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Decision Tree

Decision tree:

The decision tree is the classification algorithm in ML(machine learning). A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.

To understand the algorithm of the decision tree we need to know about the classification.

What is Classification?

Classification is the process of dividing the datasets into different categories or groups by adding a label. It adds the data point to a particular labeled group on the basis of some condition.

As we see in daily life there are three categories in an email(Spam, Promotions, Personal) they are classified to get the proper information. Here decision tree is used to classify the mail type and fix it the proper one.

Types of classification 

  • DECISION TREE
  • RANDOM FOREST
  • NAIVE BAYES
  • KNN

Decision tree:

  1. Graphical representation of all the possible solutions to a decision.
  2. A decision is based on some conditions.
  3. The decision made can be easily explained.

There are following steps to get a decision with the decision tree

1. Entropy:

Entropy is basically used to create a tree. We find our entropy from attribute or class. A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar values (homogeneous). ID3 algorithm uses entropy to calculate the homogeneity of a sample.

2.Information Gain:

The information gain is based on the decrease in entropy after a data-set is split on an attribute. Constructing a decision tree is all about finding an attribute that returns the highest information gain.

  • The information gain is based on the decrease in entropy after a dataset is split on an attribute.
  • Constructing a decision tree is all about finding an attribute that returns the highest information gain (i.e., the most homogeneous branches).
  • Gain(S, A) = Entropy(S) – ∑ [ p(S|A) . Entropy(S|A) ]
  • We intend to choose the attribute, splitting by which information gain will be the most
  • Next step is calculating information gain for all attributes
Here the short example of a Decision tree:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
play_data=pd.read_csv('data/tennis.csv.txt')
print(play_data)
play_data=pd.read_csv('data/tennis.csv.txt')
play_data

Output:

outlook temp humidity windy play
0 sunny hot high False no
1 sunny hot high True no
2 overcast hot high False yes
3 rainy mild high False yes
4 rainy cool normal False yes
5 rainy cool normal True no
6 overcast cool normal True yes
7 sunny mild high False no
8 sunny cool normal False yes
9 rainy mild normal False yes
10 sunny mild normal True yes
11 overcast mild high True yes
12 overcast hot normal False yes
13 rainy mild high True no 

Entropy of play:

  • Entropy(play) = – p(Yes) . log2p(Yes) – p(No) . log2p(No)

play_data.play.value_counts()
Entropy_play=-(9/14)*np.log2(9/14)-(5/14)*np.log2(5/14)
print(Entropy_play)

output:
0.94028595867063114

Information Gain on splitting by Outlook

  • Gain(Play, Outlook) = Entropy(Play) – ∑ [ p(Play|Outlook) . Entropy(Play|Outlook) ]
  • Gain(Play, Outlook) = Entropy(Play) – [ p(Play|Outlook=Sunny) . Entropy(Play|Outlook=Sunny) ] – [ p(Play|Outlook=Overcast) . Entropy(Play|Outlook=Overcast) ] – [ p(Play|Outlook=Rain) . Entropy(Play|Outlook=Rain) ]

play_data[play_data.outlook == 'sunny'] 

# Entropy(Play|Outlook=Sunny)
Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)
Entropy_Play_Outlook_Sunny
play_data[play_data.outlook == 'overcast'] # Entropy(Play|Outlook=overcast)
# Since, it's a homogenous data entropy will be 0
play_data[play_data.outlook == 'rainy'] # Entropy(Play|Outlook=rainy)
Entropy_Play_Outlook_Rain = -(2/5)*np.log2(2/5) - (3/5)*np.log2(3/5)
print(Entropy_play_Outlook_Rain)
# Entropy(Play_Sunny|)
Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)
#Gain(Play, Outlook) = Entropy(Play) – [ p(Play|Outlook=Sunny) . Entropy(Play|Outlook=Sunny) ] –
#[ p(Play|Outlook=Overcast) . Entropy(Play|Outlook=Overcast) ] – [ p(Play|Outlook=Rain) . Entropy(Play|Outlook=Rain) ]

Other gains

  • Gain(Play, Temperature) – 0.029
  • Gain(Play, Humidity) – 0.151
  • Gain(Play, Wind) – 0.048

Conclusion – Outlook is winner & thus becomes root of the tree

Time to find the next splitting criteria

play_data[play_data.outlook == 'overcast'] play_data[play_data.outlook == 'sunny'] # Entropy(Play_Sunny|)
Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)
print(Entropy_Play_Outlook_Sunny)
# Entropy(Play_Sunny|)
Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)
print(Entropy_Play_Outlook_Sunny)

Information Gain for humidity

#Entropy for attribute high = 0, also entropy for attribute normal = 0
Entropy_Play_Outlook_Sunny - (3/5)*0 - (2/5)*0 

Information Gain for windy

  • False -> 3 -> [1+ 2-]
  • True -> 2 -> [1+ 1-]

Entropy_Wind_False = -(1/3)*np.log2(1/3) - (2/3)*np.log2(2/3)
print(Entropy_Wind_False)
Entropy_Play_Outlook_Sunny - (3/5)* Entropy_Wind_False - (2/5)*1  

Information Gain for temperature

  • hot -> 2 -> [2- 0+]
  • mild -> 2 -> [1+ 1-]
  • cool -> 1 -> [1+ 0-]

Entropy_Play_Outlook_Sunny - (2/5)*0 - (1/5)*0 - (2/5)* 1]

Conclusion : Humidity is the best choice on sunny branch:

play_data[(play_data.outlook == 'sunny') & (play_data.humidity == 'high')] 

Output:

outlook temp humidity windy play
0 sunny hot high False no
1 sunny hot high True no
7 sunny mild high False no 

play_data[(play_data.outlook == 'sunny') & (play_data.humidity == 'normal']

Output:
outlook temp humidity windy play
8 sunny cool normal False yes
10 sunny mild normal True yes

Splitting the rainy branch:

play_data[play_data.outlook == 'rainy'] # Entropy(Play_Rainy|)
Entropy_Play_Outlook_Rainy =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)outlook temp humidity windy play
3 rainy mild high False yes
4 rainy cool normal False yes
5 rainy cool normal True no
9 rainy mild normal False yes
13 rainy mild high True no 

Information Gain for temp

  • mild -> 3 [2+ 1-]
  • cool -> 2 [1+ 1-]

Entropy_Play_Outlook_Rainy - (3/5)*0.918 - (2/5)*1

Output:
0.020150594454668602

Information Gain for Windy:

Entropy_Play_Outlook_Rainy - (2/5)*0 - (3/5)*0

Output:
0.97095059445466858 

Information Gain for Humidity

  • High -> 2 -> [1+ 1-]
  • Normal -> 3 -> [2+ 1-]

Entropy_Play_Outlook_Rainy_Normal = -(1/3)*np.log2(1/3) - (2/3)*np.log2(2/3)
Entropy_Play_Outlook_Rainy_Normal
Entropy_Play_Outlook_Rainy - (2/5)*1 - (3/5)*Entropy_Play_Outlook_Rainy_Normal
Entropy_Play_Outlook_Rainy_Normal
Entropy_Play_Outlook_Rainy_Normal

Output:
0.91829583405448956
0.019973094021974891 

Final tree:

Decision trees are popular among non-statisticians as they produce a model that is very easy to interpret. Each leaf node is presented as an if/then rule. Cases that satisfy the if/then the statement is placed in the node. Are non-parametric and therefore do not require normality assumptions of the data. Parametric models specify the form of the relationship between predictors and response. An example is a linear relationship for regression. In many cases, however, the nature of the relationship is unknown. This is a case in which non-parametric models are useful. Can handle data of different types, including continuous, categorical, ordinal, and binary. Transformations of the data are not required. It can be useful for detecting important variables, interactions, and identifying outliers. It handles missing data by identifying surrogate splits in the modeling process. Surrogate splits are splitting highly associated with the primary split. In other models, records with missing values are omitted by default.

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Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.

Support vector machine

Introduction of  Support Vector Machine

Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression.

SVMs were introduced initially in the 1960s and were later refined in 1990s. However, it is only now that they are becoming extremely popular, owing to their ability to achieve brilliant results. SVMs are implemented uniquely when compared to other machine learning algorithms.

Support vector machine(SVM) is a supervised learning algorithm that is used to classify the data into different classes, now unlike most algorithms SVM makes use of hyperplane which acts as a decision boundary between the various classes. In general, SVM can be used to generate multiple separating the hyperplane so that the data is divided into segments. These segments contain some kind of data.SVM used to classify the data into two different segments depending on the feature of data.

Feature of SVM-

SVM studies the labeled data & then classify any new input data depending on what it learned into the training phase.

It can be used for both classification and regression problems. As SVC supports vector classification SVR stands for support vector regression. One of the main features of SVM is kernel function, it can be used for nonlinear data by using the kernel trick.  The working of the kernel trick is to transform the data into another dimension so that we can draw a hyperplane that classifies the data.

How SVM work??

SVM works by mapping data to a high-dimensional feature space so that data points can be classified, even when the data are not linearly separable. A separator between the classifies is found, then the data are transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.

Importing Libraries:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
bankdata = pd.read_csv("D:/Datasets/bill_authentication.csv")

Exploratory Data Analysis:

bankdata.shape
bankdata.head()

VarianceSkewnessCurtosisEntropyClass
03.621608.6661-2.8073-0.446990
14.545908.1674-2.4586-1.462100
23.86600-2.63831.92420.106450
33.456609.5228-4.0112-3.594400
40.32924-4.45524.5718-0.988800

Data preprocessing:

X = bankdata.drop('Class', axis=1)
y = bankdata['Class'] from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20) 

Training the Algorithm:

from sklearn.svm import SVC
svclassifier = SVC(kernel='linear')
svclassifier.fit(X_train, y_train)

Making prediction

y_pred = svclassifier.predict(X_test)

Evaluating the Algorithm:

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test,y_pred))
print(classification_report(y_test,y_pred))

Output:

[[152 0] [ 1 122]] precision recall f1-score support

0 0.99 1.00 1.00 152
1 1.00 0.99 1.00 123

avg / total 1.00 1.00 1.00 275

SVM Linear Classifier:

In the linear classifier model, we assumed that training examples plotted in space. These data points are expected to be separated by an apparent gap. It predicts a straight hyperplane dividing 2 classes. The primary focus while drawing the hyperplane is on maximizing the distance from hyperplane to the nearest data point of either class. The drawn hyperplane called a maximum-margin hyperplane.

SVM Non-Linear Classifier:

In the real world, our dataset is generally dispersed up to some extent. To solve this problem separation of data into different classes based on a straight linear hyperplane can’t be considered a good choice. For this Vapnik suggested creating Non-Linear Classifiers by applying the kernel trick to maximum-margin hyperplanes. In Non-Linear SVM Classification, data points plotted in a higher-dimensional space.

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Data Preprocessing

Data Preprocessing:

Introduction to Preprocessing:- Before modeling the data we need to clean the data to get a training sample for the modeling. Data preprocessing is a data mining technique that involves transforming the raw data into an understandable format. It provides the technique for cleaning the data from the real world which is often incomplete, inconsistent, lacking accuracy and more likely to contain many errors. Preprocessing provides a clean the data before it gets to the modeling phase.

Preprocessing of data in a stepwise fashion in scikit learn.

1.Introduction to Preprocessing:

  • Learning algorithms have an affinity towards a certain pattern of data.
  • Unscaled or unstandardized data have might have an unacceptable prediction.
  • Learning algorithms understand the only number, converting text image to number is required.
  • Preprocessing refers to transformation before feeding to machine learning.

2. StandardScaler

  • The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centered around 0, with a standard deviation of 1.
  • Calculate – Subtract mean of column & div by the standard deviation
  • If data is not normally distributed, this is not the best scaler to use.

3. MinMaxScaler

  • Calculate – Subtract min of column & div by the difference between max & min
  • Data shifts between 0 & 1
  • If distribution not suitable for StandardScaler, this scaler works out.
  • Sensitive to outliers.

4. Robust Scaler

  • Suited for data with outliers
  • Calculate by subtracting 1st-quartile & div by difference between 3rd-quartile & 1st-quartile.

5. Normalizer

  • Each parameter value is obtained by dividing by magnitude.
  • Enabling you to more easily compare data from different places.

6. Binarization

  • Thresholding numerical values to binary values ( 0 or 1 )
  • A few learning algorithms assume data to be in Bernoulli distribution – Bernoulli’s Naive Bayes

7. Encoding Categorical Value

  • Ordinal Values – Low, Medium & High. Relationship between values
  • LabelEncoding with the right mapping

8. Imputation

  • Missing values cannot be processed by learning algorithms
  • Imputers can be used to infer the value of missing data from existing data

9. Polynomial Features

  • Deriving non-linear feature by converting data into a higher degree
  • Used with linear regression to learn a model of higher degree

10. Custom Transformer

  • Often, you will want to convert an existing Python function into a transformer to assist in data cleaning or processing.
  • FunctionTransformer is used to create one Transformer
  • validate = False, is required for the string column.

11. Text Processing

  • Perhaps one of the most common information
  • Learning algorithms don’t understand the text but only numbers
  • Below methods convert text to numbers

12. CountVectorizer

  • Each column represents one word, count refers to the frequency of the word
  • A sequence of words is not maintained

13.Hyperparameters

  • n_grams – Number of words considered for each column
  • stop_words – words not considered
  • vocabulary – only words considered

13. TfIdfVectorizer

  • Words occurring more frequently in a doc versus entire corpus is considered more important
  • The importance is on the scale of 0 & 1

14. HashingVectorizer

  • All the above techniques convert data into a table where each word is converted to column
  • Learning on data with lakhs of columns is difficult to process
  • HashingVectorizer is a useful technique for out-of-core learning
  • Multiple words are hashed to limited column
  • Limitation – Hashed value to word mapping is not possible

15. Image Processing using skimage

  • skimage doesn’t come with anaconda. install with ‘pip install skimage’
  • Images should be converted from 0-255 scale to 0-1 scale.
  • skimage takes image path & returns numpy array
  • images consist of 3 dimensions.

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Necessity of Machine Learning in Retail

Nowadays data proves to be a powerful pushing force of the industry. Big companies representing diverse trade spheres seek to make use of the beneficial value of the data. Thus, data has become of great importance for those willing to take profitable decisions concerning business. Moreover, a thorough analysis of a vast amount of data allows influencing or rather manipulating the customers decisions. Numerous flows of information, along with channels of communication, are used for this purpose.

The sphere of the retail develops rapidly. The retailers manage to analyze data and develop a peculiar psychological portrait of a customer to learn his or her sore points. Thereby, a customer tends to be easily influenced by the tricks developed by the retailers.

This article presents top 10 data science use cases in the retail, created for you to be aware of the present trends and tendencies.

  1. Recommendation engines

    Recommendation engines proved to be of great use for the retailers as the tools for customers’ behavior prediction. The retailers tend to use recommendation engines as one of the main leverages on the customers’ opinion. Providing recommendations enables the retailers to increase sales and to dictate trends.Recommendation engines manage to adjust depending on the choices made by the customers. Recommendation engines make a great deal of data filtering to get the insights. Usually, recommendation engines use either collaborative or content-based filtering. In this regard, the customer’s past behavior or the series of the product characteristics are under consideration. Besides, various types of data such as demographic data, usefulness, preferences, needs, previous shopping experience, etc. go via the past data learning algorithm.Then the collaborative and content filtering association links are built. The recommendation engines compute a similarity index in the customers’ preferences and offer the goods or services accordingly. The up-sell and cross-sell recommendations depend on the detailed analysis of an online customer’s profile.

  2. Market basket analysis

    Market basket analysis may be regarded as a traditional tool of data analysis in the retail. The retailers have been making a profit out of it for years. This process mainly depends on the organization of a considerable amount of data collected via customers’ transactions. Future decisions and choices may be predicted on a large scale by this tool. Knowledge of the present items in the basket along with all likes, dislikes, and previews is beneficial for a retailer in the spheres of layout organization, prices making and content placement. The analysis is usually conducted via rule mining algorithm. Beforehand the data undertakes transformation from data frame format to simple transactions. A specially tailored function accepts the data, splits it according to some differentiating factors and deletes useless. This data is input. On its basis, the association links between the products are built. It becomes possible due to the association rule application.The insight information largely contributes to the improvement of the development strategies and marketing techniques of the retailers. Also, the efficiency of the selling efforts reaches its peak.

  3. Warranty analytics
    Warranty analytics entered the sphere of the retail as a tool of warranty claims monitoring, detection of fraudulent activity, reducing costs and increasing quality. This process involves data and text mining for further identification of claims patterns and problem areas. The data is transformed into actionable real-time plans, insight, and recommendations via segmentation analysis.The methods of detecting are quite complicated, as far as they deal with vague and intensive data flows. They concentrate on the detecting anomalies in the warranty claims. Powerful internet data platforms speed up the analysis process of a significant amount of warranty claims. This is an excellent chance for the retailers to turn warranty challenges into actionable intelligence.
  4. Price optimization
    Having a right price both for the customer and the retailer is a significant advantage brought by the optimization mechanisms. The price formation process depends not only on the costs to produce an item but on the wallet of a typical customer and the competitors’ offers. The tools for data analysis bring this issue to a new level of its approaching. Price optimization tools include numerous online tricks as well as secret customers approach. The data gained from the multichannel sources define the flexibility of prices, taking into consideration the location, an individual buying attitude of a customer, seasoning and the competitors’ pricing. The computation of the extremes in values along with frequency tables are the appropriate instruments to make the variable evaluation and perfect distributions for the predictors and the profit response.The algorithm presupposes customers segmentation to define the response to changes in prices. Thus, the costs that meet corporate goals may be determined. Using the model of a real-time optimization the retailers have an opportunity to attract the customers, to retain the attention and to realize personal pricing schemes.
  5. Inventory management
    Inventory, as it is, concerns stocking goods for their future use. Inventory management, in its turn, refers to stocking goods in order to use them in time of crisis. The retailers aim to provide a proper product at a right time, in a proper condition, at a proper place. In this regard, the stock and the supply chains are deeply analyzed. Powerful machine learning algorithms and data analysis platforms detect patterns, correlations among the elements and supply chains. Via constantly adjusting and developing parameters and values the algorithm defines the optimal stock and inventory strategies. The analysts spot the patterns of high demand and develop strategies for emerging sales trends, optimize delivery and manage the stock implementing the data received.
  6. Location of new stores
    Data science proves to be extremely efficient about the issue of the new store’s location. Usually, to make such a decision a great deal of data analysis is to be done. The algorithm is simple, though very efficient. The analysts explore the online customers’ data, paying great attention to the demographic factor. The coincidences in ZIP code and location give a basis for understanding the potential of the market. Also, special settings concerning the location of other shops are taken into account. As well as that, the retailer’s network analysis is performed. The algorithms find the solution by connection all these points. The retailer easily adds this data to its platform to enrich the analysis opportunities for another sphere of its activity.
  7. Customer sentiment analysis
    Customer sentiment analysis is not a brand-new tool in this industry. However, since the active implementation of data science, it has become less expensive and time-consuming. Nowadays, the use of focus groups and customers polls is no longer needed. Machine learning algorithms provide the basis for sentiment analysis.The analysts can perform the brand-customer sentiment analysis by data received from social networks and online services feedback. Social media sources are readily available. That is why it is much easier to implement analytics on social platforms. Sentiment analytics uses language processing to track words bearing a positive or negative attitude of a customer. These feedback become a background for services improvement.

    The analysts perform sentiment analysis on the basis of natural language processing, text analysis to extract defining positive, neutral or negative sentiments. The algorithms go through all the meaningful layers of speech. All the spotted sentiments belong to certain categories or buckets and degrees. The output is the sentiment rating in one of the categories mentioned above and the overall sentiment of the text.

  8. Merchandising
    Merchandising has become an essential part of the retail business. This notion covers a vast majority of activities and strategies aimed at increase of sales and promotion of the product. The implementation of the merchandising tricks helps to influence the customer’s decision-making process via visual channels. Rotating merchandise helps to keep the assortment always fresh and renewed. Attractive packaging and branding retain customers attention and enhance visual appeal. A great deal of data science analysis remains behind the scenes in this case.The merchandising mechanisms go through the data picking up the insights and forming the priority sets for the customers, taking into account seasonality, relevancy and trends.
  9. Lifetime value prediction
    In retail, customer lifetime value (CLV) is a total value of the customer’s profit to the company over the entire customer-business relationship. Particular attention is paid to the revenues, as far as they are not so predictable as costs. By the direct purchases two significant customer methodologies of lifetime predictions are made: historical and predictive.All the forecasts are made on the past data leading up to the most recent transactions. Thus the algorithms of a customer’s lifespan within one brand are defined and analyzed. Usually, the CLV models collect, classify and clean the data concerning customers’ preferences, expenses, recent purchases and behavior to structure them into the input. After processing this data we receive a linear presentation of the possible value of the existing and possible customers. The algorithm also spots the inter dependencies between the customer’s characteristics and their choices. The application of the statistical methodology helps to identify the customer’s buying pattern up until he or she stops making purchases. Data science and machine learning assure the retailer’s understanding of his customer, the improvement in services and definition of priorities.
  10. Fraud detection
    The detection of fraud and fraud rings is a challenging activity of a reliable retailer. The main reason for fraud detection is a great financial loss caused. And this is only a tip of an iceberg. The conducted profound National Retail Security Survey goes deeply into details. The customer might suffer from fraud in returns and delivery, the abuse of rights, the credit risk and many other fraud cases that do nothing but ruin the retailer’s reputation. Once being a victim of such situations may destroy a precious trust of the customer forever.The only efficient way to protect your company’s reputation is to be one step ahead of the fraudsters. Big data platforms provide continuous monitoring of the activity and ensure the detection of the fraudulent activity. The algorithm developed for fraud detection should not only recognize fraud and flag it to be banned but to predict future fraudulent activities. That is why deep neural networks prove to be so efficient. The platforms apply the common dimensionality reduction techniques to identify hidden patterns, to label activities and to cluster fraudulent transactions. Using the data analysis mechanisms within fraud detection schemes brings benefits and somewhat improves the retailer’s ability to protect the customer and the company as it is.
Learnbay is a Data Science and Artificial Intelligence training institute that provides the essential and highly recommended topics of Machine Learning.

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