Abstract— The movie making is a multibillion-dollar industry. In 2018, the global movie business has generated nearly $41.5 billion in box office and more than that in merchandise revenues. But it is not a guaranteed business: every year we witness big buster and budget movies that become either a “hit” or a “flop”. The success of a movie is mainly judged by looking at ratio of its gross revenue over its budget, but some may also call a movie successful if it bagged critics praise and awards, both of which do not necessarily convert to financial revenue. In our project we look from an investor point of view, who largely favour financial return over any other attribute. But to predict the success of a movie, an investor can’t only rely on superficial attributes, a typical reason why Machine Learning (ML) prediction will prove to be very useful. We are going to implement this prediction using two ML methods that we have studied during the subject CMPE542, namely Random Forest and Neural Network. These are very adapted for discriminating classes, and can thus help us very effectively in pointing to successful or failed movies after being trained on a set of 5043 movies which data have been scraped from IMDB. At the end of the project, we should be able to know which method has the highest accuracy, what movies sell the best at the box office and most importantly for movies producers, what movie features are the most decisive in making a movie profitable.
Data Annotation in Machine Learning – Key Challenges and How to Overcome ThemAndrew Leo
Explore the complexities of data annotation for Machine Learning on Damco’s insightful page. Delve into the key challenges faced in this crucial process and uncover effective solutions. Our formal guide provides a comprehensive understanding, aiding businesses in refining their Machine Learning models. Stay informed and stay ahead in the dynamic realm of technology.
Smarter Documentation: Shedding Light on the Black Box of Reporting DataKelly Raposo
Developing reports to make sense of project data can be a difficult task. IBM’s reporting tools enable users to report on the data from Rational Team Concert, Rational Quality Manager, and Rational Requirements Composer, but our clients often have trouble determining how to get the right data into the right reports. Through a collaborative effort between our clients and several Rational teams (incl. Support, Development, User Experience and Documentation), we explored the challenges and developed a plan to get all the necessary information into our users’ hands. Using tools to automate documentation of the data models, collect and expose SME knowledge about the product REST APIs, and filter the information based on goals, the team delivered a full set of guidance and reference material in the Information Centres that sheds some light on the black box of data. Ongoing efforts will connect the pieces using linked data, allowing fast and easy exploration of the data relationships.
Data Annotation in Machine Learning – Key Challenges and How to Overcome ThemAndrew Leo
Explore the complexities of data annotation for Machine Learning on Damco’s insightful page. Delve into the key challenges faced in this crucial process and uncover effective solutions. Our formal guide provides a comprehensive understanding, aiding businesses in refining their Machine Learning models. Stay informed and stay ahead in the dynamic realm of technology.
Smarter Documentation: Shedding Light on the Black Box of Reporting DataKelly Raposo
Developing reports to make sense of project data can be a difficult task. IBM’s reporting tools enable users to report on the data from Rational Team Concert, Rational Quality Manager, and Rational Requirements Composer, but our clients often have trouble determining how to get the right data into the right reports. Through a collaborative effort between our clients and several Rational teams (incl. Support, Development, User Experience and Documentation), we explored the challenges and developed a plan to get all the necessary information into our users’ hands. Using tools to automate documentation of the data models, collect and expose SME knowledge about the product REST APIs, and filter the information based on goals, the team delivered a full set of guidance and reference material in the Information Centres that sheds some light on the black box of data. Ongoing efforts will connect the pieces using linked data, allowing fast and easy exploration of the data relationships.
A Holistic Approach to Property ValuationsCognizant
A brief guide to incorporating all available data, structured and unstructured, in the property valuation activity, by choosing key variables and running textual analysis.
Explaining the Explainability: ‘Why’ and ‘How’ of Explainability in ResearchMelih Bahar
The harder the question we are trying to solve, the more sophisticated the machine learning models tend to become, making it almost impossible to interpret. This might mean more features, complex algorithms or complex patterns.
E(X)plainableAI (XAI) has been a very trending topic recently. To explain the outcomes of these models, mostly focusing on the point of view of the end user. For research, however, the machine learning models we use are mostly taken for granted as black-boxes because we usually focus on performance and don’t really need to explain the predictions to anyone else.
In this talk, I will cover why explainability (specifically using SHAP values) of a model is important also for the research phase and how it can help not just the end user but also us data scientists that are building the models. We will see several different ways of looking at a model or its predictions can help us improve performance even before the production phase.
Melih is a data scientist at Riskified where he joined almost 2.5 years ago. Today, he is working mainly on the research and improvement of the ATO product.
Originally from Turkey and coming from an engineering background, he pivoted his way into the Data Science/Machine Learning world to follow his passion for data and AI.
He believes in constant learning and endless curiosity. When not doing DS/ML, you can find him doing any kind of sports or tasting new whisky.
1 Exploratory Data Analysis (EDA) by Melvin Ott, PhD.docxhoney725342
1
Exploratory Data Analysis (EDA)
by Melvin Ott, PhD
September, 2017
Introduction
The Masters in Predictive Analytics program at Northwestern University offers
graduate courses that cover predictive modeling using several software products
such as SAS, R and Python. The Predict 410 course is one of the core courses and
this section focuses on using Python.
Predict 410 will follow a sequence in the assignments. The first assignment will ask
you to perform an EDA(See Ratner1 Chapters 1&2) for the Ames Housing Data
dataset to determine the best single variable model. It will be followed by an
assignment to expand to a multivariable model. Python software for boxplots,
scatterplots and more will help you identify the single variable. However, it is easy
to get lost in the programming and lose sight of the objective. Namely, which of
the variable choices best explain the variability in the response variable?
(You will need to be familiar with the data types and level of measurement. This
will be critical in determining the choice of when to use a dummy variable for model
building. If this topic is new to you review the definitions at Types of Data before
reading further.)
This report will help you become familiar with some of the tools for EDA and allow
you to interact with the data by using links to a software product, Shiny, that will
demonstrate and interact with you to produce various plots of the data. Shiny is
located on a cloud server and will allow you to make choices in looking at the plots
for the data. Study the plots carefully. This is your initial EDA tool and leads to
your model building and your overall understanding of predictive analytics.
Single Variable Linear Regression EDA
1. Become Familiar With the Data
2
Identify the variables that are categorical and the variables that are quantitative.
For the Ames Housing Data, you should review the Ames Data Description pdf file.
2. Look at Plots of the Data
For the variables that are quantitative, you should look at scatter plots vs the
response variable saleprice. For the categorical variables, look at boxplots vs
saleprice. You have sample Python code to help with the EDA and below are some
links that will demonstrate the relationships for the a different building_prices
dataset.
For the boxplots with Shiny:
Click here
For the scatterplots with Shiny:
Click here
3. Begin Writing Python Code
Start with the shell code and improve on the model provided.
http://melvin.shinyapps.io/SboxPlot
http://melvin.shinyapps.io/SScatter/
http://melvin.shinyapps.io/SScatter/
3
Single Variable Logistic Regression EDA
1. Become Familiar With the Data
In 411 you will have an introduction to logistic regression and again will ask you to
perform an EDA. See the file credit data for more info. Make sure you recognize
which variables are quantitative and which are catego ...
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
BDW16 London - Scott Krueger, skyscanner - Does More Data Mean Better Decisio...Big Data Week
We have seen vast improvements to data collection, storage, processing and transport in recent years. An increasing number of networked devices are emitting data and all of us are preparing to handle this wave of valuable data.
Have we, as data professionals, been too focused on the technical challenges and analytical results?
What about the data quality? Are we confident about it? How can we be sure we are making good decisions?
We need to revisit methods of assessing data quality on our modernized data platforms. The quality of our decision making depends on it.
Module 9: Natural Language Processing Part 2Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
ML Drift - How to find issues before they become problemsAmy Hodler
Over time, our AI predictions degrade. Full Stop.
Whether it's concept drift where the relationships of our data to what we're trying to predict as changed or data drift where our production data no longer resembles the historical training data, identifying meaningful ML drift versus spurious or acceptable drift is tedious. Not to mention the difficulty of uncovering which ML features are the source of poorer accuracy.
This session looked at the key types of machine learning drift and how to catch them before they become a problem.
IST365 - Project Deliverable #3Create the corresponding relation.docxpriestmanmable
IST365 - Project Deliverable #3
Create the corresponding relational data model by implementing the database for myFlicks.com within the course's mySQL site, linked on the course's homepage within Blackboard. Be sure to populate your tables with dummy data, providing me with the SQL scripts used to create the tables and insert the data into the actual database within a Word document. You need only to have enough data so that your queries (explained below) can be processed and not a complete product or customer inventory.
For this project, you will also create at a minimum 10 important SELECT queries that you need for the day to day management and maintenance of the operation of the site to turn raw data into useful information. In other words, these should all be SELECT statements that myFlicks.com administration or the site itself would actually process to present data to the user or management. Run the queries against your database to ensure the results are correct. Justify why each of the queries you created are important to the application in a written report. Be sure that you make the best use of the data that you can.
I will look for at least two major areas in your implementation. (a) First, I will check the structure of the tables - has entity and referential integrity been enforced and does the structure of your relational database match the ER diagram you submitted as deliverable #2 (b) most importantly, do you have queries that support the transactions associated with myFlicks.com. Remember that I will be looking for and grading 10 queries, but at the same time, I grade on a difficulty scale. Points will be awarded based on complexity, meaning I am looking for aggregate information, joins, etc. and not just statements such as select * from users, etc. As an example, you may have 10 queries, but if one is a very simple and not very useful query, it may get only 2 points (each is worth 7 with the other points given to the database's integrity structure). Also, make sure no query or report produces a null output. I must see data in the output for all queries/reports. Remember, and I reiterate, grading for the queries/reports will be based on the complexity of the queries. Simple queries are allowed, but are awarded very few points.
Creating database
4
Creating a database MyFlicks.com IST 365
December 1, 2014
The database will store information about a catalogue of movies. Information to be stored about each movie includes their titles, description, genre, artists and directors. Each artist and director may be involved in several movies. Movies will have a title and a running time (in minutes). Actors will have names associated with them and it should be possible to search the database by artist names. The same case will apply to directors and producers. Finally, in order to search the catalogue by title, each movie will have a number of keywords, which are the words in the title of the movie.Identifying Entities
The firs ...
Modern Oracle DBAs have spent years acquiring extremely valuable skills, even while facing increased responsibility for growing numbers of diverse multi-version databases, demands to transition to public cloud computing Infrastructure, and a never-ending drumbeat for upskilling and relevance in our industry. It’s the perfect time to consider a transition in your career by leveraging your expertise with the Oracle database in a new role as a Data Engineer (DE).
A Holistic Approach to Property ValuationsCognizant
A brief guide to incorporating all available data, structured and unstructured, in the property valuation activity, by choosing key variables and running textual analysis.
Explaining the Explainability: ‘Why’ and ‘How’ of Explainability in ResearchMelih Bahar
The harder the question we are trying to solve, the more sophisticated the machine learning models tend to become, making it almost impossible to interpret. This might mean more features, complex algorithms or complex patterns.
E(X)plainableAI (XAI) has been a very trending topic recently. To explain the outcomes of these models, mostly focusing on the point of view of the end user. For research, however, the machine learning models we use are mostly taken for granted as black-boxes because we usually focus on performance and don’t really need to explain the predictions to anyone else.
In this talk, I will cover why explainability (specifically using SHAP values) of a model is important also for the research phase and how it can help not just the end user but also us data scientists that are building the models. We will see several different ways of looking at a model or its predictions can help us improve performance even before the production phase.
Melih is a data scientist at Riskified where he joined almost 2.5 years ago. Today, he is working mainly on the research and improvement of the ATO product.
Originally from Turkey and coming from an engineering background, he pivoted his way into the Data Science/Machine Learning world to follow his passion for data and AI.
He believes in constant learning and endless curiosity. When not doing DS/ML, you can find him doing any kind of sports or tasting new whisky.
1 Exploratory Data Analysis (EDA) by Melvin Ott, PhD.docxhoney725342
1
Exploratory Data Analysis (EDA)
by Melvin Ott, PhD
September, 2017
Introduction
The Masters in Predictive Analytics program at Northwestern University offers
graduate courses that cover predictive modeling using several software products
such as SAS, R and Python. The Predict 410 course is one of the core courses and
this section focuses on using Python.
Predict 410 will follow a sequence in the assignments. The first assignment will ask
you to perform an EDA(See Ratner1 Chapters 1&2) for the Ames Housing Data
dataset to determine the best single variable model. It will be followed by an
assignment to expand to a multivariable model. Python software for boxplots,
scatterplots and more will help you identify the single variable. However, it is easy
to get lost in the programming and lose sight of the objective. Namely, which of
the variable choices best explain the variability in the response variable?
(You will need to be familiar with the data types and level of measurement. This
will be critical in determining the choice of when to use a dummy variable for model
building. If this topic is new to you review the definitions at Types of Data before
reading further.)
This report will help you become familiar with some of the tools for EDA and allow
you to interact with the data by using links to a software product, Shiny, that will
demonstrate and interact with you to produce various plots of the data. Shiny is
located on a cloud server and will allow you to make choices in looking at the plots
for the data. Study the plots carefully. This is your initial EDA tool and leads to
your model building and your overall understanding of predictive analytics.
Single Variable Linear Regression EDA
1. Become Familiar With the Data
2
Identify the variables that are categorical and the variables that are quantitative.
For the Ames Housing Data, you should review the Ames Data Description pdf file.
2. Look at Plots of the Data
For the variables that are quantitative, you should look at scatter plots vs the
response variable saleprice. For the categorical variables, look at boxplots vs
saleprice. You have sample Python code to help with the EDA and below are some
links that will demonstrate the relationships for the a different building_prices
dataset.
For the boxplots with Shiny:
Click here
For the scatterplots with Shiny:
Click here
3. Begin Writing Python Code
Start with the shell code and improve on the model provided.
http://melvin.shinyapps.io/SboxPlot
http://melvin.shinyapps.io/SScatter/
http://melvin.shinyapps.io/SScatter/
3
Single Variable Logistic Regression EDA
1. Become Familiar With the Data
In 411 you will have an introduction to logistic regression and again will ask you to
perform an EDA. See the file credit data for more info. Make sure you recognize
which variables are quantitative and which are catego ...
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
BDW16 London - Scott Krueger, skyscanner - Does More Data Mean Better Decisio...Big Data Week
We have seen vast improvements to data collection, storage, processing and transport in recent years. An increasing number of networked devices are emitting data and all of us are preparing to handle this wave of valuable data.
Have we, as data professionals, been too focused on the technical challenges and analytical results?
What about the data quality? Are we confident about it? How can we be sure we are making good decisions?
We need to revisit methods of assessing data quality on our modernized data platforms. The quality of our decision making depends on it.
Module 9: Natural Language Processing Part 2Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
ML Drift - How to find issues before they become problemsAmy Hodler
Over time, our AI predictions degrade. Full Stop.
Whether it's concept drift where the relationships of our data to what we're trying to predict as changed or data drift where our production data no longer resembles the historical training data, identifying meaningful ML drift versus spurious or acceptable drift is tedious. Not to mention the difficulty of uncovering which ML features are the source of poorer accuracy.
This session looked at the key types of machine learning drift and how to catch them before they become a problem.
IST365 - Project Deliverable #3Create the corresponding relation.docxpriestmanmable
IST365 - Project Deliverable #3
Create the corresponding relational data model by implementing the database for myFlicks.com within the course's mySQL site, linked on the course's homepage within Blackboard. Be sure to populate your tables with dummy data, providing me with the SQL scripts used to create the tables and insert the data into the actual database within a Word document. You need only to have enough data so that your queries (explained below) can be processed and not a complete product or customer inventory.
For this project, you will also create at a minimum 10 important SELECT queries that you need for the day to day management and maintenance of the operation of the site to turn raw data into useful information. In other words, these should all be SELECT statements that myFlicks.com administration or the site itself would actually process to present data to the user or management. Run the queries against your database to ensure the results are correct. Justify why each of the queries you created are important to the application in a written report. Be sure that you make the best use of the data that you can.
I will look for at least two major areas in your implementation. (a) First, I will check the structure of the tables - has entity and referential integrity been enforced and does the structure of your relational database match the ER diagram you submitted as deliverable #2 (b) most importantly, do you have queries that support the transactions associated with myFlicks.com. Remember that I will be looking for and grading 10 queries, but at the same time, I grade on a difficulty scale. Points will be awarded based on complexity, meaning I am looking for aggregate information, joins, etc. and not just statements such as select * from users, etc. As an example, you may have 10 queries, but if one is a very simple and not very useful query, it may get only 2 points (each is worth 7 with the other points given to the database's integrity structure). Also, make sure no query or report produces a null output. I must see data in the output for all queries/reports. Remember, and I reiterate, grading for the queries/reports will be based on the complexity of the queries. Simple queries are allowed, but are awarded very few points.
Creating database
4
Creating a database MyFlicks.com IST 365
December 1, 2014
The database will store information about a catalogue of movies. Information to be stored about each movie includes their titles, description, genre, artists and directors. Each artist and director may be involved in several movies. Movies will have a title and a running time (in minutes). Actors will have names associated with them and it should be possible to search the database by artist names. The same case will apply to directors and producers. Finally, in order to search the catalogue by title, each movie will have a number of keywords, which are the words in the title of the movie.Identifying Entities
The firs ...
Modern Oracle DBAs have spent years acquiring extremely valuable skills, even while facing increased responsibility for growing numbers of diverse multi-version databases, demands to transition to public cloud computing Infrastructure, and a never-ending drumbeat for upskilling and relevance in our industry. It’s the perfect time to consider a transition in your career by leveraging your expertise with the Oracle database in a new role as a Data Engineer (DE).
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
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.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
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1. Building a Movie Success Predictor
Youness Lahdili
TED University
Project Paper for
CMPE542 - Machine Learning
Prof. Venera Adanova
Abstract— The movie making is a multibillion-dollar
industry. In 2018, the global movie business has
generated nearly $41.5 billion in box office and more
than that in merchandise revenues. But it is not a
guaranteed business: every year we witness big buster
and budget movies that become either a “hit” or a
“flop”. The success of a movie is mainly judged by
looking at ratio of its gross revenue over its budget, but
some may also call a movie successful if it bagged critics
praise and awards, both of which do not necessarily
convert to financial revenue. In our project we look
from an investor point of view, who largely favour
financial return over any other attribute. But to predict
the success of a movie, an investor can’t only rely on
superficial attributes, a typical reason why Machine
Learning (ML) prediction will prove to be very useful.
We are going to implement this prediction using two
ML methods that we have studied during the subject
CMPE542, namely Random Forest and Neural
Network. These are very adapted for discriminating
classes, and can thus help us very effectively in pointing
to successful or failed movies after being trained on a
set of 5043 movies which data have been scraped from
IMDB. At the end of the project, we should be able to
know which method has the highest accuracy, what
movies sell the best at the box office and most
importantly for movies producers, what movie features
are the most decisive in making a movie profitable.
Keywords— Movie Industry, Data Scraping, Machine
Learning, Random Forest, Neural Network
I. INTRODUCTION
A. Overview
More than entertainment, the cinema industry is
becoming vital to economies of some countries and has
became an indispensable weapon in psychological war and
the soft power exerted by some countries. So it is
imperative to be able to maximize the financial gains from
movies, and keep movies as crowd-alluring as possible.
B. Data Extraction and Parsing
To run our ML analysis, we need raw data on all movies
that have been judged either successful of failed. For this
end, we turn to IMDB, an online repository of all movies
that have been released to date and even those in pre-
production phase. We can tap into this database to extract
key information about the movie budget, gross revenue,
ratings, names of people who are taking part in the movie,
year of release, and so on. We will have to use some tools
such as BeautifulSoup which allow to read data of interest
from HTML webpages and create tabular data out of it, in
this project it is a .CSV datafile conveniently named
“movie_metadata” and ready to be treated by SciKit or
other ML utilitaries.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
from nltk.corpus import stopwords
from nltk.util import ngrams
from sklearn.feature_extraction.text import TfidfVectorizer
dataRaw = pd.read_csv(movie_metadata.csv",sep=',')
2. The dataset is composed of a wide array of attributes on
all films, with separable values and annotations. It has 28
variables belonging to 5043 movies. The dataset we
obtained show the movie titles, investment that was placed
to produce the movie, the revenue that was earned, and a
lot more regarding the visual characteristics, the leading
actors/actress in the movie. It should be notes that those
movies originate from 66 countries, but with a clear
prevalence of USA movies.
An important goal of this project is to forecast the
critics’ score of a movie using the raw data have at our
disposal. It is very essential to understand which factors
have the highest weight in determining the rating of a
movie. So we will present the results in a bar chart so to
have a better grasp of this analysis.
dataRaw.info()
RangeIndex: 5043 entries, 0 to 5042
Data columns (total 28 columns):
color 5024 non-null object
director_name 4939 non-null object
num_critic_for_reviews 4993 non-null float64
duration 5028 non-null float64
director_facebook_likes 4939 non-null float64
actor_3_facebook_likes 5020 non-null float64
actor_2_name 5030 non-null object
actor_1_facebook_likes 5036 non-null float64
gross 4159 non-null float64
genres 5043 non-null object
actor_1_name 5036 non-null object
movie_title 5043 non-null object
num_voted_users 5043 non-null int64
cast_total_facebook_likes 5043 non-null int64
actor_3_name 5020 non-null object
facenumber_in_poster 5030 non-null float64
plot_keywords 4890 non-null object
movie_imdb_link 5043 non-null object
num_user_for_reviews 5022 non-null float64
language 5031 non-null object
country 5038 non-null object
content_rating 4740 non-null object
budget 4551 non-null float64
title_year 4935 non-null float64
actor_2_facebook_likes 5030 non-null float64
imdb_score 5043 non-null float64
aspect_ratio 4714 non-null float64
movie_facebook_likes 5043 non-null int64
As we can observe from the listing of data displayed
above, not all features are describe all the movies. Most
column are short 5043, which means there is missing data
that will compromise our ML analysis. There is a
possibility that some values are actually redundant. Data
analysist often encounter such situation of data mismatch,
which compels them to make up for this missing data or
duplicates, by either standardizing, interpolating, pruning
or panning their data.
We run these commands to have a peek at the number
of redundant data and the missing value in our dataset.
dataRaw.isnull().sum()
color 19
director_name 104
num_critic_for_reviews 50
duration 15
director_facebook_likes 104
actor_3_facebook_likes 23
actor_2_name 13
actor_1_facebook_likes 7
gross 884
3. genres 0
actor_1_name 7
movie_title 0
num_voted_users 0
cast_total_facebook_likes 0
actor_3_name 23
facenumber_in_poster 13
plot_keywords 153
movie_imdb_link 0
num_user_for_reviews 21
language 12
country 5
content_rating 303
budget 492
title_year 108
actor_2_facebook_likes 13
imdb_score 0
aspect_ratio 329
movie_facebook_likes 0
dataRaw.duplicated().sum()
45
We realize that 45 data are duplicates and that there is a
handful of missing data. It is evident that we can just erase
the duplicate data, but it is not as obvious how to treat the
missing data, since there exist a significant number of
them and we cannot just afford to just do away with them,
lest we run the risk of having un underfitted predictor, and
loose its accuracy that early. Our solution is as follow: we
can easily see that the feature “gross” exhibit the largest
number of missing data at a number of 884. This is
significantly larger than the second most feature with
missing data “budget” at 492, which is not a negligible
number either. Since the void values in “gross” and
“budget” are considerably big, we will just resort to
ablating those rows from our dataset altogether so to avoid
any irregularity down the line, and causing erroneous
implications.
dataRaw = dataRaw.drop_duplicates()
dataRaw = dataRaw.dropna(subset=['gross', 'budget'])
dataRaw.shape
(3857, 28)
After the unwanted data is been discarded, we still end
up with 3857 rows of data belonging to 28 features which
is amply enough to go ahead with our analysis.
We carried on with cleaning the data even further, since
other features are still not yet adapted to be inputs for our
ML algorithm. Some features like “aspect ratio” will
undergo averaging in order to reduce the intricacy of our
datasets, and bring a highly sparse data to become
consolidated into two or three ranges of data under one
mean value.
If we were to display the “language” column, we will
realize that 3644/5043 of the movies have English as their
language, which suggests that this feature will have little
to no effect in our prediction, and so we can go ahead and
eliminate it from our set of features, like we did with
“gross” and “budget”. A similar observation can be as
to the origin of the movies, which is dominated by USA
made films with a staggering number of 3025 out of 5043
largely in front of UK and France respectively with 316
and 103 respectively, and other nations contribution is
almost unaccountable at this scale. This is a opportunity to
reduce the complexity of our data, and create just four
“countries” group: 'USA', 'UK', 'France' and 'Others'.
To make our data fully interpretable by our ML
algorithms, we need to associate a numerical arbitrary
value to some of our data that are in string form. This
applies to “country”, “language” and
“content_rating”.
When we are done with all this preliminary steps of data
cleaning, rearranging and parsing, we get a dataset
perfectly suitable for ML processing, and we can then
proceed.
4. II. DATA VISUALISATION
One last step before we execute our ML processing over
the final dataset, it is judicious to understand first how the
data is correlated, that is to say how some features can
outweigh others in making a movie more or less
successful. Despite the fact that Random Trees and Neural
Networks can seamlessly create linkages between those
features, it will not be able to identify the semantic
connotation of each feature, but rather sees all features as
equal placeholder with no special meaning. Therefor, we
will make an even better assessment if we help ourselves
by recognizing the different key connections that exist.
This can be achieved by visualization of the data. We shall
begin by first laying down how many films have been
produced since the beginning of cinematography.
plt.figure(figsize=(30, 10))
sns.distplot(dataRaw.title_year, kde=False);
There is a clear sharp increase of movie productions
starting from the 80s. This is a direct result of the
cinematographic technical advance that coincided with
this decade, and most specially the market being flooded
with VHS cassettes which popularized home movies.
Another foretelling connection is the movie score with
regard to the genre. Here we can see series of plots that
best illustrate that, and clearly show the normal
distribution of this connection.
temp_act = dataRaw.loc[dataRaw.Action == 1][['imdb_score']]
temp_adv = dataRaw.loc[dataRaw.Adventure == 1][['imdb_score']]
temp_fan = dataRaw.loc[dataRaw.Fantasy == 1][['imdb_score']]
temp_sci = dataRaw.loc[dataRaw['Sci-Fi'] == 1][['imdb_score']]
temp_thr = dataRaw.loc[dataRaw.Thriller == 1][['imdb_score']]
temp_rom = dataRaw.loc[dataRaw.Romance == 1][['imdb_score']]
temp_com = dataRaw.loc[dataRaw.Comedy == 1][['imdb_score']]
temp_ani = dataRaw.loc[dataRaw.Animation == 1][['imdb_score']]
temp_fam = dataRaw.loc[dataRaw.Family == 1][['imdb_score']]
temp_hor = dataRaw.loc[dataRaw.Horror == 1][['imdb_score']]
temp_dra = dataRaw.loc[dataRaw.Drama == 1][['imdb_score']]
temp_crime = dataRaw.loc[dataRaw.Crime == 1][['imdb_score']
sns.set(style="white", palette="muted", color_codes=True)
f, axes = plt.subplots(3, 4, figsize=(20, 20), sharex=True)
sns.despine(left=True)
sns.distplot(temp_act.imdb_score, kde=False, color="blue", ax=axes[0, 0]).set_title('Action
Movies')
sns.distplot(temp_adv.imdb_score, kde=False, color="red", ax=axes[0, 1]).set_title('Adventur
e Movies')
sns.distplot(temp_fan.imdb_score, kde=False, color="green", ax=axes[0, 2]).set_title('Fantas
y Movies')
sns.distplot(temp_sci.imdb_score, kde=False, color="orange", ax=axes[0, 3]).set_title('Sci-F
i Movies')
6. III. IMPLEMENTATION OF THE ML TECHNIQUES
The prediction can now properly start, and we can
implement our two ML algorithm as per the requirement
of our paper introduction. We will compare the
performance of both algorithms and give our evaluation
and interpretation about their usage. In this project, we are
going to build the Random Forest and Neural Networks by
means of the specialized Python frameworks of Keras and
Tensorflow. We could also resort to SciKit but we wanted
to explore other tools that have are meant for advanced
ML implementation.
At this point, we have to split the train and test data. We
are using a hold-out rule that keep 25% of the data for the
testing, while being trained on 75% of it.
Y=dataRaw.imdb_score
X=dataRaw.drop(['imdb_score'], axis=1)
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)
A. Random Forest
Our Random Forest algorithm takes a sample of data
that is randomly selected, and generate a multitude of
decision trees based on it. It computes the prediction for
each single tree and pick the one which has the best cut.
Random Forest make for an ideal classifier. The movie
success predictor seems to be a textbook case of
classification, but we must note that among the 28
features, some are not easily separable and thus a clear cut
can’t easily be achieved by Random Forest models.
from sklearn.ensemble import RandomForestC
lassifier
model = RandomForestClassifier(n_estimator
s=10)
model = model.fit(x_train, y_train)
feature_imp = pd.Series(model.feature_impo
rtances_,index=x_train.columns.values).sor
t_values(ascending=False)
A fundamental task when it comes to performing
supervised learning on a dataset is establishing which
features offers the most predictive power. Through
extrapolating the relationship between only a few crucial
features and the target label (success, failure) we break
down our understanding of the phenomenon into elements
that we are familiar with. For the dataset we are
studying, our aim is to narrow down it down to a couple
of features that impact the successful rate of the film.
# Creating a bar plot
plt.figure(figsize=(10, 10))
sns.barplot(x=feature_imp, y=feature_imp.i
ndex)
# Add labels to your graph
plt.xlabel('Feature Importance Score')
plt.ylabel('Features')
plt.title("Visualizing Important Features"
)
plt.legend()
plt.show()
7. # predictions
pred_train = model.predict(x_train)
rms_train = accuracy_score(y_train, pred_train)
pred_test = model.predict(x_test)
rms_test = accuracy_score(y_test, pred_test)
print('Train Accuracy: {0} Test Accuracy: {1}'.format(rms_train*100, rms_test*100))
Train Accuracy: 98.60813704496788 Test Accuracy: 76.12419700214133
Random Forest implementation leads to a training
accuracy of over 98%, which might be an indicator that an
overfitting has occurred. This can be further corroborated
by the fact that the accuracy on testing data is only
76.12%, which suggests that some testing data was out of
range due to this unrealistic training accuracy.
B. Neural Networks
The favourite ML algorithm for sophisticated analysis
is Neural Networks. They are reliable and can be
employed for both performing a regression on linear data,
or a classification of clustered data. There bio-inspired
nature makes them easy to interpret in certain applications,
but also computationally expensive.
We will gauge both Random Forest and Neural
Networks in the light of this movies success predictor
project.
One last step prior to running the Neural Network
algorithm, is to render the data compatible with the
algorithm. First, we have to standardize all 'X' data so that
they fit in a[0,1] window. This is an important part in the
process of normalisation, which is a perquisite of ML
techniques such as Neural Network and PCM. The, the 'Y'
data which was represented in one column, will undergo a
transformation into five columns associated with each
classes.
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)
Y = to_categorical(Y, num_classes=5)
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM, Activation
from keras.optimizers import Adam, RMSprop, SGD
from keras.utils.np_utils import to_categorical
from keras.utils import np_utils
import tensorflow as tf
nn_model = Sequential()
nn_model.add(Dense(10, input_dim=39 , activation = 'relu'))
nn_model.add(Dense(10, input_dim=10 , activation = 'relu'))
nn_model.add(Dense(10, input_dim=10 , activation = 'relu'))
nn_model.add(Dense(5, activation ='softmax'))
adam = optimizers.Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.05)
nn_model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
nn_model.fit(x_train, y_train, nb_epoch=100, batch_size=10)
trainScore, trainAcc = nn_model.evaluate(x_train, y_train)
testScore, testAcc = nn_model.evaluate(x_test, y_test)
print('Train Accuracy: {0} Test Accuracy: {1}'.format(trainAcc*100, testAcc*100))
Train Accuracy: 85.08208422555317 Test Accuracy: 75.2676659528908
8. The Neural Network model has yielded a realistic 85%
accuracy on its training routine, and a 75.26% accuracy for
the testing data. Compared to Random Forest algorithm
which boast a 76.12%, one would say that this is slightly
lower, but Neural Network are more robust and thus can
be trusted for datasets of large quantities, which is not the
case for Random Forest. The neural network we used here
make use of a Keras optimized called Adam which
previous researchers has shown to be more lenient on the
computational power, and thus on the power consumption.
This is something to remember if we ever want to
implement this predictor on more disparate features (list
of all actors, timing of movie release with respect to other
global events, etc..).
This table can summarize our final result on the
accuracy of both of our ML approaches:
Slice of Dataset
used for
Random
Forest
Neural
Networks
Testing 98.60 85.08
Training 76.12 75.26
IV.CONCLUSION
Random Forest exhibit the highest accuracy on testing
data which is a far cry from the 76% accuracy on training
data. Neural Networks have a relatively high accuracy for
testing data 85% and so it makes sense when training
accuracy is of 75%. This is a typical issue with random
forest because it can overfit on data easily.
From all the above, here we list some of the key
takeaways that one can make:
- Estimating the success of a film is not as
straightforward as it seems. It does not correlate intimately
with any of the obvious features that a movie rely on
(genre, country of origin, shooting quality).
- That being said, there are certain factors that have
more impact than others (choice of actor/actress is more
decisive than the directors, drama genre movies are likely
to be more successful, and movies released during summer
have better chances than movies released in regular
months).
The model built here can be said to be a minimalistic
model, and there are a number of additions if one want to
make it better. I would suggest:
- Training on a larger dataset. Our study was conducted
only on IMDB datasets, but one can also look at Rotten
Tomatos and Box Office Mojo datasets. In which case
some of the features we removed here during data cleaning
(like “gross” and “budget”) would probably still make
it to the final training set.
- Add as a feature the keywords that make up a movie
synopsis. This text describing the plot is one the first
elements an audience consult for choosing which movie to
go watch, along with the movie poster. But this latter is
complicated to evaluate as a numerical score, unless we
use image segmentation and learn the different
components on the poster image, in order to compare
posters across movies.
- Other critical features can be inserted as well, such as
number of theatres screening a particular movie, the
number of previously successful movie from a particular
director or actor/actress.
The ML implementation that we designed in this
project can be extended to the Turkish movie industry and
help local production studios understand the parameters
that can help boost the commercial success of their films.
V. REFERENCES
CMPE542 Course Notes
www.imdb.com
www.kaggle.com
www.stackoverflow.com