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5 AI Tricks to Grow Your Online Sales

April 16, 2021/in Artificial Intelligence, Data Mining, Data Science, Insights, Main Category, Use Case, Use Cases/by Alex Husar

The way people shop is currently changing. This only means that online stores need optimization to stay competitive and answer to the needs of customers. In this post, we’ll bring up the five ways in which you can use artificial intelligence technology in an online store to grow your revenues. Let’s begin!

1. Personalization with AI

Opening the list of AI trends that are certainly worth covering deals with a step up in personalization. Did you know that according to the results of a survey that was held by Accenture, more than 90% of shoppers are likelier to buy things from those stores and brands that propose suitable product recommendations?

This is exactly where artificial intelligence can give you a big hand. Such progressive technology analyzes the behavior of your consumers individually, keeping in mind their browsing and purchasing history. After collecting all the data, AI draws the necessary conclusions and offers those product recommendations that the user might like.

Look at the example below with the block has a carousel of neat product options. Obviously, this “move” can give a big boost to the average cart sizes.

Screenshot taken on the official Reebok website

2. Smarter Search Options

With the rise of the popularity of AI voice assistants and the leap in technology in general, the way people look for things on the web has changed. Everything is moving towards saving time and getting faster better results.

One of such trends deals with embracing the text to speech and image search technology. Did you notice how many search bars have “microphone icons” for talking out your request?

On a similar note, numerous sites have made a big jump forward after incorporating search by picture. In this case, uploaded photos get analyzed by artificial intelligence technology. The system studies what’s depicted on the image and cross-checks it with the products sold in the store. In several seconds the user is provided with a selection of similar products.

Without any doubt, this greatly helps users find what they were looking for faster. As you might have guessed, this is a time-saving feature. In essence, this omits the necessity to open dozens of product pages on multiple sites when seeking out a liked item that they’ve taken a screenshot or photo of.

Check out how such a feature works on the official Amazon website by taking a look at the screenshots of StyleSnap provided below.

Screenshot taken on the official Amazon StyleSnap website

3. Assisting Clients via Chatbots

The next point on the list is devoted to AI chatbots. This feature can be a real magic wand with client support which is also beneficial for online sales.

Real customer support specialists usually aren’t available 24/7. And keeping in mind that most requests are on repetitive topics, having a chatbot instantly handle many of the questions is a neat way to “unload” the work of humans.

Such chatbots use machine learning to get better at understanding and processing client queries. How do they work? They’re “taught” via scripts and scenario schemes. Therefore, the more data you supply them with, the more matters they’ll be able to cover.

Case in point, there’s such a chat available on the official Victoria’s Secret website. If the user launches the Digital Assistant, the messenger bot starts the conversation. Based on the selected topic the user selects from the options, the bot defines what will be discussed.

Screenshot taken on the official Victoria’s Secret website

4. Determining Top-Selling Product Combos

A similar AI use case for boosting online revenues to the one mentioned in the first point, it becomes much easier to cross-sell products when artificial intelligence “cracks” the actual top matches. Based on the findings by Sumo, you can boost your revenues by 10 to 30% if you upsell wisely!

The product database of online stores gets larger by the month, making it harder to know for good which items go well together and complement each other. With AI on your analytics team, you don’t have to scratch your head guessing which products people are likely to additionally buy along with the item they’re browsing at the moment. This work on singling out data can be done for you.

As seen on the screenshot from the official MAC Cosmetics website, the upselling section on the product page presents supplement items in a carousel. Thus, the chance of these products getting added to the shopping cart increases (if you compare it to the situation when the client would search the site and find these products by himself).

Screenshot taken on the official MAC Cosmetics website

5. “Try It On” with a Camera

The fifth AI technology in this list is virtual try on that borrowed the power of augmented reality technology in the world of sales.

Especially for fields like cosmetics or accessories, it is important to find ways to help clients to make up their minds and encourage them to buy an item without testing it physically. If you want, you can play around with such real-time functionality and put on makeup using your camera on the official Maybelline New York site.

Consumers, ultimately, become happier because this solution omits frustration and unneeded doubts. With everything evident and clear, people don’t have the need to take a shot in the dark what will be a good match, they can see it.

Screenshot taken on the official Maybelline New York website

In Closing

To conclude everything stated in this article, artificial intelligence is a big crunch point. Incorporating various AI-powered features into an online retail store can be a neat advancement leading to a visible growth in conversions.

Bag of Words: Convert text into vectors

April 11, 2021/in Data Mining, Data Science, Data Science Hack, Main Category, Python, Python, Text Mining/by Ram Tavva

In this blog, we will study about the model that represents and converts text to numbers i.e. the Bag of Words (BOW). The bag-of-words model has seen great success in solving problems which includes language modeling and document classification as it is simple to understand and implement.

After completing this particular blog, you all will have an overview of: What does the bag-of-words model mean by and why is its importance in representing text. How we can develop a bag-of-words model for a collection of documents. How to use the bag of words to prepare a vocabulary and deploy in a model using programming language.

The problem and its solution…

The biggest problem with modeling text is that it is unorganised, and most of the statistical algorithms, i.e., the machine learning and deep learning techniques prefer well defined numeric data. They cannot work with raw text directly, therefore we have to convert text into numbers.

Word embeddings are commonly used in many Natural Language Processing (NLP) tasks because they are found to be useful representations of words and often lead to better performance in the various tasks performed. A huge number of approaches exist in this regard, among which some of the most widely used are Bag of Words, Fasttext, TF-IDF, Glove and word2vec. For easy user implementation, several libraries exist, such as Scikit-Learn and NLTK, which can implement these techniques in one line of code. But it is important to understand the working principle behind these word embedding techniques. As already said before, in this blog, we see how to implement Bag of words and the best way to do so is to implement these techniques from scratch in Python . Before we start with coding, let’s try to understand the theory behind the model approach.

Theory Behind Bag of Words Approach

In simple words, Bag of words can be defined as a Natural Language Processing technique used for text modelling or we can say that it is a method of feature extraction with text data from documents. It involves mainly two things firstly, a vocabulary of known words and, then a measure of the presence of known words.

The process of converting NLP text into numbers is called vectorization in machine learning language.A lot of different ways are available in converting text into vectors which are:

Counting the number of times each word appears in a document, and Calculating the frequency that each word appears in a document out of all the words in the document.

Understanding using an example

To understand the bag of words approach, let’s see how this technique converts text into vectors with the help of an example. Suppose we have a corpus with three sentences:

“I like to eat mangoes”
“Did you like to eat jellies?”
“I don’t like to eat jellies”

Step 1: Firstly, we go through all the words in the above three sentences and make a list of all of the words present in our model vocabulary.

I
like
to
eat
mangoes
Did
you
like
to
eat
Jellies
I
don’t
like
to
eat
jellies

Step 2: Let’s find out the frequency of each word without preprocessing our text.

But is this not the best way to perform a bag of words. In the above example, the words Jellies and jellies are considered twice no doubt they hold the same meaning. So, let us make some changes and see how we can use ‘bag of words’ by preprocessing our text in a more effective way.

Step 3: Let’s find out the frequency of each word with preprocessing our text. Preprocessing is so very important because it brings our text into such a form that is easily understandable, predictable and analyzable for our task.

Firstly, we need to convert the above sentences into lowercase characters as case does not hold any information. Then it is very important to remove any special characters or punctuations if present in our document, or else it makes the conversion more messy.

From the above explanation, we can say the major advantage of Bag of Words is that it is very easy to understand and quite simple to implement in our datasets. But this approach has some disadvantages too such as:

Bag of words leads to a high dimensional feature vector due to the large size of word vocabulary.
Bag of words assumes all words are independent of each other ie’, it doesn’t leverage co-occurrence statistics between words.
It leads to a highly sparse vector as there is nonzero value in dimensions corresponding to words that occur in the sentence.

Bag of Words Model in Python Programming

The first thing that we need to create is a proper dataset for implementing our Bag of Words model. In the above sections, we have manually created a bag of words model with three sentences. However, now we shall find a random corpus on Wikipedia such as ‘https://en.wikipedia.org/wiki/Bag-of-words_model‘.

Step 1: The very first step is to import the required libraries: nltk, numpy, random, string, bs4, urllib.request and re.

Step 2: Once we are done with importing the libraries, now we will be using the Beautifulsoup4 library to parse the data from Wikipedia.Along with that we shall be using Python’s regex library, re, for preprocessing tasks of our document. So, we will scrape the Wikipedia article on Bag of Words.

Step 3: As we can observe, in the above code snippet we have imported the raw HTML for the Wikipedia article from which we have filtered the text within the paragraph text and, finally,have created a complete corpus by merging up all the paragraphs.

Step 4: The very next step is to split the corpus into individual sentences by using the sent_tokenize function from the NLTK library.

Step 5: Our text contains a number of punctuations which are unnecessary for our word frequency dictionary. In the below code snippet, we will see how to convert our text into lower case and then remove all the punctuations from our text, which will result in multiple empty spaces which can be again removed using regex.

Step 6: Once the preprocessing is done, let’s find out the number of sentences present in our corpus and then, print one sentence from our corpus to see how it looks.

Step 7: We can observe that the text doesn’t contain any special character or multiple empty spaces, and so our own corpus is ready. The next step is to tokenize each sentence in the corpus and create a dictionary containing each word and their corresponding frequencies.

As you can see above, we have created a dictionary called wordfreq. Next, we iterate through each word in the sentence and check if it exists in the wordfreq dictionary. On its existence,we will add the word as the key and set the value of the word as 1.

Step 8: Our corpus has more than 500 words in total and so we shall filter down to the 200 most frequently occurring words by using Python’s heap library.

Step 9: Now, comes the final step of converting the sentences in our corpus into their corresponding vector representation. Let’s check the below code snippet to understand it. Our model is in the form of a list of lists which can be easily converted matrix form using this script:

Five ways Data Science is used in Fintech

April 2, 2021/in Insights, Use Cases/by Shannon Flynn

Data science experts process and act upon data that digital resources produce. In the fintech world, data comes from mobile apps, transactions, conversations and financial standings. With this data for fintech, experts can improve the experience and success of businesses and customers alike.

Apps like PayPal, Venmo and Cash App have led the way for other fintech organizations, big and small, to grow. In fact, roughly 65% of Americans are already using digital banking in some capacity, whether it’s an app or online service. This growth, in turn, brings benefits. From personalization to integrating robotic advisors, here are five ways data scientists help fintech brands.

1. Personalization

Finance is one of the most personal industries out there as it deals with your private accounts and data. To match this uniqueness, fintechs can use data science for personalization. That way, customer service caters to individual needs.

As the fintech company gathers data from individual transactions, communications, behavior and interests, data scientists can then use said data to curate a better experience for the customer. They can advertise products and services that the customer may need to help with savings, for instance.

Contis is one example of a fintech that has integrated personalization into its services. Customers receive specific recommendations to create an efficient experience.

2. Fundraising

Fundraising had an interesting year in 2020. Amid racial justice protests and movements, crowdfunding took off on fintechs like GoFundMe and Kickstarter. These platforms helped provide funding for those who needed it. From here, data scientists can use fundraising in unique ways.

They can help raise money by targeting people who have donated in the past, or who are likely to donate based on spending habits. This data provides a more well-rounded fundraising campaign.

Then, once they do have donors, they can again use data to segment contributors by interest, demographic or engagement history. This segmentation helps advertise in a more personal, interest-specific way.

3. Fraud Detection

Cybercriminals thrive on an abundance of digital interactions. With the rise in digital banking — and the pandemic-driven shift to technology — fintechs could potentially see high rates of fraud. In fact, by the end of 2020, the United States saw about $11 billion in lost funds from credit card fraud alone.

Data for fintech brands will help address and prevent fraud like this in the future. As customers produce data from their transactions and interactions, it provides a better picture of their behavior. If there’s deviance, the data then shows potential fraud may be occurring.

If fraud does occur, data scientists can then use that instance to learn and properly recognize how data behaves during cybercriminal activity.

4. Robo-Advisors

With more people using fintech services, employees have a lot on their hands. They must properly address the customers’ needs and provide solutions. However, in the online world, employees are now getting some robotic assistance.

Robo-advisors use machine learning algorithms to interact with customers online or on mobile apps. They ask questions, understand the problems and provide solutions. They also collect data like customer goals and financial plans, which they can report back to data scientists for analysis.

Overall, roughly 75% and 46% of large and small banks, respectively, are implementing artificial intelligence to some degree. This data-driven revolution is one to keep your eye on.

5. Blockchain Governance

Blockchain governance is a somewhat newer way that experts can use data for fintech services. The blockchain is commonly known for its support of cryptocurrency services. Though crypto assets like Bitcoin and Ethereum are on the rise, the blockchain itself is still getting its footing.

Now, fintechs like PayPal are offering crypto services, which means data scientists will be able to expand what’s possible for digital banking. As customers transfer crypto funds, data scientists can monitor their activity and get a better handle on the data that exists on the blockchain. From there, they can provide personalization and prevent fraud in the same ways as they would with standard digital banking.

A Changing Landscape

As data scientists continue to help fintech services grow, you’ll notice each of these five areas begins to become more common. Some, like personalization and fraud detection, are already key focuses for fintech companies. However, alongside robo-advisor, fundraising and blockchain, they all have room to grow through the use of data science.

Data Mining Process flow – Easy Understanding

February 4, 2021/in Artificial Intelligence, Data Mining, Data Science, Deep Learning, Machine Learning, Main Category, Predictive Analytics, Statistics/by Ahamed Al Farabi

1 Overview

Development of computer processing power, network and automated software completely change and give new concept of each business. And data mining play the vital part to solve, finding the hidden patterns and relationship from large dataset with business by using sophisticated data analysis tools like methodology, method, process flow etc.

On this paper, proposed a process flow followed CRISP-DM methodology and has six steps where data understanding does not considered.

Phase of new process flow given below:-

Phase 1: Involved with collection, outliner treatment, imputation, transformation, scaling, and partition dataset in to two sub-frames (Training and Testing). Here as an example for outliner treatment, imputation, transformation, scaling consider accordingly Z score, mean, One hot encoding and Min Max Scaler.

Phase 2: On this Phase training and testing data balance with same balancing algorithm but separately. As an example here SMOTE (synthetic minority oversampling technique) is considered.

Phase 3: This phase involved with reduction, selection, aggregation, extraction. But here for an example considering same feature reduction algorithm (LDA -Linear Discriminant analysis) on training and testing data set separately.

Phase 4: On this Phase Training data set again partition into two more set (Training and Validation).

Phase 5: This Phase considering several base algorithms as a base model like CNN, RNN, Random forest, MLP, Regression, Ensemble method. This phase also involve to find out best hyper parameter and sub-algorithm for each base algorithm. As an example on this paper consider two class classification problems and also consider Random forest (Included CART – Classification and Regression Tree and GINI index impurity) and MLP classifier (Included (Relu, Sigmoid, binary cross entropy, Adam – Adaptive Moment Estimation) as base algorithms.

Phase 6: First, Prediction with validation data then evaluates with Test dataset which is fully unknown for these (Random forest, MLP classifier) two base algorithms. Then calculate the confusion matrix, ROC, AUC to find the best base algorithm.

New method from phase 1 to phase 4 followed CRISP-DM methodology steps such as data collection, data preparation then phase 5 followed modelling and phase 6 followed evaluation and implementation steps.

Structure of proposed process flow for two class problem combined with algorithm and sub-algorithm display on figure – 1.

These articles mainly focus to describe all algorithms which are going to implementation for better understanding.

Figure 1 – Data Mining Process Flow

2 Phase 1: Outlier treatment, Transform, Scaling, Imputation

This phase involved with outlier treatment, imputation, scaling, and transform data.

2.1 Outliner treatment: – Z score

Outlier is a data point which lies far from all other data point in a data set. Outlier need to treat because it may bias the entire result. Outlier treatment with Z score is a common technique. Z score is a standard score in statistics. Z score provides information about data value is smaller or grater then mean that means how many standard deviations away from the mean value. Z score equation display below:

$Z = \frac{(x - \mu)}{\sigma}$

Here x = data point
σ = Standard deviation
μ = mean value

Equation- 1 Z-Score

In a normal distribution Z score represent 68% data lies on +/- 1, 95% data point lies on +/- 2, 99.7% data point lies on +/- 3 standard deviation.

2.2 Imputation data: – mean

Imputation is a way to handle missing data by replacing substituted value. There are many imputation technique represent like mean, median, mode, k-nearest neighbours. Mean imputation is the technique to replacing missing information with mean value. On the mean imputation first calculate the particular features mean value and then replace the missing value with mean value. The next equation displays the mean calculation:

$\mu = \frac{(\sum x)}{n}$

Here x = value of each point
n = number of values
μ = mean value

Equation- 2 Mean

2.3 Transform: – One hot encoding

Encoding is a pre-processing technique which represents data in such a way that computer can understand. For understanding of machine learning algorithm categorical columns convert to numerical columns, this process called categorical encoding. There are multiple way to handle categorical variable but most widely used techniques are label encoding and one host encoding. On label encoding give a numeric (integer number) for each category. Suppose there are 3 categories of foods like apples, orange, banana. When label encoding is used then 3 categories will get a numerical value like apples = 1, banana = 2 and orange = 3. But there is very high probability that machine learning model can capture the relationship in between categories such as apple < banana < orange or calculate average across categories like 1 +3 = 4 / 2 = 2 that means model can understand average of apple and orange together is banana which is not acceptable because model correlation calculation is wrong. For solving this problem one hot encoding appear. The following table displays the label encoding is transformed into one hot encoding.

Table- 1 Encoding example

On hot encoding categorical value split into columns and each column contains 0 or 1 according to columns placement.

2.4 Scaling data: – Min Max Scaler

Feature scaling method is standardized or normalization the independent variable that means it is used to scale the data in a particular range like -1 to +1 or depending on algorithm. Generally normalization used where data distribution does not follow Gaussian distribution and standardization used where data distribution follow Gaussian distribution. On standardization techniques transform data values are cantered around the mean and unit is standard deviation. Formula for standardization given below:

$Standardization X = \frac{(X - \mu)}{\sigma}$

Equation-3 Equations for Standardization

X represent the feature value, µ represent mean of the feature value and σ represent standard deviation of the feature value. Standardized data value does not restrict to a particular range.

Normalization techniques shifted and rescaled data value range between 0 and 1. Normalization techniques also called Min-Max scaling. Formula for normalization given below:

$Normalization X = \frac{(X - X_{min})}{X_{max} - X_{min}}$

Equation – 4 Equations for Normalization

Above X, X_min, X_max are accordingly feature values, feature minimum value and feature maximum value. On above formula when X is minimums then numerator will be 0 ( is 0) or if X is maximums then the numerator is equal to the denominator ( is 1). But when X data value between minimum and maximum then is between 0 and 1. If ranges value of data does not normalized then bigger range can influence the result.

3 Phase 2: – Balance Data

3.1 SMOTE

SMOTE (synthetic minority oversampling technique) is an oversampling technique where synthetic observations are created based on existing minority observations. This technique operates in feature space instead of data space. Under SMOTE each minority class observation calculates k nearest neighbours and randomly chose the neighbours depending on over-sampling requirements. Suppose there are 4 data point on minority class and 10 data point on majority class. For this imbalance data set, balance by increasing minority class with synthetic data point. SMOTE creating synthetic data point but it is necessary to consider k nearest neighbours first. If k = 3 then SMOTE consider 3 nearest neighbours. Figure-2 display SMOTE with k = 3 and x = x1, x2, x3, x4 data point denote minority class. And all circles represent majority class.

Figure- 2 SMOTE example

4 Phase 3: – Feature Reduction

4.1 LDA

LDA stands for Linear Discriminant analysis supervised technique are commonly used for classification problem. On this feature reduction account continuous independent variable and output categorical variable. It is multivariate analysis technique. LDA analyse by comparing mean of the variables. Main goal of LDA is differentiate classes in low dimension space. LDA is similar to PCA (Principal component analysis) but in addition LDA maximize the separation between multiple classes. LDA is a dimensionality reduction technique where creating synthetic feature from linear combination of original data set then discard less important feature. LDA calculate class variance, it maximize between class variance and minimize within class variance. Table-2 display the process steps of LDA.

Table- 2 LDA process

5 Phase 5: – Base Model

Here we consider two base model ensemble random forest and MLP classifier.

5.1 Random Forest

Random forest is an ensemble (Bagging) method where group of weak learner (decision tree) come together to form a strong leaner. Random forest is a supervised algorithm which is used for regression and classification problem. Random forests create several decisions tree for predictions and provide solution by voting (classification) or mean (regression) value. Working process of Random forest given below (Table -3).

Table-3 Random Forest process

When training a Random forest root node contains a sample of bootstrap dataset and the feature is as same as original dataset. Suppose the dataset is D and contain d record and m number of columns. From the dataset D random forest first randomly select sample of rows (d^’) with replacement and sample of features (n^’) and give it to the decision tree. Suppose Random forest created several decision trees like T₁, T₂, T₃, T₄ . . . T_n. Then randomly selected dataset D^’ = d^’ + n^’ is given to the decision tree T₁, T₂, T₃, T₄ . . . T_n where D^’ < D, m > n^’ and d > d^’. After taking the dataset decision tree give the prediction for binary classification 1 or 0 then aggregating the decision and select the majority voted result. Figure-3 describes the structure of random forest process.

Figure- 3 Random Forest process

On Random forest base learner Decision Tree grows complete depth where bias (properly train on training dataset) is low and variance is high (when implementing test data give big error) called overfitting. On Random forest using multiple decision trees where each Decision tree is high variance but when combining all decision trees with the respect of majority vote then high variance converted into low variance because using row and feature sampling with replacement and taking the majority vote where decision is not depend on one decision tree.

CART (Classification and Regression Tree) is binary segmentation technique. CART is a Gini’s impurity index based classical algorithm to split a dataset and build a decision tree. By splitting a selected dataset CART created two child nodes repeatedly and builds a tree until the data no longer be split. There are three steps CART algorithm follow:

Find best split for each features. For each feature in binary split make two groups of the ordered classes. That means possibility of split for k classes is k-1. Find which split is maximized and contain best splits (one for each feature) result.
Find the best split for nodes. From step 1 find the best one split (from all features) which maximized the splitting criterion.
Split the best node from step 2 and repeat from step 1 until fulfil the stopping criterion.

For splitting criteria CART use GINI index impurity algorithm to calculate the purity of split in a decision tree. Gini impurity randomly classified the labels with the same distribution in the dataset. A Gini impurity of 0 (lowest) is the best possible impurity and it is achieve when everything is in a same class. Gini index varies from 0 to 1. 0 indicate the purity of class where only one class exits or all element under a specific class. 1 indicates that elements are randomly distributed across various classes. And 0.5 indicate equal elements distributed over classes. Gini index (GI) described by mathematically that sum of squared of probabilities of each class (p_i) deducted from one (Equation-5).

Equation – 5 Gini impurities

Here (Equation-5) p_i represent the probability (probability of p+ or yes and probability of p- or no) of distinct class with classified element. Suppose randomly selected feature (a1) which has 8 yes and 4 no. After the split right had side (b1 on equation-6) has 4 yes and 4 no and left had side (b2 on equation – 7) has 4 yes and 0 no. here b2 is a pure split (leaf node) because only one class yes is present. By using the GI (Gini index) formula for b1 and b2:-

Equation- 6 & 7 – Gini Impurity b1 & Gini Impurity b2

Here for b1 value 0.5 indicates that equal element (yes and no) distribute over classes which is not pure split. And b2 value 0 indicates pure split. On GINI impurity indicates that when probability (yes or no) increases GINI value also increases. Here 0 indicate pure split and .5 indicate equal split that means worst situation. After calculating the GINI index for b1 and b2 now calculate the reduction of impurity for data point a1. Here total yes 8 (b1 and b2 on Equation – 8) and total no 4 (b1) so total data is 12 on a1. Below display the weighted GINI index for feature a1:

Total data point on b1 with Gini index (m) = 8/12 * 0.5 = 0.3333

Total data point on b2 with Gini index (n) = 4/12 * 0 = 0

Weighted Gini index for feature a1 = m + n = 0.3333

Equation- 8 Gini Impurity b1 & b2

After computing the weighted Gini value for every feature on a dataset taking the highest value feature as first node and split accordingly in a decision tree. Gini is less costly to compute.

5.2 Multilayer Perceptron Classifier (MLP Classifier)

Multilayer perceptron classifier is a feedforward neural network utilizes supervised learning technique (backpropagation) for training. MLP Classifier combines with multiple perceptron (hidden) layers. For feedforward taking input send combining with weight bias and then activation function from one hidden layer output goes to other hidden and this process continuing until reached the output. Then output calculates the error with error algorithm. These errors send back with backpropagation for weight adjustment by decreasing the total error and process is repeated, this process is call epoch. Number of epoch is determined with the hyper-parameter and reduction rate of total error.

5.2.1 Back-Propagation

Backpropagation is supervised learning algorithm that is used to train neural network. A neural network consists of input layer, hidden layer and output layer and each layer consists of neuron. So a neural network is a circuit of neurons. Backpropagation is a method to train multilayer neural network the updating of the weights of neural network and is done in such a way so that the error observed can be reduced here, error is only observed in the output layer and that error is back propagated to the previous layers and previous layer is proportionally updated weight. Backpropagation maintain chain rule to update weight. Mainly three steps on backpropagation are (Table-4):

Step	Process
Step 1	Forward Pass
Step 2	Backward Pass
Step 3	Sum of all values and calculate updated weight value with Chain – rules.

Table-4 Back-Propagation process

5.2.2 Forward pass/ Forward propagation

Forward propagation is the process where input layer send the input value with randomly selected weight and bias to connected neuron and inside neuron selected activation function combine them and forward to other connected neuron layer after layer then give an output with the help of output layer. Below (Figure-4) display the forward propagation.

Figure-4 Forward passes

Input layer take the input of X (X₁, X₂) combine with randomly selected weight for each connection and with fixed bias (different hidden layer has different bias) send it to first hidden layer where first multiply the input with corresponding weight and added all input with single bias then selected activation function (may different form other layer) combine all input and give output according to function and this process is going on until reach in output layer. Output layer give the output like Y (Y₁, Y₂) (here output is binary classification as an example) according to selected activation function.

5.2.3 Backward Pass

After calculating error (difference between Forward pass output and actual output) backward pass try to minimize the error with optimisation function by sending backward with proportionally distribution and maintain a chain rule. Backward pass distribution the error in such a way where weighted value is taking under consideration. Below (Figure-5) diagram display the Backward pass process.

Figure-5 Backward passes

Backpropagation push back the error which is calculated with error function or loss function for update proportional distribution with the help of optimisation algorithm. Division of Optimisation algorithm given below on Figure – 6

Figure -6 Division of Optimisation algorithms

Gradient decent calculate gradient and update value by increases or decreases opposite direction of gradients unit and try to find the minimal value. Gradient decent update just one time for whole dataset but stochastic gradient decent update on each training sample and it is faster than normal gradient decent. Gradient decent can be improve by tuning parameter like learning rate (0 to 1 mostly use 0.5). Adagrad use time step based parameter to compute learning rate for every parameter. Adam is Adaptive Moment Estimation. It calculates different parameter with different learning rate. It is faster and performance rate is higher than other optimization algorithm. On the other way Adam algorithm is squares the calculated exponential weighted moving average of gradient.

5.2.4 Chain – rules

Backpropagation maintain chain-rules to update weighted value. On chain-rules backpropagation find the derivative of error respect to any weight. Suppose E is output error. w is weight for input a and bias b and ac neuron output respect of activation function and summation of bias with weighted input (w*a) input to neuron is net. So partial derivative for error respect to weight is ∂E / ∂w display the process on figure-7.

Figure- 7 Partial derivative for error respect to weight

On the chain rules for backward pass to find (error respect to weight) ∂E / ∂w = ∂E / ∂ac * ∂ac / ∂net * ∂net / ∂w. here find to error respect to weight are error respect to output of activation function multiply by activation function output respect to input in a neuron multiply by input in a neuron respect to weight.

5.2.5 Activation function

Activation function is a function which takes the decision about neuron to activate or deactivate. If the activate function activate the neuron then it will give an output on the basis of input. Input in a activation function is sum of input multiply with corresponding weight and adding the layered bias. The main function of a activate function is non-linearity output of a neuron.

Figure-8 Activation function

Figure – 8 display a neuron in a hidden layer. Here several input (1, 2, 3) with corresponding weight (w1, w2, w3) putting in a neuron input layer where layer bias add with summation of multiplication with input and weight. Equation-9 display the output of an activate function.

Output from activate function y = Activate function (Ʃ (weight * input) + bias)

y = f (Ʃ (w*x) +b)

Equation- 9 Activate function

There are many activation functions like linear function for regression problem, sigmoid function for binary classification problem where result either 0 or 1, Tanh function which is based on sigmoid function but mathematically shifted version and values line -1 to 1. RELU function is Rectified linear unit. RELU is less expensive to compute.

5.2.6 Sigmoid

Sigmoid is a squashing activate function where output range between 0 and 1. Sigmoidal name comes from Greek letter sigma which looks like letter S when graphed. Sigmoid function is a logistic type function, it mainly use in output layer in neural network. Sigmoid is non-linear, fixed output range (between 0 and 1), monotonic (never decrees or never increases) and continuously differentiated function. Sigmoid function is good at classification and output from sigmoid is nonlinear. But Sigmoid has a vanishing gradient problem because output variable is very less to change in input variable. Figure- 9 displays the output of a Sigmoid and derivative of Sigmoid. Here x is any number (positive or negative). On sigmoid function 1 is divided by exponential negative input with adding 1.

Figure – 9 Sigmoid Functions

4.5.2.7 RELU

RELU stands for Rectified Linear Units it is simple, less expensive in computation and rectifies the gradient vanishing problem. RELU is nonlinear activation function. It gives output either positive (infinity) or 0. RELU has a dying problem because if neurons stop for responding to variation because of gradient is 0 or nothing has to change. Figure- 10 displays the output of an RELU and derivative of RELU. Here x is any positive input and if x is grater then 0 give the output as x or give output 0. RELU function gives the output maximum value of input, here max (0, x).

Figure – 10 RELU Function

4.5.2.8 Cost / loss function (Binary Cross-Entropy)

Cost or loss function compare the predictive value (model outcome) with actual value and give a quantitative value which give the indication about how much good or bad the prediction is.

Figure- 11 Cost function work process

Figure-11 x1 and x2 are input in a activate function f(x) and output y1_out which is sum of weighted input added with bias going through activate function. After model output activate function compare the output with actual output and give a quantitative value which indicate how good or bad the prediction is.

There are many type of loss function but choosing of optimal loss function depends on the problem going to be solved such as regression or classification. For binary classification problem binary cross entropy is used to calculate cost. Equation-10 displays the binary cross entropy where y is actual binary value and y^p predictive outcome range 0 and 1. And i is scalar vale range between 1 to model output size (N).

Equation-10 displays the binary cross entropy

6 Phase 6: – Evaluation

6.1 Confusion matrix

In a classification confusion matrix describe the performance of actual value against predictive value. Confusion Matrix does the performance measurement. So confusion matrix classifies and display predicted and actual value (Visa, S., Ramsay 2011).

Table- 5 Confusion Matrix

Confusion Matrix (Table-5) combines with True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). True Positive is prediction positive and true. True Negative is prediction negative and that is true. False positive is prediction positive and it’s false. False negative is prediction negative and that is false. False positive is known as Type1 error and false negative is known as Type 2 error. Confusion matrix can able to calculate several list of rates which are given below on Table- 6.

Here N = Total number of observation, TP = True Positive, TN = True Negative

FP = False Positive, FN = False Negative, Total Actual No (AN) = TN + FP,

Total Predictive Yes (PY) = FP + TP. Total Actual Yes (AY) = FN + TP

Rate	Description	Mathematical Description
Accuracy	Classifier, overall how often correctly identified	(TP+TN) / N
Misclassification Rate	Classifier, overall how often wrongly identified	(FP + FN) / N
True Positive Rate (Sensitivity / Recall)	Classifier, how often predict correctly yes when it is actually yes.	TP / AY
False Positive Rate	Classifier, how often predict wrongly yes when it is actually no.	FP / AN
True Negative Rate (Specificity)	Classifier, how often predict correctly no when it is actually no.	TN / AN
Precision	Classifier how often predict yes when it is correct.	TP / PY
Prevalence	Yes conditions how often occur in a sample.	AY / N

Table – 6 Confusion matrixes Calculation

From confusion matrix F1 score can be calculated because F1 score related to precision and recall. Higher F1 score is better. If precision or recall any one goes down F1 score also go down.

$F1 = \frac{2 * Precision * Recall}{Precision + Recall}$

4.6.2 ROC (Receiver Operating Characteristic) curve

In statistics ROC is represent in a graph with plotting a curve which describe a binary classifiers performance as its differentiation threshold is varied. ROC (Equation-11) curve created true positive rate (TPR) against false positive rate (FPR). True positive rate also called as Sensitivity and False positive rate also known as Probability of false alarm. False positive rate also called as a probability of false alarm and it is calculated as 1 – Specificity.

$True Positive Rate = \frac{True Positive}{True Positive + False Negative} = Recall or Sensivity$

$False Positive Rate = \frac{True Negative}{True Negative + False Positive} = 1 - Specificity$

Equation- 11 ROC

So ROC (Receiver Operation Characteristic) curve allows visual representation between sensitivity and specificity associated with different values of the test result (Grzybowski, M. and Younger, J.G., 1997)

On ROC curve each point has different Threshold level. Below (Figure – 12) display the ROC curve. Higher the area curve covers is better that means high sensitivity and high specificity represent more accuracy. ROC curve also represent that if classifier predict more often true than it has more true positive and also more false positive. If classifier predict true less often then fewer false positive and also fewer true positive.

ROC Curve

Figure – 12 ROC curve description

4.6.3 AUC (Area under Curve)

Area under curve (AUC) is the area surrounded by the ROC curve and AUC also represent the degree of separability that means how good the model to distinguished between classes. Higher the AUC value represents better the model performance to separate classes. AUC = 1 for perfect classifier, AUC = 0 represent worst classifier, and AUC = 0.5 means has no class separation capacity. Suppose AUC value is 0.6 that means 60% chance that model can classify positive and negative class.

Figure- 13 to Figure – 16 displays an example of AUC where green distribution curve for positive class and blue distribution curve for negative class. Here threshold or cut-off value is 0.5 and range between ‘0’ to ‘1’. True negative = TN, True Positive = TP, False Negative = FN, False Positive = FP, True positive rate = TPR (range 0 to 1), False positive rate = FPR (range 0 to 1).

On Figure – 13 left distribution curve where two class curves does not overlap that means both class are perfectly distinguished. So this is ideal position and AUC value is 1. On the left side ROC also display that TPR for positive class is 100% occupied.

ROC distributions (perfectly distinguished

Figure – 14 two class overlap each other and raise false positive (Type 1), false negative (Type 2) errors. Here error could be minimize or maximize according to threshold. Suppose here AUC = 0.6, that means chance of a model to distinguish two classes is 60%. On ROC curve also display the curve occupied for positive class is 60%.

ROC distributions (class partly overlap distinguished)

Figure- 15 displayed that positive and negative overlap each other. Here AUC value is 0.5 or near to 0.5. On this position classifier model does not able distinguish positive and negative classes. On left side ROC curve become straight that means TPR and FPR are equal.

ROC distributions (class fully overlap distinguished)

Figure- 16 positive and negative class swap position and on this position AUC = 0. That means classified model predict positive as a negative and negative as a positive. On the left ROC curve display that curve on FPR side fully fitted.

ROC distributions (class swap position distinguished)

7 Summaries

This paper describes a data mining process flow and related model and its algorithm with textual representation. One hot encoding create dummy variable for class features and min-max scaling scale the data in a single format. Balancing by SMOTE data where Euclidian distance calculates the distance in-between nearest neighbour to produce synthetic data under minority class. LDA reduce the distance inside class and maximise distance in-between class and for two class problem give a single dimension features which is less costly to calculate accuracy by base algorithm (random forest and MLP classifier). Confusion matrix gives the accuracy, precision, sensitivity, specificity which is help to take a decision about base algorithm. AUC and ROC curve also represent true positive rate against false positive rate which indicate base algorithm performance.

Base algorithm Random forest using CART with GINI impurity for feature selection to spread the tree. Here CART is selected because of less costly to run. Random forest algorithm is using bootstrap dataset to grow trees, and aggregation using majority vote to select accuracy.

MLP classifier is a neural network algorithm using backpropagation chain-rule to reducing error. Here inside layers using RLU activation function. Output layers using Sigmoid activation function and binary cross entropy loss function calculate the loss which is back propagate with Adam optimizer to optimize weight and reduce loss.

References:

Visa, S., Ramsay, B., Ralescu, A.L. and Van Der Knaap, E., 2011. Confusion Matrix-based Feature Selection. MAICS, 710, pp.120-127.
Grzybowski, M. and Younger, J.G., 1997. Statistical methodology: III. Receiver operating characteristic (ROC) curves. Academic Emergency Medicine, 4(8), pp.818-826.

On the difficulty of language: prerequisites for NLP with deep learning

January 27, 2021/in Artificial Intelligence, Big Data, Data Mining, Data Science, Data Science Hack, Deep Learning, Machine Learning, Main Category, Natural Language Processing, Python, TensorFlow, Text Mining/by Yasuto Tamura

This is the first article of my article series “Instructions on Transformer for people outside NLP field, but with examples of NLP.”

1 Preface

This section is virtually just my essay on language. You can skip this if you want to get down on more technical topic.

As I do not study in natural language processing (NLP) field, I would not be able to provide that deep insight into this fast changing deep leaning field throughout my article series. However at least I do understand language is a difficult and profound field, not only in engineering but also in many other study fields. Some people might be feeling that technologies are eliminating languages, or one’s motivations to understand other cultures. First of all, I would like you to keep it in mind that I am not a geek who is trying to turn this multilingual world into a homogeneous one and rebuild Tower of Babel, with deep learning. I would say I am more keen on social or anthropological sides of language.

I think you would think more about languages if you have mastered at least one foreign language. As my mother tongue is Japanese, which is totally different from many other Western languages in terms of characters and ambiguity, I understand translating is not what learning a language is all about. Each language has unique characteristics, and I believe they more or less influence one’s personalities. For example, many Western languages make the verb, I mean the conclusion, of sentences clear in the beginning part of the sentences. That is also true of Chinese, I heard. However in Japanese, the conclusion comes at the end, so that is likely to give an impression that Japanese people are being obscure or indecisive. Also, Japanese sentences usually omit their subjects. In German as well, the conclusion of a sentences tend to come at the end, but I am almost 100% sure that no Japanese people would feel German people make things unclear. I think that comes from the structures of German language, which tends to make the number, verb, relations of words crystal clear.

Source: https://twitter.com/nakamurakihiro

Let’s take an example to see how obscure Japanese is. A Japanese sentence 「頭が赤い魚を食べる猫」can be interpreted in five ways, depending on where you put emphases on.

Common sense tells you that the sentence is likely to mean the first two cases, but I am sure they can mean those five possibilities. There might be similarly obscure sentences in other languages, but I bet few languages can be as obscure as Japanese. Also as you can see from the last two sentences, you can omit subjects in Japanese. This rule is nothing exceptional. Japanese people usually don’t use subjects in normal conversations. And when you read classical Japanese, which Japanese high school students have to do just like Western students learn some of classical Latin, the writings omit subjects much more frequently.

*However interestingly we have rich vocabulary of subjects. The subject “I” can be translated to 「私」、「僕」、「俺」、「自分」、「うち」etc, depending on your personality, who you are talking to, and the time when it is written in.

I believe one can see the world only in the framework of their language, and it seems one’s personality changes depending on the language they use. I am not sure whether the language originally determines how they think, or how they think forms the language. But at least I would like you to keep it in mind that if you translate a conversation, for example a random conversation at a bar in Berlin, into Japanese, that would linguistically sound Japanese, but not anthropologically. Imagine that such kind of random conversation in Berlin or something is like playing a catch, I mean throwing a ball named “your opinion.” On the other hand, normal conversations of Japanese people are in stead more of, I would say, “resonance” of several tuning forks. They do their bests to show that they are listening to each other, by excessively nodding or just repeating “Really?”, but usually it seems hardly any constructive dialogues have been made.

*I sometimes feel you do not even need deep learning to simulate most of such Japanese conversations. Several-line Python codes would be enough.

My point is, this article series is mainly going to cover only a few techniques of NLP in deep learning field: sequence to sequence model (seq2seq model) , and especially Transformer. They are, at least for now, just mathematical models and mappings of a small part of this profound field of language (as far as I can cover in this article series). But still, examples of language would definitely help you understand Transformer model in the long run.

2 Tokens and word embedding

*Throughout my article series, “words” just means the normal words you use in daily life. “Tokens” means more general unit of NLP tasks. For example the word “Transformer” might be denoted as a single token “Transformer,” or maybe as a combination of two tokens “Trans” and “former.”

One challenging part of handling language data is its encodings. If you started learning programming in a language other than English, you would have encountered some troubles of using keyboards with different arrangements or with characters. Some comments on your codes in your native languages are sometimes not readable on some software. You can easily get away with that by using only English, but when it comes to NLP you have to deal with this difficulty seriously. How to encode characters in each language should be a first obstacle of NLP. In this article we are going to rely on a library named BPEmb, which provides word embedding in various languages, and you do not have to care so much about encodings in languages all over the world with this library.

In the first section, you might have noticed that Japanese sentence is not separated with spaces like Western languages. This is also true of Chinese language, and that means we need additional tasks of separating those sentences at least into proper chunks of words. This is not only a matter of engineering, but also of some linguistic fields. Also I think many people are not so conscious of how sentences in their native languages are grammatically separated.

The next point is, unlike other scientific data, such as temperature, velocity, voltage, or air pressure, language itself is not measured as numerical data. Thus in order to process language, including English, you first have to map language to certain numerical data, and after some processes you need to conversely map the output numerical data into language data. This section is going to be mainly about one-hot encoding and word embedding, the ways to convert word/token into numerical data. You might already have heard about this

You might have learnt about word embedding to some extent, but I hope you could get richer insight into this topic through this article.

2.1 One-hot encoding

One-hot encoding would be the most straightforward way to encode words/tokens. Assume that you have a dictionary whose size is $|\mathcal{V}|$ , and it includes words from “a”, “ablation”, “actually” to “zombie”, “?”, “!”

In a mathematical manner, in order to choose a word out of those $|\mathcal{V}|$ words, all you need is a $|\mathcal{V}|$ dimensional vector, one of whose elements is $1$ , and the others are $0$ . When you want to choose the No. i word, which is “indeed” in the example below, its corresponding one-hot vector is $\boldsymbol{v} = (0, \dots, 1, \dots, 0 )$ , where only the No. i element is $1$ . One-hot encoding is also easy to understand, and that’s all. It is easy to imagine that people have already come up with more complicated and better way to encoder words. And one major way to do that is word embedding.

2.2 Word embedding

Source: Francois Chollet, Deep Learning with Python,(2018), Manning

Actually word embedding is related to one-hot encoding, and if you understand how to train a simple neural network, for example densely connected layers, you would understand word embedding easily. The key idea of word embedding is denoting each token with a $D$ dimensional vector, whose dimension is fewer than the vocabulary size $|\mathcal{V}|$ . The elements of the resulting word embedding vector are real values, I mean not only 0 or 1. Obviously you can encode much richer variety of tokens with such vectors. The figure at the left side is from “Deep Learning with Python” by François Chollet, and I think this is an almost perfect and simple explanation of the comparison of one-hot encoding and word embedding. But the problem is how to get such convenient vectors. The answer is very simple: you have only to train a network whose inputs are one-hot vector of the vocabulary.

The figure below is a simplified model of word embedding of a certain word. When the word is input into a neural network, only the corresponding element of the one-hot vector is 1, and that virtually means the very first input layer is composed of one neuron whose value is 1. And the only one neuron propagates to the next D dimensional embedding layer. These weights are the very values which most other study materials call “an embedding vector.”

When you input each word into a certain network, for example RNN or Transformer, you map the input one-hot vector into the embedding layer/vector. The examples in the figure are how inputs are made when the input sentences are “You’ve got the touch” and “You’ve got the power.” Assume that you have a dictionary of one-hot encoding, whose vocabulary is {“the”, “You’ve”, “Walberg”, “touch”, “power”, “Nights”, “got”, “Mark”, “Boogie”}, and the dimension of word embeding is 6. In this case $|\mathcal{V}| = 9, D=6$ . When the inputs are “You’ve got the touch” or “You’ve got the power” , you put the one-hot vector corresponding to “You’ve”, “got”, “the”, “touch” or “You’ve”, “got”, “the”, “power” sequentially every time step $t$ .

In order to get word embedding of certain vocabulary, you just need to train the network. We know that the words “actually” and “indeed” are used in similar ways in writings. Thus when we propagate those words into the embedding layer, we can expect that those embedding layers are similar. This is how we can mathematically get effective word embedding of certain vocabulary.

More interestingly, if word embedding is properly trained, you can mathematically “calculate” words. For example, $\boldsymbol{v}_{king} - \boldsymbol{v}_{man} + \boldsymbol{v}_{woman} \approx \boldsymbol{v}_{queen}$ , $\boldsymbol{v}_{Japan} - \boldsymbol{v}_{Tokyo} + \boldsymbol{v}_{Vietnam} \approx \boldsymbol{v}_{Hanoi}$ .

*I have tried to demonstrate this type of calculation on several word embedding, but none of them seem to work well. At least you should keep it in mind that word embedding learns complicated linear relations between words.

I should explain word embedding techniques such as word2vec in detail, but the main focus of this article is not NLP, so the points I have mentioned are enough to understand Transformer model with NLP examples in the upcoming articles.

3 Language model

Language models is one of the most straightforward, but crucial ideas in NLP. This is also a big topic, so this article is going to cover only basic points. Language model is a mathematical model of the probabilities of which words to come next, given a context. For example if you have a sentence “In the lecture, he opened a _.”, a language model predicts what comes at the part “_.” It is obvious that this is contextual. If you are talking about general university students, “_” would be “textbook,” but if you are talking about Japanese universities, especially in liberal art department, “_” would be more likely to be “smartphone. I think most of you use this language model everyday. When you type in something on your computer or smartphone, you would constantly see text predictions, or they might even correct your spelling or grammatical errors. This is language modelling. You can make language models in several ways, such as n-gram and neural language models, but in this article I can explain only general formulations for such models.

*I am not sure which algorithm is used in which services. That must be too fast changing and competitive for me to catch up.

As I mentioned in the first article series on RNN, a sentence is usually processed as sequence data in NLP. One single sentence is denoted as $\boldsymbol{X} = (\boldsymbol{x}^{(1)}, \dots, \boldsymbol{x}^{(\tau)})$ , a list of vectors. The vectors are usually embedding vectors, and the $(t)$ is the index of the order of tokens. For example the sentence “You’ve go the power.” can be expressed as $\boldsymbol{X} = (\boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)}, \boldsymbol{x}^{(3)}, \boldsymbol{x}^{(4)})$ , where $\boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)}, \boldsymbol{x}^{(3)}, \boldsymbol{x}^{(4)}$ denote “You’ve”, “got”, “the”, “power”, “.” respectively. In this case $\tau = 4$ .

In practice a sentence $\boldsymbol{X}$ usually includes two tokens $BOS$ and $EOS$ at the beginning and the end of the sentence. They mean “Beginning Of Sentence” and “End Of Sentence” respectively. Thus in many cases $\boldsymbol{X} = (\boldsymbol{BOS} , \boldsymbol{x}^{(1)}, \dots, \boldsymbol{x}^{(\tau)}, \boldsymbol{EOS} )$ . $\boldsymbol{BOS}$ and $\boldsymbol{EOS}$ are also both vectors, at least in the Tensorflow tutorial.

$P(\boldsymbol{X} =$ $(\boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \dots, \boldsymbol{x}^{(\tau)}, \boldsymbol{EOS})$ is the probability of incidence of the sentence. But it is easy to imagine that it would be very hard to directly calculate how likely the sentence $\boldsymbol{X}$ appears out of all possible sentences. I would rather say it is impossible. Thus instead in NLP we calculate the probability $P(\boldsymbol{X})$ as a product of the probability of incidence or a certain word, given all the words so far. When you’ve got the words $(\boldsymbol{x}^{(1)}, \dots, \boldsymbol{x}^{(t-1})$ so far, the probability of the incidence of $\boldsymbol{x}^{(t)}$ , given the context is $P(\boldsymbol{x}^{(t)}|\boldsymbol{x}^{(1)}, \dots, \boldsymbol{x}^{(t-1)})$ . $P(\boldsymbol{BOS})$ is a probability of the the sentence $\boldsymbol{X}$ being $(\boldsymbol{BOS})$ , and the probability of $\boldsymbol{X}$ being $(\boldsymbol{BOS}, \boldsymbol{x}^{(1)})$ can be decomposed this way: $P(\boldsymbol{BOS}, \boldsymbol{x}^{(1)}) =$ $P(\boldsymbol{x}^{(1)}|\boldsymbol{BOS})P(\boldsymbol{BOS})$ .

Just as well $P(\boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)}) =$ $P(\boldsymbol{x}^{(2)}| \boldsymbol{BOS}, \boldsymbol{x}^{(1)}) P( \boldsymbol{BOS}, \boldsymbol{x}^{(1)})=$ $P(\boldsymbol{x}^{(2)}| \boldsymbol{BOS}, \boldsymbol{x}^{(1)}) P(\boldsymbol{x}^{(1)}| \boldsymbol{BOS}) P( \boldsymbol{BOS})$ .

Hence, the general probability of incidence of a sentence $\boldsymbol{X}$ is $P(\boldsymbol{X})=P(\boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)}, \dots, \boldsymbol{x}^{(\tau -1)}, \boldsymbol{x}^{(\tau)}, \boldsymbol{EOS}) =$ $P(\boldsymbol{EOS}| \boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \dots, \boldsymbol{x}^{(\tau)})$ $P(\boldsymbol{x}^{(\tau)}| \boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \dots, \boldsymbol{x}^{(\tau - 1)})$ $\cdots$ $P(\boldsymbol{x}^{(2)}| \boldsymbol{BOS}, \boldsymbol{x}^{(1)})$ $P(\boldsymbol{x}^{(1)}| \boldsymbol{BOS})$ $P(\boldsymbol{BOS})$ .

Let $\boldsymbol{x}^{(0)}$ be $\boldsymbol{BOS}$ and $\boldsymbol{x}^{(\tau + 1)}$ be $\boldsymbol{EOS}$ . Plus, let $P(\boldsymbol{x}^{(t+1)}|\boldsymbol{X}_{[0, t]})$ be $P(\boldsymbol{x}^{(t+1)}|\boldsymbol{x}^{(0)}, \dots, \boldsymbol{x}^{(t)})$ , then $P(\boldsymbol{X}) = P(\boldsymbol{x}^{(0)})\prod_{t=0}^{\tau}{P(\boldsymbol{x}^{(t+1)}|\boldsymbol{X}_{[0, t]})}$ . Language models calculate which words to come sequentially in this way.

Here’s a question: how would you evaluate a language model?

I would say the answer is, when the language model generates words, the more confident the language model is, the better the language model is. Given a context, when the distribution of the next word is concentrated on a certain word, we can say the language model is confident about which word to come next, given the context.

*For some people, it would be more understandable to call this “entropy.”

Let’s take the vocabulary {“the”, “You’ve”, “Walberg”, “touch”, “power”, “Nights”, “got”, “Mark”, “Boogie”} as an example. Assume that $P(\boldsymbol{X}) =$ $P(\boldsymbol{BOS}, \boldsymbol{You've}, \boldsymbol{got}, \boldsymbol{the}, \boldsymbol{touch}, \boldsymbol{EOS}) =$ $P(\boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)}, \boldsymbol{x}^{(3)}, \boldsymbol{x}^{(4)}, \boldsymbol{EOS})=$ $P(\boldsymbol{x}^{(0)})\prod_{t=0}^{4}{P(\boldsymbol{x}^{(t+1)}|\boldsymbol{X}_{[0, t]})}$ . Given a context $(\boldsymbol{BOS}, \boldsymbol{x}^{(1)})$ , the probability of incidence of $\boldsymbol{x}^{(2)}$ is $P(\boldsymbol{x}^{2}|\boldsymbol{BOS}, \boldsymbol{x}^{(1)})$ . In the figure below, the distribution at the left side is less confident because probabilities do not spread widely, on the other hand the one at the right side is more confident that next word is “got” because the distribution concentrates on “got”.

*You have to keep it in mind that the sum of all possible probability $P(\boldsymbol{x}^{(2)} | \boldsymbol{BOS}, \boldsymbol{x}^{(1)})$ is $1$ , that is, $P(\boldsymbol{the}| \boldsymbol{BOS}, \boldsymbol{x}^{(1)}) +$ $P(\boldsymbol{You've}| \boldsymbol{BOS}, \boldsymbol{x}^{(1)}) + \cdots$ $+ P(\boldsymbol{Boogie}| \boldsymbol{BOS}, \boldsymbol{x}^{(1)}) = 1$ .

While the language model generating the sentence “BOS You’ve got the touch EOS”, it is better if the language model keeps being confident. If it is confident, $P(\boldsymbol{X})=$ $P(\boldsymbol{BOS})$ $P(\boldsymbol{x}^{(1)}|\boldsymbol{BOS}}P(\boldsymbol{x}^{(3)}|\boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)})$ $P(\boldsymbol{x}^{(4)}|\boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)}, \boldsymbol{x}^{(3)})$ $P(\boldsymbol{EOS}|\boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)}, \boldsymbol{x}^{(3)}, \boldsymbol{x}^{(4)})}$ gets higher. Thus $(-1) \{ log_{b}{P(\boldsymbol{BOS})} +$ $log_{b}{P(\boldsymbol{x}^{(1)}|\boldsymbol{BOS}}) +$ $log_{b}{P(\boldsymbol{x}^{(3)}|\boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)})} +$ $log_{b}{P(\boldsymbol{x}^{(4)}|\boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)}, \boldsymbol{x}^{(3)})} +$ $log_{b}{P(\boldsymbol{EOS}|\boldsymbol{BOS}, \boldsymbol{x}^{(1)}, \boldsymbol{x}^{(2)}, \boldsymbol{x}^{(3)}, \boldsymbol{x}^{(4)})} \}$ gets lower, where usually $b=2$ or $b=e$ .

This is how to measure how confident language models are, and the indicator of the confidence is called perplexity. Assume that you have a data set for evaluation $\mathcal{D} = (\boldsymbol{X}_1, \dots, \boldsymbol{X}_n, \dots, \boldsymbol{X}_{|\mathcal{D}|})$ , which is composed of $|\mathcal{D}|$ sentences in total. Each sentence $\boldsymbol{X}_n =$ $(\boldsymbol{x}^{(0)})\prod_{t=0}^{\tau ^{(n)}}{P(\boldsymbol{x}_{n}^{(t+1)}|\boldsymbol{X}_{n, [0, t]})}$ has $\tau^{(n)}$ tokens in total excluding $\boldsymbol{BOS}, \boldsymbol{EOS}$ . And let $|\mathcal{V}|$ be the size of the vocabulary of the language model. Then the perplexity of the language model is $b^z$ , where $z = \frac{-1}{|\mathcal{V}|}\sum_{n=1}^{|\mathcal{D}|}{\sum_{t=0}^{\tau ^{(n)}}{log_{b}P(\boldsymbol{x}_{n}^{(t+1)}|\boldsymbol{X}_{n, [0, t]})}$ . The $b$ is usually $2$ or $e$ .

For example, assume that $\mathcal{V}$ is vocabulary {“the”, “You’ve”, “Walberg”, “touch”, “power”, “Nights”, “got”, “Mark”, “Boogie”}. Also assume that the evaluation data set for perplexity of a language model is $\mathcal{D} = (\boldsymbol{X}_1, \boldsymbol{X}_2)$ , where $\boldsymbol{X_1} =(\boldsymbol{You've}, \boldsymbol{got}, \boldsymbol{the}, \boldsymbol{touch})$ $\boldsymbol{X_2} = (\boldsymbol{You've}, \boldsymbol{got}, \boldsymbol{the }, \boldsymbol{power})$ . In this case $|\mathcal{V}|=9,$ $|\mathcal{D}|=2$ . I have already showed you how to calculate the perplexity of the sentence “You’ve got the touch.” above. You just need to do a similar thing on another sentence “You’ve got the power”, and then you can get the perplexity of the language model.

*If the network is not properly trained, it would also be confident of generating wrong outputs. However, such network still would give high perplexity because it is “confident” at any rate. I’m sorry I don’t know how to tackle the problem. Please let me put this aside, and let’s get down on Transformer model soon.

Appendix

Let’s see how word embedding is implemented with a very simple example in the official Tensorflow tutorial. It is a simple binary classification task on IMDb Dataset. The dataset is composed to comments on movies by movie critics, and you have only to classify if the commentary is positive or negative about the movie. For example when you get you get an input “To be honest, Michael Bay is a terrible as an action film maker. You cannot understand what is going on during combat scenes, and his movies rely too much on advertisements. I got a headache when Mark Walberg used a Chinese cridit card in Texas. However he is very competent when it comes to humorous scenes. He is very talented as a comedy director, and I have to admit I laughed a lot.“, the neural netowork has to judge whether the statement is positive or negative.

This networks just takes an average of input embedding vectors and regress it into a one dimensional value from 0 to 1. The shape of embedding layer is (8185, 16). Weights of neural netowrks are usually implemented as matrices, and you can see that each row of the matrix corresponds to emmbedding vector of each token.

*It is easy to imagine that this technique is problematic. This network virtually taking a mean of input embedding vectors. That could mean if the input sentence includes relatively many tokens with negative meanings, it is inclined to be classified as negative. But for example, if the sentence is “This masterpiece is a dark comedy by Charlie Chaplin which depicted stupidity of the evil tyrant gaining power in the time. It thoroughly mocked Germany in the time as an absurd group of fanatics, but such propaganda could have never been made until ‘Casablanca.'” , this can be classified as negative, because only the part “masterpiece” is positive as a token, and there are much more words with negative meanings themselves.

The official Tensorflow tutorial provides visualization of word embedding with Embedding Projector, but I would like you to take more control over the data by yourself. Please just copy and paste the codes below, installing necessary libraries. You would get a map of vocabulary used in the text classification task. It seems you cannot find clear tendency of the clusters of the tokens. You can try other dimension reduction methods to get maps of the vocabulary by for example using Scikit Learn.

import tensorflow as tf

from tensorflow import keras

from tensorflow.keras import layers

import tensorflow_datasets as tfds

tfds.disable_progress_bar()

(train_data, test_data), info = tfds.load(

'imdb_reviews/subwords8k',

split = (tfds.Split.TRAIN, tfds.Split.TEST),

with_info=True, as_supervised=True)

train_batches = train_data.shuffle(1000).padded_batch(10)

test_batches = test_data.shuffle(1000).padded_batch(10)

embedding_dim=16

encoder = info.features['text'].encoder

model = keras.Sequential([

layers.Embedding(encoder.vocab_size, embedding_dim),

layers.GlobalAveragePooling1D(),

layers.Dense(16, activation='relu'),

layers.Dense(1)

])

print("\n\nThe size of the vocabulary generated from IMDb Dataset is " + str(encoder.vocab_size) + '\n\n')

model.summary()

model.compile(optimizer='adam',

loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),

metrics=['accuracy'])

history = model.fit(

train_batches,

epochs=10,

validation_data=test_batches, validation_steps=20)

word_embedding_vectors = model.layers[0].get_weights()[0]

print("\n\nThe shape of the trained weigths of the embedding layer is " + str(word_embedding_vectors.shape) + '\n\n')

from sklearn.manifold import TSNE

X_reduced = TSNE(n_components = 2, init='pca', random_state=0).fit_transform(word_embedding_vectors)

import numpy as np

embedding_dict = zip(encoder.subwords, np.arange(len(encoder.subwords)))

embedding_dict = dict(embedding_dict)

import matplotlib.pyplot as plt

plt.figure(figsize=(60, 45))

plt.scatter(X_reduced[:, 0], X_reduced[:, 1])

for i in range(0, len(encoder.subwords), 5):

plt.text(X_reduced[i, 0], X_reduced[i, 1], encoder.subwords[i], fontsize=20, color='red')

plt.title("The map of vocabulary of IMDb Dataset mapped to a 2 dimensional space by t-SNE", fontsize=60)

#plt.savefig('imdb_tsne_map.png')

plt.show()

[References]

[1] “Word embeddings” Tensorflow Core
https://www.tensorflow.org/tutorials/text/word_embeddings

[2]Tsuboi Yuuta, Unno Yuuya, Suzuki Jun, “Machine Learning Professional Series: Natural Language Processing with Deep Learning,” (2017), pp. 43-64, 72-85, 91-94
坪井祐太、海野裕也、鈴木潤著, 「機械学習プロフェッショナルシリーズ深層学習による自然言語処理」, (2017), pp. 43-64, 72-85, 191-193

[3]”Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 – Translation, Seq2Seq, Attention”, stanfordonline, (2019)
https://www.youtube.com/watch?v=XXtpJxZBa2c

[4] Francois Chollet, Deep Learning with Python,(2018), Manning , pp. 178-185

[5]”2.2. Manifold learning,” scikit-learn
https://scikit-learn.org/stable/modules/manifold.html

* I make study materials on machine learning, sponsored by DATANOMIQ. I do my best to make my content as straightforward but as precise as possible. I include all of my reference sources. If you notice any mistakes in my materials, including grammatical errors, please let me know (email: yasuto.tamura@datanomiq.de). And if you have any advice for making my materials more understandable to learners, I would appreciate hearing it.

Role of Data Science in Education

January 18, 2021/in Education / Certification, Sponsoring Partner Posts/by Editorial Staff

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Data science is a new science that appeared thanks to a lot of reasons. The first reason is that nowadays, we have enough capacity to gather data and later work with it. The second reason is that society accumulates a lot of information every minute, and gadgets can save and then send it to data centers without any communication between people. But saving data is just one step in the world of science. The main task is how to analyze and show the results and later make a conclusion and prognoses. A team of online essay experts from a professional academic writing company SmartWritingService.com say that requests for academic papers for data science research topics increase extremely. It happens because data science analysts’ knowledge is useful in a lot of spheres, and the demand for such specialists is very high. Data science is an important part of sociology, political forecasting, the theory of games, statistics and others. Students need to study it and use it for future research. That’s why the universities added the courses of big data to be modern and meet requirements. Let’s try to discover the role of data science in education to make your own conclusion about its importance.

Why is data science necessary and how to become good in it?

The improvement of the studying process.

All students are different. They use different skills for studying and perceive information differently according to a lot of nuances. For some of them, the best way of getting information from lectors is listening without interruption. Other students prefer discussion during lectures. Some prefer to make notes. Others like to listen carefully and make notes later using the audio version of the lecture. Every group is different but has the same goal, and this goal is to absorb as much information as possible. The best assistant for this goal is data science that will show the teacher the best ways of communicating with students.

Using big data for personal needs.

Have you ever thought that every one of us is a data scientist? We all use data, analyze it, and act according to conclusions. For example, shopping. Every time you go to the grocery, you notice how many people are there and how long the line is. When you plan your next shopping, you make a prognosis according to that data — the time you need to spend in the market and make a decision if it is optimal to go right now or it is better to visit the shop later when it is almost empty. The same thing works when we talk about studying. According to your observation and experience (that are both data), you make a conclusion on how much time you need to spend on every task.

Learning data science as an additional course.

To know data science as an additional profession nowadays is very helpful. For the employer, it will be a bonus that can be a decisive factor. The skill to analyze is essential for every profession and helps to understand the market now and necessary for sales. The hardest thing for a data scientist is to ask the right questions for collecting data, and if you are good at it, your salary will increase immensely.

How to become a data science specialist?

Big data and artificial intelligence (AI). The importance of development. We believe that AI is the future of studying. First of all, the machine can collect the information and repeat it as much as the student needs. It is studying with the group, and the quality of communication grows rapidly. The base for machine learning in AI is data science. The analysis of data gives the set of possible reactions and actions to AI that can be changed or improved according to new data that was processed by AI. But from the beginning, AI is a huge set of data. It consists of reactions to life situations, speed and timbre of the voice of the interlocutor, country, the hour of the day, and a lot of other data that finally lead to the reaction of AI to the situation. The development of AI is a question of time, and it will help us to move faster in all spheres of life.

Can we ignore data analytics and don’t take a part in it?

The only way to ignore data science is to throw away all gadgets and become citizens of the wood cabin. And even this step won’t help. Those interested in the amount of population of people who live in the woods will be happy to add you to their list and analyze your +1 using data science technology. Every time you buy bread or don’t buy something on the internet, you are counted. Later they will analyze why you became or not their customer and save for statistics you age, sex, country of request, and all other parameters they can catch. We can only accept this reality and try to use it to our needs.

Don’t be afraid to become a subject for data science. It doesn’t affect your privacy a lot because there are billions of us, and we are only one point for statistics. Thanks to such analytics, scientists can make better specific offers and content for you. On the one hand, it is a kind of manipulation, but it saves time and resources for research from another hand. Use it to make your life more comfortable but remember that there are no coincidences when we talk about data.

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CRISP-DM methodology in technical view

January 6, 2021/in Business Analytics, Data Mining, Data Science, Machine Learning, Main Category, Projectmanagement/by Ahamed Al Farabi

On this paper discuss about CRISP-DM (Cross Industry Standard Process for data mining) methodology and its steps including selecting technique to successful the data mining process. Before going to CRISP-DM it is better to understand what data mining is? So, here first I introduce the data mining and then discuss about CRISP-DM and its steps for any beginner (data scientist) need to know.

1 Data Mining

Data mining is an exploratory analysis where has no idea about interesting outcome (Kantardzic, 2003). So data mining is a process to explore by analysis a large set of data to discover meaningful information which help the business to take a proper decision. For better business decision data mining is a way to select feature, correlation, and interesting patterns from large dataset (Fu, 1997; SPSS White Paper, 1999).

Data mining is a step by step process to discover knowledge from data. Pre-processing data is vital part for a data mining. In pre-process remove noisy data, combining multiple sources of data, retrieve relevant feature and transforming data for analysis. After pre-process mining algorithm applied to extract data pattern so data mining is a step by step process and applied algorithm to find meaning full data pattern. Actually data mining is not only conventional analysis it is more than that (Read, 1999).

Data mining and statistics closely related. Main goal of data mining and statistic is find the structure of data because data mining is a part of statistics (Hand, 1999). However, data mining use tools, techniques, database, machine learning which not part of statistics but data mining use statistics algorithm to find a pattern or discover hidden decision.

Data mining objective could be prediction or description. On prediction data mining considering several features of dataset to predict unidentified future, on the other hand description involve identifying pattern of data to interpreted (Kantardzic, 2003).

From figure 1.1 shows data mining is the only one part of getting unknown information from data but it is the central process of whole process. Before data mining there are several processes need to be done like collecting data from several sources than integrated data and keep in data storage. Stored unprocessed data evaluated and selected with pre-processed activity to give a standard format than data mining algorithm to analysis for hidden pattern.

2 CRISP-DM Methodologies

Cross Industry Standard Process for data mining (CRISP-DM) is most popular and widely uses data mining methodology. CRISP-DM breaks down the data mining project life cycle into six phases and each phase consists of many second-level generic tasks. Generic task cover all possible data mining application. CRISP-DM extends KDD (Knowledge Discovery and Data Mining) into six steps which are sequence of data mining application (Martínez-Plumed 2019).

Data science and data mining project extract meaningful information from data. Data science is an art where a lot of time need to spend for understanding the business value and data before applying any algorithm then evaluate and deployed a project. CRISP-DM help any data science and data mining project from start to end by giving step by step process.

Present world every day billions of data are generating. So organisations are struggling with overwhelmed data to process and find a business goal. Comprehensive data mining methodology, CRISP-DM help business to achieve desirable goal by analysing data.

CRISP-DM (Cross Industry Standard Process for Data Mining) is well documented, freely available, data mining methodology. CRISP-DM is developed by more than 200 data mining users and many mining tool and service providers funded by European Union. CRISP-DM encourages organization for best practice and provides a structure of data mining to get better, faster result.

CRISP-DM is a step by step methodology. Figure-2.1 show the phases of CRISP-DM and process of data mining. Here one side arrow indicates the dependency between phases and double side arrow represents repeatable process. Six phases of CRISP-DM are Business understanding, Data understanding, Modelling, Evaluation and Deployment.

2.1 Business Understanding

Business Understanding or domain understanding is the first step of CRISP-DM methodology. On this stage identify the area of business which is going to transform into meaningful information by analysing, processing and implementing several algorithms. Business understanding identifies the available resource (human and hardware), problems and set a goal. Identification of business objective should be agreed with project sponsors and other unit of business which will be affected. This step also focuses about details business success criteria, requirements, constraints, risk, project plan and timeline.

2.2 Data Understanding

Data understanding is the second and closely related with the business understanding phase. This phase mainly focus on data collection and proceeds to get familiar with the data and also detect interesting subset from data. Data understanding has four subsets these are:-

2.2.1 Initial data collection

On this subset considering the data collection sources which is mainly divided into two categories like outsource data or internal source data. If data is from outsource then it may costly, time consuming and may be low quality but if data is collected form internal source it is an easy and less costly, but it may be contain irrelevant data. If internal source data does not fulfil the interest of analysis than it is necessary to move outsource data. Data collection also give an assumption that the data is quantitative (continuous, count) or qualitative (categorical). It also gives information about balance or imbalanced dataset. On data collection should avoid random error, systematic error, exclusion errors, and errors of choosing.

2.2.2 Data Description

Data description performs initial analysis about data. On this stage it is going to determine about the source of data like RDBMS, SQL, NoSQL, Big data etc. then analysis and describe the data about size (large data set give more accurate result but time consuming), number of records, tables, database, variables, and data types (numeric, categorical or Boolean). On this phase examine the accessibility and availability of attributes.

2.2.3 Exploratory data analysis (EDA)

On exploratory data analysis describe the inferential statistics, descriptive statistics and graphical representation of data. Inferential statistics summarize the entire population from the sample data to perform sampling and hypothesis testing. On Parametric hypothesis testing (Null or alternate – ANOVA, t-test, chi square test) perform for known distribution (based on population) like mean, variance, standard deviation, proportion and Non-parametric hypothesis testing perform when distribution is unknown or sample size is small. On sample dataset, random sampling implement when dataset is balance but for imbalance dataset should be follow random resampling (under and over sampling), k fold cross validation, SMOTE (synthetic minority oversampling technique), cluster base sampling, ensemble techniques (bagging and boosting – Add boost, Gradient Tree Boosting, XG Boost) to form a balance dataset.

On descriptive statistics analysis describe about the mean, median, mode for measures of central tendency on first moment business decision. On second moment business decision describe the measure of dispersion about the variance, standard deviation and range of data. On third and fourth moment business decision describe accordingly skewness (Positive skewness – heavier tail to the right, negative skewness – heavier tail to the left, Zero skewness – symmetric distribution) and Kurtosis (Leptokurtosis – heavy tail, platykurtosis – light tail, mesokurtic – normal distribution).

Graphical representation is divided into univariate, bivariate and multivariate analysis. Under univariate whisker plot, histogram identify the outliers and shape of distribution of data and Q-Q plot (Quantile – Quantile) plot describe the normality of data that means data is normally distribution or not. On whisker plot if data present above of Q3 + 1.5 (IQR) and below of Q1 – 1.5 (IQR) is outlier. For Bivariate correlations identify with scatter plot which describe positive, negative or no correlation and also identify the data linearity or non-linearity. Scatter plot also describe the clusters and outliers of data. For multivariate has no graphical analysis but used to use regression analysis, ANOVA, Hypothesis analysis.

2.2.4 Data Quality analysis

This phase identified and describes the potential errors like outliers, missing data, level of granularity, validation, reliability, bad metadata and inconsistency. On this phase AAA (attribute agreement analysis) analysed discrete data for data error. Continuous data analysed with Gage repeatability and reproducibility (Gage R & R) which follow SOP (standard operating procedures). Here Gage R & R define the aggregation of variation in the measurement data because of the measurement system.

2.3 Data Preparation

Data Preparation is the time consuming stage for every data science project. Overall on every data science project 60% to 70% time spend on data preparation stage. Data preparation stapes are described below.

2.3.1 Data integration

Data integration involved to integrate or merged multiple dataset. Integration integrates data from different dataset where same attribute or same columns presents but when there is different attribute then merging the both dataset.

2.3.2 Data Wrangling

On this subset data are going to clean, curate and prepare for next level. Here analysis the outlier and treatment done with 3 R technique (Rectify, Remove, Retain) and for special cases if there are lots of outliner then need to treat outlier separately (upper outliner in an one dataset and lower outliner in another dataset) and alpha (significant value) trim technique use to separate the outliner from the original dataset. If dataset has a missing data then need to use imputation technique like mean, median, mode, regression, KNN etc.

If dataset is not normal or has a collinearity problem or autocorrelation then need to implement transformation techniques like log, exponential, sort, Reciprocal, Box-cox etc. On this subset use the data normalization (data –means/standard deviation) or standardization (min- max scaler) technique to make unitless and scale free data. This step also help if data required converting into categorical then need to use discretization or binning or grouping technique. For factor variable (where has limited set of values), dummy variable creation technique need to apply like one hot encoding. On this subset also help heterogeneous data to transform into homogenous with clustering technique. Data inconsistencies also handle the inconsistence of data to make data in a single scale.

2.3.3 Feature engineering and selection/reduction

Feature engineering may called as attribute generation or feature extraction. Feature extraction creating new feature by reducing original feature to make simplex model. Feature engineering also do the normalized feature by producing calculative new feature. So feature engineering is a data pre-process technique where improve data quality by cleaning, integration, reduction, transformation and scaling.

Feature selections reduce the multicollinearity or high correlated data and make model simple. Main two type of feature selection technique are supervised and unsupervised. Principal Components Analysis (PCA) is an unsupervised feature reduction/ feature selection technique and LDA is a Linear Discriminant analysis supervised technique mainly use for classification problem. LDA analyse by comparing mean of the variables. Supervised technique is three types filter, wrapper and ensemble method. Filter method is easy to implement but wrapper is costly method and ensemble use inside a model.

2.4 Model

2.4.1 Model Selection Technique

Model selection techniques are influence by accuracy and performance. Because recommendation need better performance but banking fraud detection needs better accuracy technique. Model is mainly subdivided into two category supervised learning where predict an output variable according to given an input variable and unsupervised learning where has not output variable.

On supervised learning if an output variable is categorical than it is classification problem like two classes or multiclass classification problem. If an output variable is continuous (numerical) then the problem is called prediction problem. If need to recommending according to relevant information is called recommendation problem or if need to retrieve data according to relevance data is called retrieval problem.

On unsupervised learning where target or output variable is not present. On this technique all variable is treated as an input variable. Unsupervised learning also called clustering problem where clustering the dataset for future decision.

Reinforcement learning agent solves the problem by getting reward for success and penalty for any failure. And semi-supervised learning is a process to solve the problem by combining supervised and unsupervised learning method. On semi-supervised, a problem solved by apply unsupervised clustering technique then for each cluster apply different type of supervised machine learning algorithm like linear algorithm, neural network, K nearest neighbour etc.

On data mining model selection technique, where output variable is known, then need to implement supervised learning. Regression is the first choice where interpretation of parameter is important. If response variable is continuous then linear regression or if response variable is discrete with 2 categories value then logistic regression or if response variable is discrete with more than 2 categorical values then multinomial or ordinal regression or if response variable is count then poission where mean is equal to variance or negative binomial regression where variance is grater then mean or if response variable contain excessive zero values then need to choose Zero inflated poission (ZIP) or Zero inflated negative binomial (ZINB).

On supervised technique except regression technique all other technique can be used for both continuous or categorical response variable like KNN (K-Nearest Neighbour), Naïve Bays, Black box techniques (Neural network, Support vector machine), Ensemble Techniques (Stacking, Bagging like random forest, Boosting like Decision tree, Gradient boosting, XGB, Adaboost).

When response variable is unknown then need to implement unsupervised learning. Unsupervised learning for row reduction is K-Means, Hierarchical etc., for columns reduction or dimension reduction PCA (principal component analysis), LDA (Linear Discriminant analysis), SVD (singular value decomposition) etc. On market basket analysis or association rules where measure are support and confidence then lift ration to determine which rules is important. There are recommendation systems, text analysis and NLP (Natural language processing) also unsupervised learning technique.

For time series need to select forecasting technique. Where forecasting may model based or data based. For Trend under model based need to use linear, exponential, quadratic techniques. And for seasonality need to use additive, multiplicative techniques. On data base approaches used auto regressive, moving average, last sample, exponential smoothing (e.g. SES – simple exponential smoothing, double exponential smoothing, and winters method).

2.4.2 Model building

After selection model according to model criterion model is need to be build. On model building provided data is subdivided with training, validation and testing. But sometime data is subdivided just training and testing where information may leak from testing data to training data and cause an overfitting problem. So training dataset should be divided into training and validation whereas training model is tested with validation data and if need any tuning to do according to feedback from validation dataset. If accuracy is acceptable and error is reasonable then combine the training and validation data and build the model and test it on unknown testing dataset. If the training error and testing error is minimal or reasonable then the model is right fit or if the training error is low and testing error is high then model is over fitted (Variance) or if training error is high and testing error is also high then model is under fitted (bias). When model is over fitted then need to implement regularization technique (e.g. linear – lasso, ridge regression, Decision tree – pre-pruning, post-pruning, Knn – K value, Naïve Bays – Laplace, Neural network – dropout, drop connect, batch normalization, SVM – kernel trick)

When data is balance then split the data training, validation and testing and here training is larger dataset then validation and testing. If data set is imbalance then need to use random resampling (over and under) by artificially increases training dataset. On random resampling by randomly partitioning data and for each partition implement the model and taking the average of accuracy. Under K fold cross validation creating K times cross dataset and creating model for every dataset and validate, after validation taking the average of accuracy of all model. There is more technique for imbalance dataset like SMOTH (synthetic minority oversampling technique), cluster based sampling, ensemble techniques e.g. Bagging, Boosting (Ada Boost, XGBoost).

2.4.3 Model evaluation and Tuning

On this stage model evaluate according to errors and accuracy and tune the error and accuracy for acceptable manner. For continuous outcome variable there are several way to measure the error like mean error, mean absolute deviation, Mean squared error, Root mean squared error, Mean percentage error and Mean absolute percentage error but more acceptable way is Mean absolute percentage error. For this continuous data if error is known then it is easy to find out the accuracy because accuracy and error combining value is one. The error function also called cost function or loss function.

For discrete output variable model, for evaluation and tuning need to use confusion matrix or cross table. From confusion matrix, by measuring accuracy, error, precision, sensitivity, specificity, F1 help to take decision about model fitness. ROC curve (Receiver operating characteristic curve), AUC curve (Area under the ROC curve) also evaluate the discrete output variable. AUC and ROC curve plot of sensitivity (true positive rate) vs 1-specificity (false positive rate). Here sensitivity is a positive recall and recall is basically out of all positive samples, how sample classifier able to identify. Specificity is negative recall here recall is out of all negative samples, how many sample classifier able to identify. On AUC where more the area under the ROC is represent better accuracy. On ROC were step bend it’s indicate the cut off value.

2.4.4 Model Assessment

There is several ways to assess the model. First it is need to verify model performance and success according to desire achievement. It needs to identify the implemented model result according to accuracy where accuracy is repeatable and reproducible. It is also need to identify that the model is scalable, maintainable, robust and easy to deploy. On assessment identify that the model evaluation about satisfactory results (identify the precision, recall, sensitivity are balance) and meet business requirements.

2.5 Evaluation

On evaluation steps, all models which are built with same dataset, given a rank to find out the best model by assessing model quality of result and simplicity of algorithm and also cost of deployment. Evaluation part contains the data sufficiency report according to model result and also contain suggestion, feedback and recommendation from solutions team and SMEs (Subject matter experts) and record all these under OPA (organizational process assets).

2.6 Deployment

Deployment process needs to monitor under PEST (political economical social technological) changes within the organization and outside of the organization. PEST is similar to SWOT (strength weakness opportunity and thread) where SW represents the changes of internal and OT represents external changes.

On this deployment steps model should be seamless (like same environment, same result etc.) from development to production. Deployment plan contain the details of human resources, hardware, software requirements. Deployment plan also contain maintenance and monitoring plan by checking the model result and validity and if required then implement retire, replace and update plan.

3 Summaries

CRISP-DM implementation is costly and time consuming. But CRISP-DM methodology is an umbrella for data mining process. CRISP-DM has six phases, Business understanding, Data understanding, Modelling, Evaluation and Deployment. Every phase has several individual criteria, standard and process. CRISP-DM is Guideline for data mining process so if CRISP-DM is going to implement in any project it is necessary to follow each and every single guideline and maintain standard and criteria to get required result.

4 References

Fu, Y., (1997), “Data Mining: Tasks, Techniques and Applications”, Potentials, IEEE, 16: 4, 18–20.
Hand, D. J., (1999), “Statistics and Data Mining: Intersecting Disciplines”, ACM SIGKDD Explorations Newsletter, 1: 1, 16 – 19.
Kantardzic, M., (2003), “Data Mining: Concepts, Models, Methods, and Algorithms” John Wiley and Sons, Inc., Hoboken, New Jersey
Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., Orallo, J.H., Kull, M., Lachiche, N., Quintana, M.J.R. and Flach, P.A., 2019. CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. IEEE Transactions on Knowledge and Data Engineering.
Read, B.J., (1999), “Data Mining and Science? Knowledge discovery in science as opposed to business”, 12th ERCIM Workshop on Database Research.

Top 10 Python Libraries Of All Time

December 4, 2020/in Data Science Hack, Insights, Python, Tool Introduction/by Harikrishna Kundariya

Python is a very popular and renowned language that has replaced several programming languages in the market. Its amazing collection of libraries makes it a convenient programming language for developers.

Python is an ocean of libraries serving an ample number of purposes and as a developer; you must possess sound knowledge of the 10 libraries. One needs to familiarize themselves with the libraries to go on and work on different projects. For the data scientist, it has been a charmer now.

Here today, for you this is a curated list of 10 Python libraries that can help you along with its significant features, when to use them, and also the benefits.

10 Best Python Libraries of All Times

Pandas: Pandas is an open-source library that offers instant high performance, data analysis, and simple data structures. When can you use it? It can be used for data munging and wrangling. If one is looking for quick data visuals, aggregation, manipulation, and reading, then this library is suitable. You can impute the missing data files, plot the data, and make edits in the data column. Moreover, for renaming and merging, this tool can do wonders. It is a foundation library, and a data scientist should have in-depth knowledge about Pandas before any other library knowledge.

TensorFlow: TensorFlow is developed by Google in collaboration with the Brain Team. Using this tool, you can instantly visualize any part of the graphical representation. It comes with modularity and offers high flexibility in its operations. This library is ideal for running and operating in large scale systems. So, as long as you have good internet connectivity, you can use it because it is an open-source platform. What is the beauty of this library? It comes with an unending list of applications associated with it.

NumPy: NumPy is the most popular Python library used by developers. It is used by various libraries for conducting easy operations. What is the beauty of NumPy? Array Interface is the beauty of NumPy and it is always a highlighted feature. NumPy is interactive and very simple to use. It can instantly solve complicated mathematical problems. With this, you need not worry about daunting phases of coding and offering open-source contributions. This interface is widely used for expressing raw streams, sound waves, and other images. If you are looking to implement this into machine learning, you must possess in-depth knowledge about NumPy.

Keras: Are you looking for a cool Python library? Well, Keras is the coolest machine learning python library. It runs smoothly on both CPU and GPU. Do you want to know where Keras is used? It is used in popular applications like Uber, Swiggy, Netflix, Square, and Yelp. Keras easily supports the fully connected, pooling, convolution, and recurrent neural networks. For any innovative research, it does fine because it is expressive and flexible. Keras is completely based on a framework, which enables easy debugging and exploring. Various large scientific organizations use Keras for innovative research.

Scikit- Learn: If your project deals with complex data, it has to be the Scikit- Learn python library. This Python Machine Learning Library is associated with NumPy and SciPy. After various modifications, one such feather cross-validation is used for enabling more than one metric. It is used for extracting features and data from texts and images. It uses various algorithms to make changes in machine learning. What are its functions? It is used in model selection, classification, clustering, and regression. Various training methods like nearest neighbor and logistics regressions are subjected to minimal modification.

PyTorch: PyTorch is the largest library which conducts various computations and accelerations. Also, it solves complicated application issues that are related to the neural networks. It is completely based on the machine language Torch, which is a free and open-source platform. PyTorch is new but gaining huge popularity and very much a favorite among the developers. Why such popularity? It comes with a hybrid end-user which ensures easy usage and flexibility. For processing natural language applications, this library is used. Do you know what the best part is? It is outperforming and taking the popularity of Tensor Flow in recent times.

MoviePy: The MoviePy is a tool that offers unending functionality related to movies and visuals. It is used for exporting, modifying, and importing various video files. Do you want to add a title to your video or rotate it 90 degrees? Well, MoviePy helps you to do all such tasks related to videos. It is not a tool for manipulating data like Pillow. In any task related to movies and videos in python coding, you can no doubt rely on the functionality of MoviePy. It is designed to conduct all the aspects of a standard task and can get it done instantly. For any common task associated with videos, it has a MoviePy library.

Matplotlib: Matplotlib is no doubt a quintessential python library whose presence can never be forgotten. You can visualize data and create innovative and interesting stories. When can you use it? You can use Matplotlib for embedding different plots into the application as it provides an object-oriented application program interface. Any sort of visualization, be it bar graph, histogram, pie chart, or graphs, Matplotlib can easily depict it. With this library, you can create any type of visualization. Do you want to know what visualizations you can create? You can create a histogram, Bar graph, pie chart, area plot, stem plot, and line plot. It also facilitates the legends, grids, and labels.
Tkinter: Tkinter is a library that can help you create any Python application with the help of a graphical user interface. Tkinter is the most common and easy to use python library for developing apps with GUI. It binds python to the GUI tool kit which can be used in any modern operating system. To create a python GUI, Tkinter is the only best way to start instantly.
Plotly: The Plotly is an essential graph plotting python library for developers. Users can import, copy, paste, export the data that needs to be analyzed and visualized. When can you use it? You can use Plotly to display and create figures and visual images. What is interesting is that it has amazing features for sending data to the various cloud servers.

What are the visual charts prepared with Plotly? You can create line pie, bubble, dot, scatter, and pie. One can also construct financial charts, contours, maps, subplots, carpet, radar, and logs. Do you have anything in your mind which needs to be represented visually? Use Plotly!

Finishing Up

In a nutshell, you have the best python libraries of recent times which contribute hugely to development. If your favorite python library didn’t make it in this list of the top 10 best python libraries, do not take offense.

Python comes with unending library packages, and these 10 are some of its popular and best-used ones. If you are a python developer, these are the best libraries you must have in-depth knowledge of.

New Era of Data Science in Today’s World

November 4, 2020/in Data Science News, Insights/by Praveenkumar Yadav

In today’s digital world, most organizations are flooded with data, both structured and unstructured. Data is a commodity now, and organizations should know how to monetize that data and derive a profit from the deluge. And valuing data is one of the best ways enterprises can become successful in distinguishing themselves in the marketplace.

Data is the new oil

Indeed, data itself has become a commodity, and the mere possession of abundant amounts of data is not enough. But the ability to monetize data effectively (and not merely hoard it) can undoubtedly be a source of competitive advantage in the digital economy. However, we need to refine this data. And refinement of this “new oil” will take a reasonable amount of time. In my opinion, we are still not there. As a result, “data refinement” remains a key factor for successful advanced analytics.

If we talk about the level of activity in data and analytics space in the last two years, most advanced analytics evolved around three categories:

Descriptive, or what has happened
Predictive, or what could happen, and
Prescriptive, or what we should do.

Descriptive analytics has been the core analytics for many years. In the past, we could only describe what has happened to historical data (such as that found in a data warehouse), with dashboard reporting, using traditional analytics. But with the advent of advanced analytics, machine learning (ML), and deep learning and artificial intelligence (AI), our focus has changed to real-time analytics. In the last two years, much work has been done in predictive analytics, and as we move forward into our analytics journey, data-centric organizations will now focus on prescriptive analytics. The use of prescriptive analytics, along with predictive analytics, is very important for any organization to be successful in the future.

Current and recent trends in data and analytics

The analytics trends revolve around AI and ML. The Analytics-as-a-Service model is an essential model for any smart, data-driven organization. We can make an impact on society and try to make a better place to live with the use of advanced analytics. At NTT DATA, we strive to solve these problems to improve the quality, safety and advancement of humanity. From a business perspective, we use data analytics and predictive modeling to help companies increase their sales and revenue.

Let me give you some examples. We have been involved with several technology partners in a project for the Smart City. This project involved the use of predictive analytics for the validation of critical alerts to help reduce the time and amount of data required to be processed. It used Internet of Things (IoT) devices, high-definition video cameras, and sound sensors, as well as video and sound data captured from specific locations. Eventually, the solution also integrated with available data from data sources such as crime, weather and social media. The overall objective of the Smart City project was to use and apply advanced analytics with cognitive computing to facilitate safety decision-making, and for a responder to respond earlier based on real-time data.

Another example is the Smart ICU System developed by NTT DATA for predictive detection of threats for seriously ill patients in an ICU, based on the data. This data was consolidated from various medical devices in the ICU into one platform. From that data, we developed a model that predicts the risk of complications that might occur within the next couple of hours or so of a medical event. We have also used advanced analytics provided by weather data forecasting and used predictive models to predict natural disasters.

Data and analytics strategy

A strategy is an essential aspect of any data-driven organization. It should cover data strategy for AI, ML, statistical modeling and other data science disciplines, such as predictive and prescriptive analytics. In general, advanced analytics is more predictive and actionable than retrospective. Smart organizations see positive results when they place a strategy for data and analytics in the hands of employees who are well-positioned to make decisions, such as those who interact with customers, oversee product development, or run production processes. With data-based insight and clear decision rules, employees can deliver more meaningful services, better assess and address customer demands, and optimize production.

Smart organizations must take time to clean and update their underlying modern data architecture — along with their data governance process, for a cleaner data and analytics strategy. A modern data architecture, combined with a good governance process, can leverage AI and ML to help organizations stay ahead of their competitors.

Data analytics innovation

Machine and Deep Learning, along with AI, are all very popular, but I would like to reiterate that advanced technologies like AI and machine learning will continue to transform data analytics. The next innovation could be the use of automated analytics, which machine learning tools can use to identify hidden patterns in data. For example, customer retention issues, customer default on loans, or predicting customers who are prone to auto accidents. Also, predictive analytics and prescriptive analytics are going to be the key for any future innovations in AI and ML.

We must make targeted investments in traditional business innovation tools, along with emerging data analytics tools to derive benefits from data-driven business initiatives. We need to invest in cloud and underlying IT infrastructure to support these analytics and business initiatives. Most importantly, we also need to invest in people — cross training skilled resources and empowering the people who work closely with clients to make the right decisions for analytics.

Bias and Variance in Machine Learning

November 2, 2020/in Artificial Intelligence, Data Science, Deep Learning, Machine Learning, Main Category/by Pavan Vadapalli

Machine learning continues to be an ever more vital component of our lives and ecosystem, whether we’re applying the techniques to answer research or business problems or in some cases even predicting the future. Machine learning models need to give accurate predictions in order to create real value for a given industry or domain.

While training a model is one of the key steps in the Data Science Project Life Cycle, how the model generalizes on unseen data is an equally important aspect that should be considered in every Data Science Project Life Cycle. We need to know whether it works and, consequently, if we can trust its predictions. Could the model be merely memorizing the data it is fed with, and therefore unable to make good predictions on future samples, or samples that it hasn’t seen before?

Let’s know the importance of evaluation with a simple example, There are two student’s Ramesh and Suresh preparing for the CAT exam to get into top IIMs (Indian Institute of Management). They both are quite good friends and stayed in the room during preparation and put an equal amount of hard work while solving numerical problems.

They both prepared for almost the same number of hours for the entire year and appeared in the final CAT exam. Surprisingly, Ramesh cleared, but Suresh did not. When asked, we got to know that there was one difference in their strategy of preparation between them, Ramesh had joined a Test Series course where he used to test his knowledge and understanding by giving mock exams and then further evaluating on which portions he is lagging and making necessary adjustments to he is preparation cycle in order to do well in those areas. But Suresh was confident, and he just kept training himself without testing on the preparation he had done.

Like the above situation we can train a Machine Learning Algorithm extensively with many parameters and new techniques, but if you are skipping its evaluation step, you cannot trust your model to perform well on the unseen data. In this article, we explain the importance of Bias, Variance and the trade-off between them in order to know how well a machine learning model generalizes to new, previously unseen data.

Bias

Bias is the difference between the Predicted Value and the Expected Value or how far are the predicted values from the actual values. During the training process the model makes certain assumptions on the training data provided. After Training, when it is introduced to the testing/validation data or unseen data, these assumptions may not always be correct.

If we use a large number of nearest neighbors in the K-Nearest Neighbors Algorithm, the model can totally decide that some parameters are not important at all for the modelling. For example, it can just consider that only two predictor variables are enough to classify the data point though we have more than 10 variables.

This type of model will make very strong assumptions about the other parameters not affecting the outcome at all. You can take it as a model predicting or understanding only the simple relationship when the data points clearly indicate a more complex relationship.

When the model has high bias error, it results in a very simplistic model that does not consider the complexity of the data very well leading to Underfitting.

Variance

Variance occurs when the model performs well on the trained dataset but does not do well on an unseen data set, it is when the model considers the fluctuations or i.e. the noise as in the data as well. The model will still consider the variance as something to learn from because it learns too much from the noise inside the trained data set that it fails to perform as expected on the unseen data.

Based on the above example from Bias, if the model learns that all the ten predictor variables are important to classify a given data point then it tends to have high variance. You can take it as the model is trying to understand every minute detail making it more complex and failing to perform well on the unseen data.

When a model has High Bias error, it underfits the data and makes very simplistic assumptions on it. When a model has High Variance error, it overfits the data and learns too much from it. When a model has balanced Bias and Variance errors, it performs well on the unseen data.

Bias-Variance Trade-off

Based on the definitions of bias and variance, there is clear trade-off between bias and variance when it comes to the performance of the model. A model will have a high error if it has very high bias and low variance and have a high error if it has high variance and low bias.

A model that strikes a balance between the bias and variance can minimize the error better than those that live on extreme ends.

We can find whether the model has High Bias using the below steps:

We tend to get high training errors.
The validation error or test error will be similar to the training error.