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IBM IBM SPSS Modeler Professional(R)

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final analysis

Watson is an umbrella for complete IBM abysmal learning and synthetic intelligence, as well as machine studying. The industry changed into a pioneer in introducing AI applied sciences to the industry world. What this capability for consumers: Watson Studio is a accurate contender for any solid trying to deploy machine discovering and abysmal studying technologies.

The platform gives huge materiel and technologies for statistics scientists, developers and region matter experts that desire to explore facts, build fashions, and educate and set up computer discovering fashions at scale. The solution contains materiel to share visualizations and results with others. Watson Studio supports cloud, laptop and autochthonous deployment frameworks.

The latter resides at the back of a company’s firewall or as a SaaS solution operating in an IBM deepest cloud. IBM Watson Studio is ranked as a “chief” in the Forrester Wave. It became a valued clientele’ option 2018 recipient at Gartner Peer Insights.

Product Description

Watson Studio relies on a set of IBM materiel and technologies to construct efficacious machine getting to know applications and services. This comprises IBM Cloud pretrained laptop discovering models reminiscent of visual awareness, Watson natural Language Classifier, and others. The ambiance uses Jupyter Notebooks along with different open source tools and scripting languages to enhance developed-in collaborative challenge aspects.

https://o1.qnsr.com/log/p.gif?;n=203;c=204660772;s=9478;x=7936;f=201812281334210;u=j;z=TIMESTAMP;a=20403954;e=i

The outcome is an environment that facilitates speedy and powerful machine getting to know development and satisfactory tuning of fashions. information scientists and others can choose between a variety of capacities of Anaconda, Spark and GPU environments.

Watson Studio helps greater visual modeling through a drag-and-drop interface offered via IBM’s SPSS Modeler. additionally, it incorporates automatic abysmal gaining information of using a drag-and-drop, no-code interface in Neural network Modeler.

Overview and lines person Base

information scientists, builders and territory import experts.

Interface

Graphical drag-and-drop and command line.

Scripting Languages/formats Supported

supports Anaconda and Apache Spark. The latter presents Scala, Python and R interfaces.

formats Supported

Most fundamental statistics and file codecs are supported via open source Jupyter Notebooks.

Integration

IBM Watson Studio connects a number of IBM items, including SPSS Modeler and statistics Science journey (DSX) together with open supply equipment, in order to carry a robust Predictive Analytics and computer getting to know (PAML) answer.

The ambiance accommodates open facts sets through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud version aspects interactivity with workstation servers and R Studio, along with Python, R., and Scala coder for information scientists.

Reporting and Visualization

Visualization through SPSS Modeler. tough logging and reporting features are developed into the product.

Pricing

IBM has adopted a pay-as-you-go mannequin. Watson Studio Cloud – unprejudiced fees $99 per thirty days with 50 potential unit hours monthly protected. Watson Studio Cloud - commercial enterprise runs $6,000 per 30 days with 5,000 potential unit hours. Watson Studio desktop charges $199 per 30 days with unlimited modeling. Watson Studio local – for commercial enterprise records science teams N/A.

IBM Watson Studio Overview and lines at a glance:

vendor and lines

IBM Watson Studio

ML seat of attention

large information science focal point with cloud and computing device ML structures.

Key features and capabilities

mighty visual focus and herbal classification equipment. bendy routine that accommodates open source tools. Connects to IBM SPSS Modeler.

person comments

particularly rated for features and capabilities. Some complaints revolving around the expend of notebooks.

Pricing and licensing

Tiered mannequin from $99 per 30 days per user to $6,000 per thirty days per consumer or greater at industry level.


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IBM Watson Studio Spark executions the expend of RStudio IDE | killexams.com actual Questions and Pass4sure dumps

during this submit i will be able to betray you the artery to expend the IBM Watson Studio Spark executions using RStudio IDE, which launched from Watson Studio initiatives.

RStudio IDE

RStudio is the premier built-in development ambiance (IDE) for R programmers. Watson Studio provides a effortless approach of loading and executing R scripts.

Spark carrier

Watson Studio provides developing Spark execution environments inner tasks to execute Spark programs within the cloud.

Launch RStudio IDE

Refer RStudio launch tutorial. This documentation aims RStudio with venture and it is not yet purchasable in complete statistics centers.

working Spark programs from RStudio

RStudio makes expend of the brand unique sparklyr package (http://spark.rstudio.com/index.html) to connect with the Spark kernel gateway on the cloud the expend of Spark-as-a-service interactive APIs. The sparklyr kit includes a dplyr interface to Spark facts frames as well as an R interface to Spark’s allotted machine gaining information of pipelines.

which you could expend your latest Spark circumstances from RStudio. to get expend of this characteristic, dash the following steps:

1 Load and screen purchasable Spark cases

2 hook up with a Spark instance

three dash dplyr APIs and Spark’s disbursed computer discovering libraries

4 betray tables for Spark loaded statistics units

5 View logs for Spark kernel interplay

6 View Spark connect repute and fix or disconnect

list obtainable Spark instances

for those who start RStudio two files are created within the working listing (don’t delete them!):  1) config.yml file — Lists complete your attainable Spark cases.  2) .Rprofile file — Configures your Spark environment.

These data are created below your domestic directory, /domestic/rstudio. If the working directory is different from the home listing, which you could reproduction the config.yml and .RProfile data to your existing working directory.

you could load and monitor Spark situations by using the load_spark_kernels() and display_spark_kernels() R capabilities . illustration:

This role lists simplest your at the instant attainable Spark instances. if you want a further Spark illustration, create it in Watson Studio Environments.

connect with the selected Spark illustration

To connect with Spark, dash the spark_connect R feature. as an example:

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

After this Spark context is created, complete subsequent operations might be executed the expend of this Spark instance:

once connected to Spark they can discern connection fame like below

Run dplyr APIs and Spark’s dispensed machine gaining information of libraries

To dash dplyr functions, load the dplyr package after which dash the copy_to characteristic the expend of the Spark context. for example:

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

This creates a Spark information carcass on the far off kernel in accordance with a autochthonous R records body, and shows the autochthonous references in the Spark view:

View the desk for Spark loaded information units

Spark View shows complete the remote Spark facts frames. which you could click on the desk icon to demonstrate sample views of those tables.

View the log for Spark kernel interaction

you can opt for the Logs icon to view complete of the calls to the Spark example.

View Spark join repute and connect or disconnect a service

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

join Disconnect study mission info in Spark

RStudio provides utility characteristic get_project_asset_path() to simplify access to venture asset data from spark jobs. right here is the illustration to load project file in spark and create spark information body

# R interface for Apache Sparklibrary(sparklyr)

library(dplyr)

# load kernelskernels <- load_spark_kernels()

# parade kernelsdisplay_spark_kernels()

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

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

# study the usage of sparklyr materiel airline15krows_tbl <- spark_read_csv(sc,name = "airline15krows", path = direction, delimiter="|", infer_schema = FALSE)

# list complete tablessrc_tbls(sc)head(airline15krows_tbl,four)

Examples

that you would be able to discover the illustration R script info in the /ibm-sparkaas-demos folder under your domestic listing. These examples parade scenarios which you can dash with Spark in RStudio.

spark-kernel-primary.R

Creates primary R statistics frames and generates far off Spark records frames in line with the autochthonous R information frames. besides runs some basic filters and DBI queries.

sparkaas_mtcars.R

masses the conventional mtcars R data frame after which generates a Spark statistics carcass for the mtcars statistics body. It then does transformations to create a practicing facts set and runs a linear model on the training information set.

sparkaas_flights.R

masses some bigger facts sets, creates ggplot for deliberate and runs home windows functions. discern sparklyr — R interface for Apache Spark for extra assistance.

See additionally sparklyr Examples for more examples.

Mahesh Kurapati is an Advisory utility Engineer with the IBM Analytics crew. Mahesh’s primary focal point is on the pile of numerous micro-capabilities for IBM statistics Science journey. Mahesh is concerned within the pile of a lot of sparkling.statistics elements and SparkaaS integration with RStudio. With more than 20 years of event in utility building, Mahesh has contributed key functionalities to IBM items together with SPSS information, SPSS Modeler, and SPSS Analytics Server.

at first posted at datascience.ibm.com on September 28, 2016.


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IBM SPSS Modeler Professional(R) Certification

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See the complete list of Machine Learning SolutionsSee user reviews of IBM Watson Studio

Bottom Line

Watson is an umbrella for complete IBM abysmal learning and artificial intelligence, as well as machine learning. The company was a pioneer in introducing AI technologies to the industry world. What this means for buyers: Watson Studio is a top contender for any organization looking to deploy machine learning and abysmal learning technologies.

The platform provides extensive tools and technologies for data scientists, developers and matter matter experts that desire to explore data, build models, and train and deploy machine learning models at scale. The solution includes tools to share visualizations and results with others. Watson Studio supports cloud, desktop and local deployment frameworks.

The latter resides behind an organization’s firewall or as a SaaS solution running in an IBM private cloud. IBM Watson Studio is ranked as a “Leader” in the Forrester Wave. It was a Customers’ election 2018 recipient at Gartner Peer Insights.

Product Description

Watson Studio relies on a collection of IBM tools and technologies to build powerful machine learning applications and services. This includes IBM Cloud pretrained machine learning models such as Visual Recognition, Watson Natural Language Classifier, and others. The environment uses Jupyter Notebooks along with other open source tools and scripting languages to complement built-in collaborative project features.

https://o1.qnsr.com/log/p.gif?;n=203;c=204660772;s=9478;x=7936;f=201812281334210;u=j;z=TIMESTAMP;a=20403954;e=i

The result is an environment that facilitates lickety-split and powerful machine learning development and fine tuning of models. Data scientists and others can choose from various capacities of Anaconda, Spark and GPU environments.

Watson Studio supports enhanced visual modeling through a drag-and-drop interface provided by IBM’s SPSS Modeler. In addition, it accommodates automated abysmal learning using a drag-and-drop, no-code interface in Neural Network Modeler.

Overview and Features User Base

Data scientists, developers and matter matter experts.

Interface

Graphical drag-and-drop and command line.

Scripting Languages/Formats Supported

Supports Anaconda and Apache Spark. The latter offers Scala, Python and R interfaces.

Formats Supported

Most major data and file formats are supported through open source Jupyter Notebooks.

Integration

IBM Watson Studio connects several IBM products, including SPSS Modeler and Data Science sustain (DSX) along with open source tools, in order to deliver a robust Predictive Analytics and Machine Learning (PAML) solution.

The environment accommodates open data sets through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud version features interactivity with Notebook servers and R Studio, along with Python, R., and Scala coder for data scientists.

Reporting and Visualization

Visualization through SPSS Modeler. tough logging and reporting functions are built into the product.

Pricing

IBM has adopted a pay-as-you-go model. Watson Studio Cloud – criterion costs $99 per month with 50 capacity unit hours per month included. Watson Studio Cloud - Enterprise runs $6,000 per month with 5,000 capacity unit hours. Watson Studio Desktop costs $199 per month with unlimited modeling. Watson Studio Local – for enterprise data science teams N/A.

IBM Watson Studio Overview and Features at a Glance:

Vendor and features

IBM Watson Studio

ML Focus

Broad data science focus with cloud and desktop ML platforms.

Key Features and capabilities

Strong visual recognition and natural classification tools. resilient approach that incorporates open source tools. Connects to IBM SPSS Modeler.

User comments

Highly rated for features and capabilities. Some complaints revolving around using notebooks.

Pricing and licensing

Tiered model from $99 per month per user to $6,000 per month per user or more at enterprise level.


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Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, piece 3 | killexams.com actual questions and Pass4sure dumps

Tim Bohn and Ricardo Balduino

Piarco Airport, Trinidad in the 1950s, Copyright John Hill, Creative Commons Attribution-Share Alike 4.0

In piece 1 of this series, they wrote about their goal to explore a expend case and expend various machine learning platforms to discern how they might build classification models with those platforms to prognosticate flight cancellations. Specifically, they hoped to prognosticate the probability of the cancellation of flights between the ten U.S. airports most affected by weather. They used historical flight data and historical weather data to get predictions for upcoming flights.

In piece 2, they started their exploration with IBM SPSS Modeler and APIs from The Weather Company. With this post, they stare at IBM’s Watson Studio.

Tools used in this expend case solution

Watson Studio is a collaborative platform for data scientists, built on open-source components and IBM added value, which is available in the cloud or on-premise. In the simplest terms, Watson Studio is a managed Apache Spark cluster with a Notebook front-end. By default, it includes integration with data tools like a data catalog and data refinery, Watson Machine Learning services, collaboration capability, Model Management, and the competence to automatically review a model’s performance and refresh/retrain the model with unique data — and IBM is quickly adding more capabilities. Read here to discern what IBM is doing lately for data science.

A python notebook solution

In this case, they followed roughly the identical steps they used in the SPSS model from piece 2, only this time they wrote python code in a Jupyter notebook to earn similar results. They hearten readers to Come up with their own solutions. Let us know. We’d cherish to feature your approaches in future blog posts.

The first step of the iterative process is gathering and understanding the data needed to train and test their model. Since they did this drudgery for piece 2, they made expend of the analysis here.

Flights data — We gathered data for 2016 flights from the US Bureau of Transportation Statistics website. The website allowed us to export one month at a time, so they ended up with twelve csv (comma separated value) files. Importing those as dataframes and merging into a lone dataframe was straightforward.

Weather data — With the latitude and longitude of the 10 Most Weather-Delayed U.S. Major Airports, they used one of the Weather Company’s API’s to earn the historical hourly weather data for complete of 2016 for each of the 10 airport locations and created a csv file that became their data set in the notebook.

Combined flights and weather data — To each flight in the first data set, they added two unique columns: source and DEST, containing the respective airport codes. Next, they merged flight data and the weather data so that the resulting dataframe contained the flight data along with the weather for the corresponding source and Destination airports.

Data preparation, modeling, and evaluation

To start preparing the data, they used the combined flights and weather data from the previous step and performed some cleanup. They deleted columns of features that they didn’t need, and replaced null values in rows where flight cancellations were not related to weather conditions.

Next, they took the features they discovered when they created a model using SPSS (such as flight date, hour, day of the week, source and destination airport codes, and weather conditions) and they used them as inputs to their python model. They besides chose the target feature for the model to predict: the cancellation status. They deleted the remaining features.

Next, they ran OneHotEncoder on the four categorical features. One-hot encoding is a process by which categorical features earn converted into a format that works better with certain algorithms, like classification and regression. pattern 2 shows the number of feature columns, expanded significantly with one inflamed encoding.

Figure 2 — One-hot encoding expands 4 feature columns into many more

Interestingly, the flight data is heavily imbalanced. Specifically, as seen in pattern 3, of complete the flights in the data set only a miniature percentage are actually cancelled.

Figure 3 — Historical data: distribution of cancelled (1) and non-cancelled (0) flights

To address that skewedness in the original data, they tried oversampling the minority class, under sampling the majority class, and a combination of both — but notany of these approaches worked well. They then tried something called SMOTE (Synthetic Minority Over-Sampling Technique), an algorithm that provides an advanced over-sampling algorithm to deal with imbalanced datasets. Since it generates synthetic examples rather than just using replication, it helped their selected model drudgery more effectively by mitigating the problem of overfitting that random oversampling can cause. SMOTE isn’t considered efficacious for elevated dimensional data, but that isn’t the case here.

In pattern 4, they notice a balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

Figure 4 — Distribution of cancelled and non-cancelled flights after using SMOTE

It’s censorious to mention is that they applied SMOTE only to the training data set, not the test data set. A circumstantial blog by Nick Becker guided their choices in the notebook.

At this point, they used the Random Forest Classifier for their model. It did the best when they used SPSS so they used again in their notebook. They abide several ideas for a second iteration of their model in order to tune it, one of which is to try multiple algorithms to discern how they compare.

Since this expend case deals with classification analysis, they used some of the common ways to evaluate the performance of the model: the confusion matrix, F1 score and ROC curve, among some others. Figures 5 and 6 demonstrate the results.

Figure 5 — Test/Validation Results Figure 6 — ROC curve for training data set

Figure 6 is the ROC curve from the training data set. pattern 5 shows us that the results from the training and test data sets are pretty close, which is a superb indication of consistency, though they realize that with some tuning it could earn better. Nevertheless, they decided that the results were quiet superb for the purposes of their discussion in this blog, and they stopped their iterations here. They hearten readers to refine the model further or even to expend other models to resolve this expend case.

Conclusion

This was a project to compare creating a model in IBM’s SPSS with IBM’s Watson Studio. SPSS offers a no-code sustain while Watson Studio offers the best of open-source coding capability with many IBM value adds. SPSS is an fabulous product and gets better with every release, adding many unique capabilities.

IBM’s Watson Studio is a powerful platform for both the genesis and experienced data scientist. Anyone can log in and abide immediate access to a managed Spark cluster with a election of a Jupyter notebook front-end using Scala, Python or R, SPSS and visual data modeler (no coding). It offers simple collaboration with other users, including adding other data scientists who could then stare over their shoulders and get suggestions. The community is dynamic and has already contributed dozens of tutorials, data sets and notebooks. If they had added Watson Machine Learning, they could very easily abide deployed and managed their model with an instant rest endpoint to muster from any application. If their data was changing, they could abide WML review their model periodically and retrain it with any unique data if their metric (ROC Curve) value fell below a given threshold. That, along with unique data cataloging and data refinery tooling added recently, get this a platform worth checking out for any data science project.

SPSS has a lot, but not everything. Writing the python code in a notebook was a bit more time-consuming than what they did in SPSS, but it besides gave quite a bit more flexibility and freedom. They had access to everything in the python libraries, and of course, one of the benefits of python as an open-source language is the trove of helpful examples.

I would grunt both platforms abide their place, and neither can pretense to be better for everything. Those doing data science for the first time will probably find SPSS an easier station to start given its drag-and-drop user interface. Those who abide Come out of school as programming wizards will want to write code, and DSX will give them a powerful artery to accomplish that without worrying about installing, configuring, and correctly integrating various product versions.

Resources

The IBM notebook and data that form the basis for this blog are available on Github.



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