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IBMSPSSMPRO - IBM SPSS Modeler Professional(R) Certification - BrainDump Information

Vendor Name : IBM
Exam Code : IBMSPSSMPRO
Exam Name : IBM SPSS Modeler Professional(R) Certification
Questions and Answers : 73 Q & A
Updated On : December 14, 2018
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IBMSPSSMPRO exam Dumps Source : IBM SPSS Modeler Professional(R) Certification

Test Code : IBMSPSSMPRO
Test Name : IBM SPSS Modeler Professional(R) Certification
Vendor Name : IBM
Q&A : 73 Real Questions

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IBM IBM SPSS Modeler Professional(R)

A Pure Play On Self-carrier massive facts Prep And Analytics: look forward to Smarter Valuation Entry aspect | killexams.com Real Questions and Pass4sure dumps

No result discovered, are trying new keyword!the usual analytics professional reports basic use of 5 tools – the leaders being, open supply statistical programming language R (which is supported by using Alteryx), IBM SPSS facts, the SAS p...

IBM Watson Studio Spark executions using RStudio IDE | killexams.com Real Questions and Pass4sure dumps

in this publish i'll reveal you the way to use the IBM Watson Studio Spark executions the usage of RStudio IDE, which launched from Watson Studio initiatives.

RStudio IDE

RStudio is the premier built-in building environment (IDE) for R programmers. Watson Studio offers a convenient way of loading and executing R scripts.

Spark service

Watson Studio offers creating Spark execution environments interior projects to execute Spark classes in the cloud.

Launch RStudio IDE

Refer RStudio launch tutorial. This documentation pursuits RStudio with venture and it is not yet purchasable in all information facilities.

running Spark classes from RStudio

RStudio makes use of the new sparklyr equipment (http://spark.rstudio.com/index.html) to join with the Spark kernel gateway on the cloud the use of Spark-as-a-service interactive APIs. The sparklyr package comprises a dplyr interface to Spark statistics frames in addition to an R interface to Spark’s dispensed desktop researching pipelines.

you can use your current Spark cases from RStudio. to make use of this function, run right here steps:

1 Load and reveal attainable Spark situations

2 connect to a Spark illustration

3 Run dplyr APIs and Spark’s distributed desktop gaining knowledge of libraries

four monitor tables for Spark loaded records sets

5 View logs for Spark kernel interplay

6 View Spark join repute and fix or disconnect

checklist available Spark cases

for those who birth RStudio two info are created within the working listing (don’t delete them!):  1) config.yml file — Lists all of your attainable Spark circumstances.  2) .Rprofile file — Configures your Spark environment.

These information are created beneath your home directory, /domestic/rstudio. If the working listing is different from the domestic listing, you can reproduction the config.yml and .RProfile information to your current working directory.

that you could load and reveal Spark cases through the use of the load_spark_kernels() and display_spark_kernels() R features . instance:

This characteristic lists handiest your at the moment accessible Spark cases. if you need an additional Spark example, create it in Watson Studio Environments.

join with the chosen Spark illustration

To connect with Spark, run the spark_connect R function. for instance:

sc <- spark_connect(config = kernels[1])

After this Spark context is created, all subsequent operations may be executed the usage of this Spark example:

once connected to Spark we will see connection reputation like beneath

Run dplyr APIs and Spark’s dispensed machine researching libraries

To run dplyr features, load the dplyr package after which run the copy_to feature the use of the Spark context. for instance:

library(dplyr) localDF <- data.body(name=c("John", "Smith", "Sarah", "Mike", "Bob"), age=c(19, 23, 18, 25, 30)) sampletbl <- copy_to(sc, localDF, "sampleTbl")

This creates a Spark data frame on the faraway kernel in accordance with a native R statistics frame, and shows the native references within the Spark view:

View the desk for Spark loaded data units

Spark View shows the entire faraway Spark facts frames. which you could click on the table icon to reveal sample views of these tables.

View the log for Spark kernel interaction

you can choose the Logs icon to view the entire calls to the Spark instance.

View Spark join reputation and connect or disconnect a service

you could view the connection popularity on the Spark View, and you can connect with or disconnect from a Spark provider.

connect Disconnect examine task info in Spark

RStudio provides utility feature get_project_asset_path() to simplify access to venture asset files from spark jobs. here is the illustration to load venture file in spark and create spark facts frame

# R interface for Apache Sparklibrary(sparklyr)

library(dplyr)

# load kernelskernels <- load_spark_kernels()

# display kernelsdisplay_spark_kernels()

# hook up with our spark kernelsc <- spark_connect(config = kernels[1])

# create a path to Tim's check bucketpath <- get_project_asset_path("airline15krows.csv")

# examine the usage of sparklyr package airline15krows_tbl <- spark_read_csv(sc,identify = "airline15krows", route = path, delimiter="|", infer_schema = FALSE)

# listing all tablessrc_tbls(sc)head(airline15krows_tbl,4)

Examples

you could locate the example R script data within the /ibm-sparkaas-demos folder beneath your domestic listing. These examples demonstrate scenarios that you could run with Spark in RStudio.

spark-kernel-simple.R

Creates standard R statistics frames and generates far flung Spark records frames according to the local R records frames. additionally runs some basic filters and DBI queries.

sparkaas_mtcars.R

hundreds the well-known mtcars R facts body and then generates a Spark records body for the mtcars information frame. It then does transformations to create a training records set and runs a linear mannequin on the training facts set.

sparkaas_flights.R

loads some larger facts sets, creates ggplot for lengthen and runs home windows features. See sparklyr — R interface for Apache Spark for greater information.

See also sparklyr Examples for extra examples.

Mahesh Kurapati is an Advisory utility Engineer with the IBM Analytics crew. Mahesh’s primary center of attention is on the development of a variety of micro-features for IBM statistics Science experience. Mahesh is concerned within the construction of quite a few sparkling.facts facets and SparkaaS integration with RStudio. With greater than twenty years of adventure in application construction, Mahesh has contributed key functionalities to IBM products together with SPSS records, SPSS Modeler, and SPSS Analytics Server.

in the beginning published at datascience.ibm.com on September 28, 2016.


comparing the leading huge records analytics utility alternate options | killexams.com Real Questions and Pass4sure dumps

there are many providers selling products classified as big information analytics application. despite the fact, it's difficult to...

differentiate these items in response to performance alone, as lots of the tools share equivalent facets and capabilities. additionally, one of the vital tools show extremely refined changes.

That being spoke of, your key differentiating elements will possible focal point on balancing ease of use, algorithmic sophistication and price when it comes to your corporation's ability and stage of maturity in analytics.

listed here, we verify products from nine big records analytics utility carriers: Alteryx Inc., IBM, KNIME AG, Microsoft, Oracle, RapidMiner Inc., SAP, SAS Institute Inc. and Teradata Corp. Some of these companies deliver multiple device. See the "leading carriers of huge information analytics software" sidebar below for greater details about their selected product offerings.

These carriers symbolize diverse facets of the big information analytics market. Let's evaluate and distinction the ways that these products meet the business wants of consumer corporations.

Analyst capabilities and talents 

Some information analytics tools are centered to newbie clients, some are focused to knowledgeable data analysts and some are engineered to appeal to each kinds of clients.

products akin to IBM SPSS Modeler, RapidMiner's equipment, Oracle superior Analytics and the automated Analytics edition of SAP BusinessObjects Predictive Analytics are frequently designed to enable clients with a restricted historical past in facts or records analysis to investigate data, increase analytical models and design analytics workflows with little or no coding.

whereas each and every dealer wraps its core analytics accessories with an intuitive user interface to guide the analyst's growth in facts guidance, analysis, and then mannequin design and validation, the method taken may additionally fluctuate, peculiarly when comparing a stand-alone product, equivalent to RapidMiner, with one that is a part of a bigger suite, such as the Oracle product.

tools equivalent to IBM SPSS information, KNIME Analytics Platform, the knowledgeable Analytics module of SAP BusinessObjects Predictive Analytics, Microsoft R and the Teradata Aster Analytics platform give the greater sophisticated performance that expert users are expecting. Oracle R superior Analytics for Hadoop (ORAAH), one of the crucial accessories within the Oracle massive facts software Connectors suite, provides an R interface for manipulating Hadoop distributed File device information and writing mapper and reducer capabilities in R. this pliability may well be appealing to greater advanced data scientists.

Alteryx and SAS enterprise Miner offer functionality tailored to the consumer's level of talents, and well-nigh fall into both categories. Alteryx has brought advancements to information profiling to support records scientists improved be mindful their statistics sources. average, SAS enterprise Miner and IBM's SPSS equipment stand out when it involves assisting extra superior analytical recommendations and model scoring, in addition to a broader array of analysis features, including neural networks, association analysis and visualization capabilities.

Analytical variety

reckoning on the use case and application, your firm's clients could be required to support different types of analytics capabilities as a way to use specific types of modeling, corresponding to regression, clustering, segmentation, conduct modeling and resolution trees.

while this has resulted in large support for the a number of forms of analytical modeling at a excessive level, some vendors have invested many years of work into tweaking distinct models of their algorithms and including greater subtle performance. it's essential to take into account which models are most valuable to your enterprise problems and to consider the items when it comes to how they optimum serve your users' company wants.

it be essential to take into account which models are most crucial to your enterprise complications and to consider the products in terms of how they top of the line serve your users' company wants.

The greater mature and higher-end -- and, accordingly, higher-priced -- equipment will demonstrate the gold standard analytical breadth. Oracle records Miner contains an array of time-honored desktop gaining knowledge of processes to support clustering, predictive mining and textual content mining. each variants of IBM's SPSS product supply a various set of analytical thoughts and fashions. And SAS commercial enterprise Miner helps many algorithms and strategies, together with determination bushes, time sequence, neural networks, linear and logistic regression, sequence and web course analysis, market basket analysis, and hyperlink analysis.

The newer era -- and, in some instances, decrease-priced -- products aid distinct models, however in all probability with a narrower range of algorithmic sophistication.

The mannequin stock in Alteryx Analytics Gallery includes such capabilities as regression analysis, determination timber, affiliation rule evaluation, classification and time sequence analysis. KNIME includes strategies for textual content mining, image mining and time collection analysis, and additionally integrates machine learning algorithms from other open supply initiatives, equivalent to Weka and JFreeChart.

another factor of analytical diversity is integration with programming languages and statistical equipment, such as R, for incorporating latest libraries, in addition to user-described functionality. in reality, integration with R may be considered an more and more critical differentiator.

Alteryx designer, Microsoft R, SAS business Miner, Teradata Aster Analytics, Oracle's ORAAH and KNIME's Analytics Platform all interface and guide integration with R. a couple of of the companies, including IBM, Oracle, Microsoft, RapidMiner and SAP, give a starting to be library of extensions to R and Python, enabling clients to take competencies of free libraries.

Scope of the statistics to be analyzed

There are diverse aspects of the scope of the statistics to be analyzed, including the subject of structured vs. unstructured counsel, in addition to entry to standard on-premises databases and statistics warehouses, cloud-based mostly data sources, and facts managed in huge facts structures, such as Hadoop.

youngsters, there are varying levels of guide for records managed inside less-regularly occurring information lakes -- either managed within Hadoop or in a different NoSQL facts management equipment intended to deliver horizontal scaling. The elements for distinguishing among the many products have to be in accordance with your firm's particular requirements for getting access to and processing facts volumes and statistics variety.

In consciousness of the growing diversity of enter sources and the range of underlying programs used to condominium those statistics units, an extra set of rising elements it really is being adopted through these carriers comprises facts accessibility. IBM, RapidMiner, Alteryx, Oracle and Microsoft have all superior their tools' facts import, export and connectivity capabilities. These enhancements should still permit clients to entry a more comprehensive checklist of data sources while simplifying and dashing up the procedure of loading records into the products.

support for scalability and high performance

The want for scalable performance is driven by your organization's facts volumes and urge for food for analysis. Smaller groups with less records could be able to tolerate items that shouldn't have performance features that scale with the available materials, such because the entry-stage types of the lessen-conclusion equipment, including RapidMiner, KNIME, Microsoft R Open and Alteryx dressmaker, that can run on laptop techniques and do not require additional server accessories.

greater businesses usually tend to have a better inventory of records units to analyze, as well as broader communities of users. This introduces two additional requirements -- high performance and facilitation of collaboration. The adaptability of a product to high-efficiency architectures is a superb indication of scalability, and most of the products can be adapted to the parallelism of Hadoop or employ every other capacity of achieving faster computation.

all of the products do have some guide for Hadoop, together with IBM SPSS Modeler and SPSS information; RapidMiner's business component Radoop, which connects the Studio front end and Server analysis engine to information stored in Hadoop; Oracle's huge data Discovery and ORAAH tools; and KNIME's large information Extensions and Cluster Execution add-ins.

IBM SPSS now additionally provides better support for a couple of multithreaded analytical algorithms that might also velocity efficiency. Teradata Aster Analytics addresses high-efficiency requirements through its vastly Parallel Processing structure. SAP's expert Analytics edition of SAP BusinessObjects Predictive Analytics can execute in-reminiscence data mining for handling massive-quantity statistics evaluation successfully. Microsoft R Server leverages its ScaleR module, a finished library of huge facts analytics algorithms that guide parallelization. Scoring algorithms implemented using SAS commercial enterprise Miner will also be deployed and performed inside a Hadoop ambiance.

in addition, integration with Apache Spark appears to be of growing importance. SPSS, KNIME, Oracle, RapidMiner and SAP all provide entry to Apache Spark libraries to support analytics applications that should scale with exploding records volumes. This allows for developed applications to take capabilities of a high-performance cluster platform to distribute the workflow across the cluster.

Collaboration

As referred to, the greater the organization, the more doubtless there should be a necessity to share analyses, models and applications throughout diverse businesses and among many analysts. organizations which have many analysts dispensed across the business might also search for accelerated skill to share fashions and collaborate related to the interpretation of outcomes.

IBM's SPSS Modeler Gold version provides collaboration capabilities, and RapidMiner's Server product offers guide for sharing and collaboration. Alteryx Analytics Gallery gives a mechanism for sharing subtle analytics applications in the cloud with participants of a protracted organization. KNIME presents industrial extensions to assist crew collaboration, in addition to extensions helping operational collaboration, such as faraway-scheduled execution, record generation, shared facts area and a workflow repository. SAS enterprise Miner's customer-server architecture allows for enterprise users and information analysts to work collaboratively by means of sharing fashions and different work products.

Alteryx, KNIME and Teradata Aster have added capabilities to assist control analytical workflows. additionally, one of the vital companies have started to study the right way to permit their tools to combine with others that may have complementary practical sweet spots. for instance, Teradata Aster now has an extension to integrate with KNIME that allows users to leverage the KNIME workflow editor and incorporate Aster Analytics functions into these workflows.

supplier measurement and product integration

carriers can be compared when it comes to their size. One may compare and distinction what could be observed because the mega-providers, whose big facts analytics tools are only one product amongst a massive portfolio of tools. if you work for a bigger firm that typically negotiates site-large, commercial enterprise licenses for the entire suite of a supplier's equipment from a mega-vendor akin to IBM, SAS, SAP or Oracle may well be an inexpensive alternative.

The massive providers promote massive facts analytics tools that are a part of a tons larger tool ecosystem. most likely, the products from a mega-vendor might be at the least a bit built-in and intended to work together. furthermore, some people suppose extra comfortable with larger providers, with an expectation of steadiness and constant consumer carrier. then again, you may simplest be able to purchase these large records analytics tools as a part of a tons higher software licensing arrangement.

Smaller providers, corresponding to KNIME, Alteryx and RapidMiner, have revenues that are commonly based on licensing and help for a small variety of large records analytics items. A smaller dealer might also deliver closer contact with their product management and innovation groups, and you can be able to affect the direction of the product roadmap or stronger functionality.

A smaller seller could even be more flexible when it comes to fee and the facets included within the licensing association. You should recognise, besides the fact that children, that working with a smaller supplier does present some chance in terms of balance, the resources obtainable for help and the opportunity that the business may well be bought, which can have an effect on the client relationship.

The larger vendors are clearly attentive to user wants for integration with different programs, although that regularly facilities on different products inside each and every dealer's stock. as an instance, SAP Predictive Analytics has stronger integration with SAP HANA and BusinessObjects Cloud. SAS business Miner has added nodes to execute code in a SAS open, cloud-capable, in-memory Viya environment. Microsoft presents SQL Server R capabilities, an R installation that runs alongside SQL Server and allows for users to combine Microsoft R Server statistics with SQL Server and Microsoft's different enterprise intelligence tools.

finances for licensing and protection

nearly all of the providers sell distinctive models or editions of their products, with a range of expenses for acquisition and complete can charge of operation. IBM, Oracle, RapidMiner, Teradata and Microsoft sell editions at distinctive tiers, with the license cost proportional to the features, capabilities and freedom from obstacles when it comes to the volumes of records to be analyzed or the number of processing nodes the product can use.

KNIME and RapidMiner deliver free and open source models of their items, either charging for aid features or for versions supporting commercial enterprise-class purposes. KNIME, RapidMiner and Alteryx have notably low licensing fees for a smaller variety of clients. if you're for the reason that SAS or SAP, you need to contact them for pricing alternate options.

The marketplace for large statistics analytics software may also be a complicated vicinity, but confidently this text has helped you keep in mind the advantages huge facts analytics utility can supply your firm, and assisted you in differentiating between the specific equipment examined right here.




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IBMSPSSMPRO exam Dumps Source : IBM SPSS Modeler Professional(R) Certification

Test Code : IBMSPSSMPRO
Test Name : IBM SPSS Modeler Professional(R) Certification
Vendor Name : IBM
Q&A : 73 Real Questions

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IBM SPSS Modeler Professional(R) Certification

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A look at the IBM SPSS Modeler and IBM SPSS Statistics analytics tools | killexams.com real questions and Pass4sure dumps

IBM's SPSS predictive analytics tools consist of IBM SPSS Modeler and IBM SPSS Statistics. SPSS Modeler provides...

data mining and text analysis software, while SPSS Statistics is an integrated family of products. Both tools enable users to build predictive models and execute other analytics tasks.

The IBM SPSS Modeler targets users who have little or no programming skills. Users are provided with a drag-and-drop user interface, enabling them to build predictive models and perform other data analytics. Modeler can apply different processes and algorithms to help the user discover information hidden in the data. The tool can also assist in integrating and consolidating all types of data sets from dispersed data sources across the organization.

The IBM SPSS Statistics suite is an integrated set of products geared toward more professional data analysts. SPSS Statistics addresses the complete analytical process, from planning to data collection, analysis, reporting and deployment.

IBM SPSS Modeler features

Version 18 provides the following features:

  • More than 30 base machine learning algorithms.
  • Extensions that provide continued improvements for use with open source products, such as R and Python.
  • Enhanced support for several multithreaded analytical algorithms, including Random Trees, Tree-AS, Generalized Linear Engine, Linear-AS, Linear Support Vector Machine and Two-Step-AS clustering.
  • The ability to run a variety of Python and Spark machine learning, as well as other Python analytics libraries natively in Modeler without requiring the use of the Analytic Server, as was required in the previous version.
  • SPSS Modeler bundles are deployed on premises, and SPSS Modeler Gold is available as a cloud offering. The client front end of SPSS Modeler runs under Windows and macOS, while the server component runs on Unix, Linux and Windows.

    IBM SPSS Modeler provides the following editions:

  • SPSS Modeler Personal: A single-user desktop product.
  • SPSS Modeler Professional: A desktop product that works with IBM SPSS Analytic Server, providing improved scalability and performance and enabling applications to be used across an organization.
  • SPSS Modeler Premium: This edition includes advanced algorithms and capabilities, such as text analytics, entity analytics and social network analysis, that increase model accuracy with unstructured data.
  • SPSS Modeler Gold: This edition provides analytical decision management, collaboration and deployment capabilities. SPSS Modeler Gold is also available as a cloud offering.
  • IBM SPSS Statistics features

    SPSS Statistics version 24 includes the following new features:

  • The ability to access more than 100 extensions, enabling users to take advantage of free libraries written in R, Python and SPSS syntax.
  • The IBM SPSS Extension Hub to browse, download, update, remove and generally manage extensions.
  • A major upgrade to the Custom Dialog Builder, enabling users to more easily build and install their own extensions. Enhancements include new controls and new properties for existing controls and several other improvements to the user interface.
  • Improvements that enable users to more easily and quickly import and export data into SPPS Statistics.
  • Improvements to the Custom Tables module, including new statistical functionality and customer-requested features.
  • IBM SPSS Statistics offers the following three editions (each with additional modules):

  • SPSS Statistics Standard tools provide advanced statistical procedures that support linear and nonlinear statistical models, as well as predictive simulation modeling, which accounts for uncertain inputs, geospatial analytics and customized tables.
  • SPSS Statistics Professional tools support data preparation, missing values and data validity, decision trees, and forecasting.
  • SPSS Statistics Premium adds advanced analytical techniques, including structural equation modeling, in-depth sampling assessment and testing. This bundle also includes procedures that target direct marketing and high-end charts and graphs.
  • Pricing for the SPSS Modeler and SPSS Statistics predictive analytics tools vary depending on the bundle options, the number of users and the license period. SPSS Statistics is now available as a subscription option or a perpetual license. IBM offers free trials of both IBM SPSS Modeler and IBM SPSS Statistics.

    Next Steps

    Why the term unstructured data is a misnomer

    How big data is changing data modeling techniques

    Big data systems pose new challenges to data governance

    Related Resources View More

    Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, Part 2 | killexams.com real questions and Pass4sure dumps

    Ricardo Balduino and Tim Bohn

    Early Flight, Creative Commons Introduction

    As we described in Part 1 of this series, our objective is to help predict the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. We use historical flights data and historical weather data to make predictions for upcoming flights.

    Over the course of this four-part series, we use different platforms to help us with those predictions. Here in Part 2, we use the IBM SPSS Modeler and APIs from The Weather Company.

    Tools used in this use case solution

    IBM SPSS Modeler is designed to help discover patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a range of advanced algorithms and analysis techniques, including text analytics, entity analytics, decision management and optimization to deliver insights in near real-time. For this use case, we used SPSS Modeler 18.1 to create a visual representation of the solution, or in SPSS terms, a stream. That’s right — not one line of code was written in the making of this blog.

    We also used The Weather Company APIs to retrieve historical weather data for the ten airports over the year 2016. IBM SPSS Modeler supports calling the weather APIs from within a stream. That is accomplished by adding extensions to SPSS, available in the IBM SPSS Predictive Analytics resources page, a.k.a. Extensions Hub.

    A proposed solution

    In this blog, we propose one possible solution for this problem. It’s not meant to be the only or the best possible solution, or a production-level solution for that matter, but the discussion presented here covers the typical iterative process (described in the sections below) that helps us accumulate insights and refine the predictive model across iterations. We encourage the readers to try and come up with different solutions, and provide us with your feedback for future blogs.

    Business and data understanding

    The first step of the iterative process includes understanding and gathering the data needed to train and test our model later.

    Flights data — We gathered 2016 flights data from the US Bureau of Transportation Statistics website. The website allows us to export one month at a time, so we ended up with 12 csv (comma separated value) files. We used IBM SPSS Modeler to merge all the csv files into one set and to select the ten airports in our scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in Figure 1.

    Figure 1 — gathering and preparing flights data in IBM SPSS Modeler

    Weather data — From the Extensions Hub, we added the TWCHistoricalGridded extension to SPSS Modeler, which made the extension available as a node in the tool. That node took a csv file listing the 10 airports latitude and longitude coordinates as input, and generated the historical hourly data for the entire year of 2016, for each airport location, as seen in Figure 2.

    Figure 2 — gathering and preparing weather data in IBM SPSS Modeler

    Combined flights and weather data — To each flight in the first data set, we added two new columns: ORIGIN and DEST, containing the respective airport codes. Next, flight data and the weather data were merged together. Note: the “stars” or SPSS super nodes in Figure 3 are placeholders for the diagrams in Figures 1 and 2 above.

    Figure 3 — combining flights and weather data in IBM SPSS Modeler Data preparation, modeling, and evaluation

    We iteratively performed the following steps until the desired model qualities were reached:

    · Prepare data

    · Perform modeling

    · Evaluate the model

    · Repeat

    Figure 4 shows the first and second iterations of our process in IBM SPSS Modeler.

    Figure 4 — iterations: prepare data, run models, evaluate — and do it again First iteration

    To start preparing the data, we used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took care of null values). In order to better train the model later on, we filtered out rows where flight cancellations were not related to weather conditions (e.g. cancellations due to technical issues, security issues, etc.)

    Figure 5 — imbalanced data found in our input data set

    This is an interesting use case, and often a hard one to solve, due to the imbalanced data it presents, as seen in Figure 5. By “imbalanced” we mean that there were far more non-cancelled flights in the historical data than cancelled ones. We will discuss how we dealt with imbalanced data in the following iteration.

    Next, we defined which features were required as inputs to the model (such as flight date, hour, day of the week, origin and destination airport codes, and weather conditions), and which one was the target to be generated by the model (i.e. predict the cancellation status). We then partitioned the data into training and testing sets, using an 85/15 ratio.

    The partitioned data was fed into an SPSS node called Auto Classifier. This node allowed us to run multiple models at once and preview their outputs, such as the area under the ROC curve, as seen in Figure 6.

    Figure 6 — models output provided by the Auto Classifier node

    That was a useful step in making an initial selection of a model for further refinement during subsequent iterations. We decided to use the Random Trees model since the initial analysis showed it has the best area under the curve as compared to the other models in the list.

    Second iteration

    During the second iteration, we addressed the skewedness of the original data. For that purpose, we chose one of the SPSS nodes called SMOTE (Synthetic Minority Over-sampling Technique). This node provides an advanced over-sampling algorithm that deals with imbalanced datasets, which helped our selected model work more effectively.

    Figure 7 — distribution of cancelled and non-cancelled flights after using SMOTE

    In Figure 7, we notice a more balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

    As mentioned earlier, we picked the Random Trees model for this sample solution. This SPSS node provides a model for tree-based classification and prediction that is built on Classification and Regression Tree methodology. Due to its characteristics, this model is much less prone to overfitting, which gives a higher likelihood of repeating the same test results when you use new data, that is, data that was not part of the original training and testing data sets. Another advantage of this method — in particular for our use case — is its ability to handle imbalanced data.

    Since in this use case we are dealing with classification analysis, we used two common ways to evaluate the performance of the model: confusion matrix and ROC curve. One of the outputs of running the Random Trees model in SPSS is the confusion matrix seen in Figure 8. The table shows the precision achieved by the model during training.

    Figure 8 — Confusion Matrix for cancelled vs. non-cancelled flights

    In this case, the model’s precision was about 95% for predicting cancelled flights (true positives), and about 94% for predicting non-cancelled flights (true negatives). That means, the model was correct most of the time, but also made wrong predictions about 4–5% of the time (false negatives and false positives).

    That was the precision given by the model using the training data set. This is also represented by the ROC curve on the left side of Figure 9. We can see, however, that the area under the curve for the training data set was better than the area under the curve for the testing data set (right side of Figure 9), which means that during testing, the model did not perform as well as during training (i.e. it presented a higher rate of errors, or higher rate of false negatives and false positives).

    Figure 9 — ROC curves for the training and testing data sets

    Nevertheless, we decided that the results were still good for the purposes of our discussion in this blog, and we stopped our iterations here. We encourage readers to further refine this model or even to use other models that could solve this use case.

    Deploying the model

    Finally, we deployed the model as a REST API that developers can call from their applications. For that, we created a “deployment branch” in the SPSS stream. Then, we used the IBM Watson Machine Learning service available on IBM Bluemix here. We imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can also call The Weather Company APIs directly from their application code to retrieve the forecast data for the next day, week, and so on, in order to pass the required data to the scoring endpoint and make the prediction.

    A typical scoring endpoint provided by the Watson Machine Learning service would look like the URL shown below.

    https://ibm-watson-ml.mybluemix.net/pm/v1/score/flights-cancellation?accesskey=<provided by WML service>

    By passing the expected JSON body that includes the required inputs for scoring (such as the future flight data and forecast weather data), the scoring endpoint above returns if a given flight is likely to be cancelled or not. This is seen in Figure 10, which shows a call being made to the scoring endpoint — and its response — using an HTTP requester tool available in a web browser.

    Figure 10 — actual request URL, JSON body, and response from scoring endpoint

    Notice in the JSON response above that the deployed model predicted this particular flight from Newark to Chicago would be 88.8% likely to be cancelled, based on forecast weather conditions.

    Conclusion

    IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this use case without writing a single line of code. We were able to follow an iterative process that helped us understand and prepare the data, then model and evaluate the solution, to finally deploy the model as an API for consumption by application developers.

    Resources

    The IBM SPSS stream and data used as the basis for this blog are available on GitHub. There you can also find instructions on how to download IBM SPSS Modeler, get a key for The Weather Channel APIs, and much more.


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