Wine Quality Analysis Using Machine LearningMahima -
Wine industries use Product Quality Certification to promote their products and become a concern for every individual who consumes any product. But it's not possible to ensure wine quality by experts with such a huge demand for the product as it will increase the cost. It allows building a model using machine learning techniques with a user interface which predicts the quality of the wine by selecting the important parameters.
Predicting wine quality using data analyticsGautam Sawant
This project develops predictive models through numerous machine learning algorithms to predict the quality of wines based on its components. This info can be used by wine makers to make good quality new wines. I did this project as part of the course MIS- 636, Knowledge Discovery in Databases at Stevens Institute of Technology in Hoboken, New Jersey. I am uploading the for the project which was submitted as part of the final presentation along with the project itself.
Predicting Wine Quality Using Different Implementations of Decision Tree Algo...Mohammed Al Hamadi
Using R programming language's three packages: tree, rpart and C50, we try to predict the quality of wine on a publicly available data set. Then, we evaluate the performance of each package using misclassification error, sensitivity, fall-out, ROC Curve and Area Under Curve (AUC).
Wine Quality Analysis Using Machine LearningMahima -
Wine industries use Product Quality Certification to promote their products and become a concern for every individual who consumes any product. But it's not possible to ensure wine quality by experts with such a huge demand for the product as it will increase the cost. It allows building a model using machine learning techniques with a user interface which predicts the quality of the wine by selecting the important parameters.
Predicting wine quality using data analyticsGautam Sawant
This project develops predictive models through numerous machine learning algorithms to predict the quality of wines based on its components. This info can be used by wine makers to make good quality new wines. I did this project as part of the course MIS- 636, Knowledge Discovery in Databases at Stevens Institute of Technology in Hoboken, New Jersey. I am uploading the for the project which was submitted as part of the final presentation along with the project itself.
Predicting Wine Quality Using Different Implementations of Decision Tree Algo...Mohammed Al Hamadi
Using R programming language's three packages: tree, rpart and C50, we try to predict the quality of wine on a publicly available data set. Then, we evaluate the performance of each package using misclassification error, sensitivity, fall-out, ROC Curve and Area Under Curve (AUC).
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Data Science Case Studies: The Internet of Things: Implications for the Enter...VMware Tanzu
The Internet of Things: Implications for the Enterprise
The Internet Of Things (IoT) is already a reality but getting value out of that is still in its infancy. This session analyzes the implications of IoT for the enterprise with examples from the work we have done.
Rashmi Raghu is a Principal Data Scientist at Pivotal with a focus on the Internet-of-Things and applications in the Energy sector. Her work has spanned diverse industry problems including uncovering patterns & anomalies in massive datasets to predictive maintenance. She holds a Ph.D. in Mechanical Engineering with a minor in Management Science & Engineering from Stanford University. Her doctoral work focused on the development of novel computational models of the cardiovascular system to aid disease research. Prior to that she obtained Master’s and Bachelor’s degrees in Engineering Science from the University of Auckland, New Zealand.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Accelerate AI w/ Synthetic Data using GANsRenee Yao
Strata Data Conference in Sep 2018 Presentation
Description:
Synthetic data will drive the next wave of deployment and application of deep learning in the real world across a variety of problems involving speech recognition, image classification, object recognition and language. All industries and companies will benefit, as synthetic data can create conditions through simulation, instead of authentic situations (virtual worlds enable you to avoid the cost of damages, spare human injuries, and other factors that come into play); unparalleled ability to test products, and interactions with them in any environment.
Join us for this introductory session to learn more about how Generative Adversarial Networks (GAN) are successfully used to improve data generation. We will cover specific real-world examples where customers have deployed GAN to solve challenges in healthcare, space, transportation, and retail industries.
Renee Yao explains how generative adversarial networks (GAN) are successfully used to improve data generation and explores specific real-world examples where customers have deployed GANs to solve challenges in healthcare, space, transportation, and retail industries.
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
YouTube Link: https://youtu.be/aGu0fbkHhek
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Science Full Course" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science PPT will start with basics of Statistics and Probability and then moves to Machine Learning and Finally ends the journey with Deep Learning and AI. For Data-sets and Codes discussed in this PPT, drop a comment.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Liquor is normally known as a mixture of water and alcohol. The term alcohol is often used for ethyl alcohol.
The liquor is manufactured by the fermentation process in which carbohydrates are fermented in presence of enzymes as per their specifications given in Bureau of Indian Standards (BIS).
The Titanic - machine learning from disasterMostafa Nizam
• Historical context to understand "What does the data mean?"
• Learn one data set well, and then apply different algorithms and modelling tools.
• This is a true event and everybody knows about the Titanic.
• Whole information is in the internet and the data is verified.
• Built data mining methods of CART, bagging and random forest to evaluate the quality of Portuguese "Vinho Verde" red wine;
• Found out test error rate decreased by 7% by using random forest over CART methods and the red wine quality were mainly determined by the physicochemical factors, alcohol and sulphates.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Data Science Case Studies: The Internet of Things: Implications for the Enter...VMware Tanzu
The Internet of Things: Implications for the Enterprise
The Internet Of Things (IoT) is already a reality but getting value out of that is still in its infancy. This session analyzes the implications of IoT for the enterprise with examples from the work we have done.
Rashmi Raghu is a Principal Data Scientist at Pivotal with a focus on the Internet-of-Things and applications in the Energy sector. Her work has spanned diverse industry problems including uncovering patterns & anomalies in massive datasets to predictive maintenance. She holds a Ph.D. in Mechanical Engineering with a minor in Management Science & Engineering from Stanford University. Her doctoral work focused on the development of novel computational models of the cardiovascular system to aid disease research. Prior to that she obtained Master’s and Bachelor’s degrees in Engineering Science from the University of Auckland, New Zealand.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Accelerate AI w/ Synthetic Data using GANsRenee Yao
Strata Data Conference in Sep 2018 Presentation
Description:
Synthetic data will drive the next wave of deployment and application of deep learning in the real world across a variety of problems involving speech recognition, image classification, object recognition and language. All industries and companies will benefit, as synthetic data can create conditions through simulation, instead of authentic situations (virtual worlds enable you to avoid the cost of damages, spare human injuries, and other factors that come into play); unparalleled ability to test products, and interactions with them in any environment.
Join us for this introductory session to learn more about how Generative Adversarial Networks (GAN) are successfully used to improve data generation. We will cover specific real-world examples where customers have deployed GAN to solve challenges in healthcare, space, transportation, and retail industries.
Renee Yao explains how generative adversarial networks (GAN) are successfully used to improve data generation and explores specific real-world examples where customers have deployed GANs to solve challenges in healthcare, space, transportation, and retail industries.
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
YouTube Link: https://youtu.be/aGu0fbkHhek
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Science Full Course" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science PPT will start with basics of Statistics and Probability and then moves to Machine Learning and Finally ends the journey with Deep Learning and AI. For Data-sets and Codes discussed in this PPT, drop a comment.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Liquor is normally known as a mixture of water and alcohol. The term alcohol is often used for ethyl alcohol.
The liquor is manufactured by the fermentation process in which carbohydrates are fermented in presence of enzymes as per their specifications given in Bureau of Indian Standards (BIS).
The Titanic - machine learning from disasterMostafa Nizam
• Historical context to understand "What does the data mean?"
• Learn one data set well, and then apply different algorithms and modelling tools.
• This is a true event and everybody knows about the Titanic.
• Whole information is in the internet and the data is verified.
• Built data mining methods of CART, bagging and random forest to evaluate the quality of Portuguese "Vinho Verde" red wine;
• Found out test error rate decreased by 7% by using random forest over CART methods and the red wine quality were mainly determined by the physicochemical factors, alcohol and sulphates.
This presentation provides basic knowledge on wine, such as (1) Classifications of Wine, (2) Wine Production, (3) Grapes, (4) Wine Terms; (5) Quality Control, and (6) Quiz
Wine is a drink to get more enthuse, it may promote a longer lifespan, protect against certain cancers, and improves mental health and to provide benefits to the heart.
8 maneras en las que francisco cambia la iglesia catolicaRicardo Castillo
Este artículo nos habla de cómo un líder puede renovar la marca de una institución en crisis. Con energía. Con coraje. Con voluntad. Con persistencia. Con un estilo propio. Este artículo nos habla del Papá Francisco y la Iglesia Católica, pero perfectamente aplica a las transformaciones que requieren los partidos políticos.
Scottish Public Opinion Monitor January 2015 - Holyrood Voting IntentionIpsos UK
On the day that the UK Government publishes draft legislation to devolve additional powers to the Scottish Parliament, our new poll for STV News suggests that voters are not won over by the proposals.
Target variable: Quality
Parameters associated: Alcohol, pH, Acidity, Volatility
The following Quality can be achieved
Pricing based on the chemical and physiometric properties.
Segmentation: Defining new markets.
DFV Wines is steadfastly committed in crafting and representing wines of the highest quality produced in accordance with sustainable wine growing practice using data mining.
Using Adaboost Algorithm to Enhance the Prediction Accuracy of Decision TreesMohammed Al Hamadi
Using R programming language's package fastAdaboost, we use the adaboost algorithm created by Yoav Freund and Robert Schapire on a public data set (white wine quality) to see if we can enhance the performance of a single decision tree.
Presentation of CDR WineLab®, Wine Analysis SystemCDR S.r.l.
CDR WineLab® system is an easy and fast tool for your wine making QC. You can realize a complete in house quality control of the process, so you can take decisions quickly in a few minutes about the wine making process.
The analyzer can be used by everyone. You don’t need any chemical expertise. You don’t need any glassware. With only a small desk you can check the whole production process.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
3. OBJECTIVE
• The dataset contains information about red
and white wine.
• The dataset has 1000 data points of each
wine with 13 characteristics(attributes).
• Wine quality is measured on 0(low)-10(high)
scale.
• Management want
– To understand the characteristics of these wines.
– How different ingredients affect the quality.
4. DATA DESCRIPTION
• The attributes are as follows
• fixed.acidity
• volatile.acidity
• citric.acid
• residual.sugar
• chlorides
• free.sulfur.dioxide
• total.sulfur.dioxide
• density
• pH
• sulphates
• alcohol
• quality
• wine_type
5. DATA ANALYSIS
• To understand the characteristics of the
wine and find ingredients contributing more
for quality, we have done the following
– Subset the data based on wine type and find
descriptive statistics.
– Subset the data based on good quality(7,8,9)
and bad quality(3,4,5,6) and find descriptive
statistics.
– Decision tree.
– Clustering the dataset.
6. DATA ANALYSIS
• Subset the data based on wine type and find
descriptive statistics.
• Subset the data based on good quality1(7,8,9)
and bad quality0(3,4,5,6) and found descriptive
statistics.
7. DATA ANALYSIS
• Subset the data based on wine quality and find
descriptive statistics.
• Volatile.acidity, citric.acid, chlorides are
showing some pattern. Alcohol being the best.
8. DATA ANALYSIS
• Decision tree
– Using rapid miner, we build a classification model
to find the ingredients which are important for
predicting red wine and white wine.
accuracy: 97.05% +/- 1.63% (mikro: 97.05%)
true Red Wine true White Wine class precision
pred. Red Wine 961 20 97.96%
pred. White Wine 39 980 96.17%
class recall 96.10% 98.00%
Ingredients
chlorides
sulphates
free.sulfur.dioxide
volatile.acidity
total.sulfur.dioxide
9. DATA ANALYSIS
• Decision tree
– Using rapid miner, we build a classification model
to find the ingredients which are important for
predicting the quality is high or low.
Ingredients
citric.acid
chlorides
volatile.acidity
density
alcohol
true 0 true 1
class
precision
pred. 0 1431 183 88.66%
pred. 1 211 175 45.34%
class recall 87.15% 48.88%
10. DATA ANALYSIS
• Clustering(Code is on next slide)
• The cluster divides the whole data based
on which wine it belongs.
• We can see there is significant difference
in all attributes.
12. INSIGHTS FROM ANALYSIS
• Characteristics
– Red wine or white wine
– High or low quality
• Ingredients which contribute more for type
of the wine.
– Chlorides, sulphates, free.sulfur.dioxide, total
sulfur dioxide, volatile.acidity.
• Ingredients which contribute more for
quality of the wine.
– Alcohol(major), density.