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Category: Analytics

What Are The Important Ways That AI Is Helping E-Commerce Stores?

Posted on by Imarticus Learning

What Are The Important Ways That AI Is Helping E-Commerce Stores?

The e-commerce industry has proved to be a boon for all the shopaholics who are too lethargic for a regular brick and motor engagement. Growing in double digits the expansion in the e-commerce industry is unmatched by any other and with the potential to grow multiple folds in the coming years it has set new highs.

In a broad sense of things, the concept behind the e-commerce world is simple, creating on an online marketplace with multiple stores available to shop anytime using the means of smartphones and other computerized devices that support web surfing.

The virtual market is not bounded by geography, having its customer base all across the world. What’s different about this shopping escapade is that it makes the entire store available for you to facilitate your shopping spree, all with a few clicks. I wonder how many times it happens that I am not sure about what exactly I need to purchase unless acquainted with the varieties available.

Now if we have to walk by several stores to find out what could be bought it will be tiresome, to say the least. Let’s assume that we somehow managed to step into each of them, how will we compare all the available products in real-time? That’s where the e-commerce industry adds value and steals the show with convenience.

The e-commerce stores not only help to bring everything together but also helps to search select and choose by providing valuable suggestions and insightful product descriptions. It also lets you read into the feedback provided by the users of the products that might help you buy better.

In the tangible world, we have a shop for every need, we have shopping complexes for multiple segments. This evolution went a little further in the era of the internet with e-commerce where we have all the product segments from all the known brands under a few keystrokes.

AI applications in the e-commerce industry

While shopping at stores with a physical address on the map, what attracts the most apart from quality goodies is the presentation and organization of the products.

Similarly when buying goods online what helps increase engagement and purchase? The answer is better to search for tools and classified product segments. This is where AI fits into the e-commerce must-have tools.

The high-tech tech AI-enabled solutions can also help in searching product descriptions and other relevant details to form a variety of keywords that might match with the user’s search and help discover the product better. This doesn’t stop here, the AI-powered solutions also help with product selection by asking some intelligent questions and narrowing down the list for us.

At times it so happens that we know what we are looking for but the name is unknown to us and thus we feed in a variety of keywords to complete our search. The predictive search mechanism provided by AI technology uses our past search and purchase history helping us identify what we might be looking for with relative ease saving a lot of time and keystroke efforts.

Arrangement of products and tidiness are some of the key drivers of customers in the traditional brick motors store, how do you implicate this approach online? Well, the answer doesn’t require a brainstorming session, it is through the website design.

Making the website aesthetic needs a well-planned web design that not only looks good but also goes along with the objective of the website. From optimized website design testing to improving decisions with auto traffic analysis & better sales funnel structuring, Artificial Intelligence Training delivers on all aspects of customer conversions and engagement.

In the present-day scenario conversational chatbots are mainstream for better customer servicing, it could also be seen as a norm, whatever site you visit for your purchase you are bound to be greeted by a bot. This evolution has been propelled further by a new wave of intelligent sales chatbots. This new AI by-product is hyper-personal in its functioning, providing customized recommendations and suggestions for better conversion.

Conclusion

AI has improved the e-commerce industry to a great extent by providing better search options for product searches to suggesting an optimized website layout for better conversions. Apart from the mainstream chatbots for customer servicing this new AI wave has welcomed the trendy sales chatbot that uses customer preferences data for good by providing a customized and hyper-personal shopping experience.

A Look at the 3 Most Common Machine Learning Obstacles

Posted on by Imarticus Learning

A Look at the 3 Most Common Machine Learning Obstacles

When we talk about artificial intelligence (AI), the research and its findings have surpassed our little expectations. Some experts also believe that this is the golden age of AI and machine learning (ML) projects where the human mind is still surprised at all the possibilities that they bring to the table. However, it is only when you start working on a project involving these advanced technologies that you realize that there are a few obstacles that you need to address before you can start throwing a party.

Predictive assembly line maintenance, image recognition, and automatic disease detection are some of the biggest applications of ML-driven automation. But what are the hurdles that data scientists need to cross if they want to practically execute these applications and gain the desired outcome?

This article will give you an overview of the three common obstacles involved in machine learning models.

Common Machine Learning Obstacles

On theory, machine learning evangelists tend to liken the technology to magic. People scrolling through Facebook watch videos that use buzzwords in their captions and believe that AI can do wonders. Of course, it can, but when you think practically it is not as easy as it sounds. Commercial use of machine learning still has a long way to go because the reference dataset that is essential for any such model to function needs to be tidied up and organized at such a minute level it becomes tedious.

Ask any data scientist who has worked in a deep learning project and she will tell you all about it: the time, the resources, and the particular skills needed to create the database, sometimes known as a training set. But these are challenges found in any project. When you deal with machine learning, there are a few peculiar ones too.

Let’s dig deeper into these three common obstacles and find out why they are so integral to the larger machine learning problem.

The Problem of Black Box

Imagine a machine learning training program that is developed to predict if a given object is a red apple or not. During the early days of machine learning research, this meant writing a simple program with elements that involved the color red and the shape of an apple. Essentially, such a program was created through a thorough understanding of those who developed it. The problem with modern programs is that although humans developed it, they have no idea how the program actually detects the object, and that too with unprecedented accuracy. This is also one of the issues hampering the wide application of data classification models.

Experts have studied this problem and tried to crack it, but the solution still seems elusive because there is absolutely no way to get into the process while it is running. Although the program gives out fabulous results – results that are much needed to detect if a given fruit is a red apple or not from a wide range of fruits that also include non-apples – but the lack of knowledge as to how it works makes the whole science behind it feel like alchemy.

If you have been following world news related to AI-enabled products, this is probably the biggest cause of ‘accidents.’ That self-driving car hit a divider when there was no reason for it to hit it? That’s the black box problem right there.

What Classification Model to Choose?

This is another common obstacle that comes in the way of data scientists and successful AI tools. As you might know, each classification model has its own set of pros and cons. There is also the issue of the unique set of data that has been fed to it and the unique outcome that is desired.

For example, a program wanting to detect a fruit as red apple is totally different from another program that requires the observation to be classified into two different possibilities. This puts the scientists behind the program in a difficult situation.

Although there are ways to simplify this to an extent, it often ends up as a process of trial-and-error. What needs to be accomplished, what is the type and volume of data, and what characteristics are involved are some of the common questions that need to be asked. Answers to these will help a team of engineers and data scientists selects an appropriate model. Some of these popular models are a k-nearest neighbor (kNN), decision trees, and logistic regression.

Data Overfitting

Understanding this will be easier because it can be described using an example. Take, for instance, a robot who has been fed the floor plan of a Walmart store. It is the only thing that has been fed to it, and the expected outcome is that the robot can successfully follow a set of directions and reach any given point in the store. What will happen if the robot instead is brought to a Costco store that is built entirely differently? Assumption tells us that it won’t be able to go beyond the initial steps as the floor plan in its memory and the floor plan of this new store do not match.

A variation of this fallacy is what is known as data overfitting in machine learning. A model is unable to generalize well to a set of new data. There are easy solutions to this, but experts suggest prevention rather than cure. Data regularization is one of those prevention mechanisms where a model is fed data sufficient for the requests that it will handle.

The above-mentioned three obstacles are the most common, but there are many more like talent deficit, unavailability of free data, and insufficiency of research and development in the field. In that vein, it is not fair of us to demand a lot more of the technology when it is relatively new compared to the technologies that took years and decades to evolve and are part of our routine use (internet protocol, hard disks, and GPS are some examples).

If you are an aspiring data scientist, the one thing that you can do is contribute to the research and development of machine learning and engage in more discussion both online and offline.

How Predictive Analytics Help Troubleshoot Network Issues?

Posted on by Imarticus Learning

 

Ten years ago, if a person had suggested a predictive model to prevent a network failure occurring due to a planned breach, people would have not believed him. Today, that has become a reality thanks to predictive analytic tools and different technologies including big data and statistical modeling.

In simple words, a predictive system looks for irregularities or patterns in data and identifies issues in a network or a server before they transform into bigger problems. This piece of information can then be used to troubleshoot it. An example would be a network outage due to failure in the power supply that can be predicted by looking at the irregularities in the flow of power supply in the days before the outage occurred. The possibilities are immense and that is why it looks so promising.

To make this clearer, let’s understand the basics first.

Analysis of Network Behavior

The basic premise of using predictive analytics to troubleshoot network issues is to let it analyze the behavior of the network. For example, analyzing the flow of data in a communications line can help it understand if any loopholes in it could create a possible entrance for a data breach.

This information can then be used as a preventive measure; a defensive mechanism can be laid out even before the breach is attempted, thereby safeguarding the data available in the line. This not only helps in the security but also in network management and policy setting. To know what is happening inside a server network and monitoring it is real convenience for network managers. It halves their daily maintenance work.

Additionally, analytics can also give out trends and insights to organizations. If a certain type of communication mechanism is known for overloading, companies can avoid creating similar structures and instead opt for better versions or entirely different infrastructures. This information can then be utilized during infrastructure development, especially when it comes to the development of server rooms for IT organizations where data breach and upper thresholds need to be monitored by the second.

Predictive Analytics in Action

Experts suggest that such technologies should be put to use in those sectors where issues can cause discomfort to a whole crowd of people. They are referring to healthcare and other emerging sectors like power distribution and aviation traffic management. Network management in these sectors will help increase safety and security and minimize issues/accidents.

Healthcare systems actually need this technology because it can help hospitals better care for their patients who require 24×7 technical support and are continuously connected to the hospital’s server.

Other than looking at the historical data provided by the network, other parameters like weather conditions are also taken into account. There can be a possibility that a thunderstorm could switch off a hospital’s network because of a power supply failure. If the effect of weather on the network can be predicted, then alternatives can be put in place just in time. Although such alternatives are already in place for emergencies, what such models will help in is better implementation and preparation.

A popular example is the use of predictive analytics in emergency services is how General Electric Power uses AI to manage its power grids in the US. According to this example, the predictive model helps the company get rid of the scope of manual errors in its system. It says that simple errors made at the service provider level can sometimes cause outages in the whole sector. This can be avoided if the data entry is taken online and passes through a filter that is connected to such a model.

Any mismatch in the data as compared to what is expected of it will trigger an alert and the response teams can quickly get in action. This is already being executed by GE Power even as it finds ways to make the entire grid system automated. This does not necessarily mean the absence of service engineers, but just the absence of potential errors that they are sometimes bound to make.

All of this is possible only because of the presence and availability of historical data. Without it, the predictive analytics models cannot compare the new tasks. This is one of the challenges that new sectors face as they do not have sufficient data to work on.

Some Challenges in this Field

Predictive analytics don’t fare well for environments that are rapidly changing. This means that environments where the relationship between two actions is quick, the model finds it difficult to grasp it and thereby ignores it and moves to the next action. This can pose a problem because it can lead to incorrect prediction, or worse, even dangerous predictions.

Availability of data, as noted above, is another hurdle but not something to be worried about. For sectors like healthcare, power, and retail manufacturing, there is abundant data. The challenge then is to source and save it properly which can be used to create the models.

Experts also point to the lack of implementation on the part of engineers. Scientists are continuously toiling to create predictive models that help in error detection but on-field engineers and workers are not supporting the system by providing or utilizing data. This can be a field engineer working on a local transformer for GE Power or a systems engineer at the grids network office not willing to listen to the model’s alerts. This shows that there is also a need for awareness among workers and engineers. This is definitely a radical change in how things work but embracing them is the only way to make it serve us better.

Predictive analytics, despite its active use in detecting and troubleshooting network issues, is still at a nascent stage in the global scale. While some countries and corporations are innovating in the field with ample help from scientific organizations, the practice will only strengthen when it comes to the mainstream. And that might take some more time.

What are the Use Cases of Big Data in Real Estate?

Posted on by Imarticus Learning

Catching up on big data & real estate

Real estate is comprised of assets such as property, land, houses, and buildings. Real estate is a budding sector where properties are dealt with every now and then. Real estate agents facilitate the buying and selling of homes, land, etc. on the behalf of the parties whose interests are vested in it.

Big data is a common term that is widely accepted for large sets of data which is analyzed using various computer software to bring out trends and other insights to understand consumer behavior and several other aspects of the economy.

How is big data related to real estate?

Big data has transformed the way data is perceived these days. It has facilitated a smooth analysis of data and the extraction of vital information. Real estate involves a huge client base thus involving a huge amount of data. There are buyers, sellers, financial institutions and a lot of other parties who require data chunks to cater to their specialization.

Real estate is moving to an electronic mode thus becoming more data-centric. People are buying and selling properties using mobile platforms thus collecting huge amounts of data. The real estate agents through these application data can easily get to know about the properties which are in huge demand and thus control the rates of the already volatile market.

Real estate should have hands-on big data so that they can reap out the benefits of the huge data resource available. Buyers are moving to a mobile platform where they can assess various property options at the same time and improve their search experience. Realtors will also know their clients better and serve them in accordance with their needs. This data is really valuable.

The biggest challenge in the real estate industry is that technology touches this sector at a very slow pace but the roots of technology are growing so fast that the real estate sector has also got a good taste of it.

Influence of big data in the real estate sector

Big data plays a real role in fixing the prices of tangible properties. Also, people who have an intention to buy get to know about the prevailing market rates. The realtors can analyze the cash flows which can take place in the future on the basis of demand. When an interested party visits a real estate website he knows what he is searching for. He has his specific parameters in place thus giving the app controller user-specific data.

The big data analytics training the realtors with a lot of information about an individual such as his age, region to which he belongs, what kind of house does he require, etc.

Such information helps the realtors to make notifications and emails more personalized thus winning the trust of the consumers. Big data also gives an insight into people who are interested in taking properties on rent. These real estate giants have access to a database of millions of people.

With the help of big data, real estate companies are able to market their products efficiently and smartly. Big data is being used by the realtors in marketing their products and also reaching their prospective clients with the help of various marketing campaigns such as email marketing, influencer marketing, celebrity marketing, etc.

Big data also helps in improving the decision-making process for these companies and also for the individuals who are visiting the application. With a plethora of options available, an individual could get all sorts of information on a particular house such as the locality it is in, how old the property is, how far the market is and so on.

Conclusion

This shift in the outlook of real estate businesses has just begun. The more these companies analyze the data available, the more it becomes lucrative. The process of implementation of big data in the dynamics of real estate business is a little slow but all good things take time. Also, they have already started to make the best out of the data available by slowly unwinding the treasures hidden in the layers of the so-called complex data.

Difference Between Data Classification and Prediction in AI

Posted on by Imarticus Learning

 

In machine learning, it is important to understand the difference between data classification and data prediction (or regression) and apply the right concept when a task arises. This is because classification involves a premature decision (as in a combination of prediction and past decisions) and the decision-maker may end up making a decision based on incorrect elements. Not a good situation when error-free action is the main aim of using such a model.

As artificial intelligence and associated technologies evolve, for a program to make the right decision actively depends on how a particular task compares with a set of reference data. This set of data, in most cases, is crucial in the development of that system.

So, let’s take a closer look at these two separate concepts and then go through some of their core differences in the context of decision-making in AI and other related fields.

What is Data Classification?

In simple words, classification is a technique where a system determines what class or category a given observation falls in so that the future course of action can then be defined. Machine learning training uses a three-pronged approach to do this:

  • Application of a classification algorithm that identifies shared characteristics of certain classes
  • Comparison of this set of characteristics with that of the given observation
  • The conclusion to classify the given observation

Let’s take the example of a real-life event to better understand this.

If a bank wants to use its AI tool to predict if a person will default on his loan repayments or not, it will need to first classify the person. For a bank, the two classes in this context will be defaulters and non-defaulters. It does not care about any more details.

The tool will execute the above-mentioned three-step process to conclude if the person will fall in the first or the second category.

While this process may seem unbiased, there is one major drawback that data classification has. This is called the black box problem. It involves a lack of identification of the specific characteristic that must have influenced the decision to classify a given observation under one class. It can be one or more characteristics but pinpointing which ones will be impossible.

What this means is that the bank cannot use it as an example to deter other applications. It has to run the tool every time to assess the person. And for all persons, this defining characteristic may differ.

How Does Data Prediction Compare?

If data classification deals with determination based on characteristics, data prediction focuses on coming up with a more polished output (e.g.: a numeric value). Such type of regression analysis is often used for numerical prediction.

In the above example of the bank loan defaulter, a data prediction model will come up with the probability of how likely a person is to default on loan repayments rather than mere classification.

What is the Difference?

As one can observe, there is a stark difference between data classification and data prediction. Although both of them are widely used in data analysis and artificial intelligence tools, they often serve separate purposes.

According to Frank Harrell, a professor of biostatistics at Vanderbilt University, classification is a forced choice. He takes the example of the decision that a marketer has to take when she has to plan how many of the total target audience should she focus on when building a marketing plan on, say, Facebook. Here, “a lift curve is used, whereby potential customers are sorted in decreasing order of estimated probability of purchasing a product.” To gain maximum ROI, the marketer targets those that are likely to buy the product. Here, classification is not required.

When to make a forced-choice and when not to totally depends on the observation being made. This is why most algorithmic models working on data analysis cannot be used for all types of results. Classification and prediction both depend on what the required output is.

Now, let’s take a look at some pointers that will further clarify the differences between these two models:

  • In classification, a data group is divided into categories based on characteristics
    • In prediction, the reference dataset is used to predict a missing element
  • Classification can be used when the type of desired output is already known
    • Prediction, on the other hand, is used when the type is unknown
  • Classifiers are dependent on previous data sets. They require abundant information to provide better prediction
    • Numerical regression, on the other hand, can provide usable data and act as a starting point for future activities

What these differences highlight is the need to apply them cautiously. Choosing one method over another might feel like a free option, but it is much more than that. While preparing the data will involve challenges in relevance analysis and data cleaning (to chuck out as much noise as possible), one has to also consider factors such as accuracy, speed, robustness, scalability, and interpretability.

As one can assume, these two models have differing values as far as the above factors are considered. The computational cost, for example, is not an important topic at the moment in the artificial intelligence field. But once these models become a part of the everyday analysis, a discussion will surely pop up. And that’s when a more learned decision must be taken.

Finally, it is important to understand that both classification and regression (prediction of a numerical value) are types of predictive analysis. The difference is mainly in how they interact with observation as well as how the reference data set is used.

Choosing which one to go with should perfectly fit the case otherwise one will end up with the wrong choice. Building such predictive models should, therefore, be a joint project involving data scientists and business users. In the example of the bank, if loan agents can directly work with data scientists during the development of these models, it aids in removing at least the known errors out of the equation.

How Can Data Analytics Help Insurance Companies Perform Better?

Posted on by Imarticus Learning

It is the question that has already been asked after it was on everybody’s mind for a long time. How can big data help insurance companies – a heavily regulated sector in India and everywhere else in the world – and make them perform better? Especially when it comes to preventing frauds, system gaming, and other illegal activities that are prevalent.
With the competition only rising in the insurance sector, what company makes headway in properly using data analytics and acts a role model remains to be seen. So, in order for us spectators to do that, we will need more information.
How exactly can analytics help insurance companies serve their customers better? There are four major ways.

Use of Data Analytics in the Insurance Sector

Apart from gaining customer insights and helping in risk management, data analytics training can also help understand if it’s worth handing out an insurance policy to a person based on his social stature. How does his social media presence look like? What are his hobbies and adventurous choices? Has he lied in his application? All of this can also be extracted through the proper use of data capturing and analytics tools.
It seems extremely lucrative for the companies but it also poses a risk for us customers who stare at a possible invasion of our personal space and privacy. Having our social media accounts stalked by HR professionals for the purpose of employment is one thing (not a decent task, nonetheless). Having strangers do the same so that they can deny you insurance – which let us remind you is a basic necessity in today’s times – is a big event. It is not to say that this will be the majority outcome but it is what is on the mind of insurers when they consider big data.
Let us look at those four different ways in which analytics can help insurers:

Managing Claims

This is by far the biggest reason why insurers are pushing for use of predictive analytics in the sector. As you can imagine, it can help companies create a database of customer information that can then be used to compare new policy buyers and see if they fall in a bracket of people who might commit fraud such as wrongly filing for a claim.
The insurer can feed the model with past data and then use it to classify its new customers. Since the approval or rejection of a file is more or less under the authority of the insurer, this can help them denying insurance to a possible fraudulent applicant.

Generating Claims Based on Data

This involves checking the profile of a person while she applies for insurance. For example, in the case of house insurance, data can help insurers understand if this specific house is vulnerable to natural incidents; is it closer to the fire station; what is the history of the locality for the past twenty years as far as mishaps are concerned. When we talk about data, there is a lot of scopes.
And when it’s time to act, this collection of data can be extremely helpful to weed out fraudulent applications and other types of scams. It can also help them set better premiums if denying insurance is not an option.

Better Customer Support

Have you ever been in a situation where you have had to get your call to the customer care rerouted a couple of times before you finally got your problem heard? The call first moves to the respective section of the insurance (example: car insurance versus medical insurance), then it goes to the redressal section, and then finally you get someone on the other line to speak. It is extremely annoying for a customer. Even more so when she is in a situation where she needs urgent medical insurance support.
Big data can assist in this process by automatically understanding the issue of a caller and routing it to the respective section. This is possible based on the preliminary details that the customer has to fill in. The analytical model which is attached to the database of policies can better bridge the gap so that the customer gets her information quickly.
On the other hand, this can also help insurers keep a track on a particular customer. How many times in the past five years has she filed for insurance? What does her lifestyle look like now compared to when she bought it? This last piece of information can aid in guiding her should she decide to buy another policy with the same company.

Offering New Services

According to IBM, data analytics can also provide insurers with tools to market new products based on their requirements. Today, retargeting techniques and cold calling are used to push products to customers, but when companies have valuable data in their hand, they can easily club it with their marketing and advertising and even sales departments to better retain customers and make them buy more products.
This will require a lot of integration on the part of insurers, but the current market and the high competition say that companies will be willing to take the jump if they see there’s any scope to grow their customer base and tackle the menace of continued competition.
According to us, newer companies will be more desperate to try these systems out than incumbent ones that have functioned in the same way for years and even decades.
While we have talked about the scope in general tone, it makes sense to understand what specific tools will be of most use. Out of this, content analytics, discovery and exploration capabilities, predictive analytics, Hadoop, and Stream computing are some essential models that will pave the way forward for insurance companies.
Of course, all of this cannot be switched on one fine morning without the approval of IRDAI. The regulatory body is yet to come up with proper guidelines, and insurers will need to abide by those rules before they can start executing them.

Ace your next Analytics Interview

Posted on by Imarticus Learning

1. What is the importance of validation of data?

From a business perspective, at any stage, data validation is a very important tool since it
ensures reliability and accuracy. It is also to ensure that the data stored in your system is
accurate, clean and useful. Improper validation or incorrect data has a direct impact on sales,
revenue numbers and the overall economy.

2. What are the various approaches to dealing with missing values?

Missing values or missing data can be dealt with by taking the following approaches-
● Encoding NAs- this used to be a very common method initially when working with
machine learning and algorithms was not very common
● Deleting missing data case wise- this method works well for large datasets with very few
missing values
● Using mean/median value to replace missing values- this method works very well for
numerical features
● Run predictive models to impute missing values- this is highly effective as it works best
with the final model
● Linear regression- works well to provide good estimates for missing values

3. How do you know if a developed data model is good or bad?

A developed data model should fulfil the following criteria to qualify as a good model-
● Whether the data is the model can be easily consumed
● If the model is scalable in spite of good data changes
● Whether performance can be predicted or not
● How good and fast can a model adapt to changes

4. What are some of the challenges I can face if I were to perform a data analysis?

Performing data analysis may involve the following challenges-
● Too much data collection which can often overwhelm data analysts or employees
● Differentiation between meaningful and useless data
● Incoherent visual representation of data
● Collating and analysing data from multiple sources
● Storing massive amounts of generated data
● Ensuring and restoring both security and privacy of stored data as well as generated
data
● Inadequate experts or lack of industry professionals who understand big data in depth
● Exposure to poor quality or inaccurate data

5. Explain the method of KNN imputation.

The term imputation means replacing the missing values in a data set with some other possible
values. Using KNN imputation in data analysis helps in dealing with missing data by matching a
particular point with its nearest K neighbours assuming that it is a multi-dimensional space. This
has been a highly popular method in pattern recognition and statistical estimation since the
beginning of the 1970s.

6. What does transforming data mean?

Data transformation involves the process of converting data or information from a different
format into the required format in a system. While mostly transforming data involves the
conversion of documents, occasionally it also means conversion of a program from one
computer language to another in a format that is readable by the system.
Data transformation comprises of two key phases, data mapping to ensure smooth
transformation, and code generation, for the actual transformation to happen and run on
computer systems.

7. State the difference between null and alternative hypothesis.

It is a null hypothesis when there is no key significance or relationship between two variables
and is something that the researcher is trying to disprove. No effects are observed as a result of
null hypothesis and neither are there any changes in actions or opinions. The observations of
the researcher are a plain result of chance.
An alternative hypothesis on the other hand is just the opposite of a null hypothesis and has a
significant relationship between two measured and verified phenomena. Some effects are
observed as a result of an alternative hypothesis; and since this is something the researcher is
trying to prove, some amount of changes in opinions and actions are involved. An alternative
hypothesis is a result of a real effect.

8. What would you mean by principal component analysis?

Principal component analysis is a method used to reduce large data sets in dimension by
transforming larger sets of variables into smaller ones, while retaining the principal information.
This is majorly done with the intent of improving accuracy since smaller data sets are easier to
explore, as a result of which data analysis gets faster and quicker for machine learning.

9. Define the term – logistic regression.

Logistic regression is a form of predictive analysis in machine learning that attempts to identify
relationships between variables. It is used to explain the relationship between a binary variable
and one or multiple nominal, ordinal, interval or ratio-level variables, while also describing the
data. Logistic regression is used for categorical dependent variables.

10. How can I deal with multi-source problems?

Storing the same data can often cause quality hindrances in analytics. Depending on what the
magnitude of the issues are, a complete data management system needs to be put in place.
Data reconciliation, elaborate and informative databases and pooling in segmented data can
help in deal with multi-source problems. Aggregation and data integration is also helpful while
dealing with multi-source data.

11. List the most important types of clustering algorithms.

The most important types of clustering algorithms are-
● Connectivity models- based on the idea that farther data points from each other exhibit
less similarity when compared to closer data points in data space
● Centroid models- the closeness of a data point to the cluster centroid derives the notion
of similarity for this model
● Distribution models- based on the probability that all data points in the same cluster are
part of the same distribution
● Density models- search for varied density areas of data points in the data space

12. Why do we scale data?

Scaling is important because sometimes your data set will have a set of features that completely
or partially vary in terms of units, range and magnitude. While certain algorithms have minimum
or zero effects, scaling can actually have positive impacts on the data. It is an important step of
data pre-processing that also helps to normalise data within a given range. Scaling of data also
often helps in speeding up algorithm calculations.

13 Things About Interview Preparations You May Not Have Known

Posted on by Imarticus Learning

Finally, your diligence has paid off, and you have now landed an interview. Going for an interview can be quite nerve-wracking and stressful.  While there are a plethora of things that can go wrong like a limp, awkward handshake to having to answer a question you have no clue about, however, there are definitely few things that you can control with a bit of preparation on your end. Here are 13 things about interview preparations you may not have known about but can surely help you to ace the interview.

  • Pick an outfit

How you dress is quite a crucial part of preparing for your interview. A smart and well-fitted outfit makes the first impression when you walk in for the meeting. Get it properly cleaned and pressed, pair it with proper shoes and accessories. Out Tip: Try it out to see it fits you well, and you look smart and presentable in it.

  • Learn and study your resume well

All your interviewer has is your resume to know you so it is a fair game that they can ask you to elaborate on any of the jobs that you have listed in it. Even if it is something you might have held a long time back, you should be ready to talk about when asked. If it is in the resume be well prepared to explain the job you did, your responsibility and any other questions related to it, and it’s a step you cannot avoid.

  • Print Multiple Copies of Your Resume

Never take a single copy of your resume, instead take multiple copies so that you can hand it to every only person who is interviewing you.  Also, it is recommended that a copy of your resume be kept in front of you as it can be used as a cheat sheet for you to answer some behavioural questions.

  • Bullet Points to behavioural questions

You might be asked behavioural questions by your interviewer, to ensure that you do not stumble while answering make points of five to seven essential things you want to address. It is advisable that you don’t look into a piece of paper to answer your interviewer. But in case you get stuck do refer to the pointers.

  • Practice aloud

Not many do these but practice some general questions aloud so that you can modulate your voice. You would not want to be too loud or too soft for your interviewers.

  • Carry a notebook and pen

One of the essential parts of preparing for an interview is to carry a couple of pens and a laptop. This establishes your desire to perform and your sincerity.

  • In-depth research of the company

While preparing for the interview learn about the company in details. The sector that the company represents how it fits into the market and the company’s mission and vision. This will help you to align your answers to the question asked in the interview with the company’s profile.

  • Think of an intelligent question

While you are studying the company profile and your job description in details if you are unsure about anything make some points. You can bring up those points and ask your interviewer questions related to them. When you ask intelligent questions, it represents that effort has been made by you to prepare for the interview.

  • Know the type of interview

Talk to your recruiter if it is not already mentioned when you applied for the job about the kind of conversation you would be part of. Do not assume that it will be one-on-one; since there are group discussions and behavioural interviews that a firm can conduct.

  • Know the direction of the venue

Even if you have a GPS print out the instructions to the site, because you never know when your internet might fail to work. Study the flow of traffic you might face during the day and area when your interview has been scheduled. Do have the phone number of the contact person of the company in case you are lost or late. You should always be on time, do not be more than 10 minutes early, as your interviewer might not be ready.

  • Do not use certain words

It’s a big no to use phrases like ‘I am nervous’ instead say I am excited. Instead of ‘I don’t know’ use the phrase, “I am not sure of the answer, or I need to learn about it’. Never say‘I don’t’ have a question’ rather you can ask ‘ What is the biggest challenge of working in this industry’ or some open-ended question that is based on what you observed and learned about the interviewer or the company. Instead of using a word like ‘um…’ when not sure about our reply, and you need some time to think, say ‘ That’s an interesting question.’

  • Carry Some Gums or Mints

The last thing you would want is your interviewer to breathe in some pungent odour from your mouth. A fresh smell is always recommended; so keep some gums or mints that you can use before entering the interview.

  • Keep the basic hygiene items

No matter what the venue of the interview is, keep the essential hygiene items with you like tissues, hand sanitizer, lotion, chapstick and any small things that give you confidence. Remember to pack them in the bag you will be carrying to the interview.

At an interview be sure to be articulate and natural with your answers. A well-prepared discussion will give you a better chance of securing the job than the others. As said by some experts it is important to remember the 5Ps for preparing for an interview: Prior Planning Prevents Poor Performance.

5 ways AI is Utilized in Advancing Cancer Research

Posted on by Imarticus Learning

When it comes to the health of a person, life and death become a matter of problem. Health care centers and medical professionals all over the world are now leveraging the power of AI, to research a plethora of ailments. One such case is cancer research. Cancer is a disease which results in the uncontrollable division of cells and hence the destruction of body tissues. This problem can be solved with the help of artificial intelligence as it is nowadays providing favorable outcomes in every field. It can help in early detection of cancer and the treatment can prove to be very successful.
 Here’s a list of 5 ways Ai is being utilized in advancing cancer research: 

  1. Machines fed with adequate data and programmed with advanced algorithms can make use of past medical records during surgery of a patient. This is possible only with the help of artificial training. Researchers have found that there are approximately 5 times fewer complications in a robotic procedure of surgery in comparison to surgeons operating alone.
  1. Artificial intelligence can be used to interact with patients by directing them the most effective care, answering the questions, monitoring them and providing quick solutions to their problems. Most applications of virtual nursing include fewer visits to hospitals and 24 hours of care to the patients.
  1. Healthcare providers also make use of artificial intelligence to diagnose patients. Early diagnosis of cancer has now become a necessity, as any delay can cause a difference between life and death.

According to a recent study, artificial learning methods can help to classify the patients into high or low-risk groups. The study further added that AI has a great impact in the area of cancer imaging as artificial intelligence can analyze more than 10000 skin images with higher sensitivity.

  1. Complicated tests and analysis, such as CT scan and internal imaging have turned out to be hassle-free with the help of AI-enabled systems. It reduces the chances of any manual error and helps the doctors to diagnose the condition before it becomes critical.

According to a study AI has proved to be 99% accurate and more than 25 times faster in detecting breast cancer. Artificial intelligence can also be used to find out vertebral fractures if any.

  1. AI has the potential of developing lifesaving drugs and saving billions. Engineers have developed algorithms that can analyze the potency and effectiveness of the medicines developed for treatment. It also helps them to make better decisions related to healthcare.

Most of the people even use wearable technology based on artificial intelligence to check out their sleep patterns and heart rate. Applying artificial intelligence to detect cancer can inform healthcare providers about specific chronic conditions and manage the disease in a better way.
So there are various cases where artificial intelligence can find its application. Artificial intelligence training can help the individual to enhance their skills and knowledge in the field of artificial intelligence.
Imarticus Learning is one of the leading institutes that provide numerous courses in data science, machine learning, blockchain, etc. The institute takes pride in helping students make a career in artificial intelligence. AI has improved its application in the past few years and is expected to revolutionize the world in many ways in the coming years. Thus, having good artificial intelligence training will prove to be useful in all fields. You can have such good knowledge with the help of experts and qualified staff at the institute which can help you to shape your career in a better way.

Which Are The Powerful Applications of Machine Learning in Retail?

Posted on by Imarticus Learning

Introduction

Machine Learning is one of the top technological trends in the retail world. It is having a great impact on the retail industries, especially in e-commerce companies like Flipkart, Amazon, eBay, Alibaba, etc. These companies completely rely on online sales, where it is common to use Machine Learning or Artificial Intelligence nowadays.

Companies like Flipkart, eBay, Amazon or Alibaba are the successful companies who have integrated AI technologies across the entire sales cycle. Not limited to these big companies, there are also numerous small companies, some of which are already using this technology and inclined towards using this technology for the growth and development of their businesses.

How can Machine Learning Change Retail

We can think of three key scenarios when Machine Learning comes into play:

  • Finding the Right Product; Enabling your users or your customers to find the right product at the right time. We can move people away from the regular textual searches and help them find the products more visually.
  • Recommending the next product; The other aspect is how do we help them recommend the right product at the right time. One of the things that we increasingly deal with  is a choice. We can help customers by giving them the right product at the right time on the basis of their prior user behaviour.
  • Understanding the feedback; Once the product is released into the wild, we are interested in knowing how the product fares, people’s opinion about it, their suggestions in relation to the particular product. We can get a better feeling of sentiment, understanding what they do with the product, and get a better answer that can drive your life cycle from a product’s development perspective, marketing perspective and multiple downstream activities.

Applications of Machine Learning in Retail

As discussed above, those three are the key scenarios when Machine Learning is used in Retail. Some other applications of Machine Learning in retail are discussed below:

  • Market Basket Analysis: This is the traditional tools of data analysis in retail. The retailers have been making huge profits out of it for years. This is totally dependent on the amount of organization’s data that is collected by customer’s transactions. This analysis is done using Association Rule Mining algorithm.
  • Price Optimization: The formation of price not only depends on the cost to produce an item but also on the different types of customers and their budgets as well as other competitor’s offers. The data received from various sources define the flexibility of prices at different locations, different customers’ buying attitude, seasoning, and the competitor’s pricing. The retailers attract customers, retain the attention and realize personal pricing schemes with the help of real-time optimization.
  • Inventory Management: Inventory is nothing but stocking goods for their future use. This means retailers stock goods in order to use them in times of crisis. Their aim is to provide a proper product at the right time at the right place and in proper condition. A powerful machine learning algorithms and data analysis platforms help in finding not just the patterns and correlation but also the optimal stock and inventory strategies.
  • Customer Sentiment analysis: Sentiment analysis is performed on the basis of Natural Language Processing, and text analysis to extract positive, neutral or negative sentiments. The algorithms run through all the meaningful layers of speech. Here, the output is the sentiment ratings.
  • Fraud Detection: Fraud detection is one of the challenging activities for retailers. The reason for fraud detection is an immense financial loss. The algorithms developed for fraud detection not only recognize the fraud and flag it to be banned, but it also predict future fraudulent activities.

Conclusion

In the article, we briefly discussed the applications of Machine Learning in retail.