How can Marketing use Data Science and AI?

marketing data science
https://www.affexpro.com/blog/why-marketers-should-think-like-data-scientists/

Data Science is a field that extracts meaningful information from data and helps marketers in discerning the best insights. These insights can be on different marketing elements such as client intent, experience, habits, etc. that would assist them in efficiently optimizing their marketing strategies and derive optimum income.


Over the previous decade, online information consumption has dramatically soared due to the full price of the World Wide Web. It is estimated that there are over 6 billion devices linked to the web right now. Couple of million terabytes of data are generated every single day.
For online marketers, this staggering amount of data is a gold mine. If this data might be appropriately processed and evaluated, it can provide valuable insights which marketers can use to target customers. Nevertheless, decoding significant portions of data is a mammoth job. This is where Data Science can profoundly help.


Over the previous decade, online information consumption has dramatically soared due to the full price of the World Wide Web. It is estimated that there are over 6 billion devices linked to the web right now. Couple of million terabytes of data are generated every single day.
For online marketers, this staggering amount of data is a gold mine. If this data might be appropriately processed and evaluated, it can provide valuable insights which marketers can use to target customers. Nevertheless, decoding significant portions of data is a mammoth job. This is where Data Science can profoundly help.

How can data science be implemented in Marketing?

Optimizing Marketing budget

Marketers are constantly under a rigorous spending plan. The main objective of every online marketer is to derive optimum ROI from their designated budgets. Accomplishing this is constantly difficult and lengthy. Things don’t always go according to strategy and efficient budget plan usage is not accomplished.
By analyzing a marketer’s spend and acquisition data, a data scientist can develop a costs design that can assist make use of the budget better. The design can help marketers disperse their spending plan across areas, channels, mediums, and campaigns to optimize for their essential metrics.

Good example of optimizing marketing budget using data science is Increase Marketing ROI with Multi-touch Attribution Modelling

Identify the right channels for a specific Marketing campaign

Data science can be utilized to figure out which channels are giving an appropriate lift for the online marketer. Utilizing a time series model, a data scientist can compare and determine the sort of lift seen in various channels. This can be extremely advantageous as it informs the marketer precisely which channel and medium are delivering appropriate returns.

Increase Marketing ROI with Multi-touch Attribution Modelling and Modeling marketing multi-channel attribution in practice are talking about identifying the right marketing channels.

Marketing to the Right Audience

Typically, marketing campaigns are broadly distributed regardless of the location and audience. As a result, there are high opportunities for online marketers to overshoot their spending plan. They likewise may not be able to achieve any of their objectives and revenue targets.
However, if they use data science to analyze their data correctly, they will be able to understand which areas and demographics are providing the greatest ROI.
Clustering comes as a good tool for creating the right audience using Machine Learning.

Matching the right Marketing Strategies with Customers

To obtain maximum worth out of their marketing strategies, marketers require to match them with the ideal customer. To do this, data researchers can create a consumer lifetime value model that can section consumers by their habits. Marketers can use this model for a variety of usage cases. They can send out referral codes, and cash back provides to their most significant worth consumers. They can apply retention strategies to users who are likely to leave their consumer base and so on.
Another even more powerful tool is Marketing Personalization. Marketing Personalization will mathe your offers with the best customer. This will guarantee the best ROI of your marketing campaign.
Read more about Personalization – How much do you understand your customer? and building marketing recommendation systems.

Customer Segmentation and Profiling

While marketing a product/service, marketers take a look at developing client profiles. They are continually constructing specific lists of prospects to target. With data science, they can accurately decide which personas require to be targeted. They can find out the variety of personas and the type of qualities they need to produce their client base.
Clustering is the most used tool for creating marketing customer segments and profiles.

Email Campaigns

Data science can be utilized to find out which emails interest which customers. How frequently are these emails check out, when to send them out, what sort of content resonates with the consumer, and so on. Such insights make it possible for online marketers to send contextualized email campaigns and target customers with the ideal deals.
Creating personalized email campaigns is another example how personalization can be used.

Sentiment Analysis

Marketers can utilize the data science to do sentiment analysis. This means that they can gain much better insights into their customer beliefs, opinions, and attitudes. They can likewise keep track of how customers respond to marketing campaigns and whether they’re engaging with their company.
With the recent advances in deep learning, the capability of algorithms to examine text has improved considerably. Innovative use of sophisticated artificial intelligence strategies can be a reliable tool for doing much more effective marketing offers.

Recommender Systems – the start of marketing personalization

recommendation systems

Recommender systems are tools for communicating with big and intricate info spaces. They provide a customized view of such areas, prioritizing products most likely to be of interest to the user. The field, christened in 1995, has grown enormously in the range of problems attended to and methods employed, as well as in its useful applications.

We are dealing with personalized offer every day, whether we are aware of it or not. I talk here about Personalization – How much do you understand your customer?.
Recommender system help companies to give personalized offers and display to their customers.

Research study has incorporated a broad variety of artificial intelligence methods consisting of machine learning, data mining, user modeling, case-based thinking, and customer satisfaction, among others. Personalized suggestions are a vital part of numerous online e-commerce applications such as Amazon.com, Netflix, and Spotify. This wealth of practical application experience has supplied the motivation to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of this unique issue is to take stock of the current landscape of recommender systems research study and recognize instructions the field is now taking. This post supplies an overview of the current state of the area and presents the different articles in a particular concern.

The prototypical usage case for a recommender system frequently occurs in e-commerce settings. A user, Jane, visits her preferred online bookstore. The homepage notes existing bestsellers and also a list consisting of advised products. This list might include, for instance, a new book published by one of Jane’s preferred authors, a cookbook by a brand-new author and a supernatural thriller. Whether Jane will find these recommendations beneficial or distracting is a function of how well they match her tastes. Is the cookbook for a style of cuisine that she likes (and is it different enough from ones she already owns)? Is the thriller too violent? A vital function of a recommender system, therefore, is that it supplies an individualized view of the data, in this case, the bookstore’s stock. If we eliminate the customization, we are entrusted to the list of best-sellers– a file that is independent of the user. The recommender system aims to lower the user’s search effort by noting those items of the highest utility, those that Jane might be probably to acquire. This is beneficial to Jane in addition to the e-commerce shopkeeper.

Recommender systems research study encompasses scenarios like this and various other info access environments in which a user and shopkeeper can benefit from the presentation of customized alternatives. The field has seen an incredible growth of interest in the previous decade, catalyzed in part by the Netflix Prize and evidenced by the fast development of the annual ACM Recommender Systems conference. At this point, it is rewarding to take stock, to consider what differentiates recommender systems research from other associated areas of the research study in artificial intelligence, and to take a look at the field’s successes and new challenges.

What is a Recommender System?

In a typical recommender system people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients. In some cases the primary transformation is in the aggregation; in others the system’s value lies in its ability to make good matches between the recommenders and those seeking recommendations.

Two basic concepts stand out that differentiate recommender systems research:

  • A recommender system is individualized. The suggestions it produces are implied to optimize the experience of one user, not to represent group consensus for all.
  • A recommender system is meant to help the user choose among discrete alternatives. Usually, the products are already understood in advance and not created in a bespoke fashion.

The personalization aspect of recommender systems distinguishes this line of research study most strongly from what is typically comprehended as research in online search engine and other details retrieval applications. In a search engine or other details retrieval system, we expect the set of outcomes relevant to a specific query to be the same despite who issued it. Many recommender systems accomplish personalization by maintaining profiles of a user’s activity (long-term or short-term) or mentioned preferences. Others achieve a tailored result through conversational interaction.

A Recommender System Typology

A common problem location identifies a recommender systems research study instead of a joint innovation or technique. Variety of research study methods have actually been used to the recommender systems problem, from analytical methods to ontological thinking, and a wide range of the issues have been tackled, from selecting consumer products to discovering good friends and lovers. One lesson that has been found out over the previous years of recommender systems research study is that the application domain exerts a strong influence over the kinds of methods that can be effectively used.
Domain characteristics like the persistence of the user’s energy function have a significant effect: for instance, a users’ taste in music may change slowly however his interest in celebrity news stories may change much more. Hence, the dependability of preferences collected in the past might differ. Likewise, some products, such as books, are offered for suggestion and intake over a long duration of time, typically years.
On the other hand, in a technological domain, such as a mobile phone or cams, old items become rapidly outdated and cannot be usefully advised. This is also real of locations where timeliness matters such as news and cultural events. It is not surprising for that reason that there are many hairs of a research study in recommender systems, as researchers take on a range of suggestion domains. To merge these different approaches, it is helpful to consider the AI elements of suggestion, in specific, the understanding basis underlying a recommender system.

Knowledge Sources

Every AI system makes use of several sources of knowledge to do its work. A supervised machine learning system, for instance, would have a labeled collection of data as its primary understanding source, but the algorithm and its parameters can be considered another implicit type of understanding that is brought to bear on the classification task. Suggestion algorithms can likewise be categorized according to the knowledge sources that they utilize.

There are three basic types of knowledge:

  • social knowledge about the user base in primary,
  • individual understanding about the particular user for whom recommendations are looked for (and possibly knowledge about the specific requirements those suggestions need to fulfill), and lastly
  • real expertise about the items being recommended, ranging from easy feature lists to more complicated ontological expertise and means-ends knowledge that allows the system to factor about how an object can fulfill a user’s needs.

Types of Recommender Systems

Collaborative Recommendation system

collaborative filtering recommendation system

The most popular strategy in the recommendation systems is a collaborative recommendation.
The standard insight for this strategy is a sort of connection in the world of taste – if users Alice and Bob have the same energy for items 1 through k, then the opportunities are excellent that they will have the same utility for detail k +1.
Typically, these energies are based on rankings that users have supplied for products with which they are currently familiar. The critical advantage of collaborative recommendation is its simpleness. The issue of calculating utility is transformed into the problem of theorizing missing out on worths in the scores matrix, the sporadic matrix where each user is a row, each product a column and the qualities are the recognized scores. This insight can be operationalized in several ways. Originally, clustering techniques, like nearest-neighbor, were applied to find communities of like-minded peers. However, matrix factorization and other dimensionality-reduction strategies are now acknowledged as superior in precision
Some problems with collaborative recommendation are well-established: – New items cannot be suggested without depending on some additional understanding source. Extrapolation depends on having some values from which to task. Indeed, sparsely-rated items, in general, present a problem because the system lacks information on which to base forecasts. Users who have provided few rankings will get noisier recommendations than those with more significant histories. The issues of new users and brand-new ratings are jointly called the “cold start” problem in the collaborative proposal. – The circulation of grades and user choices in numerous customer taste domains is relatively concentrated: a small number of “blockbuster” products receive a terrific deal of attention, and there are lots of, various hardly ever rated products.

Malicious users may be able to generate significant sales of pseudonymous profiles and utilize them to predisposition the suggestions of the system in one method or another. There is still a good deal of algorithmic research study focused on the problems of collaborative recommendation: more precise and useful price quotes of the scores matrix, better handling of brand-new users and new products, and the extension of the fundamental collaborative recommendation idea to brand-new kinds of data consisting of multi-dimensional rankings and user-produced tags, among others.

Content-based Recommendation system

content-based-recommendation-system

Before the development of collaborative recommendation in the 1990s, earlier research in individualized info access had focused on combining knowledge about products with info about user’s choices to find proper products. This technique, because of its reliance on the content knowledge source, in particular, product features, has come to be known as a content-based recommendation. A content-based recommendation is carefully related to supervised machine learning.
We can see the issue as one of discovering a set of user-specific classifiers where the classes are “useful to user X” and “not beneficial to user X.” One of the important concerns in the content-based recommendation is feature quality. The items to be advised need to be explained so that significant knowledge of user preferences can take place.
Ideally, every item would be described at the same level of detail, and the feature set would include descriptors that associate with the discriminations made by users. Unfortunately, this is frequently not the case. Descriptions may be partial, or some parts of the things space may be described in higher information than others. The match in between the function set and the user’s utility function likewise requires to be good. Among the strengths of the popular Pandora, streaming music service is that music-savvy listeners manually select the feature set it uses for musical choices. Automatic music processing is not yet significant enough to reliably draw out functions like “bop feel” from a Charlie Parker recording. In addition to the development and application of brand-new intelligent algorithms for the recommendation task, research study in content-based recommendation also takes a look at the problem of feature extraction in various domains.
A further subtype of content-based recommendation is a knowledge-based recommendation, in which the dependence on product features is extended to another sort of understanding about products and their possible energies for users. An example of this type of system is the financial investment recommender mentioned earlier that needs to understand about the threat profiles and tax consequences of various investments and how these connect with the financial position of the investor. As with other knowledge-based systems, understanding acquisition, maintenance, and validation are crucial problems. Also, considering that knowledge-based recommenders can utilize detailed requirements from the user, user interface research has been paramount in developing knowledge-based recommenders that do not position excessive of a burden on users.

Because of the difficulties of running large-scale user studies, recommender systems have conventionally been evaluated on one or both of the following measures:

  • Prediction accuracy. How well do the system’s predicted ratings compare with those that are known, but withheld?
  • Precision of recommendation lists. Given a short list of recommendations produced by the system (typically all a user would have patience to examine), how many of the entries match known “liked” items?

Both of these conventional measures are lacking in some essential aspects, and numerous of the brand-new locations of exploration in recommender systems have caused experimentation with new evaluation metrics to supplement these common ones. Among the most significant issues occurs because of the long-tailed nature of the distribution of the scores in lots of datasets.

A recommendation strategy that optimizes for high accuracy over the entire data set therefore consists of implicit bias towards popular items, and for that reason might stop working on recording elements of utility associated with novelty. An accurate forecast on a topic that the user already understands is naturally less helpful than a prediction on an unknown item. To address this problem, some researchers are taking a look at the balance between accuracy and diversity in a set of suggestions and dealing with algorithms that are sensitive to product distributions. Another issue with traditional recommender systems examination is that it is substantially fixed.
A set database of ratings is divided into training and test sets and used to demonstrate the effectiveness of an algorithm. However, the user experience of recommendation is entirely various.
In an application like movie recommendation, the field of products is always expanding; a user’s tastes are progressing; brand-new users concern the system. Some recommendation applications need that we take the dynamic nature of the recommendation environment into account and evaluate our algorithms appropriately. Another location of assessment that is reasonably under examined is the interaction between the utility functions of the shopkeeper and the user, which necessarily look slightly different. Owners carry out recommender systems to achieve business objectives, usually increased earnings. The owner, therefore, might prefer an imperfect match with a high-profit margin to a perfect game with a limited profit.
On the other hand, a user who exists with low energy suggestions may cease to rely on the recommendation function or the whole website. Owners with high volume websites can field algorithms side-by-side in randomized trials and observe sales and earnings differentials. However, such outcomes rarely filter out into the research study neighborhood.

Before implementing a Recommendation system in your organization you must make good planning of what you will recommend to whom and on which way. This is the most important first step that makes the foundation of success for your personalized offers.
I strongly recommend you to continue reading Personalization – How much do you understand your customer? as well as Increase Marketing ROI with Multi-touch Attribution Modelling. They are both really important parts of the personalization system.

Read more on Wiki: https://en.wikipedia.org/wiki/Recommender_system

Increase Marketing ROI with Multi-touch Attribution Modelling

With using Multi-touch Attribution Modelling advertisers typically realize a 15% – 44% improvement in marketing ROI with advanced multi-channel machine-learning algorithm. Using advanced machine learning techniques for marketing can give you true insight on performance all down to channel, campaign and device-level.

Implementing advanced machine learning models for marketing attribution will give you the following advantages:

  • Data-driven multi-touchpoint attribution.
  • Transparent and neutral: agency-independent and media-independent.
  • Connect the results of every touchpoint and campaign to your revenue stream.
  • Calculate the total cost of individual marketing campaigns to determine your ROI per campaign.
  • Channel-neutral: we evaluate both online and offline marketing channels and campaigns.
  • Budget optimiser to maximize your marketing ROI

Even small adjustments can have a noticeable effect on your marketing effectiveness and marketing ROI. With our Attribution Modelling machine learning model, we make the seemingly-complicated task of tracking the monetary impact of every touchpoint, every channel and every campaign easy. We can combine online and offline customer journeys and touchpoints to give you the full picture.

Non AI attribution modeling approaches

 undefined The Last Action Click model attributes 100% of the conversion value to the most recent Action ad that the customer clicked before buying or converting.

When it’s useful: If you want to identify and credit the Action that closed the most conversions, use the Last Action Click model.

undefined The First Interaction model attributes 100% of the conversion value to the first channel with which the customer interacted.

When it’s useful: This model is appropriate if you run ads or campaigns to create initial awareness. For example, if your brand is not well known, you may place a premium on the keywords or channels that first exposed customers to the brand.

 undefined The Linear model gives equal credit to each channel interaction on the way to conversion.

When it’s useful: This model is useful if your campaigns are designed to maintain contact and awareness with the customer throughout the entire sales cycle. In this case, each touchpoint is equally important during the consideration process.

 If the sales cycle involves only a short consideration phase, the Time Decay model may be appropriate. This model is based on the concept of exponential decay and most heavily credits the touchpoints that occurred nearest to the time of conversion. The Time Decay model has a default half-life of 7 days, meaning that a touchpoint occurring 7 days prior to a conversion will receive 1/2 the credit of a touchpoint that occurs on the day of conversion. Similarly, a touchpoint occuring 14 days prior will receive 1/4 the credit of a day-of-conversion touchpoint. The exponential decay continues within your lookback window (default of 30 days).

When it’s useful: If you run one-day or two-day promotion campaigns, you may wish to give more credit to interactions during the days of the promotion. In this case, interactions that occurred one week before have only a small value as compared to touchpoints near the conversion.

  The Position Based model allows you to create a hybrid of the Last Interaction and First Interaction models. Instead of giving all the credit to either the first or last interaction, you can split the credit between them. One common scenario is to assign 40% credit each to the first interaction and last interaction, and assign 20% credit to the interactions in the middle.

When it’s useful: If you most value touchpoints that introduced customers to your brand and final touchpoints that resulted in sales, use the Position Based model.

Stepping away from simplistic models like Last Click lets you take into account the full customer journey and properly account for all your marketing investments.

A Last Click attribution model leads to overvaluing and undervaluing certain channels, while Markov models – which take into account the full customer journey – is far more accurate. Using a good attribution model that takes into account the full customer journey lets you optimise marketing decisions based on the most valuable touchpoints.With Windsor.ai’s Attribution insights solutions, backed by a team of experienced data scientists who honed their craft at well-known multinational corporations. We extract valuable insights and recommendations so you can take action where it matters most to increase your MROI.

Algorithmic multi channel attribution

Here we take more in detail about how I use Machine Learning modeling to build multi channel attribution models.

multi-channel marketing graph with probabilities.png

Using machine learning models we can develop many models for comparing, such as Last Click, First Click, Linear, and the algorithmic Markov model to deliver the right solution for you, in the way that suits you best. This gives you the best possible understanding of your strongest and weakest customer touchpoints, so you can optimize them for maximum effect.

Data Scientists need to become an Adaptive Thinkers

Data Scientist adaptive thinkers

Google exposed AutoML, an automated machine learning system that could produce a synthetic intelligence solution without the help of a human engineer. IBM Cloud and Amazon Web Services (AWS) offer machine learning options that do not need AI developers. GitHub and other cloud platforms already provide thousands of machine learning programs, minimizing the requirement of having an AI professional at hand. These cloud platforms will slowly, however, undoubtedly decrease the need for expert system developers. Google Cloud’s AI supplies automated machine learning services. Microsoft Azure provides easy to use machine learning user interfaces. At the same time, Massive Open Online Courses (MOOC) are thriving all over. Anybody anywhere can choose up a machine learning option on GitHub, follow a MOOC without even going to college, and beat any engineer to the job. Today, the expert system is primary mathematics translated into source code that makes it challenging to discover for traditional developers. That is the primary reason why Google, IBM, Amazon, Microsoft, and others have ready-made cloud solutions that will need fewer engineers in the future. As you will see, you can occupy a primary function in this brand-new world as an adaptive thinker. There is no time at all to waste. In this article, we are going to dive rapidly and directly into reinforcement learning, among the pillars of Google Alphabet’s DeepMind asset (the other being neural networks).
Reinforcement learning often utilizes the Markov Decision Process (MDP). MDP contains a memoryless and unlabeled action-reward formula with a learning criterion. This equation, the Bellman equation (frequently created as the Q function), was used to beat first-rate Atari players. The objective here is not to take the easy path. We’re aiming to break intricacy into reasonable parts and challenge them with the truth.
You are going to find out right from the start how to apply an adaptive thinker’s procedure that will lead you from an idea to service in reinforcement learning, and right into the center of gravity of Google’s DeepMind jobs.

I wrote before about What are the most important soft skills for data scientists? Adaptive thinking is one more.

How to be an adaptive thinker?

Reinforcement learning, among the foundations of machine learning, expects to learn through trial and mistake by communicating with an environment. This sounds familiar, ideal? That is what we humans do all our lives in discomfort! Attempt things, examine, and after that continue; or try something else. In reality, you are the agent of your idea procedure. In a machine learning model, the agent is the function of computing through this trial-and-error procedure. This believed process in machine learning is the MDP. This form of action-value education is often called Q. To master the outcomes of MDP in theory and practice, a three-dimensional approach is a requirement. The three-dimensional technique that will make you an artificial expert, in basic terms, means: Starting by describing an issue to resolve with real-life cases Then, developing a mathematical design Then, compose source code and/or using a cloud platform option It is a method for you to go into any project with an adaptive mindset from the beginning.

Addressing real-life issues before coding a solution

You can find tons of source code and examples on the web. However, most of them are toy experiments that have nothing to do with real life. For example, reinforcement learning can be applied to an e-commerce business delivery person, self-driving vehicle, or a drone. You will find a program that calculates a drone delivery. However, it has many limits that need to be overcome. You as an adaptive thinker are going to ask some questions:
What if there are 5,000 drones over a major city at the same time? Is a drone-jam legal?
What about the noise over the city?
What about tourism?
What about the weather?
Weather forecasts are difficult to make, so how is this scheduled?
In just a few minutes, you will be at the center of attention, among theoreticians who know more than you on one side and angry managers who want solutions they cannot get on the other side. Your real-life approach will solve these problems.

A foolproof method is the practical three-dimensional approach:

  • Be a subject professional: First, you have to be a topic professional. If a theoretician geek comes up with a hundred Google DeepMind TensorFlow operates to resolve a drone trajectory issue; you now know it is going to be a hard ride if real-life specifications are considered. An SME knows the subject and hence can rapidly identify the crucial elements of a given field. The expert system typically needs finding a solution to a severe issue that even a professional in a given area can not reveal mathematically. Machine learning, in some cases, means finding an option to a problem that humans do not understand how to explain. Deep knowing, including complex networks, resolves a lot more challenging issues.
  • Have enough mathematical knowledge to comprehend AI concepts: Once you have the appropriate natural language analysis, you require to build your abstract representation rapidly. The very best way is to browse in your everyday life and make a mathematical design of it. Mathematics is not an option in AI, but a prerequisite. The effort is worthwhile. Then, you can start writing a reliable source code or begin executing a cloud platform ML service.
  • Know what source code is about as well as its perspective and limitations: MDP is an excellent method to go and begin working in the three measurements that will make you adaptive: describing what is around you in information in words, translating that into mathematical representations, and then executing the result in your source code.

Change and uncertainty are the only definites. The ability to change behavior when faced with unpredicted circumstances is crucial in the technological future unfolding around us. The Internet and social media have changed the way we connect and communicate. Machines are taking over jobs in the service industry, and global outsourcing is the new normal.  As a result, high and low skilled jobs are now flooding the market. One essential both have in common is the need for workers to develop novel and adaptive thinking in order to survive in the fast-paced fast-changing global world we now live in.

Daily we are confronted with new possibilities and unpredictability. The ability to think through problems, acting swiftly, while negotiating fear of the unknown is the foundation of novel and adaptive thinking.

The more you practice adaptive thinking the easier it will come. Follow these steps and you will surely be on your way to perfecting a powerful skill for the workplace.

image source: https://miamioh.instructure.com/courses/62208

Your Machine Learning project needs good Data. How to solve the problem of lack of data?

Machine learning applications are reliant on, and sensitive to, the data they train on. These most excellent practices will help you ensure that training data is of high quality.
To be efficient, machine learning (ML) need a significant amount of data.
We can anticipate a child to comprehend what a feline is and identify other cats after just a couple of encounters or by being revealed a couple of examples of felines, but Machine Learning algorithms need numerous, much more examples. Unlike humans, these algorithms can’t quickly develop reasonings on their own. For instance, machine learning algorithms analyze an image of a feline version.

The algorithms need a lot of data to separate the pertinent “features” of the cat from the background sound. It is the very same for other noise such as lighting and weather condition. Regrettably, such data cravings do not stop at the separation of signal from the sound. The algorithms also need to recognize significant functions that differentiate the feline itself. Variations that human beings do not require additional data to comprehend– such as a cat’s color or size– are challenging for machine learning.

Without an adequate number of samples, machine learning supplies no advantage.

Not all Machine Learning methods require loads of data

Many types of machine learning strategies exist, and some have been around for numerous years. Each has its strengths and weak points. These distinctions likewise reach the nature and amount of data required to build efficient models. For example, deep learning neural networks (DLNNs) are a fantastic area of machine learning because they can be delivering dramatic results. deep learning neural networks require a higher quantity of data than more established machine learning algorithms along with a large amount of calculating horsepower. In reality, deep learning neural networks were thought about practical only after the introduction of big data (which supplied the large data sets) and cloud computing (which offered the number-crunching capability).

Other aspects affect the need for data. General machine learning algorithms do not include domain-specific information; they must conquer this constraint through big, representative data sets. Referring back to the feline example, these machine learning algorithms do not comprehend the fundamental functions of felines, nor do they understand that backgrounds are sound. So they need many cases of this data to learn such differences.

To decrease the data needed in these scenarios, machine learning algorithms can consist of a level of domain data so important features, and characteristics of the target data are currently known. Then the focus of understanding can be strictly on optimizing output. This requirement to “imbue” human understanding into the machine learning system from the start is a direct outcome of the data-hungry nature of machine learning.

Training Data Sets Need Improvement

To truly drive innovation using machine learning, a significant amount of change requires to first happen around how to input data is chosen.

Curating (that is, selecting the data for a training data set) is, at heart, about keeping an eye on data quality. “Garbage-in, garbage-out” is specially true with machine learning. Intensifying this issue is the relative “black box” nature of machine learning, which avoids understanding why machine learning produces a specific output. When machine learning creates unexpected output, it is since the input data was not suitable, however, identifying the particular nature of the issue data is an obstacle.

Two typical problems caused by poor data curation are overfitting and bias. Overfitting is the outcome of a training data set that does not adequately represent the actual variation of production data; it, therefore, produces output that can deal with a portion of the full data stream.

Bias is a much deeper issue that connects to the same root cause as overfitting; however, is harder to determine and understand partial data sets are not representative, have skewed circulation, or do not include the proper data in the very first place. This incomplete training data results in partial output that makes incorrect conclusions that may be difficult to determine as inaccurate. Although there is much optimism about machine learning applications, data quality problems should be a significant concern as machine-learning-as-a-service offerings come online.

A related problem is having access to premium data sets. Big data has produced various data sets; however, rarely do these sets involve the type of details needed for machine learning. Data utilized for machine learning needs both the data and the outcome connected with the data. Using the feline example, images need to be tagged showing whether a feline exists.

Other machine learning tasks can need much more complex data. The need for large volumes of sample data integrated with the need to have this data sufficiently and accurately explained produces an environment of data haves and have-nots. Only the large companies with access to the finest data and deep pockets to curate it will be able to benefit from machine learning quickly. Unless the playing field is level, the development will be muted.

How to solve Data problems using Innovation?

Just as machine learning can be used to real problem resolving, the very same technologies and strategies utilized to sort through countless pages of data to identify key insights can be used to assist with the issues of finding high-quality training data.

To enhance data quality, some attractive options are available for automating problem detection and correction. For example, clustering or regression algorithms can be utilized to scan proposed input data sets to discover unseen anomalies. Alternatively, the procedure of identifying whether data is representative can be automated. If not appropriately addressed, hidden abnormalities and unrepresentative data can result in overfitting and bias.

If the input data stream is suggested to be reasonably consistent, regression algorithms can identify outliers that might represent garbage data that might negatively affect a knowing session. Clustering algorithms can assist examine a data set that includes a specific number of file categories to recognize if the data indeed comprises more or fewer types– either of which can result in poor results. Other ML techniques can be used to validate the accuracy of the tags on the sample data. We are still at the early phases of automated input data quality assurance. However, it looks promising.

To increase access to helpful data sets, one brand-new strategy offers with artificial data. Rather than an effort to collect genuine sample sets and after that tag them, companies use generative adversarial networks to produce and tag the data. In this circumstance, one neural network produces the data, and another neural network tries to figure out if the data is genuine. This procedure can be left unattended with impressive results.

Reinforcement learning is also getting real traction to address the absence of data. Systems that employ this technique can take data from interactions with their immediate environment to find out. Over time, the system can develop brand-new reasonings without needing curated sample data.

Data Is Driving Innovation

Promising and ongoing work using machine learning technologies is solving a variety of problems and automating work that is expensive, time-consuming, and complex (or a mix of all three). Yet without the necessary source data, machine learning can go nowhere. Efforts to simplify and broaden access to large volumes of high-quality input data are essential to increase the use of ML in a much broader set of domains and continue to drive innovation.

Reasons Why Your Data Science Project is Likely to Fail

Businesses are creating ahead with digital improvement at an unmatched rate. A current survey by Gartner Research discovered that 49 percent of CIOs are reporting that their company has already altered their business designs to scale their digital undertakings or are in the procedure of doing so.

As companies create ahead with these changes, they are instilling data science and machine learning into various company functions. This is not a simple job. A typical enterprise data science task is extremely complicated and requires the release of an interdisciplinary team that includes assembling data engineers, developers, data scientists, topic specialists, and people with other special abilities and understanding.

Additionally, this talent is limited and costly. In reality, only a little number of companies have actually been successful in building a skilled data science practice. And, while making this team takes time and resources, there is an even more significant problem faced by a number of these companies: more than 85 percent of big data jobs fail.

A variety of factors add to these failures, including human aspects, and challenges with time, ability, and impact.

Lack of Resources to Execute Data Science Projects

Data science is an interdisciplinary method that includes mathematicians, statisticians, data engineering, software application engineers, and notably, subject matter specialists. Depending upon the size and scope of the project, companies may release numerous data engineers, an option architect, a domain specialist, a data scientist (or several), company analysts and perhaps additional resources. Lots of business do not have and/or can not manage to release sufficient funds because employing such skills is ending up being increasingly-challenging and also because company frequently has many data science tasks to carry out, all of which take months to complete.

Heavy Dependence on Data Scientists abilities, Experiences of Particular People

Traditional data science much relies on skills, experiences, and intuitions of experienced people. In specific, the data and feature engineering procedure now are mostly based upon manual efforts and instincts of domain experts and data scientists. Although such gifted individuals are valuable, the practices relying on these individuals are not sustainable for enterprise business, given the hiring challenge of such skilled talents. As such, companies need to seek solutions to help equalize data science, allowing more individuals with different ability levels to carry out on tasks effectively.

Misalignment of Technical and Company Expectations

A lot of data science projects are carried out to provide crucial insights to the business group. Nevertheless, often a task begins without precise alignment between the service and data science groups on the expectations and goals of the job, resulting in that the data science team is focused primarily on model accuracy, while the company team is more thinking about metrics such as the monetary advantages, business insights, or model interpretability. In the end, the business team does not accept the outcomes of the data science team.

Data science projects take long turnaround time and upfront effort without exposure into the possible value

Among the most significant obstacles of data science projects is the big in advance effort required, despite an absence of presence into the eventual outcome and its business value. The traditional data science process takes months to finish until the result can be examined. In specific, data and function engineering process to transform service data into a machine learning, ready format takes a huge quantity of iterative efforts. The long turnaround time and significant upfront efforts related to this approach typically lead to job failure after months of investment. As an outcome, business executives are reluctant to apply more resources.

Absence of Architectural Consideration for Production and Operationalization on Data Science projects

Numerous data science tasks begin without consideration for how the established pipelines will be deployed in production. This takes place since the company pipeline is often handled by the IT group, which does not have insight into the data science process, and the data science team is concentrated on verifying its hypotheses and does not have an architectural view into production and option integration. As an outcome, instead of getting integrated into the pipeline, many data science tasks wind up as one-time, proof-of-concept exercises that fail to provide real business effect or triggers substantial cost-increases to productionalize the jobs.

End-to-end Data Science Automation is a Solution

The pressure to attain higher ROI from expert system (AI) and machine-learning (ML) initiatives has actually pressed more magnate to look for innovative options for their data science pipeline, such as machine learning automation. Picking the right service that delivers end-to-end automation of the data science procedure, including automated data and feature engineering, is the key to success for a data-driven business. Data science automation makes it possible to perform data science processes quicker, often in days instead of months, with more transparency, and to deliver minimum practical pipelines that can be improved continuously. As a result, companies can quickly scale their AI/ML initiatives to drive transformative business modifications.
However, Data science and machine learning automation can bring new types of problems, that is why I wrote before that : Guided analytics are the future of Data Science and AI

Guided analytics are the future of Data Science and AI

Nowadays, people are used to and take it for granted, the added value in their life, from using Siri, or Google’s Assistant or Alexa for all sorts of things: answering odd trivia concerns, inspecting the weather condition, purchasing groceries, getting driving instructions, turning on the lights, and even inspiring a dance celebration in the cooking area. These are splendidly beneficial (typically fun) AI-based gadgets that have boosted individuals’ lives. Nevertheless, human beings are not partaking in deep, significant conversations with these gadgets. Instead, automated assistants address the specific requests that are made from them. If you’re exploring AI and artificial intelligence in your enterprise, you may have experienced the claim that, if entirely automated, these innovations can replace data scientists entirely. It’s time to rethink this assertion.

The Issue with Fully Automated Analytics

How do all the driverless, automated, automatic AI, and machine learning systems suit the enterprise? Their objective is either to encapsulate (and hide) existing data researchers’ expertise or to apply advanced optimization plans to the fine-tuning of information science tasks.

Automated systems can be useful if no private data science competence is readily available, but they are likewise somewhat limiting. Business experts who depend on data to do their tasks get locked into the prepackaged competence and a limited set of hard-coded circumstances.

In my experience as a data scientist, automation tends to miss the most crucial and fascinating pieces, which can be very important in today’s extremely competitive marketplace. If data scientists are permitted to take a somewhat more active method and guide the analytics process, however, the world opens considerably.

Why a Guided Analytics Method Makes Sense?

In order for companies to get the most out of AI and data science, to effectively anticipate future outcomes and make much better organization choices, completely automatable information science sandboxes need to be left. Instead, enterprises need to begin interactive exchanges between a data scientist, organization analysts, and the devices doing the operate in the middle. This needs a procedure referred to as “assisted analytics,” in which personal feedback and assistance can be used whenever required– even while an analysis is in development.

The objective of guided analytics is to enable a team of data researchers with various choices and skills to collaboratively construct, preserve, and continuously refine a set of analytics applications that offer company users with various degrees of user interaction. Put, all stakeholders work together to create a better analysis.

Companies that wish to create a system that facilitates this type of interaction while still establishing a practical analytics application face a huge– but not overwhelming– obstacle.

Common Attributes

I have determined four typical properties that help data scientists successfully develop the right environment for the next type of wise applications– the ones that will assist them to obtain real service value from AI and machine learning.

The applications that offer company users just the correct amount of guidance and interaction allow groups of information scientists to merge their proficiency collaboratively. When specific residential or commercial properties collaborate, data researchers can build interactive analytics applications that reveal adaptive potential.

The perfect environment for guided analytics shares these 4 characteristics:

Open: Applications shouldn’t be strained with restrictions on the kinds of tools utilized. With an open environment, collaboration can occur between scripting masters and those who want to recycle their proficiency without diving into their code. Besides, it’s a plus to be able to connect to other tools for specific data types as well as interfaces specialized for high-performance or big information algorithms (such as H2O or Spark) from within the very same environment.

Agile: Once the application is deployed, new demands will emerge rapidly: more automation here, more customer feedback there. The environment used to develop these analytics applications requires likewise to make it easy for other members of the data science group to quickly adjust existing analytics applications to brand-new and changing requirements, so they continue to yield significant results over the long term.
Putting It into Practice

Versatile: Below the application, the environment should also be able to run simple regression designs or manage complicated specification optimization and ensemble designs– ranging from one to thousands of designs. It’s worth noting that this piece (or a minimum of some elements of it) can be hidden totally from the business user.

Uniform: At the same time, the specialists creating data science ought to have the ability to perform all their operations in the very same environment. They need to mix data, run the analysis, mix and match tools, and develop the facilities to deploy the resulting analytics applications all from that very same intuitive and nimble environment.

Some AI-based applications will merely provide an introduction or projection at journalism of a button. Others will allow completion user to select the data sources to be used. Still, others will ask the user for feedback that ends up improving the design( s) trained beneath the hood, factoring in the users’ knowledge. Those models can be easy or arbitrarily complicated ensembles or entire design families, and the end user might or might not be asked to assist fine-tune that setup. The control over how much of such interaction is required to depend on the hands of the information researchers who developed the underlying analytics procedure with their target audience, the actual organization users’ interests (and abilities), in mind.

The big concern you may be asking is, how do I do this in my organization? You might think this is not realistic for your team to construct on its own; you are resource-constrained as it is. The good news is that you do not have to.

Software, particularly open source software, is available that makes it useful to execute guided analytics. Utilizing it, teams of data researchers can work together utilizing visual workflows. They can give their expert service associates access to those workflows through web interfaces. Additionally, there is no need to use another tool to develop a web application; the workflow itself models the interaction points that consist of an analytics application. Workflows are the glue holding it all together: various tools utilized by different members of the information science team, information mixed from numerous sources by the information engineering experts, and interaction points modeling the UI parts noticeable to the end user. It is all quickly within your grasp.

Guided analytics in the following years

Interest in guided analytics is growing, permitting users not only to wrangle information; however, likewise, fine-tune their analyses. It is exciting to see just how much cooperation this sets off. It will also be fascinating to witness how information researchers build progressively practical analytics applications that help users in developing analyses with real organization effect.

Instead of taking experts out of the chauffeur’s seat and trying to automate their wisdom, assisted analytics aims to combine the best of both. This is good for data scientists, company analysts, and the practice of data analytics in general. Eventually, it will be necessary for development too. Although it might appear challenging now, the effort will be worth it to make sure a better future.

Use Factor Analysis to better understand your data

Surveys get used for a wide range of applications within marketing. It might be to comprehend consumers political choices. It might be to comprehend your brand name choices. It may be utilized in the design of brand-new items. It might be used to figure out what is the ideal credit to be focusing on marketing interactions. Well, think about the last time that you received a survey to submit. May have been 10, 20 questions. Other surveys might be 50 to 100 questions. Surveys can be long and for each participant that might be 100 specific products that they’re responding to. Well, as an online marketer what we’re attempting to do are derive insights from those surveys.
Moreover, I do not care about how you reacted to an individual item. What I appreciate is what’s driving you, what are your beliefs. Also, the idea is that the individual items on a survey are manifestations of those underlying beliefs.

Using Factor Analysis to Identify Underlying Constructs

So what we’re going to be doing is first looking at a tool called factor analysis that’s intended to allow us to go from a large number of survey items, narrow that down, retain as much information as possible to identify underlying preferences, underlying beliefs that consumers have. No once we’ve done that, then we can go about forming market segments using cluster analysis. We can also look to identify individuals that belong to different segments using discriminate analysis. And lastly we’re going to look at perceptual mapping as a means of understanding how our brand is seen relative to other brands. 
So to start out we’re going to look at how we identify those underlying constructs using factor analysis.

Suppose that we’re interested in understanding consumer preferences for local retailers versus large national chains. And in this case we’ve got five survey items that were included. 

  1. First asking about whether or not respondents agreed with the statement that local retailers have more variety compared to retail chains. 
  2. Second question, ask whether or not you agree with the statement that the associates at retail chains tend to be less knowledgeable than the associates at local businesses. 
  3. And the last three questions, questions three through five, get into the courtesy and the level of personal attention that you might expect when you patronize local retailers versus when you patronize national chains. 

Now if we collected these five responses to these five questions, you might have it from a sample of respondents. In this case we have 15 responses. What we might begin to do is look for patterns among the responses. That is, for when people respond above average to question one, how do they tend to respond to question two? When people respond above average for question three do they tend to respond above or below average for questions four or five. And so the technique that we might default to using is correlation analysis. 

Correlation Analysis

What correlation analysis let’s us look at is is there a pairwise linear relationship? That is do the two items, when one goes up does the other tend to go up? When one goes down does the other tend to go down?That’d be indicative of a positive relationship. Negative relationship would be when one goes up the other tends to go down and vice versa. And if we’re dealing with a small number of survey items such as the case here that might be all right. So what we could look at first is the correlation matrix. And we can see along the diagonal, we have ones, that’s to be expected because we are taking the correlation between, let say item one and itself, so that’s why we’re getting the ones along the diagonal.

Correlation matrix from customer surveys

 Then we look below the diagonal 0.61, fairly strong positive relationship between items one and two. If we look for other high or very low values of correlation, we might see question three is correlated with question four.

Now, in this case we might say, let’s identify those items that tend to move together. And it looks like items three, four and five tend to move together and Items one and two tend to move together. Now in this case we happen to get lucky with the correlation matrix. The items that are correlated with each other are directly adjacent to each other. We’re dealing with a small enough number of items that we can just stare at the correlation matrix and see which items tend to move together. 

Factor analysis

But what about a lengthier survey? What about a survey that’s several pages long if we’re dealing with 20, 50, 100 items? 
Staring at that matrix is going to be very difficult to identify the patterns that exist. All right, so that’s where factor analysis is going to come into play for us.It’s going to allow us to draw these boxes around items. That tend to move together without us having to do that work. So what factor analysis is going to take as an input is all of the survey responses. It doesn’t matter if you have ten survey items, doesn’t matter if you have 50, 100, 200 items factor analysis doesn’t care about that. What it’s going to do is take those individual items the responses from all of the individuals on those items, and identify which sets of items tend to move together. So think of this as correlation analysis on steroids.

Example 2

Let’s say we were looking at the young urban professionals. And how do you go about designing branding and targeting consumers with a message that’s going to ultimately resonate with them. So one way we might go about trying to understand the consumer is to administer a survey.
One way we might go about trying to understand our consumers is to administer a survey. So let’s take a look at the survey that we might administer.

Automotive Example

Based on Automotive survey items what could we ultimately do with it? Well, if we could identify those people who are likely to buy a car or expressing interest in this car. And what are the perceptions of themselves, perceptions of the society, the perceptions of their finances are associated with people who are likely to buy this car, right? And so we might afford to say let’s run one regression. let’s take all of these survey responses as inputs or outcome variable, or y variable, that’s can be the purchase intention. And conceptually that makes sense.That’s what we’re trying to do. We’re trying to relate the individual survey items to the outcome of interest. The problem is some of the survey items are going to be highly correlated with each other. And we may run into problems of multicollinearity, if we were to run that large regression.The other problem that we might run into is supposed that we are able to run the regression.Well, what do we ultimately do with it? So, suppose that the government should restrict import or products from Japan is a significant driver of purchase intentions. How do we act on that? That’s different from saying that somebody’s who is likely to buy this car has a lot of patriotism.

Saying that, we’re going after consumers or a patriotic that’s something that we can design a marketing campaign around. Saying that we’re going after people who are against imports, not as clear.

So what can factor analysis do for us? 

What we ultimately want to do is we want to group those variables together, those survey items together that are highly correlated with each other, the ones that tend to move together.Now that movement maybe in the same direction, that movement maybe an opposite direction.But the assumption that we’re going to make is that items that tend to move together, there’s some underlying construct. There’s some high order belief that consumers have or some set of preferences that they have that cause all of those survey items to move together. And if we can identify those underlying beliefs, those constructs, those are what we’re going to put into our regression analysis as well as the subsequent analyses that we might conduct. Now while we’re doing that, we want to make sure that we retain as much information as possible. 

Exploratory Factor Analysis

Factor analysis is a method for investigating whether a number of variables of interest X1, X2,……., Xl, are linearly related to a smaller number of unobservable factors F1, F2,..……, Fk.

Let’s say we’ve got our 50 survey items that we’re looking at. We want to make that a more manageable number. We want to cut that down to identify what’s really driving those responses, and maybe it’s ultimately five constructs that are ultimately driving those 50 responses. Well those five constructs, that’s a lot smaller than the 50 survey items that we began with. And so any time that we engage with dimension reduction we are going to be throwing away information. Our goal is to retain as much information as possible.
We’re going to ask factor analysis to do for us is two things:

1. Reveal to us how many constructs are appropriate. What is the appropriate number K?

2. Reveal which constructs and which survey items are ultimately related to each other.

One of the ways that factor analysis is commonly used when it comes to analyzing survey data as I had mentioned, is to group these similar items (items that tend to move together) together. 

So maybe I can go from a 150 survey items down to 50 surveys items after the first pass. Well, factor analysis will help us identify which items tend to move together and as such, identify which ones are potentially redundant. I can eliminate those redundancies and administer my survey in the second wave and continue to refine it until I have a number of survey items that I’m comfortable with. The other way that factor analysis gets used is to produce measures that are uncorrelated with each other. Multicollinearity is a big problem when it comes to regression analysis.

Steps for Factor Analysis

  1. Decide how many factors are necessary, 
  2. Conduct the analysis, derive that solution. 
  3. Rotate the factor solution 
  4. Interpreting the factors or naming the factors -This is where a person needs to be involved
  5. Evaluate the quality of the fit
  6. Save the factor scores for use in subsequent data

Types of Factor Analysis

  • Exploratory Factor Analysis: It is the most popular factor analysis approach among social and management researchers. Its basic assumption is that any observed variable is directly associated with any factor.
  • Confirmatory Factor Analysis (CFA): Its basic assumption is that each factor is associated with a particular set of observed variables. CFA confirms what is expected on the basic.

Terminology

A factor is a latent variable which describes the association among the number of observed variables. The maximum number of factors are equal to a number of observed variables. Every factor explains a certain variance in observed variables. The factors with the lowest amount of variance were dropped. Factors are also known as latent variables or hidden variables or unobserved variables or Hypothetical variables.

Factor loadings – The factor loading is a matrix which shows the relationship of each variable to the underlying factor. It shows the correlation coefficient for observed variable and factor and variance explained by the observed variables.

Eigenvalues – represent variance explained each factor from the total variance. It is also known as characteristic roots.

Communalities – are the sum of the squared loadings for each variable. It represents the common variance. It ranges from 0-1 and value close to 1 represents more variance.

Factor Rotation is a tool for better interpretation of factor analysis. Rotation can be orthogonal or oblique. It re-distributed the commonalities with a clear pattern of loadings.

Introduction to Factor Analysis in Python

In this tutorial, you’ll learn the basics of factor analysis and how to implement it in python.

Factor Analysis (FA) is an exploratory data analysis method used to search influential underlying factors or latent variables from a set of observed variables. It helps in data interpretations by reducing the number of variables. It extracts maximum common variance from all variables and puts them into a common score.

Factor analysis is widely utilized in market research, advertising, psychology, finance, and operation research. Market researchers use factor analysis to identify price-sensitive customers, identify brand features that influence consumer choice, and helps in understanding channel selection criteria for the distribution channel.

In this tutorial, you are going to cover the following topics:

  • Factor Analysis
  • Types of Factor Analysis
  • Determine Number of Factors
  • Factor Analysis Vs. Principle Component Analysis
  • Factor Analysis in python
  • Adequacy Test
  • Interpreting the results
  • Pros and Cons of Factor Analysis
  • Conclusion

Factor Analysis

Factor analysis is a linear statistical model. It is used to explain the variance among the observed variable and condense a set of the observed variable into the unobserved variable called factors. Observed variables are modeled as a linear combination of factors and error terms (Source). Factor or latent variable is associated with multiple observed variables, who have common patterns of responses. Each factor explains a particular amount of variance in the observed variables. It helps in data interpretations by reducing the number of variables.

Factor analysis is a method for investigating whether a number of variables of interest X1, X2,……., Xl, are linearly related to a smaller number of unobservable factors F1, F2,..……, Fk.

Source: This image is recreated from an image that I found in factor analysis notes. The image gives a full view of factor analysis.

Assumptions:

  1. There are no outliers in data.
  2. Sample size should be greater than the factor.
  3. There should not be perfect multicollinearity.
  4. There should not be homoscedasticity between the variables.

Types of Factor Analysis

  • Exploratory Factor Analysis: It is the most popular factor analysis approach among social and management researchers. Its basic assumption is that any observed variable is directly associated with any factor.
  • Confirmatory Factor Analysis (CFA): Its basic assumption is that each factor is associated with a particular set of observed variables. CFA confirms what is expected on the basic.

How does factor analysis work?

The primary objective of factor analysis is to reduce the number of observed variables and find unobservable variables. These unobserved variables help the market researcher to conclude the survey. This conversion of the observed variables to unobserved variables can be achieved in two steps:

  • Factor Extraction: In this step, the number of factors and approach for extraction selected using variance partitioning methods such as principal components analysis and common factor analysis.
  • Factor Rotation: In this step, rotation tries to convert factors into uncorrelated factors — the main goal of this step to improve the overall interpretability. There are lots of rotation methods that are available such as: Varimax rotation method, Quartimax rotation method, and Promax rotation method.

Terminology

What is a factor?

A factor is a latent variable which describes the association among the number of observed variables. The maximum number of factors are equal to a number of observed variables. Every factor explains a certain variance in observed variables. The factors with the lowest amount of variance were dropped. Factors are also known as latent variables or hidden variables or unobserved variables or Hypothetical variables.

What are the factor loadings?

The factor loading is a matrix which shows the relationship of each variable to the underlying factor. It shows the correlation coefficient for observed variable and factor. It shows the variance explained by the observed variables.

What is Eigenvalues?

Eigenvalues represent variance explained each factor from the total variance. It is also known as characteristic roots.

What are Communalities?

Commonalities are the sum of the squared loadings for each variable. It represents the common variance. It ranges from 0-1 and value close to 1 represents more variance.

What is Factor Rotation?

Rotation is a tool for better interpretation of factor analysis. Rotation can be orthogonal or oblique. It re-distributed the commonalities with a clear pattern of loadings.

How many factors do we need to include in our analysis? 

There are a couple of different criteria that can be used. 
One criteria is to say, we want to capture, we want to retain at least a given percentage of the original variation in the service. So we might say, okay, I want to retain at least 50% of the variation in the survey.
Another criterion that we could use is to say, well let’s include as many factors as are necessary such that each factor that we include is doing its fair share of explaining variation. Mathematically, what this maps on to is saying that all of the eigenvalues in the analysis have to be greater than 1.
Or saying that the amount of variation, a given factor explains has to be greater than 1 over j where j is the number of survey items that we have. So if I have 20 survey items, we’re going to include as many factors as necessary until a survey item falls below the 5% threshold or the 1 over 20 threshold.

Kaiser criterion

Kaiser criterion is an analytical approach, which is based on the more significant proportion of variance explained by factor will be selected. The eigenvalue is a good criterion for determining the number of factors. Generally, an eigenvalue greater than 1 will be considered as selection criteria for the feature.

The graphical approach is based on the visual representation of factors’ eigenvalues also called scree plot. This scree plot helps us to determine the number of factors where the curve makes an elbow.

Source

Factor Analysis Vs. PCA

  • PCA components explain the maximum amount of variance while factor analysis explains the covariance in data.
  • PCA components are fully orthogonal to each other whereas factor analysis does not require factors to be orthogonal.
  • PCA component is a linear combination of the observed variable while in FA, the observed variables are linear combinations of the unobserved variable or factor.
  • PCA components are uninterpretable. In FA, underlying factors are labelable and interpretable.
  • PCA is a kind of dimensionality reduction method whereas factor analysis is the latent variable method.
  • PCA is a type of factor analysis. PCA is observational whereas FA is a modeling technique.

Factor Analysis in python using factor_analyzer package

import pandas as pd
from sklearn.datasets import load_iris
from factor_analyzer import FactorAnalyzer
import matplotlib.pyplot as plt

https://vincentarelbundock.github.io/Rdatasets/datasets.html

data = 'bfi.csv'
df= pd.read_csv(data)

Dropping unnecessary columns

df.drop([‘gender’, ‘education’, ‘age’],axis=1,inplace=True)

Dropping missing values rows

df.dropna(inplace=True)
df.info()

Adequacy Test

Before you perform factor analysis, you need to evaluate the “factorability” of our dataset. Factorability means “can we found the factors in the dataset?”. There are two methods to check the factorability or sampling adequacy:

  • Bartlett’s Test
  • Kaiser-Meyer-Olkin Test

Bartlett’s test of sphericity checks whether or not the observed variables intercorrelated at all using the observed correlation matrix against the identity matrix. If the test found statistically insignificant, you should not employ a factor analysis.

from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity
chi_square_value,p_value=calculate_bartlett_sphericity(df)
chi_square_value, p_value

#In this Bartlett ’s test, the p-value is 0. The test was statistically significant, indicating that the observed correlation matrix is not an identity matrix.

Kaiser-Meyer-Olkin (KMO) Test measures the suitability of data for factor analysis.

from factor_analyzer.factor_analyzer import calculate_kmo
kmo_all,kmo_model=calculate_kmo(df)
kmo_model

If Kaiser-Meyer-Olkin gives value over 0.6 then we can proceed with the factor analysis.

Create factor analysis object and perform factor analysis

fa = FactorAnalyzer()
fa.analyze(df, 25, rotation=None)

Check Eigenvalues

ev, v = fa.get_eigenvalues()

From here, we pick number of factors where eigenvalues are greater than 1.

Create factor analysis object and perform factor analysis, Note that Varimax rotation is used under the assumption that the factors are completely uncorrelated.

fa = FactorAnalyzer()
fa.analyze(df, 6, rotation=”varimax”)
fa.loadings

Naming the Factors

After establishing the adequacy of the factors, it’s time for us to name the factors. This is the theoretical side of the analysis where we form the factors depending on the variable loadings. In this case, here is how the factors can be created:

Looking at the description of data: https://vincentarelbundock.github.io/Rdatasets/doc/psych/bfi.dictionary.html, we can come up with the names:

  • Factor 1 has high factor loadings for E1,E2,E3,E4, and E5 (Extraversion)
  • Factor 2 has high factor loadings for N1,N2,N3,N4, and N5 (Neuroticism)
  • Factor 3 has high factor loadings for C1,C2,C3,C4, and C5 (Conscientiousness)
  • Factor 4 has high factor loadings for O1,O2,O3,O4, and O5 (Openness)
  • Factor 5 has high factor loadings for A1,A2,A3,A4, and A5 (Agreeableness)
  • Factor 6 has none of the high loadings for any variable and is not easily interpretable. Its good if we take only five factors.

What is next?

Now that we have seen how to perform factor analysis, we can use the same technique to analyze other data.

Possible applications would be, reducing the dimensionality when building predictive model using high dimensional data. Reducing dimensionality will significantly improve models performance like I write in this article: Improve performance on Machine learning models

Improving Data Exploration like mentioned here:

Machine learning in practice – Quick data analysis
Starting with Data Science

Practical Predictive Analytics in everyday enterprises

Predictive analytics is now part of the analytics fabric of companies. Even as companies continue to adopt predictive analytics, many are struggling to make it stick. Lots of organizations have not thought about how to virtually put predictive analytics to work, provided the organizational, technology, procedure, and deployment concerns they face.

These can be some of the biggest challenges organizations face today:

Skills development. Organizations are concerned about abilities for predictive modeling. These abilities consist of comprehending how to train a model, interpret output, and determine what algorithm to utilize in what circumstance. Skills are the most significant barrier to adoption of predictive analytics; many of the times, this is the top difficulty.

Model deployment. Companies are utilizing predictive analytics and machine learning throughout a series of use cases. Those checking out the technology are likewise preparing for a diverse set of use cases. Many participants are ruling out what it requires to build a valid predictive model and put it into production. Just a small number of Data Science Teams have a DevOps group, or another group that puts machine learning designs into production maintains versioning or monitors the designs. From experience, operating in this team structure, it can take months to put models into production.

Facilities. On the facilities side, the vast bulk of companies use the data storage facility, along with a variety of other innovations such as Hadoop, data lakes, or the cloud, for developing predictive designs. The bright side is that business appears to be looking to broaden their data platforms to support predictive analytics and machine learning. The relocation to contemporary data architecture to support the diverse type of data makes good sense and is required to prosper in predictive analytics.

New Practices for Predictive Analytics and Machine Learning

Since predictive analytics and machine learning abilities are in such high need, vendors are offering tooling to assist make predictive modeling easier, particularly for brand-new users. Essential to ease of usage are these functions:

  • Collaboration features. Anyone from a business analyst to a data scientist building a model often wants to collaborate with others. A business analyst may want to get input from a data scientist to validate a model or help build a more sophisticated one. Vendors provide collaboration features in their software that enable users to share or comment on models. Collaboration among analysts is an important best practice to help democratize predictive analytics.
  • Workflows and versioning. Lots of products supply workflows that can be saved and reused, including data pipeline workflows for preparing the data in addition to analytics workflows. If a data researcher or another model home builder develops a model, others can recycle the model. This frequently consists of a point-and-click interface for model versioning– crucial for monitoring the newest designs and model history– and for analytics governance.
  • GUIs. Lots of users do not like to program or even write scripts; this stimulated the movement toward GUIs (graphical user interfaces) decades earlier in analytics items. Today’s GUIs typically offer a drag-and-drop and point-and-click interface that makes it easy to construct analytics workflows. Nodes can be picked, defined, dragged onto a canvas, and linked to form a predictive analytics workflow. Some supplier GUIs enable users to plug in open source code as a node to the workflow. This supports models integrated into R or Python, for example.
  • Persona-driven features. Various users desire different user interfaces. A data scientist may want a notebook-based interface, such as Juypter note pads (e.g., “live” Web coding and collaboration user interfaces) or just a programming user interface. A business analyst may prefer a GUI user interface. A business analyst may desire a natural language-based interface to ask questions quickly and discover insights (even predictive ones) in the data. New analytics platforms have tailored environments to satisfy the requirements of various personas while maintaining reliable data stability beneath the platform. This makes structure models more efficient.

Next to read is:

What should you consider when choosing the right machine learning and AI platforms?

Important things to consider before building your machine learning and AI project

Current State of the market

In order to go in-depth on what exactly data science and machine learning (ML) tools or platforms are, why companies small and large are moving toward them, and why they matter in the Enterprise AI journey, it’s essential to take a step back and understand where we are in the larger story of AI, ML, and data science in the context of businesses:

 1. Enterprise AI is at peak hype.

Of course, the media has been talking about consumer AI for years. However, since 2018, the spotlight has turned to the enterprise. The number and type of devices sending data are skyrocketing while the cost of storing data continues to decrease, which means most businesses are collecting more data in more types and formats than ever before. Moreover, to compete and stay relevant among digital startups and other competition, these companies need to be able to use this data not only to drive business decisions but drive the business itself. Now, everyone is talking about how to make it a reality.

2. AI has yet to change businesses.

Despite the hype, the reality is that most businesses are struggling to leverage data at all, much less build machine learning models or take it one step further into AI systems. For some, it’s because they find building just one model is far more expensive and time-consuming that they planned for. However, the great majority struggle with fundamental challenges, like even organizing controlled access to data or efficient data cleaning and wrangling.

3. Successful enterprises have democratized.

 Those companies that have managed to make progress toward Enterprise AI have realized that it’s not one ML model that will make the difference; it’s thousands or hundreds. Also, that means scaling up data efforts in a big way that will require everyone at the company to be involved. Enter democratization. In August 2018, Gartner identified Democratized AI as one of the five emerging trends in their Hype Cycle for Emerging Technologies. Since then, we have seen the word “democratization” creep into the lexicon of AI-hopefuls everywhere, from the media to the board room. Also, to be sure, it’s an essential piece of the puzzle when it comes to an understanding of data science and machine learning (ML) platforms.

Is hiring Data Scientist enough to fulfil your AI and Machine learning goals?

Employing for data functions is at an all-time high. Currently in 2019, according to career listing data, a data scientist is the hottest career out there. Moreover, though statistics on Chief Data Offers (CDOs) vary, some put the figures as high as 100-fold growth in the function over the past 10 years.

Hiring data experts is a crucial element to a robust Enterprise AI strategy; however, hiring alone does not guarantee the expected outcomes, and it isn’t a factor not to invest in data science and ML platforms. For one thing, working with data scientists is costly – often excessively so – and they’re only getting more so as their need grows.

The truth is that when the intention is going from producing one ML model a year to tens, hundreds, or even thousands, a data team isn’t enough because it still leaves a big swath of employees doing day-to-day work without the capability to take advantage of data. Without democratization, the result of a Data team – even the very best one comprised of the leading data scientists – would be restricted.

As a response to this, some companies have decided to leverage their data team as sort of an internal contractor, working for lines of business or internal groups to complete projects as needed. Even with this model, the data team will need tools that allow them to scale up, working faster, reusing parts of projects where they can, and (of course) ensuring that all work is properly documented and traceable. A central data team that is contracted out can be a good short-term solution, but it tends to be a first step or stage; the longer-term model of reference is to train masses of non-data people to be data people.

Choosing the right tools for Machine Learning and AI

Opens Source – critical, but not always giving what you need

In order to be on the bleeding edge of technological developments, using open source makes it easier to onboard a team and hire. Not only are data scientists interested in growing their skills with the technologies that will be the most used in the future, but also there is less of a learning curve if they can continue to work with tools they know and love instead of being forced to learn an entirely different system. It’s important to remember, that keeping up with that rapid pace of change is difficult for big-sized corporations.
The latest innovations are usually highly technical, so without some packaging or abstraction layers that make the innovations more accessible, it’s challenging to keep everybody in the organization on board and working together.
A business might technically adopt the open source tool, but only a small number of people will be able to work with it. Not to mention that governance can be a considerable challenge if everyone is working with open source tools on their local machines without a way to have work centrally accessible and auditable.
Data science and ML platforms have the advantage of being usable right out of the box so that teams can start analyzing data from the first day. Sometimes, with open source tools (mostly R and Python), you need to assemble a lot of the parts by hand, and as anyone who’s ever done a DIY project can attest to, it’s often much more comfortable in theory than in practice. Choosing a data science and ML platform wisely (meaning one that is flexible and allows for the incorporation and continued use of open source) can allow the best of both worlds in the enterprise: cutting-edge open source technology and accessible, governable, control over data projects.

What should Machine Learning and AI platforms provide?

Data science and ML platforms allow for the scalability, flexibility,
and control required to thrive in the era of Machine Learning and AI because they provide a framework for:

  • Data governance: Clear workflows and a method for group
    leaders to monitor those workflows and data jobs.
  • Efficiency: Finding little methods to save time throughout the data-to-insights process gets business to organization value much faster.
  • Automation: A specific type of performance is the growing field
    of AutoML, which is broadening to automation throughout the data pipeline to ease inefficiencies and maximize personal time.
  • Operationalization: Effective ways to release data jobs into production quickly and safely.
  • Collaboration: A method for additional personnel working with data,
    much of whom will be non-coders, to add to data tasks in addition to data scientists (or IT and data engineers).
  • Self-Service Analytics: A system by which non-data expert from various industries can access and deal with data in a regulated environment.

Some things to consider before choosing the AI and MAchine Learning platform

Governance is becoming more challenging

With the quantity of information being accumulated today, data safety and security (particularly in specific sectors like financing) are crucial. Without a central area to access and collaborate with information that has correct user controls, data might be saved across different individuals’ laptop computers. And also if an employee or specialist leaves the company, the threats raise not just because they could still have accessibility to sensitive data, however since they might take their collaboration with them as well as leave the group to go back to square one, uncertain of what the individual was servicing. On top of these concerns, today’s enterprise is afflicted by shadow IT; that is, the suggestion that for years, different divisions have invested in all kinds of various innovations and are accessing as well as utilizing information in their ways. A lot to make sure that also IT groups today do not have a central sight of that is using what, just how. It’s a problem that becomes dangerously amplified as AI efforts scale and points to the requirement for governance at a more significant as well as much more fundamental scale throughout all industries in the business.

AI Needs to Be Responsible

We learn from a young age that topics like science and mathematics are all goal, which implies that naturally, individuals think that data science is as well – that it’s black and white, a specific discipline with just one method to reach a “proper” service, independent of who constructs it. We’ve understood for a long time that this is not the case and that it is possible to utilize data science strategies (and, hence, produce AI systems) that do things, well … incorrect. Even as just recently as last year, we are witnessing with problems that giants like Google, Tesla and Facebook face with their AI systems. These problems can cause domino effect very fast. It can be private information leakage, photo mislabelling, or video recognition not recognizing a pedestrian on crossing the road and hitting it.
This is where AI needs to be very responsible. And for that you need to be able to discover in early stages where you AI might fail, before deploying it in the real world.
The fact that these companies might not have fixed all of the problems, showing quickly how challenging it is to get AI.

Reproducibility of Machine Learning projects as well as scaling the same projects

Absolutely nothing is extra ineffective than needlessly repeating the same processes over as well as over. This relates to both duplicating procedures within a project (like data prep work) over and over as well as repeating the same process throughout projects or – even worse – unintentionally duplicating entire jobs if the team gets large yet does not have insight right into each other’s role. As well as no service is insusceptible to this danger – as a matter of fact, this issue can become exponentially worse in huge ventures with bigger teams and also even more separate in between them. To range efficiently, data groups require a tool that helps in reducing duplicated work and makes sure that work between members of the group hasn’t currently been done before.

Utilize Data Experts to Augment Data Scientists’ Job

Today, information researcher is one of the most in-demand settings. This means that data scientists can be both (1) difficult to locate and bring in and also (2) expensive to work with as well as retain. This combination implies that to range data initiatives to pursue Venture AI, it will unavoidably need to be submitted with service or information analysts. For the two sorts of a team to collaborate appropriately, they require a central atmosphere from which to work. Experts also often tend to work in a different way than data scientists, experienced in spreadsheets as well as possibly SQL yet generally not coding. Having a tool that allows each account to leverage the tools with which (s)he is most comfortable enables the performance to range data efforts to any size.

Ad-Hoc Methodology is Unsustainable for Large Teams

Small teams can sustain themselves to a specific point
by dealing with data, ML, or larger AI tasks in an ad-hoc fashion,
indicating staff member save their work in your area and not centrally and don’t have any reproducible procedures or workflows, figuring
things out along the method.
However, with more than just a couple of employee and more than one
job, this becomes rowdy rapidly. Any business with any hope of
doing Enterprise AI requires a central location where everybody involved
with data can do all of their work, from accessing data to deploying
a design into a production environment. Permitting workers -whether directly on the data team or not – to work ad hoc without a central tool from which to work is like a construction group attempting to build a high-rise building without a primary set of blueprints.

Machine Learning models Need to be Monitored and Managed

The most significant distinction between developing traditional software application and developing machine learning models is upkeep. For the most part, the software is composed when and does not need to be continually kept – it will typically continue to work over time. Machine learning models are established, put in production, and then must be kept an eye on and fine-tuned up until performance is ideal. Even when the efficiency is optimal, model performance can still move gradually as data (and the individuals producing it) changes. This is quite a different approach, especially for companies that are used to putting software application in production.
Moreover, it’s easy to see how issues with sustainability might eventually trigger – or intensify – problems with ML design bias. In reality, the two are deeply linked, and disregarding both can be devastating to a business’s data science efforts, particularly when magnified by the scaling up of efforts. All of these factors point to having a platform that can help manage design tracking and management.

Required to Create Models that Work in Production

Investing in predictive analytics and data science means guaranteeing that data teams are productive and see projects through to completion (i.e., production) – otherwise called operationalization. Without an API-based tool that allows for a single release, data teams likely will need to hand off designs to an IT team who then will have to re-code it. This step can take lots of time and resources and be a substantial barrier to executing data tasks that genuinely affect the business in essential methods. With a tool that makes it smooth, data groups can easily have an impact, screen, fine-tune, and continue to make improvements that positively impact the bottom line.

Having all said, choosing the right platform is not always straightforward. You need to carefully measure what you really need now and what will you need in the future.
You need to do so taking in account your budget, employees skills and their willingness to learn new methodologies and technologies.

Please bare in mind that developing a challenging AI project takes time, sometimes couple of years. that means your team can start building your prototipe in easy to use open source machine learning platform. Once you have proven your hypothesis you can migrate to more complex and more expensive platform.

Good luck on your new machine learning AI project!

Data Engineering

Making Machine Learning more efficient with the cloud

In the essence, machine learning is a productivity tool for data scientists. As the heart of systems that can learn from data, machine learning permits data scientists to train design on an example data set and then utilize algorithms that immediately generalize and find out both from that example and from new data feeds. With not being watched methods, data scientists can do without training examples entirely and use machine learning to boil down insights directly and continuously from the data.

I write more here what are the advantages of using the Cloud for Building Machine Learning projects.

Machine learning can infuse every application with predictive power. Data scientists use these sophisticated algorithms to dissect, search, sort, infer, foretell, and otherwise understand the growing amounts of data in our world.

To achieve machine learning’s full capacity as a company resource, data scientists require to train it from the rich troves of data on the mainframes and other servers in your private cloud. For genuinely robust business analytics, you need machine-learning platforms that are crafted to provide the following:

  • Automation and optimization: Your enterprise machine learning platform should allow data scientists to automate creation, training, and release of algorithmic designs against high-value corporate data. The platform ought to assist them in selecting the optimal algorithm for every single data set. The way to do this is by having a system that scores their data against available algorithms and arrangements, the algorithm that best matches their requirements.
  • Efficiency and scalability: The platform needs to be able to continually develop, train, and release a high volume of machine learning models versus data kept in large business databases. It should allow data scientists to deliver better, fresher, more regular forecasts, consequently speeding time to insight.
  • Security and governance: The system ought to enable data scientists to train models without moving the data from the mainframe or another business platform where it is protected and governed. In addition to minimizing the latency and managing the cost of performing machine learning in your data center, this technique gets rid of the dangers associated with doing ETL on a platform different from the node where machine learning execution occurs.
  • Versatility and programmability: The platform ought to permit data scientists to utilize any language (e.g., Scala, Java, Python), any popular structure (e.g., Apache SparkML, TensorFlow, H2O), and any transactional data type throughout the machine learning development lifecycle.

Taking in account the above points, developing your Machine learning and AI project on the cloud can really make difference.

What are the Benefits of Machine Learning in the Cloud?

  • The cloud’s pay-per-use model is good for bursty AI or machine learning workloads.
  • The cloud makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand increases.
  • The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.
  • AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning options that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.

You don’t need to use a cloud provider to build a machine learning solution. After all, there are plenty of open source machine learning frameworks, such as TensorFlow, MXNet, and CNTK that companies can run on their own hardware. However, companies building sophisticated machine learning models in-house are likely to run into issues scaling their workloads, because training real-world models typically requires large compute clusters.

The leading cloud computing platforms are all wagering huge on democratizing artificial intelligence and ML. Over the previous 3 years, Amazon, Google, and Microsoft have actually made considerable investments in artificial intelligence (AI) and machine learning, from presenting brand-new services to performing significant reorganizations that position AI tactically in their organizational structures. Google CEO, Sundar Pichai, has even said that his company is moving to an “AI-first” world.

Having that said, as the Data Science teams grow, the cloud usage will be more eminent. Bigg teams will ask for undisturbed and performing platform where they will create and share different Machine Learning projects. On which they will compare and optimize the machine learning models performance.
This is where the cloud comes in very handy by providing centralized place to keep all big data and all ML models build on top of this data.

Another argument to take into consideration is Machine Learning project reusability.
As teams change drastically and fast nowadays, it is essential to have the machine learning models deployed on the cloud. The difference between models being deployed on servers would be the ease for giving new access to new team members while not jeopardizing the security protocols in the company. That means that a new team member can be up and running with in the first day in the team. He can see the machine learning models developed by his predecessors and use some of them to build new project. That already adds a lot of value.

Some great Machine learning platforms in the cloud available today are:
DataIku
IBM Machine Learning for z/OS
Amazon EC2 Deep Learning AMI backed by NVIDIA GPU, Google Cloud TPUMicrosoft Azure Deep Learning VM based on NVIDIA GPU, and IBM GPU-based Bare Metal Servers are examples of niche IaaS for ML.

Read more:

Machine learning in the cloud

Machine Learning in the cloud

As artificial intelligence (ML) and also artificial intelligence come to be extra prevalent, data logistics will be vital to your success.
While building Machine Learning projects, most of the effort required for success in artificial intelligence is not the algorithm, design, structure, or the learning itself. It’s the data logistics. Perhaps less amazing than these other facets of ML, it’s the data logistics that drive performance, continuous knowing, as well as success. Without data logistics, your capability to remain to refine as well as scale are significantly limited.

Data logistics is key for success in your Machine Learning and AI Projects

Great data logistics does more than drive effectiveness. It is essential to reduce prices currently and also boosted agility in the future. As ML and also AI continue to develop and also expand right into even more business processes, business have to not enable very early successes to become limitations or issues long-term. In a paper by Google scientists (Artificial intelligence: The High Rate Of Interest Credit Card of Technical Financial Debt), the writers point out that although it is simple to spin up ML-based applications, the initiative can result in expensive data dependencies. Excellent data logistics can mitigate the difficulty in managing these intricate data reliances to prevent hindering agility in the future. Using an appropriate structure such as this can also ease deployment and also administration as well as permit the advancement of these applications in ways that are difficult to predict precisely today.

When building Machine Learning and AI projects use – Keep It Simple to Start

Nowadays, we’ll see a shift from complex, data-science-heavy implementations to an expansion of efforts that can be finest called KISS (Keep It Simple to Start). Domain experience as well as data will be the chauffeurs of AI processes that will evolve and improve as experience grows. This strategy will use an additional benefit: it also improves the productivity of existing personnel along with costly, hard-to-find, -hire, as well as -preserve data researchers.

This approach additionally removes the problem over choosing “simply the right devices.” It is a fact of life that we need several devices for AI. Structure around AI the proper way allows continual adjustment to capitalize on brand-new AI tools as well as formulas as they appear. Don’t stress over performance, either (including that of applications that need to stream data in real time) due to the fact that there are constant bear down that front. For instance, NVIDIA recently announced RAPIDS, an open resource data scientific research initiative that leverages GPU-based processes to make the growth and training of models both much easier and also much faster.

Multi-Cloud Deployments will become more standard methods

To be completely agile for whatever the future may hold, the data platforms will certainly need to support the complete selection of diverse data kinds, including documents, items, tables, as well as events. The system must make input as well as outcome data available to any kind of application anywhere. Such agility will certainly make it feasible to totally utilize the worldwide sources offered in a multi cloud setting, thereby empowering organizations to attain the cloud’s complete potential to maximize efficiency, cost, as well as conformity requirements.

Organizations will move to release a common data platform to synchronize and drive converge of (and additionally preserve) all data throughout all deployments, as well as through a global namespace provide a sight into all data, any place it is. An usual data platform throughout numerous clouds will certainly also make it less complicated to explore different services for a range of ML as well as AI demands.

As companies broaden their use ML as well as AI throughout numerous industries, they will require to access the full variety of data sources, types, and also structures on any cloud while staying clear of the creation of data silos. Attaining this end result will cause releases that surpass a data lake, and also this will certainly mark the increased proliferation of worldwide data platforms that can extend data kinds and also places.

Analytics at the Cloud Will End Up Being Strategically Crucial

As the Web of Things (IoT) continues to increase and also develop, the capability to unite edge, on-premises, and cloud processing atop an usual, worldwide data platform will certainly become a tactical important.

A distributed ML/AI style efficient in coordinating data collection as well as processing at the IoT side removes the requirement to send large quantities of data over the WAN. This capability to filter, aggregate, and analyze data at the edge additionally promotes faster, much more reliable handling and also can cause better neighborhood decision making.

Organizations will certainly aim to have a typical data system– from the cloud core to the venture edge– with consistent data administration to make certain the honesty and also safety of all data. The data system picked for the cloud core will, therefore, be adequately extensible and also scalable to deal with the complexities connected with distributed processing at a scattered and also vibrant side. Enterprises will position a premium on a “light-weight” yet capable as well as compatible variation appropriate for the calculate power available at the side, especially for applications that should deliver results in real-time.

A Final Word

In the following years we will see a boosted focus for AI and also ML development in the cloud. Enterprises will maintain it basic to begin, avoid dependencies with a multicloud global data platform, as well as encourage the IoT edge so ML/AI campaigns provide more worth to business in latest years and also well right into the future.

More reads:

Where does a Data Scientist sit among all that Big Data

Predictive analytics, the process of building it

Advanced Data Science

How to turn your boring work into an amazing experience?

Have you ever found yourself stuck in boring work? Days are passing slow, and you just have no motivation to get up from your bed and go to the office. You find yourself daydreaming, searching the web for your next vacation or even playing an online game. The phenomenon of being bored at work, not enjoying going to the officer and finding millions of excuses to stay home is not new. I read somewhere that around 80% of working people don’t like their job and if they don’t have to do it, they would never do it. 80% of people hating what they do every day is too high of a number. But there is some good news.

Like everyone else, I was stuck in dread boring office work multiple times in my career. I was looking for ways to entertain myself in every possible creative scenario, from having super long breaks to playing every possible online game. I even got bored with the games and news of the web portals that I read every day. Time wasn’t passing fast enough, and I was stuck.

Where to start when things are not going your way anymore?

Make a decision to change the things in your favor by yourself. Get out of your comfort zone.

One day, after long waiting, I decided that I need to take things in my hands. I realized that my manager would never give me the exciting tasks that were nice to have, he needed the operational tasks done. I decided that I realized that the time to stop wasting my life in doing boring and to a significant extent unproductive tasks have come. Just typing what is asked from me to type and live from paycheck to paycheck isn’t my game anymore. I needed a change. And I knew I am that one who is going to bring that change in my work.

I was very aware that the management was really focused on finishing the operational tasks, fast. The business was waiting. So I was okay with the fact that I will have to put some extra hours in creating my own project.

Do ground research before you come up with a plan
The culture in every company is always fantastic if you are open to it.

People love to talk about their work, most of the time they will complain about it, but if you listen carefully enough, you will find the reason what bothers them.

That is why I think that these data scientists need to have some soft skills as I describe in my previous article.

I was already working for 6 months in this specific company. During those 6 months, I tried to meet many of my colleagues that I thought their work is exciting and I can learn from them.

In the beginning, it was a bit strange. According to me, people weren’t accustomed that someone from IT will just walk around the floors of the company and approach them and invite them for lunch. But after a dozen invitations people learned about my friendly and curious nature, so I had an easier time in making invitations. Still, on many occasions, I had to be patient and wait for a free spot on their agenda, or try to find understanding when they cancel on me six times.

In the end, persistence paid off.

In my research time, I managed to talk with my colleagues on different seniority levels from a different department and got really accustomed with the business plan, the business model and more importantly the problems that the business was facing.

Now, that I knew the core of the business, I knew that some exciting opportunities can be tackled and can bring the company on the next level.

The company I worked at that time was into the travel sector. They were selling airplane tickets and travel arrangements.

My professional interest was creating predictive algorithms and work with machine learning. Predictive algorithms can really give benefit to a company in the travel sector. However, in my surprise, predictive algorithms and machine learning weren’t used very much there.

Soon I would discover why.

After work hours I started working on a few project descriptions and use cases that I identified that would be valuable for the company.

I used our data to create working demos and presentations on how the project would help in the long run.

First, I presented this to my manager and his manager, they were both impressed by the ideas. They were pleased to give me permission to continue working on this project as long as I was working on in them after work hours. I was okay with it.

Pitching the projects to stakeholders
Naturally, I started talking with the managers I was close to and slowly started planting my ideas into them for bringing new projects in the company. I would ask them: What do you think if we could do this project to help you with that? Most of the times I would get a positive response, I realized later, more out of courtesy.

I realized, most of the time people want to stay in their comfort zone, protect their position and don’t accept gladly new ideas that might endanger their status or even worse, make them learn something new.
After a month from planting my ideas to different stakeholders, I arranged a meeting where I wanted to present the working demos to them.

On my big surprise, the response I got from them was unexpected. Mostly it was going in the lines of: “I love the idea, but I have more important things on my timeline.” or “I would not change things until they are bringing profit.”, “Excel gives me the forecasts I need, I don’t need anything more than that.”

Stay persistent – Don’t get discouraged easily
These responses were a shock to me, for months I listen people whining about their work, the processes, and faulty systems, but the time I came to them with working solutions, they rejected it immediately saying that they don’t need improvement nor changes.

However, I was determined to upgrade myself and my work, and there was no one stopping me.

Next destination was the 15th floor. 15th floor was reserved for C level people and high management.

I had the opportunity to meet a few of our C level people during company celebrations, and I left space for future unofficial conversation with them.

In my experience, in big companies, you can’t just schedule a meeting with C level person. Especially if you are coming from a low levels hierarchy, like me coming from IT at that time. So you need to make the meeting happen outside their office.

I already knew that the kitchen is the place where everyone must come past by at least once a day. My strategy was to prey on the C level people I had spoken to before and in an informal conversation mention them that I work on this exciting project that can help the business if it implemented.

After patient praying for a few weeks, I managed to meet the CEO of the company in the hallway on his way out. And in time of 6 minutes, the time to go from 15th floor to the parking lot and his car, I managed to tell him the most interesting facts about my projects that he invited me for an official meeting. I was thrilled, my plan was finally working.

The big meeting

After two weeks of meeting my CEO, the big meeting happened.

In those two weeks, I gave the maximum effort to make my demo the most exciting and eye-catching it can be.

The meeting was planned to be only 30 minutes, and it was about to happen not only with the CEO but with other stakeholders that he counted as critical people for the business.

After an hour and a half – one hour after the initially planned meeting, my big rally was over, and I got the attention I needed.

From the following Monday, I officially started working on my project half of the week. I got the promise that if the initial test phase is successful, I would be allowed to work on this full time and even form my own team. I was in the stars.

Not everyone is happy when you progress

My big news wasn’t accepted so gladly from some other managers. Now they started seeing me as their competition. The immediately scheduled meetings on which they tried very hard to prove that my project will fail.

These people were longer in the company, they already knew the business more than I do and their name was recognized more than mine was. All this made me sweat profoundly before and during the meetings, but also made me make sure that my demo is bulletproof and covered from every possible angle.

Nevertheless, I showed that I’m not bothered by their attitude and I kept the most friendly and professional character.

In the end, the only thing that matters is your goal

After a painful but exciting test phase, the final results were out. The predictive model turned out to be even more successful in the real world scenario, and the business owners were really pleased with the extra profit that the project brought.

Now I had the opportunity to work my dream work and never get bored from it again.

All the difficulties during the process, after work hours, unpleasant moments and failures now seemed like nothing else but a good experience, because all it matters is: I realized my plan.

 

What are the most important soft skills for data scientists?

Data scientist are the people who are thought to be the statistics wizards and tech gurus.

I have written about what tech skills does a data scientist need. In most cases, these beliefs are the truth. Data scientist are expected, most of the time, to perform wonders using some fancy algorithm names and tools. Everyone is focusing on their technical knowledge and expertise, their past tech knowledge and the project they have been partly from. This is all great, it is needed and it is a big part of everyday work a Data scientist should do.

Unfortunately, not so many people focus on the soft skills data scientists should have. That is why I took the time to think about and state the top three skills data scientist should have, according to me.

Read More »

How not to learn programing language like Python and R for machine learning the wrong way

Here is what you should NOT do when you start studying machine learning in Python.

  1. Get really good at Python programming and Python syntax.
  2. Deeply study the underlying theory and parameters for machine learning algorithms
  3. Avoid or lightly touch on all of the other tasks needed to complete a real project.

I think that this approach can work for some people, but it is a really slow and a roundabout way of getting to your goal. It teaches you that you need to spend all your time learning how to use individual machine learning algorithms. It also does not teach you the process of building predictive machine learning models in Python that you can actually use to make predictions.

Sadly, this is the approach used to teach machine learning that I see in almost all books and online courses on the topic.

Lessons: Learn how the sub-tasks of a machine learning project map onto Python and the best practice way of working through each task.

Projects: Tie together all of the knowledge from the lessons by working through a case study predictive modeling problems.

Recipes: Apply machine learning with a catalog of standalone recipes in Python that you

can copy-and-paste as a starting point for new projects.

1.2.1 Lessons

You need to know how to complete the specific subtasks of a machine learning project using the Python ecosystem. Once you know how to complete a discrete task using the platform and get a result reliably, you can do it again and again on the project after project. Let’s start with an overview of the common tasks in a machine learning project. A predictive modeling machine learning project can be broken down into 6 top-level tasks:

  1.  Investigate and characterize the problem in order to better understand the goals of the project.
  2. Analyze Data: Use descriptive statistics and visualization to better understand the data you have available.
  3. Prepare Data: Use data transforms in order to better expose the structure of the prediction problem to modeling algorithms.
  4. Evaluate Algorithms: Design a test harness to evaluate a number of standard algorithms on the data and select the top few to investigate further.
  5. Improve Results: Use algorithm tuning and ensemble methods to get the most out of well-performing algorithms on your data.
  6. Present Results: Finalize the model, make predictions and present results.
Time series components

Build Machine learning models using Time series data

Time series forecasting is an important area of machine learning that is often neglected. It is important because there are so many prediction problems that involve a time component. These problems are neglected because it is this time component that makes time series problems more difficult to handle.

Time series vs. normal machine learning dataset

A normal machine learning dataset is a collection of observations. For example:

observation #1
observation #2
observation #3

Predictions are made for new data when the actual outcome may not be known until some future date. The future is being
predicted, but all prior observations are treated equally. Perhaps with some very minor temporal dynamics to overcome the idea of concept drift such as only using the last year of observations rather than all data available.

A time series dataset is different. Time series adds an explicit order dependence between
observations: a time dimension. This additional dimension is both a constraint and a structure that provides a source of additional information.
A time series is a sequence of observations taken sequentially in time.

Time #1, observation
Time #2, observation
Time #3, observation

Time Series Nomenclature

it is essential to quickly establish the standard terms used when describing
time series data. The current time is defined as t, observation at the present time is defined as obs(t).

We are often interested in the observations made at prior times, called lag times or lags.

Times in the past are negative relative to the current time. For example, the previous time is t-1 and the time before that is t-2. The observations at these times are obs(t-1) and obs(t-2) respectively.

To summarize:

  • t-n: A prior or lag time (e.g. t-1 for the previous time).
  • t: A current time and point of reference.
  • t+n: A future or forecast time (e.g. t+1 for the next time).

Time Series Analysis vs. Time Series Forecasting

We have different goals depending on whether we are interested in understanding a dataset or making predictions. Understanding a dataset, called time series analysis, can help to make better predictions, but is not required and can result in a large technical investment in time and expertise not directly aligned with the desired outcome, which is forecasting the future.

Time Series Analysis

When using classical statistics, the primary concern is the analysis of time series. Time series analysis involves developing models that best capture or describe an observed time series to understand the underlying causes. This eld of study seeks the why behind a time series dataset. This often involves making assumptions about the form of the data and decomposing the time series into constitution components. The quality of a descriptive model is determined by how well it describes all available data and the interpretation it provides to better inform the problem domain.

The primary objective of time series analysis is to develop mathematical models that provide plausible descriptions from sample data.

Time Series Forecasting

Making predictions about the future is called extrapolation in the classical statistical handling of time series data. More modern fields focus on the topic and refer to it as time series forecasting.

Forecasting involves taking models t on historical data and using them to predict future observations. Descriptive models can borrow from the future (i.e. to smooth or remove noise), they only seek to best describe the data. An important distinction in forecasting is that the future is completely unavailable and must only be estimated from what has already happened.

The skill of a time series forecasting model is determined by its performance at predicting the future. This is often at the expense of being able to explain why a specific prediction was made, confidence intervals and even better understanding the underlying causes behind the problem.

Components of Time Series

Time series analysis provides a body of techniques to better understand a dataset. Perhaps the most useful of these is the decomposition of a time series into 4 constituent parts:

  • Level. The baseline value for the series if it were a straight line.
  • Trend. The optional and often linear increasing or decreasing behavior of the series over time.
  • Seasonality. The optional repeating patterns or cycles of behavior over time.
  • Noise. The optional variability in the observations that cannot be explained by the model.

All time series have a level, most have noise, and the trend and seasonality are optional.

Time series components
Time series components

Concerns of forecasting time series

When forecasting, it is important to understand your goal. Use the Socratic method and ask lots of questions to help zoom in on the specifics of your predictive modeling problem. For example:

How much data do you have available and are you able to gather it all together?

  1. Like in all Machine learning models, more data is often more helpful, offering greater opportunity for exploratory data analysis, model testing, and tuning, and model fidelity.
  2. What is the time horizon of predictions that is required? Short, medium or long term? Shorter time horizons are often easier to predict with higher confidence.
  3. Can forecasts be updated frequently over time or must they be made once and remain static? Updating forecasts as new information becomes available often results in more accurate predictions.
  4. At what temporal frequency are forecasts required? Often forecasts can be made at a lower or higher frequency, allowing you to harness down-sampling, and up-sampling of data, which in turn can offer benefits while modeling.

Time series data often requires cleaning, scaling, and even transformation. For example:

  • Frequency. Perhaps data is provided at a frequency that is too high to model or is unevenly spaced through time requiring resampling for use in some models.
  • Outliers. Perhaps there are corrupt or extreme outlier values that need to be identified and handled.
  • Missing. Perhaps there are gaps or missing data that need to be interpolated or imputed.

Often time series problems are real-time, continually providing new opportunities for prediction. This adds an honesty to time series forecasting that quickly eshes out bad assumptions, errors in modeling and all the other ways that we may be able to fool ourselves.

Examples of Time Series Forecasting

  • Forecasting the commodity, like corn, wheat etc. yield in tons by the state each year.
  • Forecasting whether an EEG trace in seconds indicates a patient is having a seizure or not.
  • Forecasting the closing price of a stock each day.
  • Forecasting the birth rate at all hospitals in a city each year.
  • Forecasting product sales in units sold each day for a store.
  • Forecasting the number of passengers through a train station each day.
  • Forecasting unemployment for a state each quarter.
  • Forecasting utilization demand on a server each hour.
  • Forecasting the size of the rabbit population in a state each breeding season.
  • Forecasting the average price of gasoline in a city each day.

Time Series as Supervised Learning

Time series forecasting can be framed as a supervised learning problem. This re-framing of your time series data allows you access to the suite of standard linear and nonlinear machine learning algorithms on your problem.

Sliding Windows

Sliding windows in time series machine leraning technique

Time series data can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem. We can do this by using previous time steps as input variables and use the next time step as the output variable.

time, measure
1, 10
2, 20
3, 30
4, 40
5, 50

We can restructure this time series dataset as a supervised learning problem by using the value at the previous time step to predict the value at the next time-step.

X, y
?, 10
10, 20
20, 30
30, 40
40, 50
50, ?

 

Univariate Time Series vs. Multivariate Time Series

Univariate Time Series: These are datasets where only a single variable is observed

at each time, such as temperature each hour. The example in the previous section is a

univariate time series dataset.

Multivariate Time Series: These are datasets where two or more variables are observed

at each time.

Most time series analysis methods and even books on the topic focus on univariate data.

This is because it is the simplest to understand and work with. Multivariate data is often more difficult to work.

 

Time series in practice

Let’s take the following data sample:

Minimum Daily Temperatures dataset. This dataset describes the minimum daily temperatures over 10 years (1981-1990) in the city Melbourne, Australia.

# create date time features of a dataset
from pandas import read_csv
from pandas import DataFrame

series = read_csv('../Datasets/daily-min-temperatures.csv', header=0, index_col=0,parse_dates=True, squeeze=True)

dataframe = DataFrame()

Please note the index_col=0 in the read_csv.
This parameter turns our first column, Date, into an index. We will use this for further feature engineering.

Date
1981-01-01 20.7
1981-01-02 17.9
1981-01-03 18.8
1981-01-04 14.6

Feature engineering

we need to do some simple feature engineering, to get the date and month from the index itself:

#Get the month out of the Index
series.index[1].month

#Get the day out of the index
series.index[1].day 

#Applied on the whole dataset
dataframe['month'] = [series.index[i].month for i in range(len(series))]
dataframe['day'] = [series.index[i].day for i in range(len(series))]
dataframe['temperature'] = [series[i] for i in range(len(series))]
print(dataframe.head(10))

Once we have the days and months, we can observe that still, we don’t have too many features that can describe our data on the best possible way. Just the month and day information alone will not give us a lot of information to predict temperature and most probably likely result in a poor model.

That is why we need to think about extracting more information from the features we have available at the moment, like at this example “Date”. You may enumerate all the properties of a time-stamp and consider what might be useful for your problem, such as:

  • Minutes elapsed for the day.
  • Hour of day.
  • Business hours or not.
  • Weekend or not.
  • Season of the year.
  • The business quarter of the year.
  • Daylight savings or not.
  • Public holiday or not.
  • Leap year or not.

How to transform a time series problem into a supervised learning problem

Lag features are the classical way that time series forecasting problems are transformed into supervised learning problems. The simplest approach is to predict the value at the next time (t+1) given the value at the current time (t). The supervised learning problem with shifted values looks as follows:

Value(t), Value(t+1)

Pandas library provides the shift() function1 to help create these shifted or lag features from a time series dataset. Shifting the dataset by 1 creates the t column, adding a NaN value for the first row. The time series dataset without a shift represents the t+1.

temps = DataFrame(series.values)
dataframe = concat([temps.shift(1), temps], axis=1)
dataframe.columns = ['t', 't+1']

Printing the data frame now will show us the original column(t) and the shifted column (t+1)

t t+1
0 NaN 20.7
1 20.7 17.9
2 17.9 18.8
3 18.8 14.6
4 14.6 15.8

The first row contains NaN because it was shifted, that is why we will have to discard this one. Please note that if you shit the data for N times because you want to predict N time intervals in future, you will create N number of rows with NaN values that you will have to discard after performing the sliding windows operation. For example:

dataframe = concat([temps.shift(3), temps.shift(2), temps.shift(1), temps], axis=1)
dataframe.columns = ['t-2', 't-1', 't', 't+1']
print(dataframe.head(5))
 t-2 t-1 t t+1
0 NaN NaN NaN 20.7
1 NaN NaN 20.7 17.9
2 NaN 20.7 17.9 18.8
3 20.7 17.9 18.8 14.6
4 17.9 18.8 14.6 15.8

Looking at this example, we can conclude that we cant expect usable data until the 4th row ( index 3).

Removing noise and improving the signal in time series

Let’s take for example one really widely used dataset: Airline dataset

from pandas import read_csv
from matplotlib import pyplot
series = read_csv('../Datasets/airline-passengers.csv', header=0, index_col=0, parse_dates=True,squeeze=True)

#lets do some plotting:
pyplot.figure(1)

# line plot
pyplot.subplot(211)
pyplot.plot(series)

# histogram
pyplot.subplot(212)
pyplot.hist(series)
pyplot.show()

 

airline time series density plot

From the graph above, we can conclude that the data set is not stationary.
What does that mean? It means that the variance and the mean of the observations are changing over time.

This can happen in business problems where there is an increasing trend and or seasonality.

What does it mean for us? Non-stationary data causes more problems in solving time series problems. It makes it difficult to model a proper statistical method to give any kind of forecasting. That is why we need to perform certain transformations on the data.

Square root transformation

The square root, x to x^(1/2) = sqrt(x), is a transformation with a moderate effect on distribution shape: it is weaker than the logarithm and the cube root. It is also used for reducing right skewness, and also has the advantage that it can be applied to zero values. Note that the square root of an area has the units of a length. It is commonly applied to counted data, especially if the values are mostly rather small.

A time series that has a quadratic growth trend, like the example above, can be made linear by taking the square root.

What we need to do is apply square root transformation to out Airline dataset and make the growth trend from quadratic to linear and change the distribution of observations to be possibly Gaussian.

first lets import two more libraries:

from pandas import DataFrame
from numpy import sqrt

Perform  square root transformation

#Visualize after the transformations
#Lets create a function to make our transformation code more elegant and short
def visualize (column):
pyplot.figure(1)
# line plot
pyplot.subplot(211)
pyplot.plot(dataframe[column])
# histogram
pyplot.subplot(212)
pyplot.hist(dataframe[column])
pyplot.show() 

dataframe = DataFrame(series.values)

#Its very important to give name to our column, 
so later we can call the column by its name
dataframe.columns = ['passengers']

#Perform Transformations
dataframe['passengers'] = sqrt(dataframe['passengers'])
visualize('passengers')

square transformation on time series machine learning
square transformation time series machine learning

Looking at the plot above, we can see that the trend was reduced, but was not removed. The line plot still shows an increasing variance from cycle to cycle. The histogram still shows a long tail to the right of the distribution, suggesting an exponential or long-tail distribution. That is why we need to look up for another type of transformation.

 

Log Transformation on time series

A class of more extreme trends is exponential. Time series with an exponential distribution can be made linear by taking the logarithm of the values.

The log transformation is, arguably, the most popular among the different types of transformations used to transform skewed data to approximately conform to normality. Maybe that is why I applied this transformation first back in university times. If the original data follows a log-normal distribution or approximately so, then the logtransformed data follows a normal or near normal distribution.

To perform a Log transformation in our python script, first, we need to import:

from  numpy import  log

Then we perform the Log transformations on the time series dataset:

dataframe['passengers'] = log(dataframe['passengers'])
visualize('passengers')

log transformation time series machine learning
log transformation on time series machine learning

Running the example results in a trend that does look a lot more linear than the square root transform above. The line plot shows a seemingly linear growth and variance. The histogram also shows a more uniform or Gaussian-like distribution of observations.

Log transforms are popular with time series data as they are effective at removing exponential variance. It is important to note that this operation assumes values are positive and non-zero.

Box-Cox Transformation on time series

The Box-Cox transformation is a family of power transformations indexed by a parameter lambda. Whenever you use it the parameter needs to be estimated from the data.

Some common values for lambda:

  • lambda = -1. is a reciprocal transform.
  • lambda = -0.5 is a reciprocal square root transform.
  •  lambda = 0.0 is a log transform.
  •  lambda = 0.5 is a square root transform.
  •  lambda = 1.0 is no transform.

Implement Box-Cox transformation on time series in python:

First, import the boxcox library:

from  scipy.stats import  boxcox

Perform Box-Cox transformation on our time series:

dataframe['passengers' ] = boxcox(dataframe['passengers' ], lmbda=0.0)
visualize('passengers')

Box-Cox transformation on time series machine learning
Box-Cox transformation on time series in machine learning

We can let lambda to None and let the function find the most statistically significant value for lambda.

dataframe['passengers'], lam = boxcox(dataframe['passengers'])
print('Lambda: %f' % lam)
visualize('passengers')
Lambda: 0.148023

BoxCox transformation time series machine learning without lambda
BoxCox transformation time series machine learning without lambda

You see using this approach, the distribution is more normal since lambda is defined automatically.

It is sometimes possible that even if after applying the Box-Cox transformation the series does not appear to be stationary, diagnostics from ARIMA modeling can then be used to decide if differencing or seasonal differencing might be useful to remove polynomial trends or seasonal trends respectively. After that, the result might be an ARMA model that is stationary. If diagnostics confirm the orders p an q for the ARMA model, the AR and MA parameters can then be estimated.

Regarding other possible uses of Box-Cox in the case of a series of iid random variables that do not appear to be normally distributed there may be a particular value of lambda that makes the data look approximately normal.

 

Moving Average Smoothing

Smoothing is a technique applied to time series to remove the fine-grained variation between time steps. The hope of smoothing is to remove noise and better expose the signal of the underlying causal processes. Moving averages are a simple and common type of smoothing used in time series analysis and time series forecasting. Calculating a moving average involves creating a new series where the values are comprised of the average of raw observations in the original time series.

A moving average requires that you specify a window size called the window width. This denes the number of raw observations used to calculate the moving average value. The moving part in the moving average refers to the fact that the window denied by the window width is slid along the time series to calculate the average values in the new series. There are two main types of moving average that is used: Centered and Trailing Moving Average.

Calculating a moving average of a time series makes some assumptions about your data. It is assumed that both trend and seasonal components have been removed from your time series. This means that your time series is stationary, or does not show obvious trends (long-term increasing or decreasing movement) or seasonality (consistent periodic structure).

A moving average can be used as a data preparation technique to create a smoothed version of the original dataset. Smoothing is useful as a data preparation technique as it can reduce the random variation in the observations and better expose the structure of the underlying causal process.

Calculating Moving average over 10 periods in python:

#Moving Average
# tail-rolling average transform over 10 periods window=10
rolling = series.rolling(window=10)
rolling_mean = rolling.mean()
print(rolling_mean.head(10))
# plot original and transformed dataset
series.plot()
rolling_mean.plot(color='red')
pyplot.show()

moving average smoothing
moving average smoothing

The moving average is one of the most common sources of new information when modeling a time series forecast. In this case, the moving average is calculated and added as a new input feature used to predict the next time step.

White Noise

If a time series is a white noise, it is a sequence of random numbers and cannot be predicted. If the series of forecast errors are not white noise, it suggests improvements could be made to the predictive model.

A time series is a white noise if the variables are independent and identically distributed with a mean of zero.

White noise is an important concept in time series analysis and forecasting. It is important for two main reasons:

  • Time series Predictability: If your time series is white noise, then, by definition, it is random. You cannot model and predict random occurrence.
  • Model evaluation: The series of errors from a time series forecast model should ideally be white noise. This means the errors are random.

Your time series is not white noise if:

  • Your series has a non-zero mean
  •  The variance change over time
  • Lag values with time series values correlate

In order to check if your time series is a white noise, it is a good idea to do some visualization and draws some statistics during the data inspection process.

  • Like we did before, create a line plot and histogram. Check for gross features like a changing mean, variance, or obvious relationship between lagged variables.

    histogram from a white noise time series
    histogram from a white noise time series
  • Calculate summary statistics. Check the mean and variance of the whole series against the mean and variance of meaningful contiguous blocks of values in the series (e.g. days, months, or years).

Create an autocorrelation plot. Check for gross correlation between lagged variables.
First import the autocorrelation library:

from pandas.tools.plotting import autocorrelation_plot

Then plot:

# autocorrelation
autocorrelation_plot(series)

You can easily spot the difference between the autocorrelation of the Airplane sales dataset to the right vs. time series with an white noise to the left.

Random Walk and time series predictability

There is a tool called a random walk that can help you understand the predictability of your time series forecast problem.

A random walk is different from a list of random numbers because the next value in the sequence is a modification of the previous value in the sequence.

This dependence provides some consistency from step-to-step rather than the large jumps that a series of independent, random numbers provide.

We can confirm that our time series data set is a random walk and not a random white noise with a statistical test, called Adufller test.

To the code from our Airline time series you need to import the adufller library:

from statsmodels.tsa.stattools import adfuller

Then perform the statistical test:

# statistical test
result = adfuller(series)
print ('ADF Statistic: %f'  % result[0])
print ('p-value: %f'  % result[1])
print ('Critical Values:' )
for  key, value in  result[4].items():
print ('\t%s: %.3f'  % (key, value))

As a result, you will get something like:

ADF Statistic: 0.815369
p-value: 0.991880
Critical Values:
        1%: -3.482
        5%: -2.884
        10%: -2.579

The null hypothesis of the test is that the time series is non-stationary. Running the test we can see that the ADF Statistic value was 0.815369. This is larger than all of the critical values at 1%, 5%, and 10% confidence levels. Therefore, we can say that the time series does appear to be non-stationary.

Predictive Analytics from research and development to a business maker

The start of predictive analytics and machine learning

Predictive analytics started in the early 90s with pattern recognition algorithms—for example, finding similar objects. Over the years, things have evolved into machine learning. In the workflow of data analysis, you collect data, prepare data, and then perform the analysis. If you employ algorithms or functions to automate the data analysis, that’s machine learning.

Read more about the process of building data analysis.

Read More »

How to boost your Machine learning model accuracy

boosting predictive machine learning algorithms

There are multiple ways to boost your predictive model accuracy. Most of these steps are really easy to implement, but yet for many reasons data scientist fail to do proper data preparation and model tuning. in the end, they end up with average or below average machine learning models.
Having domain knowledge will give you the best possible chance of getting improvements on your machine learning models accuracy. However, if every data scientist follows these simple technical steps, they will end up with a great machine learning model accuracy even without being an expert in a certain field.

Read More »