2. The number of rows and columns are displayed along with the summarization of class labelling.  A ‘for’ loop is used to print the first few classified values.  The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.  Multi-class classificationIt is a type of classification wherein the input data set is classified/labelled into more than 2 classes. Some examples of multi-class classification include:Animal species classification Facial recognition/classification Text translation (special type of multi-class classification task) This is different from binary classification in that it doesn’t have just two classes like 0 or 1, but more, and they need not be 0 or 1. They could be names or other continuous or discontinuous numbers. The data points are classified into one among many different classes given.  The number of class labels may be too high, when trying to classify a given photo into that of a specific person. Text translation also deals with a similar issue, wherein the word placement may vary widely and there maybe thousands of combinations of the same number of words. Multinoulli probability distribution is a discrete/discontinuous probability distribution, where the output could be any value within a given range. Algorithms that are used for binary classification can also be used for multi-class classification.  Code to demonstrate the multi-class classification: from numpy import where  from collections import Counter  from sklearn.datasets import make_blobs  from matplotlib import pyplot    X, y = make_blobs(n_samples=670, centers=5, random_state=1)  print("The dataset has been generated")  print("The rows and columns are ")  print(X.shape, y.shape)  my_counter = Counter(y)  print(my_counter)  for i in range(10):  print(X[i], y[i])  for my_label, _ in my_counter.items():  row_ix = where(y == my_label)[0]  pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label))  pyplot.legend()  pyplot.show() Output:  The dataset has been generated  The rows and columns are   (670, 2) (670,)  Counter({3: 134, 0: 134, 2: 134, 4: 134, 1: 134})  [-6.45785776 -3.30981436] 3  [-6.44623696 -2.90184841] 3  [-5.60217602 -0.65990849] 3 Code explanation: The required packages are imported using the ‘import’ function.  The dataset is generated using the ‘make_blobs’ function and by specifying the number of rows and columns that need to be generated.  In addition, the number of classes into which the data points need to be labelled into is also defined. Here, it is 5.  The number of rows and columns are displayed along with the summarization of class labelling.  A ‘for’ loop is used to print the first few classified values.  The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen. Multi-label classification   Multi-label classification refers to those classification problems that deal with more than one class being assigned to a single data point, i.e. every data point would belong or be labelled into more than one class/label. A simple example would be a photo that contains multiple people, not just one. This means one photo might be classified or labelled as more than one (in fact thousands) of persons. This is different from binary and multi-class classification, since the number of labels into which one data point is classified remains same, i.e one.Some multi-label classification algorithms include: Multi-label random forests Multi-label gradient boosting Code to demonstrate multi-label classification: from sklearn.datasets import make_multilabel_classification  X, y = make_multilabel_classification(n_samples=800, n_features=2, n_classes=5, n_labels=3, random_state=1)  print("The number of rows and columns are ")  print(X.shape, y.shape)  for i in range(8):  print(X[i], y[i]) Output: The number of rows and columns are   (800, 2) (800, 5)  [22. 24.] [1 0 0 1 1]  [12. 35.] [0 1 0 1 0]  [27. 30.] [1 1 0 0 1]  ..  Code explanation The required packages are imported using the ‘import’ function.  The dataset is generated using the ‘make_multilabel_classification’ function present in the scikit-learn package is used.  It is done by specifying the number of rows and columns that need to be generated.  The number of rows and columns are displayed along with the summarization of class labelling.  A ‘for’ loop is used to print the first few classified values.  The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.  Imbalanced classification This is a type of classification wherein the number of data points of the dataset in every class is not distributed equally. This means imbalanced classification is basically a binary classification problem, which doesn’t have a uniform distribution of points, one class could contains an extremely large amount of data points, and the other class might contains a very small number of data points.  Examples of imbalanced classification problem include: Fraud detection in credit cards Anomaly detection in the given dataset There are specialized algorithms that are used to classify this data into the large data point group or small data point group. Some algorithms have been listed below: Cost sensitive decision trees Cost sensitive logistic regression Cost sensitive support vector machines Code to demonstrate imbalanced binary classification #An example of imbalanced binary classification task  from numpy import where  from collections import Counter  from sklearn.datasets import make_classification  from matplotlib import pyplot  #The dataset is defined  X, y = make_classification(n_samples=800, n_features=2, n_informative=2, n_redundant=0, n_classes=2, n_clusters_per_class=1, weights=[0.99,0.01], random_state=1)  #The shape of the dataset is summarized  print("The number of rows and columns ")  print(X.shape, y.shape)  #The labelled data is summarized  my_counter = Counter(y)  print(my_counter)  #A few data points are summarized  for i in range(10):  print(X[i], y[i])  #The dataset is plotted on a graph and displayed  for my_label, _ in my_counter.items():  row_ix = where(y == my_label)[0]  pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label))  pyplot.legend()  pyplot.show() Output: The number of rows and columns   (800, 2) (800,)  Counter({0: 785, 1: 15})  [0.28622882 0.38305399] 0  [1.17971415 0.48003249] 0  [1.32658794 0.71712275] 0  Code explanation The required packages are imported using the ‘import’ function.  The dataset is generated using the ‘make_classification’ function present in the scikit-learn package is used.  It is done by specifying the number of rows and columns that need to be generated.  The number of rows and columns are displayed along with the summarization of class labelling.  A ‘for’ loop is used to print the first few classified values.  The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.  Logistic regression In this classification technique, instead of finding continuous values like that of linear regression, we are concerned with finding discrete values. It is simply a classification technique that classifies the given data points into one of the labelled classes. Usually, we are looking at a Boolean output, wherein the result is either 0 or 1, yes or no and so on. Some examples include: Classifying an email as spam or not Finding whether it would rain today or not Naïve Bayes classification Bayes theorem is way of calculating the probability of a hypothesis (situation, which might not have occurred in reality) based on our previous experiences and the knowledge we have gained by it.  Bayes theorem is stated as follows: P(hypo | data) = (P(data | hypo) * P(hypo)) / P(data)  In the above equation,  P(hypo | data) is the probability of a hypothesis ‘hypo’ when data ‘data’ is given, which is also known as posterior probability.  P(data | hypo) is the probability of data ‘data’ when the specific hypothesis ‘hypo’ is known to be true.  P(hypo) is the probability of a hypothesis ‘hypo’ being true (irrespective of the data in hand), which is also known as prior probability of ‘hypo’.  P(data) is the probability of the data (irrespective of the hypothesis). The idea here is to get the value of the posterior probability, given other data. The posterior probability for a variety of different hypotheses is found out, and the probability that has the highest value is selected. This is known as the maximum probable hypothesis, and is also known as maximum a posteriori (MAP) hypothesis.  MAP(hypo) = max(P(hypo | data))  If the value of P(hypo | data) is replaced with the value we saw before, the equation would become:  MAP(hypo) = max((P(data | hypo) * P(hypo)) / P(data))  P(data) is considered as a normalizing term that helps in determining the probability. This value can be ignored when required, since it is a constant value. Naïve Bayes classifier is an algorithm that can be used with binary or multi-class classification problems. Once a Naïve Bayes classifier has learnt from the data, it stores a list of probabilities. Probabilities such as ‘class probability’ and ‘condition probability’ is stored. Training such a model is quick since the probability of every class and its associated value needs to be determined, and this doesn’t involve any optimization processes or coefficient changing.  K-nearest neighbour (KNN)  The simplest way to understand k-nearest neighbour, is that the training data for the algorithm is all the data in its entirety. KNN doesn’t have a different model, other than the one that stores the entire dataset, which means there is no machine learning that is actually happening. This means KNN makes predictions and extracts patterns directly from the training dataset itself. When a new data point is encountered, the corresponding value for that can be found using KNN by navigating through the entire training dataset, by looking at the ‘k’ number of very similar neighbours. Once the ‘k’ neighbours have been identified, they are summarized and the output for every instance is found. In case of regression, the mean of this output is the result, and in case of classification, the mode of this output is the result.  How to determine the ‘k’ neighbours? To find ‘k’ number of instances from the training dataset that are very similar to the new data point, we use a distance factor, and the most popular metric is the Euclidean distance.  Euclidean distance can be determined by finding the square root of the sum of the square of difference between the new point and an existing point in the data set, and this sum is from values in the range (a,b). Euclidean Distance: (a,b) = square root( sum( a – b) ^ 2))  Other distances that can be used include: Hamming distance Manhattan distance Minkowski Distance When the number of data points in the training set increases, the complexity of KNN also increases.  Support vector machines (SVM) The hyperplane present in linear SVM is learnt by performing simple transformations using linear algebra. The sum of the product of every pair of input data points is multiplied, and this is known as the inner product. The basic idea behind SVM is that the inner product of two vectors can be expressed as a sum of product of the first value of every vector.  To find inner product of two input vectors: [a,b] and [c,d], we do [a*c + b*d]  In order to predict new value, the dot product can be used, and the support vector can be calculated using the below equation: f(x) = coeff-1 + sum(coeff-2 * (a,b))   Here, ‘a’ and ‘b’ are input vectors and coeff-1 and coeff-2 are coefficients that are determined with the help of the training dataset and the learning algorithm. Stochastic gradient descent or sequential minimal optimization technique can be used. All these optimization techniques break down the main problem into sub-problems and every sub problem is solved by calculating the required value.  Decision trees It is a part of predictive modelling in machine learning that is considered as one of the most powerful algorithms. It is also known as CART, i.e. classification and regression trees since this can be used in the process of classification as well as regression tasks. Decision tree can be simply visualized as a binary tree that has a root and many branches from it and leaves. It is the same as the tree data structure. The root is a single input value, and the branches that lead to leaves are used in predicting the values for the given input.  The tree structure can be stored in the form of a graph structure or a set of rules. Once the data in the form of tree is available, it is simple to make predictions on it with the help of the leaf nodes. The specific branch and its leaf node is examined to reach the node.  Data is filtered from the root of the tree and goes and sits in the branch and the leaf that is relevant to it.  No data preparation or pre-processing is required while working with CART or decision trees.  Gradient boosting It is a method to build predictive models in machine learning. The idea behind boosting is to understand whether a weak learning algorithm can be made to learn better. This involves three attributes: A weak learning algorithm that makes prediction: Decision tree is considered to be a weak learner when it comes to gradient boosting. The best splits are chosen in decision trees, thereby minimizing the loss, hence they need to be improved so that they work well even when the split is random.  A loss function that needs to be optimized: This value depends on the situation in hand. Many different loss functions can be used, such as squared error, measure squared error, logarithmic loss function and so on. A new boosting algorithm won’t have to be figured out for every loss function.  An additive model that adds weak learner to minimize the loss function: The trees to the gradient boosting technique are added one at a time, so that the existing model trees don’t have changes. This way, the loss is minimized when new trees are added. Usually, gradient descent optimization technique is used to minimize the loss.  Random forest Random forest is an ensemble machine leaning algorithm that uses bootstrap aggregation or bagging. It is a statistical method that helps in estimating the quantity from a given data sample. It is done to reduce the variance for those algorithms that seem to have a high variance. Examples of algorithms that have high variance include CART, and decision trees. Decision trees are extremely sensitive to the data on which they are trained. If the training data changes, the resultant tree would also be completely different. A small change in the input makes a huge difference to the overall training and output.  An ensemble method is the one that combines the predictions that have come from many different machine learning algorithms, thereby making sure that the predictions are more accurate in comparison to dealing with an algorithm that gives a single prediction. It is like combining the best algorithms to give the best of best values.  Random forest makes sure that the every sub-tree that learns and trains on the data and makes the predictions is less correlated to the other sub-trees that do the same. The learning algorithm is limited to be able to look at a random sample of the data points, so that it doesn’t have the opportunity to look through all the variables, and select an optimal point to split upon (which is actually the case with CART). It is seen that for classification trees, a good value for the number of randomly selected columns from the dataset is square root (p) where p refers to the number of input variables. On the other hand, for regression trees, a good value for the number of randomly selected columns from the dataset is p/3.  Neural networks It is a part of deep learning that deals with artificial neural networks. In general, the word ‘neural’ or ‘neuro’ deals with the decision making branch of the human brain. The idea behind artificial neural network, also abbreviated as ANN, is that it takes decision similar to how the neurons in the brain function while performing a function or taking a decision.  It is called deep learning since these networks have various layers, and every layer has a large number of nodes. Every layer processes some part of the data and passes on the computed data to the next layer. The input data to one layer is the output data of the previous layer. Usually, the input layer’s nodes are large in number, and the output layer has just one node indicating that the data was processed, and the output has been obtained.  ConclusionIn this post, we understood how classification works, the different types of classification and regression, their working, implementations by generating simple dataset and working through it using Python and other relevant machine learning related packages. 

Types of Classification in Machine Learning

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  • by Amit Diwan
  • 05th Sep, 2020
  • Last updated on 15th Mar, 2021
  • 15 mins read
Types of Classification in Machine Learning

Takeaways from this article 

  • In this post, we understand the concept of classification, regression, classification predictive modelling, and the different types of classification and regression 
  • We understand why and how classification is important. 
  • We also see a few classification algorithms and their implementations in Python.  
  • We understand logistic regression, decision trees, random forests, support vector machines, k nearest neighbour and neural networks. 
  • We understand their inner workings and their prominence.

Classification refers to the process of classifying the given data set into different classes or groups. The classification algorithm is placed under predictive modelling problem, wherein every class of the dataset is given a label, to indicate that it is different from other classes. Some examples include email classification as spam or not, recognition of a handwritten character as a specific character only, and not another character and so on.   

Classification algorithms need data to be trained with many inputs and their respective output, with the help of which the model learns. It is important to understand that the training data must encompass all kinds of data (options) which could be encountered in the test data set or real world. 

Classification

The 4 different prominent types of classification include the following:

  • Binary classification 
  • Multi-class classification 
  • Multi-label classification 
  • Imbalanced classification 

 Binary classification

As the name suggests, it deals with the tasks in classification that only have two class labels. Some examples include: email classification as spam or not, whether the price of a stock will go up or go down (ignoring the fact that it could also remain as is), and so on. The value obtained after classifying the data would be either 0 or 1, yes or no, normal or abnormal.  

The Bernoulli probability distribution is used as prediction to classify the data as 0 or 1. Bernoulli distribution is a discrete (discontinuous) distribution that gives a binary outcome -- a 0 or a 1. 

Algorithms that are used to perform binary classification include the following:

  • Logistic regression 
  • Decision trees 
  • Support vector machine 
  • Naïve Bayes 
  • k’nn (k nearest neighbors) 

Code to demonstrate a binary classification task:  

from numpy import where 
from collections import Counter 
from sklearn.datasets import make_blobs 
from matplotlib import pyplot 
X, y = make_blobs(n_samples=560, centers=2, random_state=1) 
print("Data has been generated ") 
print("The number of rows and columns are ") 
print(X.shapey.shape) 
my_counter = Counter(y) 
print(my_counter) 
for i in range(10): 
print(X[i], y[i]) 
for my_label, _ in my_counter.items(): 
row_ix = where(y == my_label)[0] 
pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label)) 
pyplot.legend() 
pyplot.show()

Output: 

Data has been generated  
The number of rows and columns are  
(560, 2) (560,) 
Counter({1: 280, 0: 280}) 
[-9.64384208 -4.14030356] 1 
[-0.8821407  4.2877187] 0 
 

Types of classification in Machine Learning Code explanation 

  • The required packages are imported using the ‘import’ function.  
  • The dataset is generated using the ‘make_blobs’ function and by specifying the number of rows and columns that need to be generated.  
  • In addition, the number of classes into which the data points need to be labelled into is also defined. Here, it is 2. 
  • The number of rows and columns are displayed along with the summarization of class labelling.  
  • A ‘for’ loop is used to print the first few classified values.  
  • The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.  

Multi-class classification

It is a type of classification wherein the input data set is classified/labelled into more than 2 classes. Some examples of multi-class classification include:

  • Animal species classification 
  • Facial recognition/classification 
  • Text translation (special type of multi-class classification task) 

This is different from binary classification in that it doesn’t have just two classes like 0 or 1, but more, and they need not be 0 or 1. They could be names or other continuous or discontinuous numbers. The data points are classified into one among many different classes given.  

The number of class labels may be too high, when trying to classify a given photo into that of a specific person. Text translation also deals with a similar issue, wherein the word placement may vary widely and there maybe thousands of combinations of the same number of words. Multinoulli probability distribution is a discrete/discontinuous probability distribution, where the output could be any value within a given range. Algorithms that are used for binary classification can also be used for multi-class classification.  

Code to demonstrate the multi-class classification: 

from numpy import where 
from collections import Counter 
from sklearn.datasets import make_blobs 
from matplotlib import pyplot 
 
X, y = make_blobs(n_samples=670, centers=5, random_state=1) 
print("The dataset has been generated") 
print("The rows and columns are ") 
print(X.shapey.shape) 
my_counter = Counter(y) 
print(my_counter) 
for i in range(10): 
print(X[i], y[i]) 
for my_label, _ in my_counter.items(): 
row_ix = where(y == my_label)[0] 
pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label)) 
pyplot.legend() 
pyplot.show() 

Output:  

The dataset has been generated 
The rows and columns are  
(670, 2) (670,) 
Counter({3: 134, 0: 134, 2: 134, 4: 134, 1: 134}) 
[-6.45785776 -3.30981436] 3 
[-6.44623696 -2.90184841] 3 
[-5.60217602 -0.65990849] 3 

Types of classification in Machine Learning

Code explanation: 

  • The required packages are imported using the ‘import’ function.  
  • The dataset is generated using the ‘make_blobs’ function and by specifying the number of rows and columns that need to be generated.  
  • In addition, the number of classes into which the data points need to be labelled into is also defined. Here, it is 5.  
  • The number of rows and columns are displayed along with the summarization of class labelling.  
  • A ‘for’ loop is used to print the first few classified values.  
  • The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen. 

Multi-label classification  

 Multi-label classification refers to those classification problems that deal with more than one class being assigned to a single data point, i.e. every data point would belong or be labelled into more than one class/label. A simple example would be a photo that contains multiple people, not just one. This means one photo might be classified or labelled as more than one (in fact thousands) of persons. This is different from binary and multi-class classification, since the number of labels into which one data point is classified remains same, i.e one.

Some multi-label classification algorithms include: 

  • Multi-label random forests 
  • Multi-label gradient boosting 

Code to demonstrate multi-label classification: 

from sklearn.datasets import make_multilabel_classification 
X, y = make_multilabel_classification(n_samples=800, n_features=2, n_classes=5, n_labels=3, random_state=1) 
print("The number of rows and columns are ") 
print(X.shapey.shape) 
for i in range(8): 
print(X[i], y[i]) 

Output: 

The number of rows and columns are  
(800, 2) (800, 5) 
[22. 24.] [1 0 0 1 1] 
[12. 35.] [0 1 0 1 0] 
[27. 30.] [1 1 0 0 1] 
..  

Code explanation 

  • The required packages are imported using the ‘import’ function.  
  • The dataset is generated using the ‘make_multilabel_classification’ function present in the scikit-learn package is used.  
  • It is done by specifying the number of rows and columns that need to be generated.  
  • The number of rows and columns are displayed along with the summarization of class labelling.  
  • A ‘for’ loop is used to print the first few classified values.  
  • The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.  

Imbalanced classification 

This is a type of classification wherein the number of data points of the dataset in every class is not distributed equally. This means imbalanced classification is basically a binary classification problem, which doesn’t have a uniform distribution of points, one class could contains an extremely large amount of data points, and the other class might contains a very small number of data points.  

Examples of imbalanced classification problem include: 

  • Fraud detection in credit cards 
  • Anomaly detection in the given dataset 

There are specialized algorithms that are used to classify this data into the large data point group or small data point group. Some algorithms have been listed below: 

  • Cost sensitive decision trees 
  • Cost sensitive logistic regression 
  • Cost sensitive support vector machines 

Code to demonstrate imbalanced binary classification 

#An example of imbalanced binary classification task 
from numpy import where 
from collections import Counter 
from sklearn.datasets import make_classification 
from matplotlib import pyplot 
#The dataset is defined 
X, y = make_classification(n_samples=800, n_features=2, n_informative=2, n_redundant=0, n_classes=2, n_clusters_per_class=1, weights=[0.99,0.01], random_state=1) 
#The shape of the dataset is summarized 
print("The number of rows and columns ") 
print(X.shapey.shape) 
#The labelled data is summarized 
my_counter = Counter(y) 
print(my_counter) 
#A few data points are summarized 
for i in range(10): 
print(X[i], y[i]) 
#The dataset is plotted on a graph and displayed 
for my_label, _ in my_counter.items(): 
row_ix = where(y == my_label)[0] 
pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(my_label)) 
pyplot.legend() 
pyplot.show() 

Output: 

The number of rows and columns  
(800, 2) (800,) 
Counter({0: 785, 1: 15}) 
[0.28622882 0.38305399] 0 
[1.17971415 0.48003249] 0 
[1.32658794 0.71712275] 0 

 Types of classification in Machine Learning

Code explanation 

  • The required packages are imported using the ‘import’ function.  
  • The dataset is generated using the ‘make_classification’ function present in the scikit-learn package is used.  
  • It is done by specifying the number of rows and columns that need to be generated.  
  • The number of rows and columns are displayed along with the summarization of class labelling.  
  • A ‘for’ loop is used to print the first few classified values.  
  • The entire dataset is then plotted on a graph in the form of a scatterplot using the ‘pyplot’ function and displayed on the screen.  

Logistic regression 

In this classification technique, instead of finding continuous values like that of linear regression, we are concerned with finding discrete values. It is simply a classification technique that classifies the given data points into one of the labelled classes. Usually, we are looking at a Boolean output, wherein the result is either 0 or 1, yes or no and so on. Some examples include: 

  • Classifying an email as spam or not 
  • Finding whether it would rain today or not 

Naïve Bayes classification 

Bayes theorem is way of calculating the probability of a hypothesis (situation, which might not have occurred in reality) based on our previous experiences and the knowledge we have gained by it.  

Bayes theorem is stated as follows: 

P(hypo | data) = (P(data | hypo) * P(hypo)) / P(data)  

In the above equation,  

P(hypo | data) is the probability of a hypothesis ‘hypo’ when data ‘data’ is given, which is also known as posterior probability.  

P(data | hypo) is the probability of data ‘data’ when the specific hypothesis ‘hypo’ is known to be true.  

P(hypo) is the probability of a hypothesis ‘hypo’ being true (irrespective of the data in hand), which is also known as prior probability of ‘hypo’.  

P(data) is the probability of the data (irrespective of the hypothesis). 

The idea here is to get the value of the posterior probability, given other data. The posterior probability for a variety of different hypotheses is found out, and the probability that has the highest value is selected. This is known as the maximum probable hypothesis, and is also known as maximum a posteriori (MAP) hypothesis.  

MAP(hypo) = max(P(hypo | data))  

If the value of P(hypo | data) is replaced with the value we saw before, the equation would become:  

MAP(hypo) = max((P(data | hypo) * P(hypo)) / P(data))  

P(data) is considered as a normalizing term that helps in determining the probability. This value can be ignored when required, since it is a constant value. 

Naïve Bayes classifier is an algorithm that can be used with binary or multi-class classification problems. Once a Naïve Bayes classifier has learnt from the data, it stores a list of probabilities. Probabilities such as ‘class probability’ and ‘condition probability’ is stored. Training such a model is quick since the probability of every class and its associated value needs to be determined, and this doesn’t involve any optimization processes or coefficient changing.  

K-nearest neighbour (KNN)  

The simplest way to understand k-nearest neighbour, is that the training data for the algorithm is all the data in its entirety. KNN doesn’t have a different model, other than the one that stores the entire dataset, which means there is no machine learning that is actually happening. This means KNN makes predictions and extracts patterns directly from the training dataset itself. 

When a new data point is encountered, the corresponding value for that can be found using KNN by navigating through the entire training dataset, by looking at the ‘k’ number of very similar neighbours. Once the ‘k’ neighbours have been identified, they are summarized and the output for every instance is found. In case of regression, the mean of this output is the result, and in case of classification, the mode of this output is the result.  

How to determine the ‘k’ neighbours? 

To find ‘k’ number of instances from the training dataset that are very similar to the new data point, we use a distance factor, and the most popular metric is the Euclidean distance.  

Euclidean distance can be determined by finding the square root of the sum of the square of difference between the new point and an existing point in the data set, and this sum is from values in the range (a,b). 

Euclidean Distance: 

(a,b) = square root( sum( a – b) ^ 2))  

Other distances that can be used include: 

  • Hamming distance 
  • Manhattan distance 
  • Minkowski Distance 

When the number of data points in the training set increases, the complexity of KNN also increases.  

Support vector machines (SVM) 

The hyperplane present in linear SVM is learnt by performing simple transformations using linear algebra. The sum of the product of every pair of input data points is multiplied, and this is known as the inner product. The basic idea behind SVM is that the inner product of two vectors can be expressed as a sum of product of the first value of every vector.  

To find inner product of two input vectors: 

[a,b] and [c,d], we do [a*c + b*d]  

In order to predict new value, the dot product can be used, and the support vector can be calculated using the below equation: 

f(x) = coeff-1 + sum(coeff-2 * (a,b))   

Here, ‘a’ and ‘b’ are input vectors and coeff-1 and coeff-2 are coefficients that are determined with the help of the training dataset and the learning algorithm. Stochastic gradient descent or sequential minimal optimization technique can be used. All these optimization techniques break down the main problem into sub-problems and every sub problem is solved by calculating the required value.  

Decision trees 

It is a part of predictive modelling in machine learning that is considered as one of the most powerful algorithms. It is also known as CART, i.e. classification and regression trees since this can be used in the process of classification as well as regression tasks. Decision tree can be simply visualized as a binary tree that has a root and many branches from it and leaves. It is the same as the tree data structure. The root is a single input value, and the branches that lead to leaves are used in predicting the values for the given input.  

The tree structure can be stored in the form of a graph structure or a set of rules. Once the data in the form of tree is available, it is simple to make predictions on it with the help of the leaf nodes. The specific branch and its leaf node is examined to reach the node.  

Data is filtered from the root of the tree and goes and sits in the branch and the leaf that is relevant to it.  

No data preparation or pre-processing is required while working with CART or decision trees.  

Gradient boosting 

It is a method to build predictive models in machine learning. The idea behind boosting is to understand whether a weak learning algorithm can be made to learn better. This involves three attributes: 

  1. A weak learning algorithm that makes prediction: Decision tree is considered to be a weak learner when it comes to gradient boosting. The best splits are chosen in decision trees, thereby minimizing the loss, hence they need to be improved so that they work well even when the split is random.  
  2. A loss function that needs to be optimized: This value depends on the situation in hand. Many different loss functions can be used, such as squared error, measure squared error, logarithmic loss function and so on. A new boosting algorithm won’t have to be figured out for every loss function.  
  3. An additive model that adds weak learner to minimize the loss function: The trees to the gradient boosting technique are added one at a time, so that the existing model trees don’t have changes. This way, the loss is minimized when new trees are added. Usually, gradient descent optimization technique is used to minimize the loss.  

Random forest 

Random forest is an ensemble machine leaning algorithm that uses bootstrap aggregation or bagging. It is a statistical method that helps in estimating the quantity from a given data sample. It is done to reduce the variance for those algorithms that seem to have a high variance. Examples of algorithms that have high variance include CART, and decision trees. Decision trees are extremely sensitive to the data on which they are trained. If the training data changes, the resultant tree would also be completely different. A small change in the input makes a huge difference to the overall training and output.  

An ensemble method is the one that combines the predictions that have come from many different machine learning algorithms, thereby making sure that the predictions are more accurate in comparison to dealing with an algorithm that gives a single prediction. It is like combining the best algorithms to give the best of best values.  

Random forest makes sure that the every sub-tree that learns and trains on the data and makes the predictions is less correlated to the other sub-trees that do the same. The learning algorithm is limited to be able to look at a random sample of the data points, so that it doesn’t have the opportunity to look through all the variables, and select an optimal point to split upon (which is actually the case with CART). It is seen that for classification trees, a good value for the number of randomly selected columns from the dataset is square root (p) where p refers to the number of input variables. On the other hand, for regression trees, a good value for the number of randomly selected columns from the dataset is p/3.  

Neural networks 

It is a part of deep learning that deals with artificial neural networks. In general, the word ‘neural’ or ‘neuro’ deals with the decision making branch of the human brain. The idea behind artificial neural network, also abbreviated as ANN, is that it takes decision similar to how the neurons in the brain function while performing a function or taking a decision.  

It is called deep learning since these networks have various layers, and every layer has a large number of nodes. Every layer processes some part of the data and passes on the computed data to the next layer. The input data to one layer is the output data of the previous layer. Usually, the input layer’s nodes are large in number, and the output layer has just one node indicating that the data was processed, and the output has been obtained.  

Conclusion

In this post, we understood how classification works, the different types of classification and regression, their working, implementations by generating simple dataset and working through it using Python and other relevant machine learning related packages. 

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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Types of Probability Distributions Every Data Science Expert Should know

Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution.  What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. 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. 
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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