1月 252021
 

Safety, efficacy, speed and costs must all be prioritized and balanced in the delivery of life-changing therapies to patients. A drug that's quickly and cost-efficiently delivered to market, but isn’t effective and safe is unacceptable. An effective, safe drug that doesn’t get to patients in time to save lives has [...]

The evolving role of AI in drug safety was published on SAS Voices by Cameron McLauchlin

1月 182021
 

What if you had a technology solution that creates a real-time link between the customer demand signal and what's happening on the ground? What if plans that are being steered centrally could  finally be connected to every shipping lane, while simultaneously, creating cost saving carrier adjustments? The first-of-its kind integration [...]

SAS and C.H. Robinson are rewriting the rules of transportation planning and management was published on SAS Voices by Charlie Chase

1月 112021
 

On The DO Loop blog, I write about a diverse set of topics, including statistical data analysis, machine learning, statistical programming, data visualization, simulation, numerical analysis, and matrix computations. In a previous article, I presented some of my most popular blog posts from 2020. The most popular articles often deal with elementary or familiar topics that are useful to almost every data analyst.

However, among last year's 100+ articles are many that discuss advanced topics. Did you make a New Year's resolution to learn something new this year? Here is your chance! The following articles were fun to write and deserve a second look.

Machine learning concepts

Relationship between a threshold value and true/false negatives and positives

Statistical smoothers

Bilinear interpolation of 12 data values

I write a lot about scatter plot smoothers, which are typically parametric or nonparametric regression models. But a SAS customer wanted to know how to get SAS to perform various classical interpolation schemes such as linear and cubic interpolations:

SAS Viya and parallel computing

SAS is devoting tremendous resources to SAS Viya, which offers a modern analytic platform that runs in the cloud. One of the advantages of SAS Viya is the opportunity to take advantage of distributed computational resources. In 2020, I wrote a series of articles that demonstrate how to use the iml action in Viya 3.5 to implement custom parallel algorithms that use multiple nodes and threads on a cluster of machines. Whereas many actions in SAS Viya perform one and only one task, the iml action supports a general framework for custom, user-written, parallel computations:

The map-reduce functionality in the iml action

  • The map-reduce paradigm is a two-step process for distributing a computation. Every thread runs a function and produces a result for the data that it sees. The results are aggregated and returned. The iml action supports the MAPREDUCE function, which implements the map-reduce paradigm.
  • The parallel-tasks paradigm is a way to run independent computations concurrently. The iml action supports the PARTASKS function, which implements the map-reduce paradigm.

Simulation and visualization

Decomposition of a convex polygon into triangles

Generate random points in a polygon

Your turn

Did I omit one of your favorite blog posts from The DO Loop in 2020? If so, leave a comment and tell me what topic you found interesting or useful. And if you missed some of these articles when they were first published, consider subscribing to The DO Loop in 2021.

The post Blog posts from 2020 that deserve a second look appeared first on The DO Loop.

12月 172020
 

There’s nothing worse than being in the middle of a task and getting stuck. Being able to find quick tips and tricks to help you solve the task at hand, or simply entertain your curiosity, is key to maintaining your efficiency and building everyday skills. But how do you get quick information that’s ALSO engaging? By adding some personality to traditionally routine tutorials, you can learn and may even have fun at the same time. Cue the SAS Users YouTube channel.

With more than 50 videos that show personality published to-date and over 10,000 hours watched, there’s no shortage of learning going on. Our team of experts love to share their knowledge and passion (with personal flavor!) to give you solutions to those everyday tasks.

What better way to round out the year than provide a roundup of our most popular videos from 2020? Check out these crowd favorites:

Most viewed

  1. How to convert character to numeric in SAS
  2. How to import data from Excel to SAS
  3. How to export SAS data to Excel

Most hours watched

  1. How to import data from Excel to SAS
  2. How to convert character to numeric in SAS
  3. Simple Linear Regression in SAS
  4. How to export SAS data to Excel
  5. How to Create Macro Variables and Use Macro Functions
  6. The SAS Exam Experience | See a Performance-Based Question in Action
  7. How it Import CSV files into SAS
  8. SAS Certification Exam: 4 tips for success
  9. SAS Date Functions FAQs
  10. Merging Data Sets in SAS Using SQL

Latest hits

  1. Combining Data in SAS: DATA Step vs SQL
  2. How to Concatenate Values in SAS
  3. How to Market to Customers Based on Online Behavior
  4. How to Plan an Optimal Tour of London Using Network Optimization
  5. Multiple Linear Regression in SAS
  6. How to Build Customized Object Detection Models

Looking forward to 2021

We’ve got you covered! SAS will continue to publish videos throughout 2021. Subscribe now to the SAS Users YouTube channel, so you can be notified when we’re publishing new videos. Be on the lookout for some of the following topics:

  • Transforming variables in SAS
  • Tips for working with SAS Technical Support
  • How to use Git with SAS

2020 roundup: SAS Users YouTube channel how to tutorials was published on SAS Users.

9月 222020
 

Everyone knows that SAS has been helping programmers and coders build complex machine learning models and solve complex business problems for many years, but did you know that you can also now build machines learning models without a single line of code using SAS Viya?

SAS has been helping programmers and coders build complex machine learning models and solve complex business problems over many years.

Building on the vision and commitment to democratize analytics, SAS Viya offers multiple ways to support non-programmers and empowers people with no programming skills to get up and running quickly and build machine learning models. I touched on some of the ways this can be done via SAS Visual Analytics in my previous post on analytics for everyone with SAS Viya. In addition, SAS Viya also supports more advanced pipeline-based visual modeling via SAS Visual Data Mining and Machine Learning. The combination of these different tools within SAS Viya supporting a low-code/no-code approach to modeling makes SAS Viya an incredibly flexible and powerful analytics platform that can help drive analytics usage and adoption throughout an organization.

As analytics and machine learning become more pervasive, an analytics platform that supports a low-code/no-code approach can get more people involved, drive ongoing innovations, and ultimately accelerate digital transformation throughout an organization.

Speed

I have met my fair share of coding ninjas who blew me away with their ability to build models using keyboards with lightning speed. But when it comes to being able to quickly get an idea into a model and generate all the assessment statistics and charts, there is nothing quite like a visual approach to building machine learning models.

In SAS Viya, you can build a decision tree model literally just by dragging and dropping the relevant variables onto the canvas as shown in the animated screen flow below.

Building a machine learning model via drag and drop

In this case, we were able to quickly build a decision tree model that predicts child mortality rates around the world. Not only do we get the decision tree in all its graphics glory (on the left-hand side of the image), we also get the overall model fit measure (Average Standard Error in this case), a variable importance chart, as well as a lift chart all without having to enter a single line of code in under 5 seconds!

You also get a bunch of detailed statistical outputs, including a detailed node statistics table without having to do anything extra. This is useful for when you need to review the distribution and characteristics of specific nodes when using the decision tree.

Detailed node statistics table

 

What’s more, you can leverage the same drag-and-drop paradigm to quickly tune the model. In our case, you can do simple modifications like adding a new variable by simply dragging a new data item onto the canvas or more complex techniques like manually splitting or pruning a node just by clicking and selecting a node on the canvas. The whole model and visualization refreshes instantly as you make changes, and you get instant feedback on the outputs of your tuning actions, which can help drive rapid iteration and idea testing.

Governance and collaboration

A graphical and components-based approach to modeling also has the added benefits of providing a stronger level of governance and fostering collaboration. Building machine learning model is often a team sport, and the ability to share and reuse models easily can dramatically reduce the cost and effort involved in building and maintaining models.

SAS Visual Data Mining and Machine Learning enables users to build complex, enterprise-grade pipeline models that support sophisticated variable selection, feature engineering techniques, as well as model comparison processes all within a single, easy-to-understand, pipeline-based design framework.

Pipeline modeling using SAS VDMML

The graphical, pipeline-based modeling framework within SAS Visual Data Mining and Machine Learning leverages common components, supports self-documentation, and allows users to leverage a template-based approach to building and sharing machine learning models quickly.

More importantly, as a new user or team member who needs to review, tune or reuse someone else’s model, it is much easier and quicker to understand the design and intent of the various components of a pipeline model and make the needed changes.

It is much easier and quicker to understand the design and intent of the various components of a pipeline model.

Communication and storytelling

Finally, and perhaps most importantly, a graphical, low-code/no-code approach to building machine learning models makes it much easier to communicate both the intent and potential impact of the model. Figures and numbers represent facts, but narratives and stories convey emotion and build connections. The visual modeling approaches supported by SAS Viya enable you to tell compelling stories, share powerful ideas, and inspire valuable actions.

SAS Viya enables you to make changes and apply filters on the fly within its various visual modeling environments. With the model training process and model outputs all represented visually, it makes it extremely easy to discuss business scenarios, test hypotheses, and test modeling strategies and approaches, even with people without a deep machine learning background.

There is no question that a programmatic approach to building machine learning models offers the ultimate power and flexibility and enables data scientist to build the most complex and advanced machine learning models. But when it comes to speed, governance, and communications, a graphical, low-code/no-code approach to building machine learning definitely has a lot to offer.

To learn more about a low-code/no-code approach to building machine learning models using SAS Viya, check out my book Smart Data Discovery Using SAS® Viya®.

The value of a low-code/no-code approach to building machine learning models was published on SAS Users.

9月 222020
 

Everyone knows that SAS has been helping programmers and coders build complex machine learning models and solve complex business problems for many years, but did you know that you can also now build machines learning models without a single line of code using SAS Viya?

SAS has been helping programmers and coders build complex machine learning models and solve complex business problems over many years.

Building on the vision and commitment to democratize analytics, SAS Viya offers multiple ways to support non-programmers and empowers people with no programming skills to get up and running quickly and build machine learning models. I touched on some of the ways this can be done via SAS Visual Analytics in my previous post on analytics for everyone with SAS Viya. In addition, SAS Viya also supports more advanced pipeline-based visual modeling via SAS Visual Data Mining and Machine Learning. The combination of these different tools within SAS Viya supporting a low-code/no-code approach to modeling makes SAS Viya an incredibly flexible and powerful analytics platform that can help drive analytics usage and adoption throughout an organization.

As analytics and machine learning become more pervasive, an analytics platform that supports a low-code/no-code approach can get more people involved, drive ongoing innovations, and ultimately accelerate digital transformation throughout an organization.

Speed

I have met my fair share of coding ninjas who blew me away with their ability to build models using keyboards with lightning speed. But when it comes to being able to quickly get an idea into a model and generate all the assessment statistics and charts, there is nothing quite like a visual approach to building machine learning models.

In SAS Viya, you can build a decision tree model literally just by dragging and dropping the relevant variables onto the canvas as shown in the animated screen flow below.

Building a machine learning model via drag and drop

In this case, we were able to quickly build a decision tree model that predicts child mortality rates around the world. Not only do we get the decision tree in all its graphics glory (on the left-hand side of the image), we also get the overall model fit measure (Average Standard Error in this case), a variable importance chart, as well as a lift chart all without having to enter a single line of code in under 5 seconds!

You also get a bunch of detailed statistical outputs, including a detailed node statistics table without having to do anything extra. This is useful for when you need to review the distribution and characteristics of specific nodes when using the decision tree.

Detailed node statistics table

 

What’s more, you can leverage the same drag-and-drop paradigm to quickly tune the model. In our case, you can do simple modifications like adding a new variable by simply dragging a new data item onto the canvas or more complex techniques like manually splitting or pruning a node just by clicking and selecting a node on the canvas. The whole model and visualization refreshes instantly as you make changes, and you get instant feedback on the outputs of your tuning actions, which can help drive rapid iteration and idea testing.

Governance and collaboration

A graphical and components-based approach to modeling also has the added benefits of providing a stronger level of governance and fostering collaboration. Building machine learning model is often a team sport, and the ability to share and reuse models easily can dramatically reduce the cost and effort involved in building and maintaining models.

SAS Visual Data Mining and Machine Learning enables users to build complex, enterprise-grade pipeline models that support sophisticated variable selection, feature engineering techniques, as well as model comparison processes all within a single, easy-to-understand, pipeline-based design framework.

Pipeline modeling using SAS VDMML

The graphical, pipeline-based modeling framework within SAS Visual Data Mining and Machine Learning leverages common components, supports self-documentation, and allows users to leverage a template-based approach to building and sharing machine learning models quickly.

More importantly, as a new user or team member who needs to review, tune or reuse someone else’s model, it is much easier and quicker to understand the design and intent of the various components of a pipeline model and make the needed changes.

It is much easier and quicker to understand the design and intent of the various components of a pipeline model.

Communication and storytelling

Finally, and perhaps most importantly, a graphical, low-code/no-code approach to building machine learning models makes it much easier to communicate both the intent and potential impact of the model. Figures and numbers represent facts, but narratives and stories convey emotion and build connections. The visual modeling approaches supported by SAS Viya enable you to tell compelling stories, share powerful ideas, and inspire valuable actions.

SAS Viya enables you to make changes and apply filters on the fly within its various visual modeling environments. With the model training process and model outputs all represented visually, it makes it extremely easy to discuss business scenarios, test hypotheses, and test modeling strategies and approaches, even with people without a deep machine learning background.

There is no question that a programmatic approach to building machine learning models offers the ultimate power and flexibility and enables data scientist to build the most complex and advanced machine learning models. But when it comes to speed, governance, and communications, a graphical, low-code/no-code approach to building machine learning definitely has a lot to offer.

To learn more about a low-code/no-code approach to building machine learning models using SAS Viya, check out my book Smart Data Discovery Using SAS® Viya®.

The value of a low-code/no-code approach to building machine learning models was published on SAS Users.

8月 272020
 

Decision trees are a fundamental machine learning technique that every data scientist should know. Luckily, the construction and implementation of decision trees in SAS is straightforward and easy to produce.

There are simply three sections to review for the development of decision trees:

  1. Data
  2. Tree development
  3. Model evaluation

Data

The data that we will use for this example is found in the fantastic UCI Machine Learning Repository. The data set is titled “Bank Marketing Dataset,” and it can be found at: http://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

This data set represents a direct marketing campaign (phone calls) conducted by a Portuguese banking institution. The goal of the direct marketing campaign was to have customers subscribe to a term deposit product. The data set consists of 15 independent variables that represent customer attributes (age, job, marital status, education, etc.) and marketing campaign attributes (month, day of week, number of marketing campaigns, etc.).

The target variable in the data set is represented as “y.” This variable is a binary indicator of whether the phone solicitation resulted in a sale of a term deposit product (“yes”) or did not result in a sale (“no”). For our purposes, we will recode this variable and label it as “TARGET,” and the binary outcomes will be 1 for “yes” and 0 for “no.”

The data set is randomly split into two data sets at a 70/30 ratio. The larger data set will be labeled “bank_train” and the smaller data set will be labeled “bank_test”. The decision tree will be developed on the bank_train data set. Once the decision tree has been developed, we will apply the model to the holdout bank_test data set.

Tree development

The code below specifies how to build a decision tree in SAS. The data set mydata.bank_train is used to develop the decision tree. The output code file will enable us to apply the model to our unseen bank_test data set.

ODS GRAPHICS ON;
 
PROC HPSPLIT DATA=mydata.bank_train;
 
    CLASS TARGET _CHARACTER_;
 
    MODEL TARGET(EVENT='1') = _NUMERIC_ _CHARACTER_;
 
    PRUNE costcomplexity;
 
    PARTITION FRACTION(VALIDATE=<strong>0.3</strong> SEED=<strong>42</strong>);
 
    CODE FILE='C:/Users/James Gearheart/Desktop/SAS Book Stuff/Data/bank_tree.sas';
 
    OUTPUT OUT = SCORED;
 
run;

The output of the decision tree algorithm is a new column labeled “P_TARGET1”. This column shows the probability of a positive outcome for each observation. The output also contains the standard tree diagram that demonstrates the model split points.

Model evaluation

Once you have developed your model, you will need to evaluate it to see whether it meets the needs of the project. In this example, we want to make sure that the model adequately predicts which observation will lead to a sale.

The first step is to apply the model to the holdout bank_test data set.

DATA test_scored;
 
    SET MYDATA.bank_test;
 
    %INCLUDE 'C:/Users/James Gearheart/Desktop/SAS Book Stuff/Data/bank_tree.sas';
 
RUN;

The %INCLUDE statement applied the decision tree algorithm to the bank_test data set and created the P_TARGET1 column for the bank_test data set.

Now that the model has been applied to the bank_test data set, we will need to evaluate the performance of the model by creating a lift table. Lift tables provide additional information that has been summarized in the ROC chart. Remember that every point along the ROC chart is a probability threshold. The lift table provides detailed information for every point along the ROC curve.

The model evaluation macro that we will use was developed by Wensui Liu. This easy-to-use macro is labeled “separation” and can be applied to any binary classification model output to evaluate the model results.

You can find this macro in my GitHub repository for my new book, End-to-End Data Science with SAS®. This GitHub repository contains all of the code demonstrated in the book along with all of the macros that were used in the book.

This macro on my C drive, and we call it with a %INCLUDE statement.

%INCLUDE 'C:/Users/James Gearheart/Desktop/SAS Book Stuff/Projects/separation.sas';
 
%<em>separation</em>(data = test_scored, score = P_TARGET1, y = target);

The score script that was generated from the CODE FILE statement in the PROC HPSPLIT procedure is applied to the holdout bank_test data set through the use of the %INCLUDE statement.

The table below is generated from the lift table macro.

This table shows that that model adequately separated the positive and negative observations. If we examine the top two rows of data in the table, we can see that the cumulative bad percent for the top 20% of observations is 47.03%. This can be interpreted as we can identify 47.03% of positive cases by selecting the top 20% of the population. This selection is made by selecting observations with a P_TARGET1 score greater than or equal to 0.8276 as defined by the MAX SCORE column.

Additional information about decision trees along with several other model designs are reviewed in detail in my new book End-to-End Data Science with SAS® available at Amazon and SAS.com.

Build a decision tree in SAS was published on SAS Users.

8月 252020
 

Analytics is playing an increasingly strategic role in the ongoing digital transformation of organizations today. However, to succeed and scale your digital transformation efforts, it is critical to enable analytics skills at all tiers of your organization. In a recent blog post covering 4 principles of analytics you cannot ignore, SAS COO Oliver Schabenberger articulated the importance of democratizing analytics. By scaling your analytics efforts beyond traditional data science teams and involving more people with strong business domain knowledge, you can gain more valuable insights and make more significant impacts.

SAS Viya was built from the ground up to fulfill this vision of democratizing analytics. At SAS, we believe analytics should be accessible to everyone. While SAS Viya offers tremendous support and will continue to be the tool of choice for many advanced users and programmers, it is also highly accessible for business analysts and insights team who prefer a more visual approach to analytics and insights discovery.

Self-service data management

First of all, SAS Viya makes it easy for anyone to ingest and prepare data without a single line of code. The integrated data preparation components within SAS Viya support ad-hoc, agile-oriented data management tasks where you can profile, cleanse, and join data easily and rapidly.

Automatically Generated Data Profiling Report

You can execute complex joins, create custom columns, and cleanse your data via a completely drag-and-drop interface. The automation built into SAS Viya eases the often tedious task of data profiling and data cleansing via automated data type identification and transform suggestions. In an area that can be both complex and intimidating, SAS Viya makes data management tasks easy and approachable, helping you to analyze more data and uncover more insights.

Data Join Using a Visual Interface

A visual approach supporting low-code and no-code programming

Speaking of no-code, SAS Viya’s visual approach and support extend deep into data exploration and advanced modeling. Not only can you quickly build charts such as histograms and box plots using a drag and drop interface, but you can also build complex machine learning models using algorithms such as decision trees and logistic regression on the same visual canvas.

Building a Decision Tree Model Using SAS Viya

By putting the appropriate guard rails and providing relevant and context-rich help for the user, SAS Viya empowers users to undertake data analysis using other advanced analytics techniques such as forecasting and correlation analysis. These techniques empower users to ask more complex questions and can potentially help uncover more actionable and valuable insights.

Correlation Analysis Using the Correlation Matrix within SAS Viya

Augmented analytics

Augmented analytics is an emerging area of analytics that leverages machine learning to streamline and automate the process of doing analytics and building machine learning models. SAS Viya leverages augmented analytics throughout the platform to automate various tasks. My favorite use of augmented analytics in SAS Viya, though, is the hyperparameters autotuning feature.

In machine learning, hyperparameters are parameters that you need to set before the learning processing can begin. They are only used during the training process and contribute significantly to the model training process. It can often be challenging to set the optimal hyperparameter settings, especially if you are not an experienced modeler. This is where SAS Viya can help by making building machine learning models easier for everyone one hyperparameter at a time.

Here is an example of using the SAS Viya autotuning feature to improve my decision tree model. Using the autotuning window, all I needed to do was tell SAS Viya how long I want the autotuning process to run for. It will then work its magic and determine the best hyperparameters to use, which, in this case, include the Maximum tree level and the number of Predictor bins. In most cases, you get a better model after coming back from getting a glass of water!

Hyperparameters Autotuning in SAS Viya

Under the hood, SAS Viya uses complex optimization techniques to try to find the best hyperparameter combinations to use all without you having to understand how it manages this impressive feat. I should add that hyperparameters autotuning is supported with many other algorithms in SAS Viya, and you have even more autotuning options when using it via the programmatic interface!

By leveraging a visually oriented framework and augmented analytics capabilities, SAS Viya is making analytics easier and machine learning models more accessible for everyone within an organization. For more on how SAS Viya enables everyone to ask more complex questions and uncover more valuable insights, check out my book Smart Data Discovery Using SAS® Viya®.

Analytics for everyone with SAS Viya was published on SAS Users.

8月 182020
 

As the operations director of any organization’s contact center, you’ll want to ensure that your employees have the right tools in their hands to deliver superior customer service ultimately leading to happy customers and improved revenue for your company. In a previous series on How SAS Visual Analytics’ automated analysis takes customer care to the next level, we saw how automated analysis used machine learning and natural language generation (NLG) to determine which factors were most important in predicting if telecommunications customers would be likely to upgrade to a different mobile plan. We then used this information to create a list of customers our customer care workers could contact to promote our new products. But what about making on-the-fly decisions about what offer(s) best supports a given customer’s needs? To provide support for this type of analysis, SAS recently introduced the automated prediction feature within SAS Visual Analytics on SAS Viya.

What is automated prediction?

Automated prediction, in less than a minute, runs several analytic models (such as decision trees, gradient boosting, and logistic and linear regression) on a specific variable of your choice. Most of the remaining variables in your dataset are automatically analyzed as factors that might influence your specified variable. They are called underlying factors. SAS then chooses the one model (champion model) that most accurately predicts your target variable. The model prediction and the underlying factors are then displayed. You can adjust the values of the underlying factors to determine how the model prediction changes with each adjustment.

Let’s look at how automated prediction works.

Here we have the same customer table we were working within our previous blog posts. This table contains 121 columns (i.e. variables) containing usage and demographic information from a subset of customers who have contacted our customer care centers. One of these columns is a flag that indicates whether that customer upgraded their plan or not. We’ll use this as our target variable.

We’ll right-click on the Upgrade Flag variable and choose Predict on a new page in our report (Figure 1 below).

Figure 1: Selecting automated prediction.

With the automated prediction feature, SAS Visual Analytics created this easy-to-use form in less than a few minutes—by using advanced analytic models and machine learning in the background—that can help predict whether a customer is likely to upgrade (Figure 2 below). SAS analyzed all the other variables in the dataset to determine which ones were most likely to influence our Upgrade Flag. We can modify some of the values in the form and see how it affects the outcome. The factors are listed in order of their relative importance.

Figure 2: Automated Prediction results.

Under the prediction, SAS provides further details using natural language generation (NLG) (Figure 3 below). For example, here we see that the most prevalent value in our data was Did not upgrade with 87.87% of the records having that value. We can also see what type of model was chosen as the champion model; the model that best predicts whether a customer will upgrade. In our current example, the Gradient Boosting model provides the most accurate results.

Figure 3: Natural Language Generation (NLG) provides details about the prediction.

Now, let’s say that a customer calls in. We can fill in the values for each of these parameters listed that are specific to that customer. The analysis is automatically updated using the model previously generated and provides an updated prediction.

Here we see that this customer is likely to upgrade (Figure 4 below). Now we can discuss other mobile plans with this customer.

Figure 4: Updated prediction based on values entered for a specific customer.

What if we want to learn more about the model behind the prediction and what variables were deemed the most important? We can find that information when we maximize the object (Figure 5 below).

Figure 5: Maximize to see model details.

Now we can see at the bottom the steps that were taken to create the champion model (step 2) (Figure 6 below). SAS ran a decision tree, logistic regression, and a gradient boosting model and found that the gradient boosting model provided the greatest accuracy (91.67% accurate in predicting upgrade flag).

Figure 6: Prediction model description.

If we select the Relative Importance tab, we can see the relative importance of each of the underlying factors (Figure 7 below).

Figure 7: Relative importance of underlying factors.

Total Days Over Plan has the greatest influence on our Upgrade Flag variable. Days Suspended Last 6M is the next most important variable whose impact is 43.97% of Total Days Over Plan (Figure 7 above).

Automated Prediction is a fast and easy way to gain an understanding of how variables influence a target and to consider “what if,” by seeing how modifying those underlying factors affects an outcome. Multiple models were run on the data and a champion model was chosen for us using machine learning. We were able to see the accuracy of the champion model and the relative importance of each influencer. All this within a few minutes! This provides a great foundation for moving forward to more advanced modeling techniques. For those who wish to have more control over the models, SAS Visual Analytics also provides capabilities to build and modify other advanced analytical models such as gradient boosting, linear and logistic regression, and decision trees. Furthermore, as your company’s analytic maturing increases additional products can be easily added on to provide even more model choices (Forest, Neural network, Support vector machine, etc.) and capabilities. SAS’ platform and products support the whole analytical life cycle from data preparation all the way through model deployment, model performance management, and decision intelligence.

Take the next step in learning more about SAS Visual Analytics on SAS Viya by signing up for a free trial!

How SAS Visual Analytics' automated prediction takes customer care to the next level was published on SAS Users.

8月 182020
 

As the operations director of any organization’s contact center, you’ll want to ensure that your employees have the right tools in their hands to deliver superior customer service ultimately leading to happy customers and improved revenue for your company. In a previous series on How SAS Visual Analytics’ automated analysis takes customer care to the next level, we saw how automated analysis used machine learning and natural language generation (NLG) to determine which factors were most important in predicting if telecommunications customers would be likely to upgrade to a different mobile plan. We then used this information to create a list of customers our customer care workers could contact to promote our new products. But what about making on-the-fly decisions about what offer(s) best supports a given customer’s needs? To provide support for this type of analysis, SAS recently introduced the automated prediction feature within SAS Visual Analytics on SAS Viya.

What is automated prediction?

Automated prediction, in less than a minute, runs several analytic models (such as decision trees, gradient boosting, and logistic and linear regression) on a specific variable of your choice. Most of the remaining variables in your dataset are automatically analyzed as factors that might influence your specified variable. They are called underlying factors. SAS then chooses the one model (champion model) that most accurately predicts your target variable. The model prediction and the underlying factors are then displayed. You can adjust the values of the underlying factors to determine how the model prediction changes with each adjustment.

Let’s look at how automated prediction works.

Here we have the same customer table we were working within our previous blog posts. This table contains 121 columns (i.e. variables) containing usage and demographic information from a subset of customers who have contacted our customer care centers. One of these columns is a flag that indicates whether that customer upgraded their plan or not. We’ll use this as our target variable.

We’ll right-click on the Upgrade Flag variable and choose Predict on a new page in our report (Figure 1 below).

Figure 1: Selecting automated prediction.

With the automated prediction feature, SAS Visual Analytics created this easy-to-use form in less than a few minutes—by using advanced analytic models and machine learning in the background—that can help predict whether a customer is likely to upgrade (Figure 2 below). SAS analyzed all the other variables in the dataset to determine which ones were most likely to influence our Upgrade Flag. We can modify some of the values in the form and see how it affects the outcome. The factors are listed in order of their relative importance.

Figure 2: Automated Prediction results.

Under the prediction, SAS provides further details using natural language generation (NLG) (Figure 3 below). For example, here we see that the most prevalent value in our data was Did not upgrade with 87.87% of the records having that value. We can also see what type of model was chosen as the champion model; the model that best predicts whether a customer will upgrade. In our current example, the Gradient Boosting model provides the most accurate results.

Figure 3: Natural Language Generation (NLG) provides details about the prediction.

Now, let’s say that a customer calls in. We can fill in the values for each of these parameters listed that are specific to that customer. The analysis is automatically updated using the model previously generated and provides an updated prediction.

Here we see that this customer is likely to upgrade (Figure 4 below). Now we can discuss other mobile plans with this customer.

Figure 4: Updated prediction based on values entered for a specific customer.

What if we want to learn more about the model behind the prediction and what variables were deemed the most important? We can find that information when we maximize the object (Figure 5 below).

Figure 5: Maximize to see model details.

Now we can see at the bottom the steps that were taken to create the champion model (step 2) (Figure 6 below). SAS ran a decision tree, logistic regression, and a gradient boosting model and found that the gradient boosting model provided the greatest accuracy (91.67% accurate in predicting upgrade flag).

Figure 6: Prediction model description.

If we select the Relative Importance tab, we can see the relative importance of each of the underlying factors (Figure 7 below).

Figure 7: Relative importance of underlying factors.

Total Days Over Plan has the greatest influence on our Upgrade Flag variable. Days Suspended Last 6M is the next most important variable whose impact is 43.97% of Total Days Over Plan (Figure 7 above).

Automated Prediction is a fast and easy way to gain an understanding of how variables influence a target and to consider “what if,” by seeing how modifying those underlying factors affects an outcome. Multiple models were run on the data and a champion model was chosen for us using machine learning. We were able to see the accuracy of the champion model and the relative importance of each influencer. All this within a few minutes! This provides a great foundation for moving forward to more advanced modeling techniques. For those who wish to have more control over the models, SAS Visual Analytics also provides capabilities to build and modify other advanced analytical models such as gradient boosting, linear and logistic regression, and decision trees. Furthermore, as your company’s analytic maturing increases additional products can be easily added on to provide even more model choices (Forest, Neural network, Support vector machine, etc.) and capabilities. SAS’ platform and products support the whole analytical life cycle from data preparation all the way through model deployment, model performance management, and decision intelligence.

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How SAS Visual Analytics' automated prediction takes customer care to the next level was published on SAS Users.